Reviewerly/exHarmony huggingface dataset:
Blockchain-Based Lightweight Certificate Authority for Efficient Privacy-Preserving Location-Based Service in Vehicular Social Networks

Blockchain can be utilized to enhance both security and efficiency for location-based service (LBS) in vehicular social networks (VSNs), due to its inherent decentralization, anonymity, and trust properties. Unfortunately, the existing approaches either lack effective authentication, which is vulnerable to the man-in-the-middle attack, or require an online certificate authority (CA) where frequent interactions with resource-constrained vehicles are required. To address these challenging issues, in this article, a lightweight threshold CA for consortium blockchain along with a privacy-preserving LBS protocol in blockchain enforced VSNs is proposed. First, a lightweight threshold CA framework LTCA is proposed by devising a threshold proxy signature, where the proxy signing key is issued by a coalition of threshold number of CAs playing the roles of authorized nodes in the consortium blockchain. Without the intervene of an online CA, each vehicle in the online phase can authenticate its identity by itself each time its blockchain address (i.e., account address) is updated. Then, based on the proposed LTCA, an efficient privacy-preserving LBS protocol PPVC is contrived to protect each vehicle’s conditional identity privacy with a moderate cost. Finally, both security analysis and performance evaluation demonstrate the effectiveness and efficiency of our proposed LTCA and PPVC in blockchain enforced VSNs.
Haptic Feedback Helps Me? A VR-SAR Remote Collaborative System with Tangible Interaction

Research on Augmented Reality (AR)/Mixed Reality (MR) remote collaboration for physical tasks remains a compelling and dynamic area of study. AR systems have been developed which transmit virtual annotations between remote collaborators, but there has been little research on how haptic feedback can also be shared. In this paper, we present a Virtual Reality (VR)-Spatial Augmented Reality (SAR) remote collaborative system that provides haptic feedback with tangible interaction between a local worker and a remote expert helper. Using this system, we conducted a within-subject user study to compare two interfaces for remote collaboration between a local worker and expert helper, one with mid-air free drawing (MFD) and one with tangible physical drawing (TPD). The results showed that there were no significant differences with respect to performance time and operation errors. However, users felt that the TPD interface supporting passive haptic feedback could significantly improve the remote experts' user experience in VR. Our research provides useful information on the way for gesture- and gaze-based multimodal interaction supporting haptic feedback in AR/MR remote collaboration on physical tasks.
A self-perturbed pseudo-random sequence generator based on hyperchaos

Chaotic system has been widely used in the design of pseudorandom sequence generators. However, the performance of pseudorandom sequence generators is greatly affected by the chaotic degradation, which is caused by computational accuracy. In this paper, we propose a self-perturbed pseudorandom sequence generator based on hyper-chaotic system to overcome this problem. A novel hyper-chaotic system is constructed to achieve a complex dynamic behavior. One of the feedback controllers is used to disturb the other dimensions so that the short period can be avoided. Based on the proposed hyper-chaotic system, a pseudorandom number generator is designed. The dynamic behavior of the hyper-chaotic system is analyzed. The randomness and the security of the pseudorandom sequence generated by the proposed scheme are tested and analyzed. The results show that this scheme has a larger key space and a higher level of randomness. It is suitable to be used in privacy encryption and secure communication.
Heterogeneous Network Representation Learning: Survey, Benchmark, Evaluation, and Beyond

Since real-world objects and their interactions are often multi-modal andmulti-typed, heterogeneous networks have been widely used as a more powerful,realistic, and generic superclass of traditional homogeneous networks (graphs).Meanwhile, representation learning (a.k.a. embedding) has recently beenintensively studied and shown effective for various network mining andanalytical tasks. In this work, we aim to provide a unified framework to deeplysummarize and evaluate existing research on heterogeneous network embedding(HNE), which includes but goes beyond a normal survey. Since there has alreadybeen a broad body of HNE algorithms, as the first contribution of this work, weprovide a generic paradigm for the systematic categorization and analysis overthe merits of various existing HNE algorithms. Moreover, existing HNEalgorithms, though mostly claimed generic, are often evaluated on differentdatasets. Understandable due to the application favor of HNE, such indirectcomparisons largely hinder the proper attribution of improved task performancetowards effective data preprocessing and novel technical design, especiallyconsidering the various ways possible to construct a heterogeneous network fromreal-world application data. Therefore, as the second contribution, we createfour benchmark datasets with various properties regarding scale, structure,attribute/label availability, and etc. from different sources, towards handyand fair evaluations of HNE algorithms. As the third contribution, we carefullyrefactor and amend the implementations and create friendly interfaces foreleven popular HNE algorithms, and provide all-around comparisons among themover multiple tasks and experimental settings.
DNN-Chip Predictor: An Analytical Performance Predictor for DNN Accelerators with Various Dataflows and Hardware Architectures

