Information Embedding

The purpose of this paper is to show how data mining may offer promise as a strategy for discovering and building alternative representations for the data underlying asynchronous discussion forums. Presently, the instructor's view of the output of a threaded forum is limited to reviewing a transcript or print version of the written dialogue produced by participants. With potentially hundreds of contributions to review for an entire online course, the instructor lacks a comprehensive view of the information embedded in the transcript. In this context, the authors attempt to sort out the question, ''what is data from an online forum?'' among other key questions. The present work seeks to intersect the information (i.e., participation indicators) an instructor may wish to extract from the forum with viewable and useful information that the system could produce from the instructor's query. Temporal participation indicators are used to show how using data and text mining techniques in the query process could improve the instructor's ability to evaluate the progress of a threaded discussion.

This paper introduces the Artifi cial Neural Networks (ANN) function to model probabilistic dependencies, in supervised classifi cation tasks for discrimination between earthquakes and explosions problems. ANNs are regarded as the discriminating tools to classify the natural seismic events (earthquakes) from the artifi cial ones (Man-made explosions) based on the seismic signals recorded at regional distances. The bulk of our novel is to improve the obtained numerical results using this advance technique. The ANNs, by testing the different types of seismic features, showed the potential application of this method to discriminate the classes. During the above study, we found out that the Neural Networks have been used in a fully innovative manner in this work. Here the ARMA coeffi cients fi lters detects the type of the source whenever a natural or artifi cial source changes the nature of the background noise of the seismograms. During the above study, we found out that this algorithm is sometimes capable to alarm the further natural seismological events just a little before the onset.

We examine whether a simple quantitative measure of language can be used to predict individual firms' accounting earnings and stock returns. Our three main findings are: (1) the fraction of negative words in firm-specific news stories forecasts low firm earnings; (2) firms' stock prices briefly underreact to the information embedded in negative words; and (3) the earnings and return predictability from negative words is largest for the stories that focus on fundamentals. Together these findings suggest that linguistic media content captures otherwise hard-to-quantify aspects of firms' fundamentals, which investors quickly incorporate in stock prices.

ABSTRACTWe examine whether a simple quantitative measure of language can be used to predict individual firms' accounting earnings and stock returns. Our three main findings are: (1) the fraction of negative words in firm‐specific news stories forecasts low firm earnings; (2) firms' stock prices briefly underreact to the information embedded in negative words; and (3) the earnings and return predictability from negative words is largest for the stories that focus on fundamentals. Together these findings suggest that linguistic media content captures otherwise hard‐to‐quantify aspects of firms' fundamentals, which investors quickly incorporate into stock prices.

In current optical coherent communication systems, balanced photo-detectors are typically employed, which preserve the product of the received optical signals and the local oscillators (LO) for detection but cancel out the power of both. Since only the product of the received optical signals and the LO is used to estimate the transmitted complex symbol, such detection scheme is suboptimal due to the loss of some useful information embedded in the transmitted signal power. Different from that, this paper proposes a new detection scheme which maintains full information obtained from the photo-detectors. Specifically, we first extract the transmitted signal power from the output of the photo-detector. After that, we use not only the product of the signals and the LO but also the power of signals to estimate the target signals. For 16-qudrature-amplitudemodulation (16-QAM) and 64-QAM signals, the simulation results show that our new scheme can outperform the conventional ones by more than 1 dB.

One of the promises of AI in the military domain that seems to guarantee its adoption is its broad applicability. In a military context, the potential for AI is present in all operational domains (i.e., land, sea, air, space, and cyber-space) and all levels of warfare (i.e., political, strategic, operational, and tactical). However, despite the potential, the convergence between needs and AI technological advances is still not optimal, especially in supervised machine learning for military applications. Training supervised machine learning models requires a large amount of up-to-date data, often unavailable or difficult to produce by one organization. An excellent way to tackle this challenge is federated learning by designing a data pipeline collaboratively. This mechanism is based on implementing a single universal model for all users, trained using decentralized data. Furthermore, this federated model ensures the privacy and protection of sensitive information managed by each ent...

