Reviewerly/exHarmony huggingface dataset:
Hate Speech and the Polarization of Japanese National Newspapers

Abstract In 2016, the first anti-hate speech law in Japan was introduced against the backdrop of verbal attacks on ethnic Koreans who were targeted with particular force by radical right organizations in the early 21st century. We argue that while the role of social media in the proliferation of hate speech in Japan has received considerable attention, the coverage of hate speech and related issues in mainstream news media has not been sufficiently studied. This article offers an interdisciplinary analysis, grounded in media studies and in linguistics, of the positions of five national newspapers in public discourse about hate speech, discussed as a current issue in Japan from 2016 until mid-2018. We combine agenda-setting theory with discourse analysis of factual reporting in order to evaluate the Japanese media landscape, which, when scrutinized through the lens of the hate speech issue, reveals ideological polarization.
Research on Intelligent Experimental Equipment and Key Algorithms Based on Multimodal Fusion Perception

The application of virtual reality technology in science experiment education is a research with practical significance and value in human-computer interaction.However, in some existing education tools based on virtual reality, due to the single interaction mode, the complexity of user intention and the non-physical interaction characteristics brought by virtualization, their experimental teaching ability is limited, resulting in the lack of practical value and popularity.In order to solve these problems, a multimodal interaction model is constructed by fusing gesture, speech and pressure information.Specifically, our tasks include: 1) collecting user input information and time series information to construct basic data input tuples.2) The basic interaction information is used to identify the user's basic intention, and the correlation degree between the user's intentions is considered to determine the correctness of the current identification intention.3) It allows users to alternate between multi-channel and single channel interaction.Based on this model, we build a multi-modal intelligent interactive virtual experiment platform (MIIVEP), and design and implement a kind of dropper with strong perception ability, which has been verified, tested, evaluated and applied in the intelligent virtual experiment system.In addition, in order to evaluate this work more effectively, we developed a fair scoring criterion for the virtual experimental system (Evaluation scale of virtual experiment system, ESVES), and invited middle school teachers and students to participate in the verification of the results of this work.Through the user's actual use effect verification and result research, we prove the effectiveness of the proposed model and the corresponding implementation.
Automatic processing of gaze movements to quantify gaze scanning behaviors in a driving simulator

Eye and head movements are used to scan the environment when driving. In particular, when approaching an intersection, large gaze scans to the left and right, comprising head and multiple eye movements, are made. We detail an algorithm called the gaze scan algorithm that automatically quantifies the magnitude, duration, and composition of such large lateral gaze scans. The algorithm works by first detecting lateral saccades, then merging these lateral saccades into gaze scans, with the start and end points of each gaze scan marked in time and eccentricity. We evaluated the algorithm by comparing gaze scans generated by the algorithm to manually marked "consensus ground truth" gaze scans taken from gaze data collected in a high-fidelity driving simulator. We found that the gaze scan algorithm successfully marked 96% of gaze scans and produced magnitudes and durations close to ground truth. Furthermore, the differences between the algorithm and ground truth were similar to the differences found between expert coders. Therefore, the algorithm may be used in lieu of manual marking of gaze data, significantly accelerating the time-consuming marking of gaze movement data in driving simulator studies. The algorithm also complements existing eye tracking and mobility research by quantifying the number, direction, magnitude, and timing of gaze scans and can be used to better understand how individuals scan their environment.
Dispute Resolution in Voting

In voting, disputes arise when a voter claims that the voting authority is dishonest and did not correctly process his ballot while the authority claims to have followed the protocol. A dispute can be resolved if any third party can unambiguously determine who is right. We systematically characterize all relevant disputes for a generic, practically relevant, class of voting protocols. Based on our characterization, we propose a new definition of dispute resolution for voting that accounts for the possibility that both voters and the voting authority can make false claims and that voters may abstain from voting.A central aspect of our work is timeliness: a voter should possess the evidence required to resolve disputes no later than the election's end. We characterize what assumptions are necessary and sufficient for timeliness in terms of a communication topology for our voting protocol class. We formalize the dispute resolution properties and communication topologies symbolically. This provides the basis for verification of dispute resolution for a broad class of protocols. To demonstrate the utility of our model, we analyze a mixnet-based voting protocol and prove that it satisfies dispute resolution as well as verifiability and receipt-freeness. To prove our claims, we combine machine-checked proofs with traditional pen-and-paper proofs.
A Subspace Based Transfer Joint Matching with Laplacian Regularization for Visual Domain Adaptation

In a real-world application, the images taken by different cameras with different conditions often incur illumination variation, low-resolution, different poses, blur, etc., which leads to a large distribution difference or gap between training (source) and test (target) images. This distribution gap is challenging for many primitive machine learning classification and clustering algorithms such as k-Nearest Neighbor (k-NN) and k-means. In order to minimize this distribution gap, we propose a novel Subspace based Transfer Joint Matching with Laplacian Regularization (STJML) method for visual domain adaptation by jointly matching the features and re-weighting the instances across different domains. Specifically, the proposed STJML-based method includes four key components: (1) considering subspaces of both domains; (2) instance re-weighting; (3) it simultaneously reduces the domain shift in both marginal distribution and conditional distribution between the source domain and the target domain; (4) preserving the original similarity of data points by using Laplacian regularization. Experiments on three popular real-world domain adaptation problem datasets demonstrate a significant performance improvement of our proposed method over published state-of-the-art primitive and domain adaptation methods.
Attributed Relational SIFT-Based Regions Graph: Concepts and Applications