The recent breakthroughs in deep neural networks (DNNs) have spurred a tremendously increased demand for DNN accelerators. However, designing DNN accelerators is non-trivial as it often takes months/years and requires cross-disciplinary knowledge. To enable fast and effective DNN accelerator development, we propose DNN-Chip Predictor, an analytical performance predictor which can accurately predict DNN accelerators' energy, throughput, and latency prior to their actual implementation. Our Predictor features two highlights: (1) its analytical performance formulation of DNN ASIC/FPGA accelerators facilitates fast design space exploration and optimization; and (2) it supports DNN accelerators with different algorithm-to-hardware mapping methods (i.e., dataflows) and hardware architectures. Experiment results based on 2 DNN models and 3 different ASIC/FPGA implementations show that our DNN-Chip Predictor's predicted performance differs from those of chip measurements of FPGA/ASIC implementation by no more than 17.66% when using different DNN models, hardware architectures, and dataflows. We will release code upon acceptance.
THE USE OF THE CLOUD-BASED OPEN LEARNING AND RESEARCH PLATFORM FOR COLLABORATION IN VIRTUAL TEAMS

The article highlights the promising ways of providing access to cloud-based platforms and tools to support collaborative learning and research processes. It is emphasized that the implementation of cloud computing services is a promising trend in the development of modern ICT pedagogical systems. The concept of cloud-based open learning and research platform is considered. The benchmarking studies of using ICT tools for learning and research within the pedagogical systems of higher education are fulfilled. The analysis and evaluation of the existing experience of using different types of software packages to support learning and research processes within the cloud-based environment are proposed. The issues of integration of knowledge-based services, language technologies and database network services within the open systems of learning and research are covered. A model of the cloud-based open learning and research collaborative environment involving the use of cloud-based components based on AWS virtual desktop, IBM Box, WPadV4 and other tools is substantiated. The reasonable ways of tools selection are considered and the prospects for their use within the cloud-based open learning and research platform in pedagogical systems of higher education are described. The research methods are the analysis of official international documents, publications on the subject of the research, observation, comparison, the analysis of the experience of using educational and scientific network packages, application of cloud technologies, benchmarking studies and experimental studies. Conclusions and recommendations encompass the application of cloud-based open learning and research technologies covering research platforms, scientific and educational network packages, and also cloud services for collecting, submitting and processing data as a promising trend in the development and modernization of the educational environment of higher education institutions and collaborative research.
RS-CapsNet: An Advanced Capsule Network

Capsule Network is a novel and promising neural network in the field of deep learning, which has shown good performance in image classification by encoding features into capsules and constructing the part-whole relationships.However, the original Capsule Network is not suitable for the images with complex background due to its weak ability of feature extraction, a large number of training parameters, and the characteristic of explaining everything in the image.To address the above issues, we propose an advanced Capsule Network named RS-CapsNet, which uses Res2Net block to extract multi-scale features, and Squeeze-and-Excitation (SE) block to highlight useful features and suppress useless features.Meanwhile, we adopt the method of linear combination between capsules, which enhances the representation ability of capsules for detected object and reduces the number of capsules.Moreover, we propose the method of firstly constructing intermediate capsules that can represent most of the detected object, and then using the intermediate capsules and primary capsules to construct the classification capsules together.The experimental results show that proposed RS-CapsNet has better performance on CIFAR10, CIFAR100, SVHN, FashionMNIST, and AffNIST datasets, it can also provide better translation equivariance, and the number of training parameters is reduced by 65.11%.
Using Virtual Reality in the Classroom: Reflections of STEM Teachers on the Use of Teaching and Learning Tools

The purpose of the study was to determine teachers' opinions about virtual reality (VR).Virtual Reality Interview Form (VRIF) administered to seven teachers after the implementation of VR practices.Teachers received three weeks of intensive training.Afterward, teachers applied VR for two months in their classrooms.Later, interviews held with them.Findings indicated that different VR practices, including Google Cardboard, were used in the classroom.According to the teachers, using virtual reality in the classroom captured students' interest, increased their creativity, allowed students to take virtual trips, increased students' motivation, improved students' technology literacy, individualized learning; made students easier to understand difficult concepts.The potential problems of using VR were not asked the teachers; however, teachers mentioned that online safety and security, student access, and technology gaps were the problems they faced.The findings of this study suggest that the use of VR allows teachers to visualize abstract topics and enrich instruction.A VR orientation module would be provided teachers with opportunities to learn, practice, and apply their VR skills before being placed in the classroom.
Devon: Deformable Volume Network for Learning Optical Flow

