Reviewerly/exHarmony huggingface dataset:
BSBA: Burst Series Based Approach for Identifying Fake Free-traffic

In recent years, mobile traffic has gradually become a major part of network traffic. To attract customers, mobile network operators provide free-traffic, which is a preferential policy that is free of charge for specific application traffic. Since the emergence of free-traffic, fake free-traffic also appeared soon. Fake free-traffic is a malicious behavior, which helps attackers illegally use network resources and evade network resource charging. The appearance of fake free-traffic maliciously harms the interests of operators and disrupts the rules of network resource charging. Because of the uniqueness of free-traffic, it encapsulates a layer of the HTTP protocol in addition to the actual application communication protocol, existing studies on encrypted traffic analysis are not applicable to identify fake free-traffic. In this paper, we propose Burst Series Based Approach (BSBA), a novel method for identifying fake free-traffic. The key idea behind BSBA is to construct effective features by capturing the differences of burst series among fake free-traffic, free-traffic and non-free traffic, and combine the constructed features with machine learning algorithms to identify fake free-traffic. We collect a real-world traffic dataset and conduct evaluations to verify the effectiveness of the BSBA. Experiment results demonstrate that the BSBA achieves excellent performances (96.82% Accuracy, 96.46% Precision, 96.57% Recall and 96.51% F1-score) and is superior to the state-of-the-art methods.
Multi-platform application for learning English phonetics: Serious Game

This paper presents the architecture and interface of a serious game designed for pronunciation training for Spanish-speaking university and high school learners. Set on the Way of Saint James, users will face multiple challenges, in the form of mini-games, to progress along the way. The challenges will consist of identifying sounds in words and in longer sequences, producing sounds in words and in longer sequences, the relationship between spelling-sound/phonetic symbol (and vice versa) and identifying sound/phonetic symbol-grammar (and vice versa). All these challenges are divided into three large blocks: Perception, Production and Transcription, presented in that order in each of the 33 stages along the route.
Safeguarding Unmanned Aerial Vehicles Against Side Channel Analysis Via Motor Noise Injection

We first investigate the efficacy of side channel anal-ysis (SCA) based attacks to recover the encryption key from an unmanned aerial vehicle (UAV). We then propose a method based on noise injection to safeguard against this type of attack. The proposed noise injection method is unique because it prioritizes low power consumption and uses as a noise source the motors of the UAV system. The effectiveness of the proposed method is demonstrated through experiments on a real quadcopter. It is found that when noise injection is not applied, the encryption key can be recovered through correlation power analysis. In contrast, the same key is not recoverable when the proposed noise injection method is applied. The implemented solution is efficient and does not negatively effect the flight stability or control of the UAV because its execution time overhead is negligible inside the control loop. In addition, it consumes a negligible amount of power. These results demonstrate that mitigation of SCA attacks without expanding the complexity or negatively effecting the flight operations of drones is achievable.
Detection of JavaScript Injection Eavesdropping on WebRTC communications

WebRTC is a Google-developed project that allows users to communicate directly. It is an open-source tool supported by all major browsers. Since it does not require additional installation steps and provides ultra-low latency streaming, smart city and social network applications such as WhatsApp, Facebook Messenger, and Snapchat use it as the underlying technology on the client-side both on desktop browsers and mobile apps. While the open-source tool is deemed to be secure and despite years of research and security testing, there are still vulnerabilities in the real-time communication application programming interface (API). We show in this paper how eavesdropping can be enabled by exploiting weaknesses and loopholes found in official WebRTC specifications. We demonstrate through real-world implementation how an eavesdropper can intercept WebRTC video calls by installing a malicious code onto the WebRTC webserver. Furthermore, we identify and discuss several, easy to perform, ways to detect wiretapping. Our evaluation shows that several indicators within webrtc-internals API traces can be used to detect anomalous activities, without the need for network monitoring tools.
Cyberbully Classification Based on Tweet Texts for Detection of Phishing Links

Cyberbully includes harassments or misbehaving with non-positive commands and false content about others in social media. Bullying is specified as of sharing information about someone else causing embarrassment or humiliation. In recent research works, prediction and classification of cyberbullying using various machine learning algorithms are evolved. This proposed work focuses on multimedia contents such as text, image, video, and audio for prediction of bullied content. Bullying through multimedia contents is predicted through various machine learning algorithms like K-nearest neighbor, support vector machine, naïve Bayes, and random forest. Social media suffers a lot toward text bullying. Predicting and classification of text in social media as bullied are essentially needed with higher accuracy. The personal details provided by the user in unrelated sites may lead to phishing attacks. Intimation of malicious attacks to the user reduces the further options to be aware on it. The proposed system uses the classification of text that is carried out using support vector machine to check the bullied or non-bullied text in social networks. The bullied text is blocked from phishing links are classified using logistic regression. Phishing links trap the user’s identity, in order to prevent it; the phishing link is blocked from being posted. Also, the user who continuously posting the bullied content or phishing link is blocked to prevent the unlawful activities. The experimental results show that the proposed work provides higher accuracy compared with state-of-the-art techniques.
Designing a Socially Intelligent System by Cognitive Modeling of Human-Environment Interaction

