Reviewerly/exHarmony huggingface dataset:
A Novel Approach for Authorship Verification

The Internet is increasing with huge amount of textual data. The crimes also increased in the Internet along with textual data. The authorship analysis is one important area attracted by the several researchers to reduce the problems raised through the text in the Internet. Authorship verification is one type of authorship analysis which is used to verify an author by checking whether the textual document is written by the disputed author or not. The accuracy of authorship verification majorly depends on the features that are used for distinguishing the style of writing followed in the documents. In the previous works of authorship verification, the researchers proposed various types of stylistic features to distinguish the authors writing style. The researchers analyzed that the performance of authorship verification was poor when the stylistic features were used alone in the experiment. In this work, a new approach is proposed for authorship verification where the content-based features were used in the experiment. The term importance is computed by using term weight measure, and these term weights were used to calculate the document weight. The document weights of training document and document weights of test documents were compared to verify the test document. The proposed approach accuracy is good when compared with state-of-the-art existing approaches for authorship verification.
Abnormal Activity Recognition Based on Deep Learning in Crowd

Abnormal activity recognition is considered as the most challenging task in surveillance videos. Due to the traditional method depend on the computation of artificial features, and noise data has some influence on the extracted features. In this paper, a new hybrid deep learning structure was proposed to fuse the extracted features, which integrates convolutional neural network (CNN) and long short-term memory network (LSTM). Firstly, the video was preprocessed and extracted visual features by CNN. Next, LSTM was used to learn the temporal features of visual features and added attention mechanism to select important features. Finally, the video feature vector obtained layer by layer to judge abnormal activity. An experiment is used to test the ability of the model on the standard dataset UMN to recognize abnormal activity, the result shows that our experimental demonstrate high performance of recognition and outperform the state-of-art algorithms.
How Do API Selections Affect the Runtime Performance of Data Analytics Tasks?

As data volume and complexity grow at an unprecedented rate, the performance of data analytics programs is becoming a major concern for developers. We observed that developers sometimes use alternative data analytics APIs to improve program runtime performance while preserving functional equivalence. However, little is known on the characteristics and performance attributes of alternative data analytics APIs. In this paper, we propose a novel approach to extracting alternative implementations that invoke different data analytics APIs to solve the same tasks. A key appeal of our approach is that it exploits the comparative structures in Stack Overflow discussions to discover programming alternatives. We show that our approach is promising, as 86% of the extracted code pairs were validated as true alternative implementations. In over 20% of these pairs, the faster implementation was reported to achieve a 10x or more speedup over its slower alternative. We hope that our study offers a new perspective of API recommendation and motivates future research on optimizing data analytics programs.
Microarray Data Classification using Multi Objective Genetic Algorithm and SVM

Data mining and its applications have been used in many fields. One such important filed is the medical industry where data mining techniques have been applied for improving the medical procedure and the treatment. Cancer is a dangerous disease that requires very complex treatment. In this paper, a Multi-objective Genetic Algorithm was proposed for Microarray Data feature selection. Minimizing the quantity of features and maximizing the classification accuracy are the two objectives. Existing feature selection algorithms are well suitable for evaluate the single objective and do not satisfy the actual need. The proposed algorithm is developed and compared with single objective method.
Language comprehension models at different levels of explanation

Abstract This paper reviews two recent models of language comprehension, the RI‐Val model (Cook &amp; O'Brien, 2014; O'Brien &amp; Cook, 2016b) and the dynamic generative framework (Kuperberg, 2016). Both of these models have been heavily informed by research on the processing of semantic anomalies, but they have arisen in different literatures. The RI‐Val model has roots in the text processing literature and focuses primarily on bottom–up aspects of comprehension; the dynamic generative framework has roots in the event‐related brain potential (ERP)/sentence processing literature and focuses more heavily on top–down aspects of comprehension. Relating these models to each other and considering the findings in the semantic anomaly literature that support each one pushes us to consider the possibility that these models might be describing the comprehension system at different levels of explanation (Marr, 1982). This view has a few important consequences. First, it provides new ways of thinking about, and consequently increases the importance of, the small literature about anomaly detection in fictional contexts. It also opens the door for beginning to speculate about potential mappings between these two models of language comprehension. Our hope is that by bringing together and highlighting potential connections between models from different traditions, we will provoke bigger picture points of view that raise new questions and spur new ideas for research.
Adaptive Elastic Echo State Network for Channel Prediction in IEEE802.11ah Standard-Based OFDM System