State-of-the-art neural network models estimate large displacement optical flow in multi-resolution and use warping to propagate the estimation between two resolutions. Despite their impressive results, it is known that there are two problems with the approach. First, the multi-resolution estimation of optical flow fails in situations where small objects move fast. Second, warping creates artifacts when occlusion or dis-occlusion happens. In this paper, we propose a new neural network module, Deformable Cost Volume, which alleviates the two problems. Based on this module, we designed the Deformable Volume Network (Devon) which can estimate multi-scale optical flow in a single high resolution. Experiments show Devon is more suitable in handling small objects moving fast and achieves comparable results to the state-of-the-art methods in public benchmarks.
Deep Ranking for Image Zero-Shot Multi-Label Classification

During the past decade, both multi-label learning and zero-shot learning have attracted huge research attention, and significant progress has been made. Multi-label learning algorithms aim to predict multiple labels given one instance, while most existing zero-shot learning approaches target at predicting a single testing label for each unseen class via transferring knowledge from auxiliary seen classes to target unseen classes. However, relatively less effort has been made on predicting multiple labels in the zero-shot setting, which is nevertheless a quite challenging task. In this work, we investigate and formalize a flexible framework consisting of two components, i.e., visual-semantic embedding and zero-shot multi-label prediction. First, we present a deep regression model to project the visual features into the semantic space, which explicitly exploits the correlations in the intermediate semantic layer of word vectors and makes label prediction possible. Then, we formulate the label prediction problem as a pairwise one and employ Ranking SVM to seek the unique multi-label correlations in the embedding space. Furthermore, we provide a transductive multi-label zero-shot prediction approach that exploits the testing data manifold structure. We demonstrate the effectiveness of the proposed approach on three popular multi-label datasets with state-of-the-art performance obtained on both conventional and generalized ZSL settings.
The Effectiveness of Using Mobile Learning Techniques to Improve Learning Outcomes in Higher Education

Recently Mobile technology is considered an effective way to improve students' skills such as positive thinking, collaborative, communication, as well as it is considered the main part of major innovation in many e-learning research areas. As a result of the 21&lt;sup&gt;st.&lt;/sup&gt; century requirements, skills were developed to address the rising needs in higher education which causes a shifting paradigm from the traditional methods of teaching to M-learning. In this research, we discuss the effect of using Mobile learning techniques to improve learning outcomes in Higher Education. We have implemented a web-based survey through two questionnaires. The questionnaires were distributed among 200 students in the second and third levels in the computer science department at both Community College and College of Arts and Science. This research explores a study on e-learning using mobile technology to identify students’ perceptions in the acceptance of mobile techniques and recognize the quality of mobile services for academic and social purposes to improve teaching strategy and learning performance in higher educational organizations. The outcomes of this research would support the evolution of M-learning at the university-level and cause shifting the traditional learning methods by merging M-learning methodologies as a learning management system that provides mobile learning services to students and teachers any time and from any location. The research study shows some important results towards the integration of mobile technology into teaching include: student positive perception, facilitates student concentrate, flexible access to m-services for learning materials, and increases students' skills in using mobile technology for e-learning.
Hindi-English Hate Speech Detection: Author Profiling, Debiasing, and Practical Perspectives

Code-switching in linguistically diverse, low resource languages is often semantically complex and lacks sophisticated methodologies that can be applied to real-world data for precisely detecting hate speech. In an attempt to bridge this gap, we introduce a three-tier pipeline that employs profanity modeling, deep graph embeddings, and author profiling to retrieve instances of hate speech in Hindi-English code-switched language (Hinglish) on social media platforms like Twitter. Through extensive comparison against several baselines on two real-world datasets, we demonstrate how targeted hate embeddings combined with social network-based features outperform state of the art, both quantitatively and qualitatively. Additionally, we present an expert-in-the-loop algorithm for bias elimination in the proposed model pipeline and study the prevalence and performance impact of the debiasing. Finally, we discuss the computational, practical, ethical, and reproducibility aspects of the deployment of our pipeline across the Web.
Convexity Shape Prior for Level Set-Based Image Segmentation Method

