Reviewerly/exHarmony huggingface dataset:
SmartStreamer: Preference-Aware Multipath Video Streaming Over MPTCP

Simultaneous access to multiple interfaces (e.g., WiFi and cellular networks) can significantly improve the users' quality of experience (QoE) in video streaming. However, some interfaces could be more expensive to use and less energy efficient. Therefore, in this paper, we propose a preference-aware multipath video streaming algorithm over HTTP using multipath TCP (MPTCP). First, we formulate the quality decisions of the video chunks and the chunk's download policy subject to the chunk's deadlines, the available bandwidth of the different paths, and the link preferences as a non-convex optimization problem. The objective is to optimize a novel QoE metric that maintains a tradeoff between maximizing the quality of every video's chunk and ensuring quality fairness among all chunks for the minimum re-buffering (stall) duration, and without violating the link preference constraint. Second, we develop a polynomial time complexity algorithm to solve the proposed optimization problem, and provide guarantees for the proposed algorithm. We further propose a sliding window based online algorithm where several challenges including short bandwidth prediction with prediction errors are addressed. Extensive emulated experiments with real bandwidth traces of public datasets reveal the robustness of our scheme and demonstrate its significant performance improvement compared to the state-of-the-art multi-path streaming algorithms.
Riesz fractional derivative Elite-guided sine cosine algorithm

In order to improve the calculation accuracy of the sine cosine algorithm (SCA), Riesz fractional derivative sine cosine algorithm (RFSCA) based on the Riesz fractional derivative mutation strategy is proposed. The new algorithm uses quasi-opposition learning to initialize the population, which can increase the diversity of the population. Based on the approximate formula of Riesz fractional derivative with second-order accuracy, we construct a new mutation approach to update the optimal individual and improve the calculation accuracy of the algorithm. Furthermore, the proposed method is integrated into quasi-opposition learning and opposition-based learning strategies to enhance the ability of global exploration of the population and improve the convergence speed of the algorithm. The new algorithm is tested in two sets of test sets (classical benchmark of 23 problems and standard IEEE CEC 2017). The simulation experiments demonstrate that the proposed algorithm significantly outperforms the latest heuristic-based algorithms in both exploration, exploitation and solution quality. Two engineering questions (Welded beam design, pressure vessel design) are applied to confirm the superior performance of proposed algorithm.
Adaptive Metric Learning For Zero-Shot Recognition

Zero-shot learning (ZSL) has enjoyed great popularity in recent years due to its ability to recognize novel objects, where semantic information is exploited to build up relations among different categories. Traditional ZSL approaches usually focus on learning more robust visual-semantic embeddings among seen classes and directly apply them to the unseen classes without considering whether they are suitable. It is well known that domain gap exists between seen and unseen classes. In order to tackle such problem, we propose a novel adaptive metric learning approach to measure the compatibility between visual samples and class semantics, where class similarities are utilized to adapt the visual-semantic embedding to the unseen classes. Extensive experiments on four benchmark ZSL datasets show the effectiveness of the proposed approach.
On Consensus in Public Blockchains

In this paper, we formulate the consensus problem and its solutions as seen in public blockchains and contrast them to those of the traditional distributed consensus. The Proof of Work (PoW) algorithm introduced in Bitcoin provides the first elegant and practical solution for reaching distributed consensus in a large-scale untrusted environment. Due to the enormous energy cost of PoW, many alternative consensus algorithms have been proposed for public blockchains aiming at drastically reducing the energy consumption for reaching consensus. We examine four blockchain consensus algorithms, namely Proof of Work, Proof of Stake, Proof of Space, and Proof of Elapsed Time, with respect to the consensus model we have formulated and point out the challenges of adopting them in public blockchains.
Evolutionary prototype selection for multi-output regression

A novel approach to prototype selection for multi-output regression data sets is presented. A multi-objective evolutionary algorithm is used to evaluate the selections using two criteria: training data set compression and prediction quality expressed in terms of root mean squared error. A multi-target regressor based on k-NN was used for that purpose during the training to evaluate the error, while the tests were performed using four different multi-target predictive models. The distance matrices used by the multi-target regressor were cached to accelerate operational performance. Multiple Pareto fronts were also used to prevent overfitting and to obtain a broader range of solutions, by using different probabilities in the initialization of populations and different evolutionary parameters in each one. The results obtained with the benchmark data sets showed that the proposed method greatly reduced data set size and, at the same time, improved the predictive capabilities of the multi-output regressors trained on the reduced data set.
Automated Breast Tumor Diagnosis Using Local Binary Patterns (LBP) Based on Deep Learning Classification