As a promising wireless communication technology, the IEEE802.11ah standard is designed to connect various sensors in the Internet of Things (IoT) in future. It is important to investigate adaptive transmission in the IEEE802.11ah standard. However, exact channel state information (CSI) is required. Channel prediction is an available approach. Therefore, an adaptive elastic echo state network (AEESN) for channel prediction in the IEEE802.11ah standard-based orthogonal frequency division multiplexing (OFDM) system is introduced in this paper. The AEESN includes two key components, a basic echo state network and an adaptive elastic network. The latter is imported to overcome collinearity problems due to vast neurons in the former and to avoid ill-conditioned solutions when estimating output weights in the former. Moreover, the latter can produce sparse output weights, which reduces memory storage requirements. To evaluate system performances, 1MHz and 2MHz bandwidth cases with specified parameters are tested. One-step prediction, multi-step prediction and robustness are evaluated for various signal to noise ratios (SNRs). The results indicate that the AEESN not only offers satisfactory prediction performance, but also effectively avoids ill-conditioned solutions and produces sparse output weights. Therefore, it can assure adaptive IoT communication.
Complete Visualisation, Network Modeling and Training, Web Based Tool, for the Yolo Deep Neural Network Model in the Darknet Framework

This paper presents an interface designed for the Darknet neural network, used by the state of the art YOLO (You Only Look Once) models. The object detection is still representing a challenge in the field of computer vision, and this interface has the main purpose of providing a way to generate more easily new networks, in order to obtain the desired object detection system. Furthermore, through this interface, it can be generated and displayed the feature maps at any point in the network. Therefore, the Darknet interface presented in this paper, leads to a better understanding of how a neural network makes a certain decision regarding the final predictions. And so, newer and better algorithms can be developed, in order to solve the object detection problem.
Face Image Anonymization as an Application of Multidimensional Data K-Anonymizer

Recently, the development of data communication networks and advances in computer processing capacity have significantly increased the amount of data to be managed. Although future innovations using data are expected, privacy violation of data has become a problem. For example, image disclosure in Social Networking Service may result in infringement of portrait rights. Previously, studies on anonymization have been conducted to disclose data for protecting personal information. However, conventional anonymization, even for high-dimensional data such as faced images, uses an averaging operation in anonymization and does not consider complex relationships between dimensions. Therefore, the loss of semantic meaning increases because it is caused by the anonymization process, assuming an Euclidean data space. We herein propose Multidimensional Inputs K-anonymizing Unit (MIKU), an anonymization algorithm focusing on face image anonymization as a typical example of high-dimensional datMIKU enables anonymization that retains the image quality. As the comparison method directly anonymizes images, the quality of images is deteriorated. MIKU considers the relation between dimensions using the latent space of StyleGAN. The effect of using latent space was confirmed by comparing the quantitative results of Fréchet Inception Distance(FID) and the qualitative evaluation of the output image with the comparison method. The quantitative results by FID proved that MIKU achieved better performance when K-anonymity was large. When K-anonymity is small, we clarified that the image quality of the comparison method such as FID could not be measured correctly and was overestimated because of the effect of the inception model's linearity. In addition, the qualitative evaluation of anonymized images shows that MIKU generates a more natural image in the contour and hair expressions than the comparison method, and the anonymized images of MIKU contain no unnatural edge lines on a face such as those generated in the comparison method.
Image Context Based Similarity Retrieval System

The rapid development in multimedia and imaging technology, the numbers of images uploaded and shared on the internet have increased. It leads to develop the highly effective image retrieval system to satisfy the human needs. The content-text image retrieval (CTIR) system which retrieves the image based on the high level features such as tags which are not sufficient to describe the user’s low level perception for images. Therefore reducing this semantic gap problem of image retrieval is challenging task. Some of the most important notions in image retrieval are keywords, terms or concepts. Terms are used by humans to describe their information need and it also used by system as a way to represent images. Here in this paper different types of features their advantage and disadvantages are described.
Importance Densities for Particle Filtering Using Iterated Conditional Expectations

In this letter, we consider Gaussian approximations of the optimal importance density in sequential importance sampling for nonlinear, non-Gaussian state-space models. The proposed method is based on generalized statistical linear regression and posterior linearization using conditional expectations. Simulation results show that the method outperforms the compared methods in terms of the effective sample size and provides a better local approximation of the optimal importance density.
Design and Implementation of Autonomous Energy Efficient Solar Tracking System for PV Power Plants

