Query: Entity Set Expansion via Knowledge Graphs

Relevant passages:
Improving RDF Data Through Association Rule Mining <=SEP=> Linked Open Data comprises very many and often large public data sets, which are mostly presented in the Rdf triple structure of subject, predicate, and object. However, the heterogeneity of available open data requires significant integration steps before it can be used in applications. A promising and novel technique to explore such data is the use of association rule mining. We introduce “mining configurations”, which allow us to mine Rdf data sets in various ways. Different configurations enable us to identify schema and value dependencies that in combination result in interesting use cases. We present rule-based approaches for predicate suggestion, data enrichment, ontology improvement, and query relaxation. On the one hand we prevent inconsistencies in the data through predicate suggestion, enrichment with missing facts, and alignment of the corresponding ontology. On the other hand we support users to handle inconsistencies during query formulation through predicate expansion techniques. Based on these approaches, we show that association rule mining benefits the integration and usability of Rdf data.

PathSim: Meta Path-Based Top-K Similarity Search in Heterogeneous Information Networks <=SEP=> Similarity search is a primitive operation in database and Web search engines. With the advent of large-scale heterogeneous information networks that consist of multi-typed, interconnected objects, such as the bibliographic networks and social media networks, it is important to study similarity search in such networks. Intuitively, two objects are similar if they are linked by many paths in the network. However, most existing similarity measures are defined for homogeneous networks. Different semantic meanings behind paths are not taken into consideration. Thus they cannot be directly applied to heterogeneous networks. In this paper, we study similarity search that is defined among the same type of objects in heterogeneous networks. Moreover, by considering different linkage paths in a network, one could derive various similarity semantics. Therefore, we introduce the concept of meta path-based similarity, where a meta path is a path consisting of a sequence of relations defined between different object types (i.e., structural paths at the meta level). No matter whether a user would like to explicitly specify a path combination given sufficient domain knowledge, or choose the best path by experimental trials, or simply provide training examples to learn it, meta path forms a common base for a network-based similarity search engine. In particular, under the meta path framework we define a novel similarity measure called PathSim that is able to find peer objects in the network (e.g., find authors in the similar field and with similar reputation), which turns out to be more meaningful in many scenarios compared with random-walk based similarity measures. In order to support fast online query processing for PathSim queries, we develop an efficient solution that partially materializes short meta paths and then concatenates them online to compute top-k results. Experiments on real data sets demonstrate the effectiveness and efficiency of our proposed paradigm.

Evaluating question answering over linked data <=SEP=> The availability of large amounts of open, distributed and structured semantic data on the web has no precedent in the history of computer science. In recent years, there have been important advances in semantic search and question answering over RDF data. In particular, natural language interfaces to online semantic data have the advantage that they can exploit the expressive power of Semantic Web data models and query languages, while at the same time hiding their complexity from the user. However, despite the increasing interest in this area, there are no evaluations so far that systematically evaluate this kind of systems, in contrast to traditional question answering and search interfaces to document spaces. To address this gap, we have set up a series of evaluation challenges for question answering over linked data. The main goal of the challenge was to get insight into the strengths, capabilities and current shortcomings of question answering systems as interfaces to query linked data sources, as well as benchmarking how these interaction paradigms can deal with the fact that the amount of RDF data available on the web is very large and heterogeneous with respect to the vocabularies and schemas used. Here we report on the results from the first and second of such evaluation campaigns. We also discuss how the second evaluation addressed some of the issues and limitations which arose from the first one, as well as the open issues to be addressed in future competitions.

dbrec - Music Recommendations Using DBpedia <=SEP=> This paper describes the theoretical background and the implementation of dbrec, a music recommendation system built on top of DBpedia, offering recommendations for more than 39,000 bands and solo artists. We discuss the various challenges and lessons learnt while building it, providing relevant insights for people developing applications consuming Linked Data. Furthermore, we provide a user-centric evaluation of the system, notably by comparing it to last.fm.