Breast cancer is the fifth most common cause of cancer death among women worldwide, even on Algeria that known about 12,000 new cases every year. Texture description has been a great interest in pattern recognition methods for looking deeper into features images, In this paper, we investigate the capability of the Local Binary Pattern texture and deep learning method for automated breast tumor images classification to be an efficient element for Computer aided diagnosis (CAD) system, where the extraction of meaningful information from the input image do not require features extractors. We have proposed a Convolution Neural Network (CNN) architecture based on LBP images as input after we compared their classification results by a standard CNN based on origin images as input. A 190-segmented image from (DDSM) database will be used for testing the proposed approach. Experimental results of the classification (benign or malignant tumor) gave better results than the standard CNN approach with an overall accuracy about 96.32 %.
Deep CNN-LSTM with Word Embeddings for News Headline Sarcasm Detection

Detecting sarcasm has been a problem in Artificial Intelligence (AI) because it is highly dependent on context and a human agent's knowledge of the world. This means that for conventional AI, a large number of rules must be hard coded. Furthermore, naïve machine learning methods such as logistic regression simply generate lists of words that are frequently associated and dissociated with sarcasm. Thus, logistic regression is unable to take groupings of words into account in sentiment analysis problems. In this paper, we design a deep neural network that leverages the advantages of convolutional neural networks (CNN) and Long Short-term Memory layers. This CNN-LSTM neural network with word embeddings is then trained 21,709 on word vector encodings of news headlines to determine whether a headline is sarcastic or genuine. We then applied this neural network to a corpus of 5000 test examples of real and sarcastic news headlines. This Deep CNN-LSTM neural network architecture can classify whether a news headline is real or satirical with 86.16% accuracy.
FPGA Platform applied for Facial Expression Recognition System using Convolutional Neural Networks

Emotion being a subjective thing, leveraging knowledge and science behind labeled data and extracting the components that constitute it. With the development of deep learning in computer vision, emotion recognition has become a widely-tackled research problem. In this work, we propose a Field Programmable Gate Array (FPGA) architecture applied for this task using independent method called convolutional neural network (CNN). The emotion recognition block receives the detected faces from a video stream by using VITA-2000 camera module and process the image data with the trained CNN model. The architecture is implemented on a Zynq-7000 All Programmable SoC Video and Imaging Kit. Once we have trained a network, weights from the Tensorflow model will be convert as C-arrays, to be used in Vivado HLS. After having the weights as C arrays, they can be implemented to FPGA system. We can also test the functionality of the CNN entirely, by compiling the design with C++ compiler. This method was trained on the posed-emotion dataset (FER2013). The results show that with more fine-tuning and depth, the CNN model can outperform the state-of-the-art methods for emotion recognition. We also propose some exciting ideas for expanding the concept of representational landmark features and sliding windows to improve its performance.
Bridging Screen Readers and Voice Assistants for Enhanced Eyes-Free Web Search

People with visual impairments often rely on screen readers when interacting with computer systems. Increasingly, these individuals also make extensive use of voice-based virtual assistants (VAs). We conducted a survey of 53 people who are legally blind to identify the strengths and weaknesses of both technologies, as well as the unmet opportunities at their intersection. We learned that virtual assistants are convenient and accessible, but lack the ability to deeply engage with content (e.g., read beyond the first few sentences of Wikipedia), and the ability to get a quick overview of the landscape (list alternative search results & suggestions). In contrast, screen readers allow for deep engagement with content (when content is accessible), and provide fine-grained navigation & control, but at the cost of increased complexity, and reduced walk-up-and-use convenience. In this demonstration, we showcase VERSE, a system that combines the positive aspects of VAs and screen readers, and allows other devices (e.g., smart watches) to serve as optional input accelerators. Together, these features allow people with visual impairments to deeply engage with web content through voice interaction.
Video Quality-Aware Traffic Offloading System for Video Streaming Services Over 5G Networks With Dual Connectivity

This paper presents a video quality-aware traffic offloading system that provides seamless video streaming services to users by offloading part of the macrobase station traffic into small cells. The proposed system achieves a good balance between fairness and social welfare in terms of the video quality by allocating the radio resource of the macrobase station. The data flow is split into two parts toward the macrobase station and small cell AP, and the users simultaneously receive their data from both. In the proposed system, fountain code is employed to achieve dual connectivity enhancement by overcoming frequent packet loss in error-prone mobile networks and practical issues in the dual connectivity architecture. Furthermore, software-defined networking (SDN) technology is adopted not only to quickly react to time-varying network status but also to control network resources efficiently. The proposed resource allocation process is designed to require a low running time complexity. The proposed system is implemented using C/C++, Java, and a well-known open source, ONOS. The experimental results verify that the proposed system can achieve much better performance than other offloading algorithms.
Chinese Grammatical Error Correction Based on Convolutional Sequence to Sequence Model

