Reviewerly/exHarmony huggingface dataset:
Design and Analysis of Mobile App for Large-Scale Cyber-Argumentation

People from different backgrounds share opinions about various issues over the Internet. The resulted discussions contain substantial information, from which we can derive the collective intelligence and the crowd wisdom. Several argumentation platforms have been developed to enable online deliberations with large-scale in-depth argumentation for effective online discussions. These platforms host structured argumentation networks that allow complex analytical models to mine the argumentation for collective intelligence. However, not all of those argumentation platforms were developed mobile applications. In this paper, we contribute with the design of a mobile application for cyber-argumentation. This mobile application supports intelligent cyber-argumentation and large-scale discussions and provides meaningful analytics on mobile devices. The platform has incorporated several analytical models to capture collective opinions, detect opinion polarization, and predict missing user opinions. An example is used to illustrate our design and models, and a system usability study of our application is presented. This application is an initial step to bring the multi-sided argumentation and deliberation into handheld devices and shows the potential in bringing multi-sided large-scale cyber-argumentation into the limited screen sizes platforms.
Prudential Supervisory Disclosure (PSD) with Supervisory Technology (SupTech): Lessons from a FinTech Crisis

The U.S. financial markets faced an unprecedented rapid decline and recovery on May 6, 2010, known as the May 6 flash crash. Roughly one trillion $ market value in less than thirty minutes vanished with the biggest one-day point decline in the history of the DJIA at the time.Since the market events took place in electronic markets, and algorithmic trading (AT) and high-frequency trading (HFT), parts of FinTech, played significant roles, we handle the May 6 flash crash from the FinTech, SupTech, and financial supervision perspectives.With the flashback method, we analyzed the reactions of market participants, media, and two financial supervisors, the SEC, and the CFTC, to the market crash. We find that the technological imbalance between financial markets or institutions and their supervisors drove the markets in uncertainty, hence in a fear and panic environment. Since the imbalance has not diminished yet, the same risks still exist. As a remedy, we introduce a new concept and model with a well-functioning SupTech system to cope with the May 6 type FinTech crises.
Skeleton-Based Action Recognition with Dense Spatial Temporal Graph Network

The skeleton information of human body can provide important information for human action recognition. In the field of action recognition based on skeleton, The graph convolutional network has achieved remarkable results. However, the classification methods cannot fully understand realistic and complex actions such as competitive sports. We propose a new spatiotemporal graph convolutional network named DSTG-Net. DSTG-Net optimizes the original graph convolutional network using the feature of dense structure. Our model is tested on two realistic and complex datasets, FSD-10 and video-emotion, and its effectiveness is validated.
How Similar Are Smart Contracts on the Ethereum?

Ethereum is a programmable platform that allows everyone to deploy and access the smart contracts on it. Such flexibility can lead everyone to browse or reuse the source code of the existing smart contracts on the Ethereum. In this paper, to characterize the code clone practice of the smart contract, we present a large-scale study on the smart contracts coming from the Ethereum. We firstly collect more than 700,000 open-source smart contracts, and then we employ a highly effective approach (i.e., Locality-Sensitive Hashing, LSH) to cluster the similar smart contracts. At last, we conduct a qualitative analysis to characterize the clone practice of the smart contract, and further analyze the reason why smart contracts are similar. Our analysis revealed that over 96% of the smart contracts can found similar contracts, which indicates that the smart contracts on the Ethereum are highly homogeneous.
Tightening bounds for variational inference by revisiting perturbation theory

Variational inference has become one of the most widely used methods in latent variable modeling. In its basic form, variational inference employs a fully factorized variational distribution and minimizes its KL divergence to the posterior. As the minimization can only be carried out approximately, this approximation induces a bias. In this paper, we revisit perturbation theory as a powerful way of improving the variational approximation. Perturbation theory relies on a form of Taylor expansion of the log marginal likelihood, vaguely in terms of the log ratio of the true posterior and its variational approximation. While first order terms give the classical variational bound, higher-order terms yield corrections that tighten it. However, traditional perturbation theory does not provide a lower bound, making it inapt for stochastic optimization. In this paper, we present a similar yet alternative way of deriving corrections to the ELBO that resemble perturbation theory, but that result in a valid bound. We show in experiments on Gaussian Processes and Variational Autoencoders that the new bounds are more mass covering, and that the resulting posterior covariances are closer to the true posterior and lead to higher likelihoods on held-out data.
Denstity Level Aware Network for Crowd Counting