This paper is an attempt at constructing a simple and effective macroprudential tool for policymakers. By integrating the joint occurrences of the main financial markets in Colombia into a single Financial Conditions Index (FCI), we hope to synthesize the information embedded in them regarding possible future economic outcomes. To do this, we use monthly data on 21 variables for the period comprised between July, 1991 -June, 2010 and apply PCA on their correlation matrix. On the one hand, we evaluate the predictive capacity of the FCI in forecasting GDP growth at different time horizons and find that it performs better as a leading indicator of real activity than other individual financial variables and an autoregressive model of GDP growth. Additionally, we are interested in testing the FCI's long-term capability to correctly anticipate periods of distress in the economy, and find that the index could be used as an effective early-warning indicator. Hence, our FCI seems to represent a useful instrument for both financial stability and macroprudential supervision purposes.

SUMMARYThe acoustic channel is important for communication in otariids (fur seals and sea lions). Discrimination between species, sex or individuals is essential in communication; therefore insight into the role of vocalisations in recognition is vital to understanding otariid social interactions. We measured vocalisations and their use in discriminating sex and species in male Australian sea lions (Neophoca cinerea). Barking calls of mature males were recorded and analysed based on five acoustic parameters. A discriminant function analysis classified calls to the correct individual at a classification rate of 56%, suggesting that male barking calls are individually distinctive with the potential to facilitate individual vocal recognition. Playback experiments were used to assess the role of vocalisations in sex and species recognition both in and out of the breeding season. Males showed significantly stronger reactions to both conspecific and heterospecific males than they did to c...

In recent years, mtDNA and Y chromosome studies involving human populations from South Asia and the rest of the world have revealed new insights about the peopling of the world by anatomically modern humans during the late Pleistocene, some 40,000-60,000 years ago, over the southern coastal route from Africa. Molecular studies and archaeological record are both largely consistent with autochthonous differentiation of the genetic structure of the caste and tribal populations in South Asia. High level of endogamy created by numerous social boundaries within and between castes and tribes, along with the influence of several evolutionary forces such as genetic drift, fragmentation and long-term isolation, has kept the Indian populations diverse and distant from each other as well as from other continental populations. This review attempts to summarize recent genetic studies on Indian caste and tribal populations with the focus on the information embedded in the socially defined structure of Indian populations. BioEssays 29: 91-100, 2007.

Nowadays people posting reviews are increasing and it gives us the knowledge to buy the products. People can express their sentiments through reviews posted in websites. By these reviews we can predict the movie sales performance. The question is how the sentiment factor can be analyzed in the reviews? Reviews are analyzed by Sentiment PLSA which helps us to find the unknown factors in the reviews and helps us to classify the reviews. Then we have to predict the sales performance, for the prediction regression is suitable way to do and we suggest an autoregressive sentiment (ARSA) Aware Model. To improve the prediction accuracy to enhance the best quality factor we also consider an autoregressive sentiment and quality aware model (ASQA)to build the quality for mining reviews sales performance in movie domain.

We show how the complement of white light and a suite of Fe coronal forbidden line images taken during total solar eclipses since 2006 have yielded new insights into the physical properties of the coronal plasma and the role of the magnetic field. The unique properties of these spectral lines make them ideal diagnostic tools for exploring the first few solar radii above the photosphere where the expansion of the corona and the acceleration of the solar wind occur. In particular, these observations show how: (1) localized enhancements of the ion densities relative to electrons appear in some magnetic structures, (2) the transition between a collisiondominated to a collisionless plasma occurs within a heliocentric distance of 1.2 -1.6 R s , (3) the expanding corona is characterized by an electron temperature of 10 6 K, and (4) prominences are enshrouded by hot 2 × 10 6 K coronal plasma. We discuss the implications of these observations for the source and acceleration of the solar wind.

An undesirable side effect of almost all information embedding methods is the fact that the original is distorted due to the embedding itself. For some applications no distortion is allowed (e.g. modifying medical images is not allowed for legal reasons). Reversible information embedding is an easy solution to avoid these problems. If the information (message) is reversibly embedded in a host, the receiver is able to detect the message and reconstruct the original host. In literature, several algorithms have been proposed, however little attention has been paid to the theoretical limits. We computed the capacity for the binary and Gaussian case, for other input distributions two algorithms are developed to compute the capacity. This theoretical knowledge also gives 'hints' for practical implementations. For the binary case we designed an algorithm that improves Fridrich's regular-singular method and for the non-binary case an algorithm is designed that has a better performance than the difference expansion method of Tian. Newly designed reversible embedding algorithms based on the theory have better performance than those described in literature.

