nils-herrmann/clean_scirepeval huggingface dataset:
An Investigative Study for Smart Home Security: Issues, Challenges and Countermeasures

Home security should be a top concern for everyone who owns or rents a home. Moreover, safe and secure residential space is the necessity of every individual as most of the family members are working. The home is left unattended for most of the day-time and home invasion crimes are at its peak as constantly monitoring of the home is difficult. Another reason for the need of home safety is specifically when the elderly person is alone or the kids are with baby-sitter and servant. Home security system i.e. HomeOS is thus applicable and desirable for resident’s safety and convenience. This will be achieved by turning your home into a smart home by intelligent remote monitoring. Smart home comes into picture for the purpose of controlling and monitoring the home. It will give you peace of mind, as you can have a close watch and stay connected anytime, anywhere. But, is common man really concerned about home security? An investigative study was done by conducting a survey to get the inputs from different people from diverse backgrounds. The main motivation behind this survey was to make people aware of advanced HomeOS and analyze their need for security. This paper also studied the necessity of HomeOS investigative study in current situation where the home burglaries are rising at an exponential rate. In order to arrive at findings and conclusions, data were analyzed. The graphical method was employed to identify the relative significance of home security. From this analysis, we can infer that the cases of having kids and aged person at home or location of home contribute significantly to the need of advanced home security system. At the end, the proposed system model with its flow and the challenges faced while implementing home security systems are also discussed.
Classifying and visualizing motion capture sequences using deep neural networks

The gesture recognition using motion capture data and depth sensors has recently drawn more attention in vision recognition. Currently most systems only classify dataset with a couple of dozens different actions. Moreover, feature extraction from the data is often computational complex. In this paper, we propose a novel system to recognize the actions from skeleton data with simple, but effective, features using deep neural networks. Features are extracted for each frame based on the relative positions of joints (PO), temporal differences (TD), and normalized trajectories of motion (NT). Given these features a hybrid multi-layer perceptron is trained, which simultaneously classifies and reconstructs input data. We use deep autoencoder to visualize learnt features. The experiments show that deep neural networks can capture more discriminative information than, for instance, principal component analysis can. We test our system on a public database with 65 classes and more than 2,000 motion sequences. We obtain an accuracy above 95% which is, to our knowledge, the state of the art result for such a large dataset.
Unemployment and Labor Market Rigidities : Europe versus North America

H ere is the received wisdom. The European job market is rigid and inflexible.  high unemployment. The North American job market is dynamic and flexible.  low unemployment. So Europeans had better do something about their labor markets unless they want permanent double digit unemployment. In fact, this is not totally wrong. There are features of the labor markets in some European countries that help sustain high levels of unemployment. Some of these features can be thought of as rigidities. However, there are many other so-called rigidities that do not cause high unemployment and, indeed, may serve a useful purpose. So it is important to know which features of the labor market cause high unemployment and which do not. This is the subject of what follows.
A robust layered control system for a mobile robot

A new architecture for controlling mobile robots is described. Layers of control system are built to let the robot operate at increasing levels of competence. Layers are made up of asynchronous modules that communicate over low-bandwidth channels. Each module is an instance of a fairly simple computational machine. Higher-level layers can subsume the roles of lower levels by suppressing their outputs. However, lower levels continue to function as higher levels are added. The result is a robust and flexible robot control system. The system has been used to control a mobile robot wandering around unconstrained laboratory areas and computer machine rooms. Eventually it is intended to control a robot that wanders the office areas of our laboratory, building maps of its surroundings using an onboard arm to perform simple tasks.
CIMDS: Adapting Postprocessing Techniques of Associative Classification for Malware Detection