Query modeling for entity search based on terms, categories, and examples <=SEP=> Users often search for entities instead of documents, and in this setting, are willing to provide extra input, in addition to a series of query terms, such as category information and example entities. We propose a general probabilistic framework for entity search to evaluate and provide insights in the many ways of using these types of input for query modeling. We focus on the use of category information and show the advantage of a category-based representation over a term-based representation, and also demonstrate the effectiveness of category-based expansion using example entities. Our best performing model shows very competitive performance on the INEX-XER entity ranking and list completion tasks.

Health benefits of fruit and vegetables are from additive and synergistic combinations of phytochemicals. <=SEP=> Cardiovascular disease and cancer are ranked as the first and second leading causes of death in the United States and in most industrialized countries. Regular consumption of fruit and vegetables is associated with reduced risks of cancer, cardiovascular disease, stroke, Alzheimer disease, cataracts, and some of the functional declines associated with aging. Prevention is a more effective strategy than is treatment of chronic diseases. Functional foods that contain significant amounts of bioactive components may provide desirable health benefits beyond basic nutrition and play important roles in the prevention of chronic diseases. The key question is whether a purified phytochemical has the same health benefit as does the whole food or mixture of foods in which the phytochemical is present. Our group found, for example, that the vitamin C in apples with skin accounts for only 0.4% of the total antioxidant activity, suggesting that most of the antioxidant activity of fruit and vegetables may come from phenolics and flavonoids in apples. We propose that the additive and synergistic effects of phytochemicals in fruit and vegetables are responsible for their potent antioxidant and anticancer activities, and that the benefit of a diet rich in fruit and vegetables is attributed to the complex mixture of phytochemicals present in whole foods.

Population-based norms for the Mini-Mental State Examination by age and educational level. <=SEP=> OBJECTIVE
To report the distribution of Mini-Mental State Examination (MMSE) scores by age and educational level.


DESIGN
National Institute of Mental Health Epidemiologic Catchment Area Program surveys conducted between 1980 and 1984.


SETTING
Community populations in New Haven, Conn; Baltimore, Md; St Louis, Mo; Durham, NC; and Los Angeles, Calif.


PARTICIPANTS
A total of 18,056 adult participants selected by probability sampling within census tracts and households.


MAIN OUTCOME MEASURES
Summary scores for the MMSE are given in the form of mean, median, and percentile distributions specific for age and educational level.


RESULTS
The MMSE scores were related to both age and educational level. There was an inverse relationship between MMSE scores and age, ranging from a median of 29 for those 18 to 24 years of age, to 25 for individuals 80 years of age and older. The median MMSE score was 29 for individuals with at least 9 years of schooling, 26 for those with 5 to 8 years of schooling, and 22 for those with 0 to 4 years of schooling.


CONCLUSIONS
Cognitive performance as measured by the MMSE varies within the population by age and education. The cause of this variation has yet to be determined. Mini-Mental State Examination scores should be used to identify current cognitive difficulties and not to make formal diagnoses. The results presented should prove to be useful to clinicians who wish to compare an individual patient's MMSE scores with a population reference group and to researchers making plans for new studies in which cognitive status is a variable of interest.

Face recognition based on the uncorrelated discriminant transformation <=SEP=> The extraction of discriminant features is the most fundamental and important problem in face recognition. This paper presents a method to extract optimal discriminant features for face images by using the uncorrelated discriminant transformation and KL expansion. Experiments on the ORL database and the NUST603 database have been performed. Experimental results show that the uncorrelated discriminant transformation is superior to the Foley}Sammon discriminant transformation and the new method to extract uncorrelated discriminant features for face images is very e!ective. An error rate of 2.5% is obtained with the experiments on the ORL database. An average error rate of 1.2% is obtained with the experiments on the NUST603 database. Experiments show that by extracting uncorrelated discriminant features, face recognition could be performed with higher accuracy on lower than 16 16 resolution mosaic images. It is suggested that for the uncorrelated discriminant transformation, the optimal face image resolution can be regarded as the resolution m n which makes the dimensionality N"mn of the original image vector space be larger and closer to the number of known-face classes. 2001 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.