Content fingerprinting and digital watermarking are techniques that are used for content protection and distribution monitoring. Over the past few years, both techniques have been well studied and their shortcomings understood. Recently, a new content fingerprinting scheme called active content fingerprinting was introduced to overcome these shortcomings. Active content fingerprinting aims to modify a content to extract robuster fingerprints than the conventional content fingerprinting. Moreover, contrary to digital watermarking, active content fingerprinting does not embed any message independent of contents thus does not face host interference. The main goal of this paper is to analyze fundamental limits of active content fingerprinting in an information theoretical framework.

人工知能（Artificial Intelligence：AI）の技術発展は、現在の人間の「しごと」の多くを代替することで、産業構造に変革をもたらすとされている。しかし、この変革により新たな「しごと」が生み出されるであろうことは過去の産業革命における「しごと」の変化からも明らかであろう。本稿では、単語分散表現（Word Embeddings）を用いて新聞記事等から人工知能に関連するキーワードの時系列データを抽出することで、第四次産業革命のキーファクターの一つである人工知能が、どのように日本社会で認知され注目されるようになってきたか、その変化を明らかにする。Replacing many of "work" of humanity with Artificial Intelligence（AI）is said to bring a big change in the industrial structure. However, this change will create a new “work”. It will be evident from the change of "work" in the past industrial revolution. This paper describes how artificial intelligence, one of the key factors of the Fourth Industrial Revolution, has become recognized and noticed in Japanese society. We clarify the changes of the time series by investigating the related keywords of artificial intelligence using word embeddings on newspaper articles, etc

We present a case study about the application of the inductive database approach to the analysis of Web logs. We consider rich XML Web logs -called conceptual logs -that are generated by Web applications designed with the WebML conceptual model and developed with the WebRatio CASE tool. Conceptual logs integrate the usual information about user requests with meta-data concerning the structure of the content and the hypertext of a Web application. We apply a data mining language (MINE RULE) to conceptual logs in order to identify different types of patterns, such as: recurrent navigation paths, most frequently visited page contents, and anomalies (e.g., intrusion attempts or harmful usages of resources). We show that the exploitation of the nuggets of information embedded in the logs and of the specialized mining constructs provided by the query languages enables the rapid customization of the mining procedures following to the Web developers' need. Given our on-field experience, we also suggest that the use of queries in advanced languages, as opposed to ad-hoc heuristics, eases the specification and the discovery of large spectrum of patterns.

Sterilization is a very popular word used in biomedical testing (like removal of all microorganisms on surface of an article or in fluid using appropriate chemical products). Motivated by this biological analogy, we, for the first time, introduce the concept of sterilization of an image, i.e., removing any steganographic information embedded in the image. Experimental results show that our technique succeeded in sterilizing around 76% to 91% of stego pixels in an image on average, where data is embedded using LSB-based steganography.

Due to its high popularity, Weblogs (or blogs in short) present a wealth of information that can be very helpful in assessing the general public's sentiments and opinions. In this paper, we study the problem of mining sentiment information from blogs and investigate ways to use such information for predicting product sales performance. Based on an analysis of the complex nature of sentiments, we propose Sentiment PLSA (S-PLSA), in which a blog entry is viewed as a document generated by a number of hidden sentiment factors. Training an S-PLSA model on the blog data enables us to obtain a succinct summary of the sentiment information embedded in the blogs. We then present ARSA, an autoregressive sentiment-aware model, to utilize the sentiment information captured by S-PLSA for predicting product sales performance. Extensive experiments were conducted on a movie data set. We compare ARSA with alternative models that do not take into account the sentiment information, as well as a model with a different feature selection method. Experiments confirm the effectiveness and superiority of the proposed approach.

We describe how to manage distributed file system caches based upon groups of files that are accessed together. We use file access patterns to automatically construct dynamic groupings of files and then manage our cache by fetching groups, rather than single files. We present experimental results, based on trace-driven workloads, demonstrating that grouping improves cache performance. At the file system client, grouping can reduce LRU demand fetches by 50 to 60%. At the server, cache hit rate improvements are much more pronounced, but vary widely (20 to over 1200%) depending upon the capacity of intervening caches. Our treatment includes information theoretic results that justify our approach to file grouping.