In this paper, we propose an image segmentation model that incorporates convexity shape priori using level set representations. In the past decade, several discrete and continuous methods have been developed to solve this problem. Our method comes from the observation that the signed distance function of a convex region must be a convex function. Based on this observation, we transfer the complicated geometrical convexity shape priori into some simple constraints on the signed distance function. We propose a simple algorithm to keep these constraints exactly. The proposed method could be easily applied to level set based segmentation models, such as the well-known Chan-Vese mode and the active contour models. By setting some good initial curves, the proposed method can easily segment convex objects from images with complicated background. We demonstrate the performance of the proposed methods on both synthetic images and real images, as well as the comparison to some state-of-the-art methods.
Iterative Edit-Based Unsupervised Sentence Simplification

We present a novel iterative, edit-based approach to unsupervised sentence simplification.Our model is guided by a scoring function involving fluency, simplicity, and meaning preservation.Then, we iteratively perform word and phrase-level edits on the complex sentence.Compared with previous approaches, our model does not require a parallel training set, but is more controllable and interpretable.Experiments on Newsela and WikiLarge datasets show that our approach is nearly as effective as state-of-the-art supervised approaches. 1
Hard-Coded Gaussian Attention for Neural Machine Translation

Recent work has questioned the importance of the Transformer's multi-headed attention for achieving high translation quality.We push further in this direction by developing a "hardcoded" attention variant without any learned parameters.Surprisingly, replacing all learned self-attention heads in the encoder and decoder with fixed, input-agnostic Gaussian distributions minimally impacts BLEU scores across four different language pairs.However, additionally hard-coding cross attention (which connects the decoder to the encoder) significantly lowers BLEU, suggesting that it is more important than self-attention.Much of this BLEU drop can be recovered by adding just a single learned cross attention head to an otherwise hard-coded Transformer.Taken as a whole, our results offer insight into which components of the Transformer are actually important, which we hope will guide future work into the development of simpler and more efficient attention-based models.
Discourse as a Function of Event: Profiling Discourse Structure in News Articles around the Main Event

Understanding discourse structures of news articles is vital to effectively contextualize the occurrence of a news event.To enable computational modeling of news structures, we apply an existing theory of functional discourse structure for news articles that revolves around the main event and create a human-annotated corpus of 802 documents spanning over four domains and three media sources.Next, we propose several documentlevel neural-network models to automatically construct news content structures.Finally, we demonstrate that incorporating system predicted news structures yields new state-of-theart performance for event coreference resolution.The news documents we annotated are openly available and the annotations are publicly released for future research 1 .
Deep Self-Supervised Representation Learning for Free-Hand Sketch

In this paper, we tackle for the first time, the problem of self-supervised representation learning for free-hand sketches. This importantly addresses a common problem faced by the sketch community – that annotated supervisory data are difficult to obtain. This problem is very challenging in which sketches are highly abstract and subject to different drawing styles, making existing solutions tailored for photos unsuitable. Key for the success of our self-supervised learning paradigm lies with our sketch-specific designs: (i) we propose a set of pretext tasks specifically designed for sketches that mimic different drawing styles, and (ii) we further exploit the use of the textual convolution network (TCN) together with the convolutional neural network (CNN) in a dual-branch architecture for sketch feature learning, as means to accommodate the sequential stroke nature of sketches. We demonstrate the superiority of our sketch-specific designs through two sketch-related applications (retrieval and recognition) on a million-scale sketch dataset, and show that the proposed approach outperforms the state-of-the-art unsupervised representation learning methods, and significantly narrows the performance gap between with supervised representation learning. <xref ref-type="fn" rid="fn1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><sup>1</sup></xref> <fn id="fn1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><label><sup>1</sup></label> PyTorch code of this work is available at <uri>https://github.com/zzz1515151/self-supervised_learning_sketch</uri>. </fn>
Sentiment Analysis of YouTube Movie Trailer Comments Using Naïve Bayes

Netflix has produced many TV series, one of which is Money Heist. This series has four seasons with a total of 38 episodes. The fourth season was released on April 3, 2020, which has eight episodes. The fourth season of Money Heist is 31.73 times more demand than the average series around the world. However, despite the many requests for the fourth season of the Money Heist series, there are still some negative comments made by the connoisseurs of the Money Heist series. In the YouTube comments column on the Netflix channel, there are still many who comment neutral and provide positive comments on this series. Therefore, there needs to be a method in which viewers' comments or opinions can be analyzed in order to be able to classify the opinions they make about this series by conducting sentiment analysis using the Naïve Bayes algorithm. Based on the results of research conducted, Naive Bayes can be said to be successful in conducting sentiment analysis because it achieves results of 81%, 74.83%, and 75.22% for accuracy, precision, and recall, respectively.