Movie Review Classification Based on a Multiple Classifier <=SEP=> In this paper, we propose a method to classify movie review documents into positive or negative opinions. There are several approaches to classify documents. The previous studies, however, used only a single classifier for the classification task. We describe a multiple classifier for the review document classification task. The method consists of three classifiers based on SVMs, ME and score calculation. We apply two voting methods and SVMs to the integration process of single classifiers. The integrated methods improved the accuracy as compared with the three single classifiers. The experimental results show the effectiveness of our method.

Gödel Machines: Fully Self-referential Optimal Universal Self-improvers <=SEP=> We present the first class of mathematically rigorous, general, fully self-referential, self-improving, optimally efficient problem solvers. Inspired by Kurt Gödel’s celebrated self-referential formulas (1931), such a problem solver rewrites any part of its own code as soon as it has found a proof that the rewrite is useful, where the problem-dependent utility function and the hardware and the entire initial code are described by axioms encoded in an initial proof searcher which is also part of the initial code. The searcher systematically and efficiently tests computable proof techniques (programs whose outputs are proofs) until it finds a provably useful, computable self-rewrite. We show that such a self-rewrite is globally optimal—no local maxima!—since the code first had to prove that it is not useful to continue the proof search for alternative self-rewrites. Unlike previous non-self-referential methods based on hardwired proof searchers, ours not only boasts an optimal order of complexity but can optimally reduce any slowdowns hidden by the O()-notation, provided the utility of such speed-ups is provable at all.

A UWB based Localization System for Indoor Robot Navigation <=SEP=> For robots to become more popular for domestic applications, the short comings of current indoor navigation technologies have to be overcome. In this paper, we propose the use of UWB-IR for indoor robot navigation. Various parts of an actual implementation of a UWB-IR based robot navigation system such as system architecture, RF sub-system design, antennas and localization algorithms are discussed. It is shown that by properly addressing the various issues, a localization error of less than 25 cm can be achieved at all points within a realistic indoor localization space.

A Comparative Analysis and Study of Multiview CNN Models for Joint Object Categorization and Pose Estimation <=SEP=> In the Object Recognition task, there exists a dichotomy between the categorization of objects and estimating object pose, where the former necessitates a view-invariant representation, while the latter requires a representation capable of capturing pose information over different categories of objects. With the rise of deep architectures, the prime focus has been on object category recognition. Deep learning methods have achieved wide success in this task. In contrast, object pose estimation using these approaches has received relatively less attention. In this work, we study how Convolutional Neural Networks (CNN) architectures can be adapted to the task of simultaneous object recognition and pose estimation. We investigate and analyze the layers of various CNN models and extensively compare between them with the goal of discovering how the layers of distributed representations within CNNs represent object pose information and how this contradicts with object category representations. We extensively experiment on two recent large and challenging multi-view datasets and we achieve better than the state-of-the-art.

Broder and Mitzenmacher : Network Applications of Bloom Filters : A Survey 487 <=SEP=> A Bloom filter is a simple space-efficient randomized data structure for representing a set in order to support membership queries. Bloom filters allow false positives but the space savings often outweigh this drawback when the probability of an error is controlled. Bloom filters have been used in database applications since the 1970s, but only in recent years have they become popular in the networking literature. The aim of this paper is to survey the ways in which Bloom filters have been used and modified in a variety of network problems, with the aim of providing a unified mathematical and practical framework for understanding them and stimulating their use in future applications.