Mapping gestures to digital musical instrument parameters is not trivial when the dimensionality of the sensor-captured data is high and the model relating the gesture to sensor data is unknown. In these cases, a front-end processing system for extracting gestural information embedded in the sensor data is essential. In this paper we propose an unsupervised offline method that learns how to reduce and map the gestural data to a generic instrument parameter control space. We make an unconventional use of the Self-Organizing Maps to obtain only a geometrical transformation of the gestural data, while dimensionality reduction is handled separately. We introduce a novel training procedure to overcome two main SelfOrganizing Maps limitations which otherwise corrupt the interface usability. As evaluation, we apply this method to our existing Voice-Controlled Interface for musical instruments, obtaining sensible usability improvements.

This paper presents a new photo browsing technique, dynamic collage. Although previous photo collage techniques have innate advantages for viewing several photos collectively, they only focus on a static two-dimensional arrangement of photos so that the scalability is limited. In dynamic collage, new photos are incrementally inserted into the collage one by one while the old photos are removed accordingly, the positions of photos in the canvas are updated with a local and incremental manner to form a new layout so as to maximize visibility of all the important information embedded in the current collage meanwhile maintaining the visual continuity of two successive collages. To achieve this goal, a carefully designed optimization method based on belief propagation is employed. Unlike most traditional applications of belief propagation on pairwise MRF, we apply belief propagation on factor graph to optimize terms which

Advanced technologies originally developed for the military are now commonly used to enhance urban public safety. Photonics, thermal imaging, facial and behavioral recognition systems, remote monitoring by satellite, and biometric systems like DNA testing and retinal scans have become new weapons in police agencies. These technologies are being diffused among cities by the Defense Advanced Research Projects Agency (DARPA), NASA, the US military, national research laboratories, the Office of Law Enforcement Technology Commercialization of the National Institute of Justice, and others. As a result, public police agencies are changing their methods of handling urban space and the ways such spaces are defined and controlled by police. Police technologies are evolving into, among other things, machine systems that code and decode information embedded in urban spaces, with the ability to track movements of targeted individuals and groups and identify the unseen remnants left by targets of these surveillance machines. As these systems become more automated and autonomous, critical thought needs to be directed to their impact on people and cities.

Crosslingual text classification consists of exploiting labeled documents in a source language to classify documents in a different target language. In addition to the evident translation problem, this task also faces some difficulties caused by the cultural discrepancies manifested in both languages by means of different topic distributions. Such discrepancies make the classifier unreliable for the categorization task. In order to tackle this problem we propose to improve the classification performance by using information embedded in the own target dataset. The central idea of the proposed approach is that similar documents must belong to the same category. Therefore, it classifies the documents by considering not only their own content but also information about the assigned category to other similar documents from the same target dataset. Experimental results using three different languages evidence the appropriateness of the proposed approach.

The present study investigated newbornsÕ ability to discriminate, recognize, and learn visual information embedded in the schematic face-like patterns preferred at birth. Four experiments were carried out using the visual-paired comparison paradigm. Results indicated that newborns discriminated face-like stimuli relying on their internal features (Experiments 1 and 4) and recognized a perceptual invariance between face-like configurations in conditions of low (Experiment 2) and high-perceptual variability (Experiment 3) of their inner elements. Altogether, data show that the presence of the preferred structure that schematically defines a face, displaying a triplet of elements in the correct locations for eyes and mouth, does not constitute a limit that constrains newbornsÕ face learning processes.

We describe PhotoCompas, a system that utilizes the time and location information embedded in digital photographs to automatically organize a personal photo collection. Pho-toCompas produces browseable location and event hierarchies for the collection. These hierarchies are created using algorithms that interleave time and location to produce an organization that mimics the way people think about their photo collections. In addition, the algorithm annotates the generated hierarchy with geographical names. We tested our approach in case studies of three real-world collections and verified that the results are meaningful and useful for the collection owners.

Crosslingual text classification consists of exploiting labeled documents in a source language to classify documents in a different target language. In addition to the evident translation problem, this task also faces some difficulties caused by the cultural discrepancies manifested in both languages by means of different topic distributions. Such discrepancies make the classifier unreliable for the categorization task. In order to tackle this problem we propose to improve the classification performance by using information embedded in the own target dataset. The central idea of the proposed approach is that similar documents must belong to the same category. Therefore, it classifies the documents by considering not only their own content but also information about the assigned category to other similar documents from the same target dataset. Experimental results using three different languages evidence the appropriateness of the proposed approach.