Malware is software designed to infiltrate or damage a computer system without the owner's informed consent (e.g., viruses, backdoors, spyware, trojans, and worms). Nowadays, numerous attacks made by the malware pose a major security threat to computer users. Unfortunately, along with the development of the malware writing techniques, the number of file samples that need to be analyzed, named "gray list," on a daily basis is constantly increasing. In order to help our virus analysts, quickly and efficiently pick out the malicious executables from the "gray list," an automatic and robust tool to analyze and classify the file samples is needed. In our previous work, we have developed an intelligent malware detection system (IMDS) by adopting associative classification method based on the analysis of application programming interface (API) execution calls. Despite its good performance in malware detection, IMDS still faces the following two challenges: (1) handling the large set of the generated rules to build the classifier; and (2) finding effective rules for classifying new file samples. In this paper, we first systematically evaluate the effects of the postprocessing techniques (e.g., rule pruning, rule ranking, and rule selection) of associative classification in malware detection, and then, propose an effective way, i.e., CIDCPF, to detect the malware from the "gray list." To the best of our knowledge, this is the first effort on using postprocessing techniques of associative classification in malware detection. CIDCPF adapts the postprocessing techniques as follows: first applying Chi-square testing and Insignificant rule pruning followed by using Database coverage based on the Chi-square measure rule ranking mechanism and Pessimistic error estimation, and finally performing prediction by selecting the best First rule. We have incorporated the CIDCPF method into our existing IMDS system, and we call the new system as CIMDS system. Case studies are performed on the large collection of file samples obtained from the Antivirus Laboratory at Kingsoft Corporation and promising experimental results demonstrate that the efficiency and ability of detecting malware from the "gray list" of our CIMDS system outperform popular antivirus software tools, such as McAfee VirusScan and Norton Antivirus, as well as previous data-mining-based detection systems, which employed Naive Bayes, support vector machine, and decision tree techniques. In particular, our CIMDS system can greatly reduce the number of generated rules, which makes it easy for our virus analysts to identify the useful ones.
Resting frontal EEG asymmetry as an endophenotype for depression risk: sex-specific patterns of frontal brain asymmetry.

Resting frontal electroencephalographic (EEG) asymmetry has been hypothesized as a marker of risk for major depressive disorder (MDD), but the extant literature is based predominately on female samples. Resting frontal asymmetry was assessed on 4 occasions within a 2-week period in 306 individuals aged 18-34 (31% male) with (n = 143) and without (n = 163) lifetime MDD as defined by the Diagnostic and Statistical Manual of Mental Disorders, 4th edition (American Psychiatric Association, 1994). Lifetime MDD was linked to relatively less left frontal activity for both sexes using a current source density (CSD) reference, findings that were not accounted for solely by current MDD status or current depression severity, suggesting that CSD-referenced EEG asymmetry is a possible endophenotype for depression. In contrast, results for average and linked mastoid references were less consistent but demonstrated a link between less left frontal activity and current depression severity in women.
Hyperprofile-Based Computation Offloading for Mobile Edge Networks

In recent studies, researchers have developed various computation offloading frameworks for bringing cloud services closer to the user via edge networks. Specifically, an edge device needs to offload computationally intensive tasks because of energy and processing constraints. These constraints present the challenge of identifying which edge nodes should receive tasks to reduce overall resource consumption. We propose a unique solution to this problem which incorporates elements from Knowledge-Defined Networking (KDN) to make intelligent predictions about offloading costs based on historical data. Each server instance can be represented in a multidimensional feature space where each dimension corresponds to a predicted metric. We compute features for a "hyperprofile" and position nodes based on the predicted costs of offloading a particular task. We then perform a k-Nearest Neighbor (kNN) query within the hyperprofile to select nodes for offloading computation. This paper formalizes our hyperprofile-based solution and explores the viability of using machine learning (ML) techniques to predict metrics useful for computation offloading. We also investigate the effects of using different distance metrics for the queries. Our results show various network metrics can be modeled accurately with regression, and there are circumstances where kNN queries using Euclidean distance as opposed to rectilinear distance is more favorable.
Clothing genre classification by exploiting the style elements

This paper presents a novel approach to automatically classify the upperwear genre from a full-body input image with no restrictions of model poses, image backgrounds, and image resolutions. Five style elements, that are crucial for clothing recognition, are identified based on the clothing design theory. The corresponding features of each of these style elements are also designed. We illustrate the effectiveness of our approach by showing that the proposed algorithm achieved overall precision of 92.04%, recall of 92.45%, and F score of 92.25% with 1,077 clothing images crawled from popular online stores.
Business Intelligence (Bi) Success and the Role of Bi Capabilities

Business intelligence (BI) has become the top priority for many organizations who have implemented BI solutions to improve their decision-making process. Yet, not all BI initiatives have fulfilled the expectations. We suggest that one of the reasons for failure is the lack of an understanding of the critical factors that define the success of BI applications, and that BI capabilities are among those critical factors. We present findings from a survey of 116 BI professionals that provides a snapshot of user satisfaction with various BI capabilities and the relationship between these capabilities and user satisfaction with BI. Our findings suggest that users are generally satisfied with BI overall and with BI capabilities. However, the BI capabilities with which they are most satisfied are not necessarily the ones that are the most strongly related to BI success. Of the five capabilities that were the most highly correlated with overall satisfaction with BI, only one was specifically related to data. Another interesting finding implies that, although users are not highly satisfied with the level of interaction of BI with other systems, this capability is highly correlated with BI success. Implications of these findings for the successful use and management of BI are discussed.
Evaluation of machine learning classifiers for mobile malware detection