Mechanical thrombectomy in acute ischemic stroke: Consensus statement by ESO-Karolinska Stroke Update 2014/2015, supported by ESO, ESMINT, ESNR and EAN. <=SEP=> The original version of this consensus statement on mechanical thrombectomy was approved at the European Stroke Organisation (ESO)-Karolinska Stroke Update conference in Stockholm, 16-18 November 2014. The statement has later, during 2015, been updated with new clinical trials data in accordance with a decision made at the conference. Revisions have been made at a face-to-face meeting during the ESO Winter School in Berne in February, through email exchanges and the final version has then been approved by each society. The recommendations are identical to the original version with evidence level upgraded by 20 February 2015 and confirmed by 15 May 2015. The purpose of the ESO-Karolinska Stroke Update meetings is to provide updates on recent stroke therapy research and to discuss how the results may be implemented into clinical routine. Selected topics are discussed at consensus sessions, for which a consensus statement is prepared and discussed by the participants at the meeting. The statements are advisory to the ESO guidelines committee. This consensus statement includes recommendations on mechanical thrombectomy after acute stroke. The statement is supported by ESO, European Society of Minimally Invasive Neurological Therapy (ESMINT), European Society of Neuroradiology (ESNR), and European Academy of Neurology (EAN).

PinMe: Tracking a Smartphone User around the World <=SEP=> With the pervasive use of smartphones that sense, collect, and process valuable information about the environment, ensuring location privacy has become one of the most important concerns in the modern age. A few recent research studies discuss the feasibility of processing sensory data gathered by a smartphone to locate the phone’s owner, even when the user does not intend to share his location information, e.g., when the user has turned off the Global Positioning System (GPS) on the device. Previous research efforts rely on at least one of the two following fundamental requirements, which impose significant limitations on the adversary: (i) the attacker must accurately know either the user’s initial location or the set of routes through which the user travels and/or (ii) the attacker must measure a set of features, e.g., device acceleration, for different potential routes in advance and construct a training dataset. In this paper, we demonstrate that neither of the above-mentioned requirements is essential for compromising the user’s location privacy. We describe PinMe, a novel user-location mechanism that exploits non-sensory/sensory data stored on the smartphone, e.g., the environment’s air pressure and device’s timezone, along with publicly-available auxiliary information, e.g., elevation maps, to estimate the user’s location when all location services, e.g., GPS, are turned off. Unlike previously-proposed attacks, PinMe neither requires any prior knowledge about the user nor a training dataset on specific routes. We demonstrate that PinMe can accurately estimate the user’s location during four activities (walking, traveling on a train, driving, and traveling on a plane). We also suggest several defenses against the proposed attack.

COSY: An Energy-Efficient Hardware Architecture for Deep Convolutional Neural Networks Based on Systolic Array <=SEP=> Deep convolutional neural networks (CNNs) show extraordinary abilities in artificial intelligence applications, but their large scale of computation usually limits their uses on resource-constrained devices. For CNN’s acceleration, exploiting the data reuse of CNNs is an effective way to reduce bandwidth and energy consumption. Row-stationary (RS) dataflow of Eyeriss is one of the most energy-efficient state-of-the-art hardware architectures, but has redundant storage usage and data access, so the data reuse has not been fully exploited. It also requires complex control and is intrinsically unable to skip over zero-valued inputs in timing. In this paper, we present COSY (CNN on Systolic Array), an energy-efficient hardware architecture based on the systolic array for CNNs. COSY adopts the method of systolic array to achieve the storage sharing between processing elements (PEs) in RS dataflow at the RF level, which reduces low-level energy consumption and on-chip storage. Multiple COSY arrays sharing the same storage can execute multiple 2-D convolutions in parallel, further increasing the data reuse in the low-level storage and improving throughput. To compare the energy consumption of COSY and Eyeriss running actual CNN models, we build a process-based energy consumption evaluation system according to the hardware storage hierarchy. The result shows that COSY can achieve an over 15% reduction in energy consumption under the same constraints, improving the theoretical Energy-Delay Product (EDP) and Energy-Delay Squared Product (ED^2P) by 1.33 x on average. In addition, we prove that COSY has the intrinsic ability for zero-skipping, which can further increase the improvements to 2.25× and 3.83× respectively.