This paper presents a contribution to the large problematic of integrating medical image-based information into a structured framework (such as electronic patient records or anatomofunctional databases). In neuroscience, the complexity of the cerebral anatomy, the wealth of information embedded in imaging data, as well as the difficulty of their interpretation, can benefit from the use of a structural brain model representing prior generic knowledge, which includes information on anatomical structures and their spatial relations. In this paper we describe a novel generic brain model, based on graph representations, and an instantiation procedure for individual patients, based on image segmentation. A complete patientspecific modeling framework is proposed that can be integrated into powerful computational tools to assist image data reviewing, diagnosis and therapeutic patient follow up.

Techniques to embed information in printed documents have lately attracted considerable attention due to their various applications such as copyright protection and forensic document examination. One method is to embed data in the halftone portions of a document during the printing process. The printed hardcopy might then contain information about the printer that includes manufacturer, model name, and serial number, or information about the document, such as copyright, confidentiality, and date printed. However, previous approaches have mainly focused on methods that modify only the halftoning algorithm itself without considering the printer mechanism. Embedding data at the printer mechanism-level is desirable, since it is more robust and flexible. In this paper, we describe a printer mechanism-level information embedding and detection scheme for halftone documents that is suitable for electrophotographic laser printers. After we embed the information, the printed document is scanned for image analysis and data extraction. These processes include specific means to account for the inherent instability of the EP process and resulting noise in the scanned images. Preliminary experimental results suggest that our proposed scheme is feasible although much work remains to be done to fully prove out the concept.

A computer-aided rule-based framework that restructures the unstructured information embedded in precedent designs is introduced. Based on a deductive analysis of a corpus of sixteen case studies from Mamluk architecture, the framework is represented as a generative system that establishes systematic links between the form of a case study, its visual properties, its composition syntax and the processes underlying its design. The system thus formulated contributes to the areas of design research and practice with a theoretical construct about design logic, an interactive computerized plan generator and a combination of a top-down approach for case study analysis and a bottom-up methodology for the derivation of artifacts.

Product recalls have long been recognised to have an impact on firm value. We revisit this issue using more recent data across different industries. We also examine the recall impact on the the firm's expected performance using analyst' earnings forecasts. The results show that product recall announcements negatively affect stock prices and analysts' earnings forecasts. However, there is weak evidence on the market response between characteristics-based subsamples of recalls.

This paper examines whether tone (positive and negative) and volume of firm-specific news media content provide valuable information about future stock returns, using UK news media data from 1981-2010. The results indicate that both tone and volume of news media content significantly predict next period abnormal returns, with the impact of volume more pronounced than tone. Additionally, the predictive power of tone is found to be stronger among lower visibility firms. Further, the paper finds evidence of an attention-grabbing effect for firm-specific news stories with high media coverage, mainly seen among larger firms. A simple news-based trading strategy produces statistically significant risk-adjusted returns of 14.2 to 19 basis points in the period 2003-2010. At the aggregate level, price pressure induced by semantics in news stories is corrected only in part by subsequent reversals. Overall, the findings suggest firm-specific news media content incorporates valuable information that predicts asset returns.

An electrocardiogram is a potential bioelectric record that occurs as a result of cardiac activity. QRS Detection with zero crossing calculation is one method that can precisely determine peak R of QRS wave as part of arrhythmia detection. In this paper, two experimental scheme (2 minutes duration with different activities: relaxed and, typing) were conducted. From the two experiments it were obtained: accuracy, sensitivity, and positive predictivity about 100% each for the first experiment and about 79%, 93%, 83% for the second experiment, respectively. Furthermore, the feature set of MIT-BIH arrhythmia using the support vector machine (SVM) method on the WEKA software is evaluated. By combining the available attributes on the WEKA algorithm, the result is constant since all classes of SVM goes to the normal class with average 88.49% accuracy.

In last 5 years Physical Computing has been a constant discussion in the whole world. With so many Physical Computing turned into wearable technology. We are trying to take one of thousands of example, in this case ECG processing, and put it into the test. We tried to expand all the features and pin point all the exact point of the PQRST of the ECG. We also tested it in three state of condition, which is: sitting-rest, walking, and fast-paced walk to see how our program would perform in that particular varies of activity. From the analysis conducted, it can be concluded that the output of our proposed combination of method shown a significant performances.