Mobile devices have become a significant part of people’s lives, leading to an increasing number of users involved with such technology. The rising number of users invites hackers to generate malicious applications. Besides, the security of sensitive data available on mobile devices is taken lightly. Relying on currently developed approaches is not sufficient, given that intelligent malware keeps modifying rapidly and as a result becomes more difficult to detect. In this paper, we propose an alternative solution to evaluating malware detection using the anomaly-based approach with machine learning classifiers. Among the various network traffic features, the four categories selected are basic information, content based, time based and connection based. The evaluation utilizes two datasets: public (i.e. MalGenome) and private (i.e. self-collected). Based on the evaluation results, both the Bayes network and random forest classifiers produced more accurate readings, with a 99.97 % true-positive rate (TPR) as opposed to the multi-layer perceptron with only 93.03 % on the MalGenome dataset. However, this experiment revealed that the k-nearest neighbor classifier efficiently detected the latest Android malware with an 84.57 % truepositive rate higher than other classifiers. Communicated by V. Loia. F. A. Narudin · A. Gani Mobile Cloud Computing (MCC), University of Malaya, 50603 Kuala Lumpur, Malaysia A. Feizollah (B) · N. B. Anuar Security Research Group (SECReg), Faculty of Computer Science and Information Technology, University of Malaya, 50603 Kuala Lumpur, Malaysia e-mail: ali.feizollah@siswa.um.edu.my
Applications, Architectures, and Protocol Design Issues for Mobile Social Networks: A Survey

The mobile social network (MSN) combines techniques in social science and wireless communications for mobile networking. The MSN can be considered as a system which provides a variety of data delivery services involving the social relationship among mobile users. This paper presents a comprehensive survey on the MSN specifically from the perspectives of applications, network architectures, and protocol design issues. First, major applications of the MSN are reviewed. Next, different architectures of the MSN are presented. Each of these different architectures supports different data delivery scenarios. The unique characteristics of social relationship in MSN give rise to different protocol design issues. These research issues (e.g., community detection, mobility, content distribution, content sharing protocols, and privacy) and the related approaches to address data delivery in the MSN are described. At the end, several important research directions are outlined.
Ageing rate of paper insulation used in power transformers Part 2: Oil/paper system with medium and high oxygen concentration

Utility asset managers and engineers are concerned about the life and reliability of their power transformers which depends on the continued life of the paper insulation. The ageing rate of the paper is affected by water, oxygen and acids. Traditionally, the ageing rate of paper has been studied in sealed vessels however this approach does not allow the possibility to assess the affect of oxygen on paper with different water content. The ageing rate of paper has been studied for dry paper in air (excess oxygen). In these experiments we studied the ageing rate of Kraft and thermally upgraded Kraft paper in medium and high oxygen with varying water content. Furthermore, the oxygen content of the oil in sealed vessels is low which represents only sealed transformers. The ageing rate of the paper has not been determined for free breathing transformers with medium or high oxygen content and for different wetness of paper. In these ageing experiments the water and oxygen content was controlled using a special test rig to compare the ageing rate to previous work and to determine the ageing effect of paper by combining temperature, water content of paper and oxygen content of the oil. We found that the ageing rate of paper with the same water content increased with oxygen content in the oil. Hence, new life curves were developed based on the water content of the paper and the oxygen content of the oil.
A 16 bit linear passive-charge-sharing SAR ADC in 55nm CMOS

This paper presents a first reported passive-charge-sharing SAR ADC that achieves 16 bit linearity. It is known that on chip passive-charge-sharing suffers from poor linearity due to the unregulated reference voltage during bit trials. The proposed unique ADC architecture and calibration technique addresses the issue of signal dependent reference voltage droop during SAR ADC bit trials and orthogonalize the bit weights to achieve 16bit linearity. In addition, the proposed architecture maximizes SNR by sampling on to the bit cap, the first reported in this type of SAR ADC. Measurement result from a prototype test chip shows +/−0.8 LSB (16-bit level) INL at 1MSPS.
Aesthetics and credibility in web site design

Web sites often provide the first impression of an organization. For many organizations, web sites are crucial to ensure sales or to procure services within. When a person opens a web site, the first impression is probably made in a few seconds, and the user will either stay or move on to the next site on the basis of many factors. One of the factors that may influence users to stay or go is the page aesthetics. Another reason may involve a user’s judgment about the site’s credibility. This study explores the possible link between page aesthetics and a user’s judgment of the site’s credibility. Our findings indicate that when the same content is presented using different levels of aesthetic treatment, the content with a higher aesthetic treatment was judged as having higher credibility. We call this the amelioration effect of visual design and aesthetics on content credibility. Our study suggests that this effect is operational within the first few seconds in which a user views a web page. Given the same content, a higher aesthetic treatment will increase perceived credibility.
Implementation and performance analysis of non-blocking collective operations for MPI