The Aneka platform and QoS-driven resource provisioning for elastic applications on hybrid Clouds <=SEP=> Cloud computing alters the way traditional software systems are built and run by introducing a utilitybased model for delivering IT infrastructure, platforms, applications, and services. The consolidation of this new paradigm in both enterprises and academia demanded reconsideration in the way IT resources are used, so Cloud computing can be used together with available resources. A case for the utilization of Clouds for increasing the capacity of computing infrastructures is Desktop Grids: these infrastructures typically provide best effort execution of high throughput jobs and other workloads that fit the model of the platform. By enhancing Desktop Grid infrastructures with Cloud resources, it is possible to offer QoS to users, motivating the adoption of Desktop Grids as a viable platform for application execution. In this paper,we describe howAneka, a platform for developing scalable applications on the Cloud, supports such a vision by provisioning resources fromdifferent sources and supporting different applicationmodels.We highlight the key concepts and features of Aneka that support the integration between Desktop Grids and Clouds and present an experiment showing the performance of this integration. © 2011 Elsevier B.V. All rights reserved.

Analysis of user keyword similarity in online social networks <=SEP=> How do two people become friends? What role does homophily play in bringing two people closer to help them forge friendship? Is the similarity between two friends different from the similarity between any two people? How does the similarity between a friend of a friend compare to similarity between direct friends? In this work, our goal is to answer these questions. We study the relationship between semantic similarity of user profile entries and the social network topology. A user profile in an on-line social network is characterized by its profile entries. The entries are termed as user keywords. We develop a model to relate keywords based on their semantic relationship and define similarity functions to quantify the similarity between a pair of users. First, we present a ‘forest model’ to categorize keywords across multiple categorization trees and define the notion of distance between keywords. Second, we use the keyword distance to define similarity functions between a pair of users. Third, we analyze a set of Facebook data according to the model to determine the effect of homophily in on-line social networks. Based on our evaluations, we conclude that direct friends are more similar than any other user pair. However, the more striking observation is that except for direct friends, similarities between users are approximately equal, irrespective of the topological distance between them.

An introduction to recursive partitioning: rationale, application, and characteristics of classification and regression trees, bagging, and random forests. <=SEP=> Recursive partitioning methods have become popular and widely used tools for nonparametric regression and classification in many scientific fields. Especially random forests, which can deal with large numbers of predictor variables even in the presence of complex interactions, have been applied successfully in genetics, clinical medicine, and bioinformatics within the past few years. High-dimensional problems are common not only in genetics, but also in some areas of psychological research, where only a few subjects can be measured because of time or cost constraints, yet a large amount of data is generated for each subject. Random forests have been shown to achieve a high prediction accuracy in such applications and to provide descriptive variable importance measures reflecting the impact of each variable in both main effects and interactions. The aim of this work is to introduce the principles of the standard recursive partitioning methods as well as recent methodological improvements, to illustrate their usage for low and high-dimensional data exploration, but also to point out limitations of the methods and potential pitfalls in their practical application. Application of the methods is illustrated with freely available implementations in the R system for statistical computing.

Big Data Analytics for Air Quality Monitoring at a Logistics Shipping Base via Autonomous Wireless Sensor Network Technologies <=SEP=> The indoor air quality in industrial workplace buildings, e.g. air temperature, humidity and levels of carbon dioxide (CO2), play a critical role in the perceived levels of workers' comfort and in reported medical health. CO2 can act as an oxygen displacer, and in confined spaces humans can have, for example, reactions of dizziness, increased heart rate and blood pressure, headaches, and in more serious cases loss of consciousness. Specialized organizations can be brought in to monitor the work environment for limited periods. However, new low cost wireless sensor network (WSN) technologies offer potential for more continuous and autonomous assessment of industrial workplace air quality. Central to effective decision making is the data analytics approach and visualization of what is potentially, big data (BD) in monitoring the air quality in industrial workplaces. This paper presents a case study that monitors air quality that is collected with WSN technologies. We discuss the potential BD problems. The case trials are from two workshops that are part of a large on-shore logistics base a regional shipping industry in Norway. This small case study demonstrates a monitoring and visualization approach for facilitating BD in decision making for health and safety in the shipping industry. We also identify other potential applications of WSN technologies and visualization of BD in the workplace environments; for example, for monitoring of other substances for worker safety in high risk industries and for quality of goods in supply chain management.