Crowd counting has wide applications in video surveillance and public safety, while it remains an extremely challenging task due to large scale variation and diverse crowd distributions. In this paper, we present a novel method called Density Level Aware Network (DLA-Net) to improve the density map estimation in varying density scenes. Specifically, we divide the input into multiple regions according to their density levels and handle the regions independently. Dense regions (with small scale heads) require higher resolution features from shallow layers, while sparse regions (with large heads) need deep features with broader receptive filed. Based on this requirement, we propose to predict multiple density maps focusing on regions of varying density levels correspondingly. Inspired by the U-Net architecture, our density map estimators borrow features of shallow layers to improve the estimation of dense regions. Moreover, we design a Density Level Aware Loss (DLA-Loss) to better supervise those density maps in different regions. We conduct extensive experiments on three crowd counting datasets (ShanghaiTech, UCF-CC-50 and UCF-QNRF) to validate the effectiveness of the proposed method. The results demonstrate that our DLA-Net achieves the best performance compared with other state-of-the-art approaches.
Towards Interference-Aware Dynamic Scheduling in Virtualized Environments

Our previous work shows that multiple applications contending for shared resources in virtualized environments are susceptible to cross-application interference, which can lead to significant performance degradation and consequently an increase in the number of broken SLAs. Nevertheless, state of the art in resource scheduling in virtualized environments still relies mainly on resource capacity, adopting heuristics such as bin packing, overlooking this source of overhead. However, in recent years interference-aware scheduling has gained traction, with the investigation of ways to classify applications regarding their interference levels and the proposal of static cost models and policies for scheduling co-hosted cloud applications. Preliminary results in this area already show a considerable improvement on resource usage and in the reduction of broken SLAs, but we strongly believe that there are still opportunities for improvement in the areas of application classification and pro-active dynamic scheduling strategies. This paper presents the state of the art in interference-aware scheduling for virtualized environments and the challenges and advantages of a dynamic scheme.
Use of formative assessment to improve the online teaching materials content quality

Due to the evolution of new information technologies, several courses have been made available on the web via educational platforms, websites, or educational applications. But there is no follow-up for the detection of faults in these online courses which allows the improvement of their quality. In this study, we used online formative assessment to provide useful information to writers of teaching materials to improve their quality and to identify areas for improvement. Based on the learners' responses, we calculated the failure rate per unit, per question and the frequency of false answers per question, which allowed us to detect faults in course material, and to ensure from the effectiveness of the corrective measures carried out by the teaching materials editors based on the information we have talked about, we calculated the failure rate by version of the teaching materials.
Clustering Analysis Method of Ethnic Cultural Resources Based on Deep Neural Network Model

This paper proposes a method of clustering analysis of ethnic cultural resources based on deep neural network model. Firstly, the feature word extraction and vectorization of ethnic cultural resources texts are realized by doc2vec document vectorization tool. Then K-means clustering algorithm is used to cluster the ethnic cultural resources texts after vectorization, and the Elbow method is used to determine the best aggregation. So as to obtain the correlation between the texts of ethnic cultural resources, which is used for the collection, storage and intelligent service of massive ethnic cultural resources provides technical support. At the end of the paper, the ethnic cultural resources in the specific ethnic website are taken as an example to analyze the above methods.
Human Factors in the Design of Wheelchair Tray Tables: User Research in the Co-design Process

Wheelchair tray tables offer a convenient way for wheelchair users to carry out daily tasks such as eating, reading, and using mobile devices. However, most tray tables are made to serve the majority of wheelchair users and are inaccessible to some with a limited range of motion. In our work, we address this issue by exploring the ergonomic problems and possible solutions. In this paper, we describe the human factors in the design and development of powered wheelchair tray tables. The process of humanistic co-design relies on the direct involvement of the targeted demographic in the design process. This ensures the outcome is centered around the specific needs of the individual. Our approach employs user research studies (e.g., interviews, questionnaires, and actively working with a wheelchair using co-designer) as a means towards gleaning valuable insight into the needs of wheelchair users. In these studies, we sought to explore their experiences with using tables made for wheelchairs. We also collected data about whether the tray tables required external assistance to stow and use, and the problems they faced using existing solutions. We then highlighted the various specific needs presented by the co-designers and questionnaire respondents. These needs are embodied into scenario-based personas in which they may find themselves in need of a table for use with their wheelchairs. Deriving these personas from our survey results provides an effective method of keeping the insight gathered present throughout the design process. Implications for design are discussed.
Study of robustness of information embedding into digital images DWT domain using QIM method against destructive effects and steganalysis