Chinese grammatical error correction (CGEC) is practically useful for learners of Chinese as a second language, but it is a rather challenging task due to the complex and flexible nature of Chinese language so that existing methods for English cannot be directly applied.In this paper, we introduce a convolutional sequence to sequence model into the CGEC task for the first time, since many Chinese grammatical errors are concentrated between three and four words and convolutional neural network can better capture the local context.A convolution-based model can obtain the representations of the context by fixed size kernel.By stacking convolution layers, long-term dependences can be obtained.We also propose two optimization methods, shared embedding and policy gradient, to optimize the convolutional sequence to sequence model through sharing parameters and reconstructing loss function.Besides, we collate the existing Chinese grammatical correction corpus in detail.The results show that the models we proposed two different optimization methods both achieve large improvement compared with the natural machine translation model based on a recurrent neural network.
Microsoft Concept Graph: Mining Semantic Concepts for Short Text Understanding

Knowlege is important for text-related applications. In this paper, we introduce Microsoft Concept Graph, a knowledge graph engine that provides concept tagging APIs to facilitate the understanding of human languages. Microsoft Concept Graph is built upon Probase, a universal probabilistic taxonomy consisting of instances and concepts mined from the Web. We start by introducing the construction of the knowledge graph through iterative semantic extraction and taxonomy construction procedures, which extract 2.7 million concepts from 1.68 billion Web pages. We then use conceptualization models to represent text in the concept space to empower text-related applications, such as topic search, query recommendation, Web table understanding and Ads relevance. Since the release in 2016, Microsoft Concept Graph has received more than 100,000 pageviews, 2 million API calls and 3,000 registered downloads from 50,000 visitors over 64 countries.
Construction on teaching evaluation index system of track and field general course for physical education major in light of wireless network technology

In order to perfect the evaluation index system of physical education specialized track and field general course, USES wireless network technology is used to construct it. The development status of the combination of teaching indexes and Internet is explained firstly in this paper, with the evalua tion index model of track and field general course teaching constructed by using wireless network technology. The risk evaluation indexes of students’ physical quality, class attention and self-quality are discussed in this model from three aspects and the algorithm is used to improve the evaluation data. In the test of teaching evaluation model, the model is putting into use in school evaluation system. The test has proved that after the implementation of the model, the comprehensive risk assessment data and evaluation index data measured are more robust, which is conducive to adjusting the teaching plan.
Persuasive technology for enhanced learning behavior in higher education

Obtaining quality learning behavior requires constant improvement. The development of learning methods often unsupported changes in the behavior of participants. This condition causes reluctance to use new learning methods by some users. Meanwhile, technological developments, especially Web 2.0, have the ability to change the way people communicate and interact. This study used the features and capabilities of Web 2.0 to improve learning behavior. The use of this technology will be adjusted with learning methods, especially online-learning that overgrows nowadays. The concept of an integrated system applied a persuasive strategy to bring persuasion. Modifications e-learning were done by combining the concepts of Web 2.0 and persuasive system. Tests were performed on aspects that affect the user's intentions by comparing the initial and final conditions after the intervention. The behavior changes were expected to improve user involvement and intention in online learning. The results show that the modified technology by using persuasive concept can improve the users' intention.
Traffic-Based Side-Channel Attack in Video Streaming

Video streaming takes up an increasing proportion of network traffic nowadays. Dynamic adaptive streaming over HTTP (DASH) becomes the de facto standard of video streaming and it is adopted by Youtube, Netflix, and so on. Despite of the popularity, network traffic during video streaming shows an identifiable pattern which brings threat to user privacy. In this paper, we propose a video identification method using network traffic while streaming. Though there is bitrate adaptation in DASH streaming, we observe that the video bitrate trend remains relatively stable because of the widely used variable bit-rate (VBR) encoding. Accordingly, we design a robust video feature extraction method for eavesdropped video streaming traffic. Meanwhile, we design a VBR-based video fingerprinting method for candidate video set which can be built using downloaded video files. Finally, we propose an efficient partial matching method for computing similarities between video fingerprints and streaming traces to derive video identities. We evaluate our attack method in different scenarios for various video content, segment lengths, and quality levels. The experimental results show that the identification accuracy can reach up to 90% using only three-minute continuous network traffic eavesdropping.
Discriminative Fusion Correlation Learning for Visible and Infrared Tracking