Collective operations and non-blocking point-to-point operations have always been part of MPI. Although non-blocking collective operations are an obvious extension to MPI, there have been no comprehensive studies of this functionality. In this paper we present LibNBC, a portable high-performance library for implementing non-blocking collective MPI communication operations. LibNBC provides non-blocking versions of all MPI collective operations, is layered on top of MPI-1, and is portable to nearly all parallel architectures. To measure the performance characteristics of our implementation, we also present a microbenchmark for measuring both latency and overlap of computation and communication. Experimental results demonstrate that the blocking performance of the collective operations in our library is comparable to that of collective operations in other high-performance MPI implementations. Our library introduces a very low overhead between the application and the underlying MPI and thus, in conjunction with the potential to overlap communication with computation, offers the potential for optimizing real-world applications.
Factorization-Based Texture Segmentation

This paper introduces a factorization-based approach that efficiently segments textured images. We use local spectral histograms as features, and construct an M × N feature matrix using M-dimensional feature vectors in an N-pixel image. Based on the observation that each feature can be approximated by a linear combination of several representative features, we factor the feature matrix into two matrices-one consisting of the representative features and the other containing the weights of representative features at each pixel used for linear combination. The factorization method is based on singular value decomposition and nonnegative matrix factorization. The method uses local spectral histograms to discriminate region appearances in a computationally efficient way and at the same time accurately localizes region boundaries. The experiments conducted on public segmentation data sets show the promise of this simple yet powerful approach.
SIFT: Scale Invariant Feature Transform (Review)

This paper presents a study on SIFT (Scale Invariant Feature transform) which is a method for extracting distinctive invariant features from images that can be used to perform reliable matching between different views of an object or scene. The features are invariant to image scaling, translation, and rotation, and partially invariant to illumination changes and affine or 3D projection. There are various applications of SIFT that includes object recognition, robotic mapping and navigation, image stitching, 3D modeling, gesture recognition, video tracking, individual identification of wildlife and match moving.
Bio-inspired Unsupervised Learning of Visual Features Leads to Robust Invariant Object Recognition

Retinal image of surrounding objects varies tremendously due to the changes in position, size, pose, illumination condition, background context, occlusion, noise, and non-rigid deformations. But despite these huge variations, our visual system is able to invariantly recognize any object in just a fraction of a second. To date, various computational models have been proposed to mimic the hierarchical processing gically inspired network architecture and learning rule significantly improves the models' performance when facing challenging invariant object recognition problems. Our model is an asynchronous feedforward spiking neural network. When the network is presented with natural images, the neurons in the entry layers detect edges, and the most activated ones fire first, while neurons in higher layers are equipped with spike timing-dependent plasticity. These neurons progressively become selective to intermediate complexity visual features appropriate for object categorization. The model is evaluated on 3D-Object and ETH-80 datasets which are two benchmarks for invariant object recognition, and is shown to outperform state-of-the-art models, including DeepConvNet and HMAX. This demonstrates its ability to accurately recognize different instances of multiple object classes even under various appearance conditions (different views, scales, tilts, and backgrounds). Several statistical analysis techniques are used to show that our model extracts class specific and highly informative features. & 2016 Elsevier B.V. All rights reserved.
Understanding Membership Inferences on Well-Generalized Learning Models

Membership Inference Attack (MIA) determines the presence of a record in a machine learning model’s training data by querying the model. Prior work has shown that the attack is feasible when the model is overfitted to its training data or when the adversary controls the training algorithm. However, when the model is not overfitted and the adversary does not control the training algorithm, the threat is not well understood. In this paper, we report a study that discovers overfitting to be a sufficient but not a necessary condition for an MIA to succeed. More specifically, we demonstrate that even a well-generalized model contains vulnerable instances subject to a new generalized MIA (GMIA). In GMIA, we use novel techniques for selecting vulnerable instances and detecting their subtle influences ignored by overfitting metrics. Specifically, we successfully identify individual records with high precision in real-world datasets by querying black-box machine learning models. Further we show that a vulnerable record can even be indirectly attacked by querying other related records and existing generalization techniques are found to be less effective in protecting the vulnerable instances. Our findings sharpen the understanding of the fundamental cause of the problem: the unique influences the training instance may have on the model.