Detecting Suicidal Ideation in Chinese Microblogs with Psychological Lexicons <=SEP=> Suicide is among the leading causes of death in China. However, technical approaches toward preventing suicide are challenging and remaining under development. Recently, several actual suicidal cases were preceded by users who posted micro blogs with suicidal ideation to Sina Weibo, a Chinese social media network akin to Twitter. It would therefore be desirable to detect suicidal ideations from micro blogs in real-time, and immediately alert appropriate support groups, which may lead to successful prevention. In this paper, we propose a real-time suicidal ideation detection system deployed over Weibo, using machine learning and known psychological techniques. Currently, we have identified 53 known suicidal cases who posted suicide notes on Weibo prior to their deaths. We explore linguistic features of these known cases using a psychological lexicon dictionary, and train an effective suicidal Weibo post detection model. 6714 tagged posts and several classifiers are used to verify the model. By combining both machine learning and psychological knowledge, SVM classifier has the best performance of different classifiers, yielding an F-measure of 68:3%, a Precision of 78:9%, and a Recall of 60:3%.

A Lie Never Lives to be Old: The Effects of Fake Social Information on Consumer Decision-Making in Crowdfunding <=SEP=> The growing success of social media led to a strong presence of social information such as customer product reviews and product ratings in electronic markets. While this information helps consumers to better assess the quality of goods before purchase, its impact on consumer decision-making also incentivizes sellers to game the system by creating fake data in favor of specific goods in order to deliberately mislead consumers. As a consequence, consumers could make suboptimal choices or could choose to disregard social information altogether. In this exploratory study, we assess the effects nongenuine social information has on the consumer’s decision-making in the context of reward-based crowdfunding. Specifically, we capture unnatural peaks in the number of Facebook Likes that a specific crowdfunding campaign receives on the platforms Kickstarter and Indiegogo and observe subsequent campaign performance. Our results show that fake Facebook Likes have a very short-term positive effect on the number of backers participating in the respective crowdfunding campaign. However, this peak in participation is then followed by a period in which participation is lower than prior to the existence of the non-genuine social information. We further discuss circumstances that foster this artificial manipulation of quality signals.

Provable Inductive Matrix Completion <=SEP=> Consider a movie recommendation system where apart from the ratings information, side information such as user’s age or movie’s genre is also available. Unlike standard matrix completion, in this setting one should be able to predict inductively on new users/movies. In this paper, we study the problem of inductive matrix completion in the exact recovery setting. That is, we assume that the ratings matrix is generated by applying feature vectors to a low-rank matrix and the goal is to recover back the underlying matrix. Furthermore, we generalize the problem to that of low-rank matrix estimation using rank-1 measurements. We study this generic problem and provide conditions that the set of measurements should satisfy so that the alternating minimization method (which otherwise is a non-convex method with no convergence guarantees) is able to recover back the exact underlying low-rank matrix. In addition to inductive matrix completion, we show that two other low-rank estimation problems can be studied in our framework: a) general low-rank matrix sensing using rank-1 measurements, and b) multi-label regression with missing labels. For both the problems, we provide novel and interesting bounds on the number of measurements required by alternating minimization to provably converges to the exact low-rank matrix. In particular, our analysis for the general low rank matrix sensing problem significantly improves the required storage and computational cost than that required by the RIP-based matrix sensing methods [1]. Finally, we provide empirical validation of our approach and demonstrate that alternating minimization is able to recover the true matrix for the above mentioned problems using a small number of measurements.