Socially intelligent system comprises of self-governing components that communicate with the environment to extend intelligent support for attaining specific intentions. In such kind of systems, the environment is an active part of the system itself and provides input for runtime. Modeling and representing such systems are an arduous tasks because emotional and social intelligence is unprecedented. The paper proposes a framework for development of systems that are emotionally as well as socially intelligent. The challenges faced for developing such systems and their solutions are also discussed which eventually led to a successful system design framework. These systems use collective intelligent behavior to interact dynamically with the user.
Flexible privacy‐preserving machine learning: When searchable encryption meets homomorphic encryption

Now various privacy-preserving techniques have been combined with machine learning to ensure training data security. However, during the training, users were unable to select data and labels adaptively in the server, which made the trained models challenging in meeting user needs. Repeated model training not only wastes server resources, but also reduces query efficiency. In this paper, we combine two privacy-preserving technologies, searchable encryption and homomorphic encryption, and propose a highly flexible machine training framework. Homomorphic encryption technology is used to encrypt data, and symmetric searchable encryption technology is used to generate blind indexes and trapdoors. The server can perform ciphertext data search through blind query trapdoors and indexes, and can use the searched homomorphic ciphertext for model training. This framework significantly improves the flexibility of training and the fit of the model, and also ensures the security of dynamic data management. The server locally uses the header of the query trap and the storage address of the ciphertext model to build an automatically updatable ciphertext model table. On the basis of ensuring the effectiveness of the model, it greatly improves the efficiency of obtaining the model, effectively prevents redundant training when different users send the same request, and reasonably allocates resources in the server.
Enhancement of Smart Contact on Blockchain Security by Integrating Advanced Hashing Mechanism

The demand for blockchain is growing every day. Smart contracts, on the other hand, are commonly employed in business applications. The hashing mechanism is a security method that ensures the blockchain's dependability. The purpose of the proposed article is to look at the function of hashing mechanisms in blockchain security. The current study took into account a number of previous studies in the field of hashing mechanisms and blockchain. Previous research has encountered challenges such as restricted scope, security, and performance. The suggested work is expected to create a more secure and high-performing solution. Simulation work has been done to confirm the execution time of smart contracts considering several hashing mechanisms. Comparative analysis of collision is also considered to present the security of the proposed work.
Abelian sharing, common informations, and linear rank inequalities

Abstract Dougherty et al. introduced the common information (CI) method as a method to produce non-Shannon inequalities satisfied by linear random variables, which are called linear rank inequalities. This method is based on the fact that linear random variables have CI. Dougerthy et al. asked whether this method is complete, in the sense that it can be used to produce all linear rank inequalities. We study this question, and we attack it using the theory of secret sharing schemes. To this end, we introduce the notions of Abelian secret sharing scheme and Abelian capacity. We prove that: If there exists an access structure whose Abelian capacity is smaller than its linear capacity, then the CI method is not complete. We investigate the existence of such an access structure.
Deep reinforcement learning in playing Tetris with robotic arm experiment

Tetris has been an important field for research in deep reinforcement learning (DRL). However, most studies about Tetris are focused on simulation validation, and a few attempts are conducted in the real-world environment. In this paper, the DRL algorithms are trained in the constructed Tetris simulation environment, after that they are deployed into the real-world Tetris experiments. The dynamic timesteps method is integrated into the proximal policy optimization (PPO) method to accelerate its training speed, which reaches the goal of the game within 1483 episodes. With the help of multiple recognition and segmented moving techniques, the robotic arm provides accurate and robust performance to play real-world Tetris. The effectiveness of the developed system is experimentally verified; the experimental results show that the proposed algorithm achieved superior performance compared with conventional method and Deep Q-Network (DQN) in real-world Tetris environments.
A Fast Dejittering Approach for Line Scanning Microscopy