The article develops a new method that compares activity‐travel patterns in both terms of the sequential order of activities and the shape of activity‐travel trajectory in time and space. The similarity of the list of activities and their order between activity‐travel patterns are computed by a sequence alignment method. The shape of activity‐travel trajectory is compared between the patterns using a path similarity technique that captures the direction and speed of a movement from the current location and the duration of staying at each location. The comparison results, therefore capture how people move around in three‐dimensional space–time choreography that indicates how people conduct which activities in what order. A total of 1,000 individuals are sampled from the data of 2016 Household Travel Survey, South Korea. The data provide the information of individual activity‐travel behavior and personal characteristics. The suggested method computes the pairwise distance matrix, and ...

Increasing the resolution of digital images and the frame rate of video sequences leads to an increase in the amount of required logical and memory resources necessary for digital image and video decompression. Therefore, the development of new hardware architectures for digital image decoder with a reduced amount of utilized logical and memory resources become a necessity. In this paper, a digital image decoder for efficient hardware implementation, has been presented. Each block of the proposed digital image decoder has been described. Entropy decoder, decoding probability estimator, dequantizer and inverse subband transformer (parts of the digital image decoder) have been developed in such way which allows efficient hardware implementation with reduced amount of utilized logic and memory resources. It has been shown that proposed hardware realization of inverse subband transformer requires 20% lower memory capacity and uses less logic resources compared with the best state-of-the...

Real life design situations often involve addressing complex multidisciplinary problems that are hard to formulate and solve, attempted by a diverse group of people, working in different organizations with different cultures, collaborating through a variety of contracts. Many design efforts fail due to incomplete understanding of design contexts. Can we create a framework that will help us unpack this complexity into a structure that allows us to explain, predict, and control failures and improve such situations to help organizations design better? We present a framework, called the PSI matrix that was developed by combining knowledge from diverse disciplines; following numerous case studies with industry to address the above-mentioned challenge. We describe the framework and its evolution from an earlier version, demonstrate its applicability to several diverse design examples, and mention several other cases on which it was tested. We have found it to be robust in supporting its o...

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The notion of model-free implied volatility (MFIV), constituting the basis for the highly publicized VIX volatility index, can be hard to measure with accuracy due to the lack of precise prices for options with strikes in the tails of the return distribution. This is reflected in practice as the VIX index is computed through a tail-truncation which renders it more compatible with the related concept of corridor implied volatility (CIV). We provide a comprehensive derivation of the CIV measure and relate it to MFIV under general assumptions. In addition, we price the various volatility contracts, and hence estimate the corresponding volatility measures, under the standard Black-Scholes model. Finally, we undertake the first empirical exploration of the CIV measures in the literature. Our results indicate that the measure can help us refine and systematize the information embedded in the derivatives markets. As such, the CIV measure may serve as a tool to facilitate empirical analysis of both volatility forecasting and volatility risk pricing across distinct future states of the world for diverse asset categories and time horizons.

The problem of discriminating between earthquakes and underground nuclear explosions is formulated as an exercise in pattern recognition approach analysis. An advantage of our procedure is flexibility, by combining both adaptive noise suppression and event classification incorporating feature selection criteria. The analysis has been applied to a learning set of 44 nuclear explosions (eight test sites) and 35 earthquakes in Eurasia recorded at the NORESS array (Fig. 1). The signal features considered were the normalized power in eight spectral bands in the 0.2-5.0 Hz range of the P wave (6 s) and the P coda (30 s). Physically, it means that we exploit potential differences in the shape of earthquake and explosion spectra, respectively. Other features included are peak P and P-coda amplitude frequencies and relative PIP-coda power. These 19 features were extracted either from conventional array beam traces or the optimum group filtered traces (OGFremoval of coherent low-frequency noise). Using the feature selection algorithm, based on estimates of the expected probability of misclassification (EPMC), only two to four features were needed for optimum discrimination performance. The dominant features were coda excitation and P-and P-coda power at lower signal frequencies. Furthermore, feature parameters extracted from the OGF traces had a slightly better performance in comparison to those extracted from beam traces. Finally, there were no misclassifications for OGF-derived features when the explosion population was limited to East Kazakh events, while including events from the other test sites lead to a decrease in discrimination power.