For better or for worse: neural systems supporting the cognitive down- and up-regulation of negative emotion <=SEP=> Functional neuroimaging studies examining the neural bases of the cognitive control of emotion have found increased prefrontal and decreased amygdala activation for the reduction or down-regulation of negative emotion. It is unknown, however, (1) whether the same neural systems underlie the enhancement or up-regulation of emotion, and (2) whether altering the nature of the regulatory strategy alters the neural systems mediating the regulation. To address these questions using functional magnetic resonance imaging (fMRI), participants up- and down-regulated negative emotion either by focusing internally on the self-relevance of aversive scenes or by focusing externally on alternative meanings for pictured actions and their situational contexts. Results indicated (1a) that both up- and down-regulating negative emotion recruited prefrontal and anterior cingulate regions implicated in cognitive control, (1b) that amygdala activation was modulated up or down in accord with the regulatory goal, and (1c) that up-regulation uniquely recruited regions of left rostromedial PFC implicated in the retrieval of emotion knowledge, whereas down-regulation uniquely recruited regions of right lateral and orbital PFC implicated in behavioral inhibition. Results also indicated that (2) self-focused regulation recruited medial prefrontal regions implicated in internally focused processing, whereas situation-focused regulation recruited lateral prefrontal regions implicated in externally focused processing. These data suggest that both common and distinct neural systems support various forms of reappraisal and that which particular prefrontal systems modulate the amygdala in different ways depends on the regulatory goal and strategy employed.

Using Ground Reaction Forces from Gait Analysis: Body Mass as a Weak Biometric <=SEP=> Ground reaction forces generated during normal walking have recently been used to identify and/or classify individuals based upon the pattern of the forces observed over time. One feature that can be extracted from vertical ground reaction forces is body mass. This single feature has identifying power comparable to other studies that use multiple and more complex features. This study contributes to understanding the role of body mass in identification by (1) quantifying the accuracy and precision with which body mass can be obtained using vertical ground reaction forces, (2) quantifying the distribution of body mass across a population larger than has previously been studied in relation to gait analysis, and (3) quantifying the expected identification capabilities of systems using body mass as a weak biometric. Our results show that body mass can be measured in a fraction of a second with less than a 1 kilogram standard deviation of error.

Machine learning based malware classification for Android applications using multimodal image representations <=SEP=> The popularity of smartphones usage especially Android mobile platform has increased to 80% of share in global smartphone operating systems market, as a result of which it is on the top in the attacker's target list. The fact of having more private data and low security assurance letting the attacker to write several malware programs in different ways for smartphone, and the possibility of obfuscating the malware detection applications through different coding techniques is giving more energy to attacker. Several approaches have been proposed to detect malwares through code analysis which are now severely facing the problem of code obfuscation and high computation requirement. We propose a machine learning based method to detect android malware by analyzing the visual representation of binary formatted apk file into Grayscale, RGB, CMYK and HSL. GIST feature from malware and benign image dataset were extracted and used to train machine learning algorithms. Initial experimental results are encouraging and computationally effective. Among machine learning algorithms Random Forest have achieved highest accuracy of 91% for grayscale image, which can be further improved by tuning the various parameters.

The reliability and validity of the Face, Legs, Activity, Cry, Consolability observational tool as a measure of pain in children with cognitive impairment. <=SEP=> UNLABELLED
Pain assessment remains difficult in children with cognitive impairment (CI). In this study, we evaluated the validity and reliability of the Face, Legs, Activity, Cry, Consolability (FLACC) tool for assessing pain in children with CI. Each child's developmental level and ability to self-report pain were evaluated. The child's nurse observed and scored pain with the FLACC tool before and after analgesic administration. Simultaneously, parents scored pain with a visual analog scale, and scores were obtained from children who were able to self-report pain. Observations were videotaped and later viewed by nurses blinded to analgesics and pain scores. One-hundred-forty observations were recorded from 79 children. FLACC scores correlated with parent scores (P < 0.001) and decreased after analgesics (P = 0.001), suggesting good validity. Correlations of total scores (r = 0.5-0.8; P < 0.001) and of each category (r = 0.3-0.8; P < 0.001), as well as measures of exact agreement (kappa = 0.2-0.65), suggest good reliability. Test-retest reliability was supported by excellent correlations (r = 0.8-0.883; P < 0.001) and categorical agreement (r = 0.617-0.935; kappa = 0.400-0.881; P < 0.001). These data suggest that the FLACC tool may be useful as an objective measure of postoperative pain in children with CI.