Abstract Steganography is one of effective solutions to ensure information confidentiality. For this purpose, secret information is embedded into a cover object, for example, a digital image. However, embedded data can be detected using steganalysis techniques. As a result, an attacker can apply destructive effects to the stego-image and destroy the embedded data to prevent their hidden transmission. Therefore, this paper presents a study of robustness of information embedding into digital images DWT domain using QIM method against destructive effects (JPEG compression, brightness change, etc.) and statistical steganalysis at the same time. The results will help to increase the efficiency of information embedding into DWT domain of digital images.
A Single Document Assamese Text Summarization Using a Combination of Statistical Features and Assamese WordNet

In this paper, an extractive text summarization approach using Assamese WordNet is proposed, and the difficulties faced while extracting summary in the Assamese document are discussed. The Assamese language is a low-level language. Synset is applied from Assamese WordNet. The various features used for identifying the most salient sentences to generate effective summary aspects such as TF-IDF, sentence length, sentence position and numerical identification are considered. Automatic Text Summarization in the Assamese language is still in an early stage and this language does not have its own approach. So, the text summarization approach is compared to the approaches applied in Bengali and Bangla language approaches as these languages share a script that is quite similar having slight variations in certain letters. The effectiveness of our proposed approach is demonstrated through a set of experiments carried out using ROUGE measure, and the evaluation is depicted in terms of Precision, Recall and F1-score.
WITHDRAWN: Usage of smart technologies among young adults

The development of an individual is carried out through various processes and it includes social, psychological, and biological ways. Adding upon all these individual factors the smart technologies have tremendous impact on the life an individual. The recent developments in smart technologies have paved the way of a 360 degree change among the life an individual and as well the society. The young adult, who is considered as the treasurer of a nation have been greatly influenced by this latest smart technologies. In this context, the usages of such smart technologies by the young adults need to be carefully assessed. The present study focus on the various aspects of the usage of smart technologies by the young adult group. The study was descriptive in nature. Total 100 samples were collected with a sampling procedure of simple random technique. Questionnaire was developed and used to collect the data from the sample. The study analyzed the various services provided by the smart devices and duration of usage among the young adult.
BNRDT: When Data Transmission Meets Blockchain

Data transmission exists in almost all the Internet-based applications, while few of them consider the property of nonrepudiation as part of data security. If a data transmission scheme is performed without the endorsement of a trusted third party (TTP) or a central server, it is easy to raise disputes while transmitting valuable data, especially digital goods, because a dishonest participant can deny the fact of particular data transmission instance. The above problem can be solved by signing and encrypting. However, digital signature schemes usually assume public key infrastructure (PKI), increasing the burden on certificate management and are not suitable for distributed networks without TTP such as blockchain. To solve the above problems, we propose two new schemes for nonrepudiation data transmission based on blockchain (we call it BNRDT): one for short message transmission and the other for large file transmission. In BNRDT schemes, nonrepudiation evidence of data transmission is generated and stored on the blockchain to satisfy both the properties of nonrepudiation (including nonrepudiation of origin and nonrepudiation of receipt) and data confidentiality. We implement and test the schemes on Hyperledger Fabric. The experimental results show that the proposed schemes can provide appealing performance.
A Modification of UCT Algorithm for WTN-EinStein würfelt nicht! Game

WTN-EinStein würfelt nicht! (abbreviated as EWN) chess game has been attracting much attention owing to its characteristics of randomness and incompleteness. In this study, a modified upper confidence bounds applied to trees (UCT) algorithm is proposed by optimizing selection strategy, simulation of tree nodes and establishing the game tree based on probabilistic rules and natural characteristics of the chess game. Experimental results verify that the program applying the modified UCT algorithm can greatly improve winning rate compared with others with plain UCT or Monte Carlo algorithms. The program won the first prize in 2019 Chinese University Student Computer Games Competition and 13th National Computer Games Tournament.
Floating-Point Double-Humped Chaotic Image Encryption on FPGA

Chaos-based image encryption systems have drawn the attention of researchers in the data security field tremendously over the past few years. In this paper, an image encryption system based on generalized Double-Humped logistic map is designed and implemented on Virtex-5 FPGA platform. Due to the high sensitivity of chaotic functions, floating-point data representation is adopted to achieve high precision and accuracy of results compared to system simulations. The proposed hard-ware design of the encryption system fits the simulation results exactly, and validated with practical implementation, achieving clock frequency of 350 MHz.
An Algorithm for Automatically Updating a Forsyth-Edwards Notation String Without an Array Board Representation