This study mainly focuses on the effect of mass of impactor upon dynamic response of laminated composites. For this purpose two different impactors with different masses were used. The impact velocities were selected so that impactors with different masses had the same kinetic energy. The experiments were performed on simply supported woven E-glass reinforced epoxy laminates which are made of 8 layers stacked symmetrically. The variation of impact force, impactor velocity and laminate displacement versus interaction time were obtained. The impulse and energy absorption characteristics and damage zones were also investigated. After low velocity impact, the test specimens were subjected to three-point bending testing in order to get retention mechanical properties.

The purpose of this paper is to show how data mining may offer promise as a strategy for discovering and building alternative representations for the data underlying asynchronous discussion forums. Presently, the instructor's view of the output of a threaded forum is limited to reviewing a transcript or print version of the written dialogue produced by participants. With potentially hundreds of contributions to review for an entire online course, the instructor lacks a comprehensive view of the information embedded in the transcript. In this context, the authors attempt to sort out the question, ''what is data from an online forum?'' among other key questions. The present work seeks to intersect the information (i.e., participation indicators) an instructor may wish to extract from the forum with viewable and useful information that the system could produce from the instructor's query. Temporal participation indicators are used to show how using data and text mining techniques in the query process could improve the instructor's ability to evaluate the progress of a threaded discussion.

Increasing the resolution of digital images and the frame rate of video sequences leads to an increase in the amount of required logical and memory resources necessary for digital image and video decompression. Therefore, the development of new hardware architectures for digital image decoder with a reduced amount of utilized logical and memory resources become a necessity. In this paper, a digital image decoder for efficient hardware implementation, has been presented. Each block of the proposed digital image decoder has been described. Entropy decoder, decoding probability estimator, dequantizer and inverse subband transformer (parts of the digital image decoder) have been developed in such way which allows efficient hardware implementation with reduced amount of utilized logic and memory resources. It has been shown that proposed hardware realization of inverse subband transformer requires 20% lower memory capacity and uses less logic resources compared with the best state-of-the...

The cyclic guanosine-3′,5′-monophosphate (cGMP)-dependent protein kinase (PKG) was identified >25 y ago; however, efforts to obtain a structure of the entire PKG enzyme or catalytic domain from any species have failed. In malaria parasites, cooperative activation of PKG triggers crucial developmental transitions throughout the complex life cycle. We have determined the cGMP-free crystallographic structures of PKG from Plasmodium falciparum and Plasmodium vivax , revealing how key structural components, including an N-terminal autoinhibitory segment (AIS), four predicted cyclic nucleotide-binding domains (CNBs), and a kinase domain (KD), are arranged when the enzyme is inactive. The four CNBs and the KD are in a pentagonal configuration, with the AIS docked in the substrate site of the KD in a swapped-domain dimeric arrangement. We show that although the protein is predominantly a monomer (the dimer is unlikely to be representative of the physiological form), the binding of the AI...

The PSI spaces are a framework for studying designing as practiced in the real world: framing and solving technical, social or organizational goals embedded in the existing socioeconomic and institutional cultures and practices. Given the interconnected nature of the design product, knowledge and activities, we should anticipate that understanding designing is at least as complex as designing itself. Consequently, understanding designing involves mobilizing multiple knowledge sources, with different perspectives and diversity of participants orchestrated to achieve an effective outcome. We call the study of the PSI spaces the PSI framework. We introduce the PSI spaces, and their language resting on diverse disciplines such as psychology, engineering, economics, and sociology. We introduce some of its methodological tools; how the PSI spaces might be used to explain design challenges through misalignments of the spaces and how these misalignments could be resolved. The PSI framework has significant implication to the development of design science; it demands that design science be a trans-disciplinary endeavor, in need of a flexible community that will study it.