IMPLICATIONS
The FLACC pain assessment tool may facilitate reliable and valid observational pain assessment in children with cognitive impairment who cannot self-report their pain. Objective pain assessment is important to facilitate effective postoperative pain management in these vulnerable children.

A 10-Gb/s receiver with a continuous-time linear equalizer and 1-tap decision-feedback equalizer <=SEP=> This paper presents a wireline receiver design of CMOS I/O at 10-Gb/s data rate. A power efficient continuous-time linear equalizer (CTLE) and 1-tap lookahead decision feedback equalizer (DFE) are designed and implemented in a 45 nm CMOS technology. The DFE employs a sampler including a current injection section that makes no use of summer as a separated block. In addition, cascode structure increases kick-back noise immunity and reduces power consumption by 11%. The PLL used in the proposed receiver drives 5 GHz clock frequency with 12.62 pspk-pk jitter. The core receiver circuit consumes 14.3 mW at a 1.1 V supply voltage when processing 10 Gb/s data rate with 15 dB of channel loss at Nyquist frequency.

Transaction aggregation as a strategy for credit card fraud detection <=SEP=> The problem of preprocessing transaction data for supervised fraud classification is considered. It is impractical to present an entire series of transactions to a fraud detection system, partly because of the very high dimensionality of such data but also because of the heterogeneity of the transactions. Hence, a framework for transaction aggregation is considered and its effectiveness is evaluated against transaction-level detection, using a variety of classification methods and a realistic cost-based performance measure. These methods are applied in two case studies using real data. Transaction aggregation is found to be advantageous in many but not all circumstances. Also, the length of the aggregation period has a large impact upon performance. Aggregation seems particularly effective when a random forest is used for classification. Moreover, random forests were found to perform better than other classification methods, including SVMs, logistic regression and KNN. Aggregation also has the advantage of not requiring precisely labeled data and may be more robust to the effects of population drift.

Rapid MPPT for Uniformly and Partial Shaded PV System by Using JayaDE Algorithm in Highly Fluctuating Atmospheric Conditions <=SEP=> In photovoltaic (PV) array, the output power and the power–voltage (P–V ) characteristic of PV array are totally dependent on the temperature and solar insolation. Therefore, if these atmospheric parameters fluctuate rapidly, then the maximum power point (MPP) of the P–V curve of PV array also fluctuates very rapidly. This rapid fluctuation of the MPP may be in accordance with the uniform shading of the PV panel or may be in accordance to the partially shaded due to the clouds, tall building, trees, and raindrops. However, in both cases, the MPP tracking (MPPT) is not only a nonlinear problem, this becomes a highly nonlinear problem, which solution is time bounded. Because the highly fluctuating atmospheric conditions change the P–V characteristic after every small time duration. This paper introduces a hybrid of “Jaya” and “differential evolution (DE)” (JayaDE) technique for MPPT in the highly fluctuating atmospheric conditions. This JayaDE algorithm is tested on MATLAB simulator and is verified on a developed hardware of the solar PV system, which consists of a single peak and many multiple peaks in the voltage–power curve. Moreover, the tracking ability is compared with the recent state of the art methods. The satisfactory steady-state and dynamic performances of this new hybrid technique under variable irradiance and temperature levels show the superiority over the state-of-the-art control methods.