This paper concerns the design and implementation of an autonomous dual-axis energy-efficient solar tracking system dedicated to photovoltaic panels. The proposed tracking system ensures optimum generation of electrical power by proper orientation of PV panels while consuming minimal energy. The design criteria are based on controlling the panel's position by automatic rotation throughout two DC motors only at certain times during the day. The followed methodology uses a microcontroller algorithm to redirect the solar panels in accordance with the actual coordinates of the sun. The idea is applicable to any PV system at any geographical location as panel rotation is changed according to the tilt and azimuth angles. This leads to capturing maximum radiation at any given time. Moreover, its power consumption is low due to its working mechanism and automatic sleep mode feature while not in use. The sun tracker system is implemented for Jeddah, Saudi Arabia area and tested for other four cities around the world. Results have proved its cost-effectiveness, energy-efficiency, simplicity, and reliability in operation under different weather conditions.
Satellite ASTER Mineral Mapping the Provenance of the Loess Used by the Ming to Build their Earthen Great Wall

The earthen border wall (Great Wall) built by the Ming is largely made of wind-blown loess. However, does the composition of this loess change along the length of the wall in response to variations in regional sediment transport pathways and impacting on the wall’s erosional durability? To date, defining these sediment transport pathways has been a challenge because of the paucity of spatially-comprehensive, compositional information. Here, we show that satellite ASTER mineral maps, combined with field sample measurements along a 1200 km section of the Ming’s earthen wall, reveal both the compositional heterogeneity of loess as well as the complexity of the sediment transport pathways of individual loess components, including: (i) quartz sand from Cretaceous sandstones in the Gobi Desert; (ii) gypsum from evaporative lakes in the Tengger Desert; (iii) kaolinite from Devonian Molasse in the Qilian Shan; and (iv) chlorite and muscovite from meta-volcanic rocks exposed across the Alashan Block. Sediment transport pathways involve a combination of colluvial, aeolian and fluvial (ephemeral and permanent) processes shaped by the topography. ASTER enabled mapping of compositional gradients related to two pathways, namely: (i) quartz sand driven by aeolian saltation in concert with the Yellow River; and (ii) clay and fine silt travelling large distances (&gt;500 km) by long-term wind suspension. The most intact section of wall is found along the Hexi Corridor, which is poor in quartz sand and rich in (kaolinitic) clay and fine-silt, driven by wind-shielding by the Alashan Block. We also found evidence that the Ming: (i) mined loess from close by the wall (&lt;1 km); (ii) targeted loess richer in finer fractions; and (iii) routinely applied a Ca-rich additive (probably lime).
Integrating Peripheral Interaction Into Augmented Reality Applications

We address in this paper software architecture design to enable peripheral interactions in Augmented Reality applications. To this end, we rely on SAPIENS, a recently introduced software architecture for engineering peripheral interactions in smart environments, which we reuse to our purpose. Our approach can be readily tested using the online simulator available from the SAPIENS home page.
Extremely Short Chinese Text Classification Method Based on Bidirectional Semantic Extension

Abstract Short text classification methods have achieved significant progress and wide application on text data such as Twitter and Weibo. However, the extremely short chinese texts like tax invoice data are different with traditional short texts in lackness of contextual semantic information, feature sparseness and extremely short length. The existing short text classification methods are difficult to achieve a satisfactory performance in these texts. To address these problems, this paper proposes a text classification method based on bidirectional semantic extension for extremely short texts like Chinese tax invoice data. More specifically, firstly, the Chinese knowledge graph is introduced for extending bidirectional semantic of texts and label data to expand the extremely short texts and ease the problem of feature sparseness; secondly, the hash vectorization is used to avoid the semantic problem caused by the lackness of contextual information. Experimental results conducted the real tax invoice dataset demonstrate the effectiveness of our proposed method.
Human Face Recognition Using Local Binary Pattern Algorithm - Real Time Validation

A real time face recognition using LBP algorithm and image processing techniques are proposed. Face image is represented by utilizing information about shape and texture. In order to represent the face effectively, area of the face is split into minute sections, then histograms of Local Binary Pattern (LBP) are extorted which are then united into a single histogram. Secondly, the recognition is carried out on computed feature space using nearest neighbor classifier. The developed algorithm is validated in real time by developing a prototype model using Raspberry Pi single board computer and also in simulation mode using MATLAB software. The above obtained results match with each other. On comparing both the results, recognition time taken by the prototype model is more than that of the simulation results because of hardware limitations. The real time experimental results demonstrated that the face recognition rate of LBP algorithm is 89%.
Game Design Principles Influencing Stroke Survivor Engagement for VR-Based Upper Limb Rehabilitation

Engagement with one's rehabilitation is crucial for stroke survivors. Serious games utilising desktop Virtual Reality could be used in rehabilitation to increase stroke survivors' engagement. This paper discusses the results of a user experience case study that was conducted with six stroke survivors to determine which game design principles are or would be important for engaging them with a desktop VR serious games designed for the upper limb rehabilitation. The results of our study showed the game design principles that warrant further investigation are awareness, feedback, interactivity, flow and challenge; and also important to a great extent are attention, involvement, motivation, effort, clear instructions, usability, interest, psychological absorption, purpose and a first-person view.
Applications of Deep Learning to Road Sign Detection in DVR Images