We present an algorithm that correctly updates the Forsyth-Edwards Notation (FEN) chessboard character string after any move is made without the need for an intermediary array representation of the board. In particular, this relates to software that have to do with chess, certain chess variants and possibly even similar board games with comparable position representation. Even when performance may be equal or inferior to using arrays, the algorithm still provides an accurate and viable alternative to accomplishing the same thing, or when there may be a need for additional or side processing in conjunction with arrays. Furthermore, the end result (i.e. an updated FEN string) is immediately ready for export to any other internal module or external program, unlike with an intermediary array which needs to be first converted into a FEN string for export purposes. The algorithm is especially useful when there are no existing array-based modules to represent a visual board as it can do without them entirely. We provide examples that demonstrate the correctness of the algorithm given a variety of positions involving castling, en passant and pawn promotion.
Active Interaction Design For Stress Therapy Virtual Environment

Virtual reality technology has been used as an alternative approach for stress therapy. However, designing a virtual environment for the purpose of stress therapy is still an issue since not many articles on designing a suitable 3D environment for stress relief are available. Existing literature published only focused on identifying the effectiveness of virtual reality as a stress therapy tool. This situation has left the researchers with the problem of having very little guidelines to be used as reference when designing the virtual environment. In our previous publication, an initial framework for designing a virtual environment for stress therapy was proposed. In this paper, a concept on one of the design elements proposed in the framework, which is active interaction, is presented. A pilot study to evaluate the usefulness of the element was carried out. Both discussion and findings from the evaluation process could serve as a guideline in designing the active interaction element in a virtual environment developed for stress therapy purpose.
Trajectory Planning for Unmanned Aircraft Vehicle via Set-Valued Filter

Trajectory planning in complex environments with different kinds of obstacles, like static obstacles, dynamic obstacles and noncooperative agents, is of great pratical importance for Unmanned Aircraft Vehicle (UAV). Although various algorithms are proposed to solve the obstacle avoidance problem, these methods only consider one or two kinds of obstacles. In this paper, a novel algorithm is proposed to plan a trajectory for UAV in such an environment which simultaneously includes static obstacles, dynamic obstacles and especially noncooperative agents. Besides, we consider different types of approach modes of noncooperative agents and propose corresponding strategies to avoid collisions with them. A set-valued filter-based method is proposed to predict the position of dynamic obstacles and noncooperative agents whose motion model is not available to UAV. Meanwhile, the measurement noise is considered in the set-valued filter to improve the safety of UAV. Some simulations are implemented to verify the effectiveness of the proposed method and they confirm that the proposed method can plan a safe trajectory for UAV in complex environments.
Better Paracoherent Answer Sets with Less Resources

Abstract Answer Set Programming (ASP) is a well-established formalism for logic programming. Problem solving in ASP requires to write an ASP program whose answers sets correspond to solutions. Albeit the non-existence of answer sets for some ASP programs can be considered as a modeling feature, it turns out to be a weakness in many other cases, and especially for query answering. Paracoherent answer set semantics extend the classical semantics of ASP to draw meaningful conclusions also from incoherent programs, with the result of increasing the range of applications of ASP. State of the art implementations of paracoherent ASP adopt the semi-equilibrium semantics, but cannot be lifted straightforwardly to compute efficiently the (better) split semi-equilibrium semantics that discards undesirable semi-equilibrium models. In this paper an efficient evaluation technique for computing a split semi-equilibrium model is presented. An experiment on hard benchmarks shows that better paracoherent answer sets can be computed consuming less computational resources than existing methods.
Malaysian Banknote Reader For Visually Impaired Person

Visually impaired person facing difficulty in shopping, running business and activities that dealing with handling banknote. This paper presents a Malaysian banknote reader targeted for visually impaired person to allow them identifying Malaysian banknotes value and dealing with dealers that performing business transaction in Ringgit. This paper proposes a Malaysian banknote reader for visually impaired person using perceptual hashing based image searching and fuzzy logic algorithm. the banknote image captured by the reader will be sent to cloud/internet server for image processing, via Wi-Fi to eliminate the bulky size reader problem. A server in cloud is set up to receive the captured image and return the analysis results after running the image processing algorithm. This will eventually reduce the costs and size of the banknote reader. The banknote reader reached about 95% accuracy by testing 600 different worn, torn and new Malaysian banknotes. The proposed reader managed to recognize banknotes at about 480ms to 560ms for single-sided recognition, after image taken by the reader's camera. The test of the reader was also run with both visually impaired and visually good participants to recognize 100 pieces of different Malaysian banknote. The result shown that the recognition responses is comparable, only with slight difference of 1.0908 second per banknote reading time.