Discriminative correlation filter- (DCF-) based trackers are computationally efficient and achieve excellent tracking in challenging applications. However, most of them suffer low accuracy and robustness due to the lack of diversity information extracted from a single type of spectral image (visible spectrum). Fusion of visible and infrared imaging sensors, one of the typical multisensor cooperation, provides complementarily useful features and consistently helps recognize the target from the background efficiently in visual tracking. Therefore, this paper proposes a discriminative fusion correlation learning model to improve DCF-based tracking performance by efficiently combining multiple features from visible and infrared images. Fusion learning filters are extracted via late fusion with early estimation, in which the performances of the filters are weighted to improve the flexibility of fusion. Moreover, the proposed discriminative filter selection model considers the surrounding background information in order to increase the discriminability of the template filters so as to improve model learning. Extensive experiments showed that the proposed method achieves superior performances in challenging visible and infrared tracking tasks.
Sentiment Classifier: Logistic Regression for Arabic Services’ Reviews in Lebanon

This paper proposes a logistic regression approach paired with term and inverse document frequency (TF*IDF) for Arabic sentiment classification on services' reviews in Lebanon country. Reviews are about public services, including hotels, restaurants, shops, and others. We collected manually from Google reviews and Zomato, which have reached to 3916 reviews. Experiments show three core findings: 1) The classifier is confident when used to predict positive reviews. 2) The model is biased on predicting reviews with negative sentiment. Finally, the low percentage of negative reviews in the corpus contributes to the diffidence of logistic regression model.
Impact of Text Pre-processing and Ensemble Learning on Arabic Sentiment Analysis

Nowadays, with the rapid growth and spread of web platforms such as social networks, online review websites and blogs, people can openly express and share their opinions. They can rate products or comment various subjects. Thus, a new field called web based Sentiment Analysis (SA) or Opinion Mining has emerged. In general, SA is the process of classifying opinions and sentiments as positive, negative or neutral. Many studies were performed on SA for languages such as English, Spanish and French. However, the research on SA of Arabic text is very limited. The goal of this paper is to measure the impact of the preprocessing phase on Arabic Sentiment Analysis in terms of various aspects such as accuracy, precision and recall. We have conducted experimentations using different stemming (Khoja, ISRI, Tashaphyne, Light10, and MOTAZ), n-gram, and stop words. The second goal is to study the impact of combining multiple classifiers on Arabic sentiment analysis. For this reason, the vote algorithm in conjunction with three classifiers, namely Naive Bayes, Support Vector Machine (SVM), and Maximum Entropy have been used and evaluated using k-fold cross-validation.
Narrative review and meta-analysis of MALL research on L2 skills

Abstract This study employed a narrative review and a meta-analysis to synthesize the literature on mobile-assisted language learning (MALL). Following a systematic retrieval of literature from 2008 to 2017, 17 studies with 22 effect sizes were included based on predetermined inclusion and exclusion criteria. By categorizing the characteristics of the studies retrieved, the narrative review revealed a detailed picture of MALL research in terms of the language aspects targeted, theoretical frameworks addressed, mobile technologies adopted, and multimedia components used. The qualitative review helped to contextualize and interpret the results found in the meta-analysis, which revealed a large effect for mobile technologies in language learning, identified three variables (i.e. type of activities, modality of delivery, and duration of treatment) that might influence the effectiveness of mobile technologies, and confirmed the existence of a redundancy effect and a novelty effect in MALL practice. Implications for future research and pedagogy are discussed.
Efficient nonparametric belief propagation for pose estimation and manipulation of articulated objects

A “pull” message passing algorithm is an efficient nonparametric belief propagation for pose estimation of articulated objects.
Real-Time GPU-Based Digital Image Correlation Sensor for Marker-Free Strain-Controlled Fatigue Testing

Digital image correlation (DIC) is a highly accurate image-based deformation measurement method achieving a repeatability in the range of σ = 10−5 relative to the field-of-view. The method is well accepted in material testing for non-contact strain measurement. However, the correlation makes it computationally slow on conventional, CPU-based computers. Recently, there have been DIC implementations based on graphics processing units (GPU) for strain-field evaluations with numerous templates per image at rather low image rates, but there are no real-time implementations for fast strain measurements with sampling rates above 1 kHz. In this article, a GPU-based 2D-DIC system is described achieving a strain sampling rate of 1.2 kHz with a latency of less than 2 milliseconds. In addition, the system uses the incidental, characteristic microstructure of the specimen surface for marker-free correlation, without need for any surface preparation—even on polished hourglass specimen. The system generates an elongation signal for standard PID-controllers of testing machines so that it directly replaces mechanical extensometers. Strain-controlled LCF measurements of steel, aluminum, and nickel-based superalloys at temperatures of up to 1000 °C are reported and the performance is compared to other path-dependent and path-independent DIC systems. According to our knowledge, this is one of the first GPU-based image processing systems for real-time closed-loop applications.