We propose two efficient optimization approaches to correct jitter effects appearing in a specific type of line scanning microscopy. In this modality, even lines suffer from a non uniform and non integer distortion with respect to odd lines, creating significant visual artifacts. The huge image size make this problem highly challenging. To handle it, we propose two techniques. One is based on dynamic programming and has a complexity linear w.r.t. the number of pixels. The second is based on a convex relaxation and can be particularly efficient for parallel architectures. Both algorithms provide globally optimal solutions. The empirical reconstruction results are of high quality.
Low-Power Feature-Attention Chinese Keyword Spotting Framework with Distillation Learning

In this paper, we propose a novel Low-Power Feature-Attention Chinese Keyword Spotting Framework based on a depthwise separable convolution neural network (DSCNN) with distillation learning to recognize speech signals of Chinese wake-up words. The framework consists of a low-power feature-attention acoustic model and its learning methods. Different from the existing model, the proposed acoustic model based on connectionist temporal classification (CTC) focuses on the reduction of power consumption by reducing model network parameters and multiply-accumulate (MAC) operations through our designed feature-attention network and DSCNN. In particular, the feature-attention network is specially designed to extract effective syllable features from a large number of MFCC features. This could refine MFCC features by selectively focusing on important speech signal features and removing invalid speech signal features to reduce the number of speech signal features, which helps to significantly reduce the parameters and MAC operations of the whole acoustic model. Moreover, DSCNN with fewer parameters and MAC operations compared with traditional convolution neural networks is adopted to extract effective high-dimensional features from syllable features. Furthermore, we apply a distillation learning algorithm to efficiently train the proposed low-power acoustic model by utilizing the knowledge of the trained large acoustic model. Experimental results thoroughly verify the effectiveness of our model and show that the proposed acoustic model still has better accuracy than other acoustic models with the lowest power consumption and smallest latency measured by NVIDIA JETSON TX2. It has only 14.524 KB parameters and consumes only 0.141 J energy per query and 17.9 ms latency on the platform, which is hardware-friendly.
LUCIE

Selection in genetic algorithms is difficult for stochastic problems due to noise in the fitness space. Common methods to deal with this fitness noise include sampling multiple fitness values, which can be expensive. We propose LUCIE, the Lower Upper Confidence Intervals Elitism method, which selects individuals based on confidence. By focusing evaluation on separating promising individuals from others, we demonstrate that LUCIE can be effectively used as an elitism mechanism in genetic algorithms. We provide a theoretical analysis on the convergence of LUCIE and demonstrate its ability to select fit individuals across multiple types of noise on the OneMax and LeadingOnes problems. We also evaluate LUCIE as a selection method for neuroevolution on control policies with stochastic fitness values.
On optimal static and dynamic parameter choices for fixed-target optimization

Various flavours of parameter setting, such as (static) parameter tuning and (dynamic) parameter control, receive a lot of attention both in applications and in theoretical investigations. It is widely acknowledged that the choice of parameter values influences the efficiency of evolutionary algorithms (and other search heuristics) a lot, and a considerable amount of work has been dedicated to finding (near-)optimal choices of parameters, or of parameter control strategies.
Mobile Edge Computing Application in Enterprise Human Resource Management Platform Based on Task Scheduling Algorithm

With the development of the social economy and the acceleration of economic globalization, human resources of enterprises have become an important factor restricting the development of enterprises. This paper creates an enterprise human resource management platform application based on twin network and mobile edge computing. By allocating computing and storage resources to the network edge close to users or data sources, mobile edge computing supports mobile to complete the computing offload of wireless access network applications. This process significantly reduces the end-to-end delay of the network and effectively reduces the processing load of the core network and data center. The simulation results can see that the average user benefit of all algorithms increases with the increase of network transmission speed and VCPU processing power. In addition to the introduction and popularization of information technology, the construction of enterprise human resources informatization also requires continuous and comprehensive monitoring and analysis of existing internal data, information, and data. The use of network communication technology to build a management information system, through information management, can significantly optimize and improve the efficiency of enterprise human resource management.
Consistency of morphological dictionary MorfFlex

Abstract Language corpora usually contain, in addition to their own texts, various types of annotations. The most common one is a morphological annotation, which consists in assigning a lemma and a morphological tag to each wordform. For morphological tagging, morphological dictionaries are traditionally used. Our paper presents a new version of the so-called “Prague” morphological dictionary MorfFlex used for tagging many Czech corpora (particularly Prague Dependency Treebanks, corpora published by the Institute of the Czech National Corpus in Prague or large Czech web corpora of the Aranea series). Three basic principles were used to update the dictionary: the Golden Rule of Morphology, the Principle of Paradigm Unity, and the Principle of Paradigm Uniqueness.
<i>Kladenští</i> type as a problem of automatic morphological analysis