A Directional Virtual Coordinate System (DVCS) is proposed based on a novel transformation that restores the lost directionality information in a Virtual Coordinate System (VCS). VCS is an attractive option to characterize the node locations in Wireless Sensor Networks (WSNs), instead of using geographical coordinates, which are expensive or difficult to obtain. A VCS characterizes each node in a network with the minimum hop distances to a set of anchor nodes. Proposed transformation supplements the VCS, thus preserving all its inherent properties such as geodesic distance information embedded in the coordinates. Virtual directionality introduced alleviates the local minima issue present in original VCS. Properties of this virtual directional domain are discussed. With these directional properties, it is possible, for the first time, to consider deterministic algorithms for routing in the virtual domain, as illustrated with a constrained tree network example. A novel routing scheme, Directional Virtual Coordinate Routing (DVCR) that illustrates the effectiveness of directional virtual coordinate domain is presented. DVCR significantly outperforms existing VCS routing schemes Convex Subspace Routing (CSR) and Logical Coordinate Routing (LCR), while achieving a performance similar to geographical routing scheme Greedy Perimeter Stateless Routing (GPSR), but without the need for node location information.

The PSI spaces are a framework for studying designing as practiced in the real world: framing and solving technical, social or organizational goals embedded in the existing socio-economic and institutional cultures and practices. Given the interconnected nature of the design product, knowledge and activities, we should anticipate that understanding designing is at least as complex as designing itself. Consequently, understanding designing involves mobilizing multiple knowledge sources, with different perspectives and diversity of participants orchestrated to achieve an effective outcome. We call the study of the PSI spaces the PSI framework. We introduce the PSI spaces, and their language resting on diverse disciplines such as psychology, engineering, economics, and sociology. We introduce some of its methodological tools; how the PSI spaces might be used to explain design challenges through misalignments of the spaces and how these misalignments could be resolved. The PSI framework...

Agroforestry is often discussed as a strategy that can be used both for the adaptation to and the mitigation of climate change effects. The climate of southern Africa is predicted to be severely affected by such changes. With agriculture noted as the continent’s largest economic sector, issues such as food security and land degradation are in the forefront. In the light of such concerns we review the current literature to investigate if agroforestry systems (AFS) are a suitable response to the challenges besetting traditional agricultural caused by a changing climate. The benefits bestowed by AFS are multiple, offering ecosystem services, influence over crop production and positive impacts on rural livelihoods through provisioning and income generation. Nevertheless, knowledge gaps remain. We identify outstanding questions requiring further investigation such as the interplay between trees and crops and their combination, with a discussion of potential benefits. Furthermore, we iden...

Real life design situations often involve addressing complex multidisciplinary problems that are hard to formulate and solve, attempted by a diverse group of people, working in different organizations with different cultures, collaborating through a variety of contracts. Many design efforts fail due to incomplete understanding of design contexts. Can we create a framework that will help us unpack this complexity into a structure that allows us to explain, predict, and control failures and improve such situations to help organizations design better? We present a framework, called the PSI matrix that was developed by combining knowledge from diverse disciplines; following numerous case studies with industry to address the above-mentioned challenge. We describe the framework and its evolution from an earlier version, demonstrate its applicability to several diverse design examples, and mention several other cases on which it was tested. We have found it to be robust in supporting its o...

The application of the steerable pyramid transform in image watermarking has many useful properties. In this paper, we will try to address some properties of steerable pyramid transform that are relevant for use in image watermarking; these properties include: (1) invariance properties; (2) multiresolution aspect; (3) capture of multi-scale and multiresolution structures in the image. All the above mentioned properties make this steerable pyramid transform appropriate for the design of a robust watermarking scheme. This paper proposes an image watermarking scheme based on steerable pyramid transform to embed invisible and robust watermark. We can summarize the basic principles of our method as follow: a host image is first transformed by the steerable pyramid transform. The different features are then extracted by thresolding the different subbands. The watermark sequence is inserted into disjoint blocks centered on the extracted feature points. The original host image is needed in watermark detection mainly for extracting the featured coefficients necessary for robust detection and determining the value of one bit of the watermark spread into a block. It has been confirmed by experiments and comparisons with many existing non-blind techniques that the watermark information embedded by the proposed technique is robust to JPEG compression, additive noise, and median filtering.

A computer-aided rule-based framework that restructures the unstructured information embedded in precedent designs is introduced. Based on a deductive analysis of a corpus of sixteen case studies from Mamluk architecture, the framework is represented as a generative system that establishes systematic links between the form of a case study, its visual properties, its composition syntax and the processes underlying its design. The system thus formulated contributes to the areas of design research and practice with a theoretical construct about design logic, an interactive computerized plan generator and a combination of a top-down approach for case study analysis and a bottom-up methodology for the derivation of artifacts.