This paper applies deep learning to road sign detection based on the images obtained from DVRs. Three convolutional neural network based state-of-the-art real-time object detection systems are explored for road sign detection, where the road sign plates are vertical or horizontal. First, collect the images of street scenes including road signs as an image library, which is divided into three parts for training, validation and testing. Secondly, according to the exposure values (EVs) of images, divide the images into three categories, high exposures labeled as EV -H, middle exposures labeled as EV-M, and low exposures labeled as EV-L. Thirdly, the road signs in each image are labeled to perform supervised learning. Fourthly, apply three object detection systems to road sign detection, You Only Look Once (yOLO) version 2 (v2), YOLO version 3 (v3), and Single-Step Multi-Box Detection (SSD), where an improved method based on YOLO v2 is proposed by dividing the images into three kinds of meshes and recalculating the values of the anchor boxes by k-means algorithm. Finally, analyze the mean average precisions and the frame rates of all images to conclude the best models for the three conditions EV-L, EV-M and EV-H.
Task Scheduling Techniques for Deep Learning in Heterogeneous Environment

The training of deep learning requires heavy computation, and is the focus of deep learning system optimization. The training of deep learning has three characteristics. First, the training repeats a sequence of identical tasks. Second, the training is done in batches, i.e., the training performs the same computation on a batch of input data. Third, the training system usually has several different types of computation devices, e.g., CPU, GPU, and FPGA, so the environment is heterogeneous. An efficient deep learning system must consider the three characteristics mentioned above, and address three issues. First, since the system is heterogeneous, every task can run on every computation device. As a result, we need to assign every task to an appropriate device to reduce computation cost. Second, the computation devices are exclusive, therefore, we need to carefully schedule tasks from different batches so that no tasks that require the same device are assigned to the device at the same time. Finally, two tasks A and B have their own most appropriate devices. However, the task B may need the output of A as input, so if A and B are mapped to different devices, heavy communication overhead may incur. We develop two algorithms to address these issues. We first solve the task-device mapping problem by transforming it into a generalized shortest path problem. We then use a concatenation algorithm to schedule tasks from multiple batches in such a way that no device conflict will happen. We show that both algorithms are efficient and optimal, in terms of scheduling quality.
Deterrence: Concepts and Approaches for Current and Emerging Threats

By exploring and analysing the complexities associated with the development and application of the concept of deterrence in resolving conflicts, this chapter sets the context of the book. Deterrence has to do with maintaining the status quo by convincing an opponent or ally that the cost of an unwanted action is greater than the rewards. Deterrence, on the one hand can act as a delaying mechanism in dealing effectively with opponents, in which case the aim would be to contain a conflict and the focus is zero-sum and more short term. On the other, deterrence can have the role of a proactive mechanism, where the focus is longer term. To be able however, to make shifts from zero-sum to a positive-sum the deterring party needs to be aware of context specific variables such as the opponent’s values’ system, the mind-set, and decision making processes. Routinely, mirror-imaging influences decision making leading states to develop deterrence policies with limited impact and effectiveness, as deterrence requires an understanding of the other’s as well as one’s own motives, objectives, and decision-making processes. Mirror-imaging leads to questionable assumptions about opponents’ values and how they will behave under pressure.
Certificateless Broadcast Multisignature Scheme Based on MPKC

Broadcast multisignature allows multiple signers to sign the same message, which can be used in many areas, such as electronic contract signing, educational administration management system and grade management system.At present, the security of most broadcast multisignature schemes mainly depends on the intractability of large integer factoring (LIF) or discrete logarithm (DL) problem.Thus, broadcast multisignature schemes will suffer from the potential threat of the quantum computing attacks.Hence, it is an important problem how to solve the quantum computing attacks in traditional broadcast multisignature.In this paper, we construct the first certificateless broadcast multisignature scheme based on multivariate public key cryptosystem (MPKC-CLBMSS), whose security is based on the hardness of the isomorphism of polynomials (IP) problem.MPKC-CLBMSS not only solves the problem of quantum computing attacks, but also avoids the key escrow issue in IB-PKC along with the certificate management problem in traditional PKI.In MPKC-CLBMSS, the signature length is as same as that of the partial signature, regardless of the number of signers; the verification time of signature is as same as for a partial signature.MPKC-CLBMSS has higher computational efficiency than the existing broadcast multisignature scheme.Moreover, the security proof shows that MPKC-CLBMSS satisfies the unforgeability in the random oracle model.