Abstract The aim of our paper is to demonstrate the procedures by which the data needed to refine tools for automatic morphological analysis of Czech can be obtained using a corpus, namely the Araneum Bohemicum IV Maximum (Czech, 20.03) 7.10 G web corpus of the ARANEA series and Araneum Bohemicum Maximum (Czech, 15.04) 3,20 G (hereinafter Araneum). Particularly, we will focus on propria of the Kladenští type, i.e., substantivized adjectives of denoting groups of persons according to affiliation. The goal of the probe into the Aranea web corpus is: 1) a corpus-based description of frequented properties of the Kladenští type, which can be used as a starting point for rule disambiguation; 2) creating a list of the most frequent lemmas belonging to the Kladenští type, which can then be included into dictionaries of automatic morphological analyzers (e.g. the MorfFlex dictionary by Hajič and Hlaváčová). We believe that the probe can help improve the results of tools for automatic morphological analysis of Czech.
Chinese Language Word Embeddings Based on the Corpus Hanku

Abstract Vector models based on word embeddings are an indispensable part of advanced Natural Language Processing research and language analysis. We describe several Chinese language (Pǔtōnghuà) word embeddings, the differences from “western” language models caused by specific orthographic and linguistic features of the written Chinese language, and introduce a publicly available web interface for querying the vector models, aimed at linguistically or pedagogically oriented users.
Thematic words in the Slovak pre-election campaign on Facebook

Abstract The aim of this paper is 1. to describe/specify and compare thematic orientations of 735 pre-election microblogs published on the virtual profiles of major six Slovak political parties and 2. based on this description and comparison to identify and sketch features of Slovak political discourse. The conceptual and methodological frame consists of thematic words, that is, autosemantics above the so-called h-point, and the qualitative analysis of these thematic words. The identified features of the general Slovak pre-election communication include: populist communication, ego presentation of the party, leader or the candidate, conflict between government parties and opposition parties, image of Slovakia as a country facing troubles but also hiding potential to solve them.
Markov chain Monte Carlo based adaptive Rauch–Tung–Striebel smoother

For linear state space model, the covariance matrix setting errors of process and measurement noise deteriorate the estimation performance of Rauch–Tung–Striebel (RTS) smoother. To address this problem, the Markov Chain Monte Carlo is utilized to sample the state vector and noise covariance matrices simultaneously in this study. The Gibbs sampler is adopted and the corresponding adaptive RTS smoother is designed. Simulation results confirm the performance of proposed smoother.
Generation of Non-isomorphic Connected Graphs by Successive Edge Removals from a Complete Graph

Subgraphs of a graph can be generated in two ways, either removing edges from the graph or adding valid edges to the isolated vertices. In this paper, we have proposed an algorithm which follows the first method, i.e., starting from a complete graph of a given order and removing possible edges in successive levels; it generates non-isomorphic connected subgraphs of same order but of different sizes. It generates at least one connected subgraph of a particular size and the subgraphs of the same size are non-isomorphic. The set of graphs generated by the proposed algorithm can be utilized to determine the performance of spanning tree generation algorithms, approximation algorithms, graph algorithms, algorithms for modeling and simulation, and so on.
Facial Recognition-Based Automated Classroom Attendance System

The paper proposes a method for recording attendance automatically by taking pictures of the students or people in an organization, with high accuracy. Taking attendance manually leaves room for human error and is a labor-intensive task in itself, which allows it to be automated. Our methodology uses the technology of deep learning-based facial recognition. The images of each and every member have to be provided, for automation to be possible. When the image of a member is taken during class, the face in it is located and then the face is encoded. The encodings of the located face are then contrasted with the encodings of all the member faces provided earlier. The one which is akin to the captured image is taken, and the corresponding member is given the attendance. Using these encodings proves useful because the faces will not always be aligned straight. There is one case where this system can break which is the case of identical or monozygous twins. This is because their facial characteristics would nearly be alike. It is also cost-effective, as to deploy it, only a camera and a device to run the system are required.
Analytical Study on Application of Deep Learning for Sentiment Analysis

In recent years with growth of opinion contents over Internet had raised the scope for data analysis and creating new opportunities for new challenges. Analysis of user generated task is very crucial and time taking task that needed to be focused. Any business need is an objective to research in sentiment analysis and comes up with high and improved accuracy in prediction. The sentiment expression is used to classify the polarity of the text review on a scale of negative to positive. This research work is focused toward analytical study of deep learning approach for big data sentiment analysis on multiple domain platforms.