In the real world, structured data are increasingly represented by graphs. In general, the applications concern the most varied fields, and the data need to be represented in terms of local and spatial connections. In this scenario, the goal is to provide a structure for the representation of a digital image, called the Attributed Relational SIFT-based Regions Graph (ARSRG), previously introduced. ARSRG has not been described in detail, and for this purpose, it is important to explore unknown aspects. In this regard, the goal is twofold: first, to provide a basic theory, which presents formal definitions, not yet specified above, clarifying its structural configuration; second, experimental, which provides key elements about adaptability and flexibility to different applications. The combination of the theoretical and experimental vision highlights how the ARSRG is adaptable to the representation of the images including various contents.
Generating Random Binary Bit Sequences for Secure Communications between Constraint Devices under the IOT Environment

The researchers found random numbers beneficial to implement a secure IoT environment. To secure the communications between constrained devices, researchers can use either true random number generators (TRNGs) or pseudorandom number generators (PRNGs) to generate the secure key. Encryption or decryption of the plain text and the mutual authentication between devices uses these keys. PRNGs are highly dependable on TRNGs for the perfect randomness because the randomness of the natural sources or TRNGs is not traceable. Although TRNGs are much safe to generate random numbers, programmers write codes for pseudorandom number generation, commonly known as PRNGs. These PRNGs should use less complicated methods for use in the IoT environment. Authors are writing the programs to generate random numbers independently, without any hardware interruption. In this paper, authors are going to use these concepts to form a new lightweight mechanism for the generation of cryptographically secure random binary bit sequences. Here authors are trying to incorporate the goodness of every flavor at one platform.
An experimental study on inverse adaptive neural fuzzy control for nonlinear systems

In this paper, a type-2 fuzzy neural network is used to adaptive inverse control of a class of nonlinear systems. The proposed method has proper performance for real-time control of nonlinear and time-invariant systems and has a quick response to following changes. In this method, the high approxim ation ability of type 2 fuzzy systems is used to reverse system modeling and this inverse model is used immediately to control the system. In order to measure the efficiency of the proposed method, the control of dynamic, highly nonlinear and time varying water both temperature system with certain and uncertain water pressure is experimentally considered. The results show the accuracy of the proposed method.
Context Based MTS for Translating Gujarati Trigram and Bigram Idioms to English

Gujarati language is the official language of the state of Gujarat located on the western region of India. Machine Translation System (MTS) translates text from one language to other language. Based on our review, we found that very few machine translation systems are available that converts Gujarati text into English language. This paper focuses on the translation of Gujarati trigram idioms. Idiom is defined as a token-sequence whose meaning is different from the literal meaning of the individual tokens. The proposed Gujarati to English Idioms translator accurately translates the trigram and bigram idioms. We have created the corpus of nearly 3000 n-gram idioms and from this corpus we have found nearly 890 trigram idioms and 1735 bigram idioms. This paper studies the translation of trigram and bigram idioms.
Structured feature sparsity training for convolutional neural network compression

Convolutional neural networks (CNNs) with large model size and computing operations are difficult to be deployed on embedded systems, such as smartphones or AI cameras. In this paper, we propose a novel structured pruning method, termed the structured feature sparsity training (SFST), to speed up the inference process and reduce the memory usage of CNNs. Unlike other existing pruning methods, which require multiple iterations of pruning and retraining to ensure stable performance, SFST only needs to fine-tune the pretrained model with additional regularization on the less important features and then prune them, no multiple pruning and retraining needed. SFST can be deployed to a variety of modern CNN architectures including VGGNet, ResNet and MobileNetv2. Experimental results on CIFAR, SVHN, ImageNet and MSTAR benchmark dataset demonstrate the effectiveness of our scheme, which achieves superior performance over the state-of-the-art methods.
Analysis of Haptic Feedback and its Influences in Virtual Reality Learning Environments

In this work we present our approach of measuring the influence, importance and immersion of haptic feedback in a virtual reality learning environment. Participants were given the goal to improve themselves to their best ability on a task involving virtual weights supported by real world objects in a mixed reality environment. Conducting pre- and post-surveys, assessing the immersion and putting the resulting data in comparison with the test group, which used a pure virtual reality environment. We found correlations bolstering our initial assumption of the importance of haptic feedback in understanding and usage of virtual reality learning environments for specific functions.
Blockchain Technology: Applications and Research Challenges

Blockchain technology has become a catchword in the recent technological era. It is used in industry as well as by researchers. Blockchain technology concept was used first time in bitcoin application and now its application areas are expanding through all directions in the digital world. Blockchain technology has characteristics as transparency, decentralization, persistency, auditability. There are pros and cons of these characteristics. Based on the advantages, the various domains are adopting the blockchain technology for the development of applications. Different industries and governments are developing their projects using blockchain technology to fulfil their goals as they are getting solutions to their problems through this technology. For developing the applications, various platforms are introduced. These development platforms support by various feature and there is an add-on in that features as per requirements come up. Toward the end of the discussion, the paper is enlightening the challenges in blockchain technology. As the demand of the technology increases, the issues are raising and attackers are attracting to break the system and find out the loopholes in that. The report mentioned the challenges with examples which will be pointing towards various areas of the research.
What Happens in Blockchain Stays in Blockchain. A Legal Solution to Conflicts Between Digital Ledgers and Privacy Rights

Blockchain is a disruptive technology presented in 2008 that allows both scarcity and timestamps to be introduced to the digital world. Whereas many technological applications may benefit from this architecture, it involves direct conflict with both Privacy rights and Data Protection rules, as introduced by the General Data Protection Regulation (GDPR). This study first provides an overview of what blockchain is, how it works, and how it can affect privacy. It describes how this technology functions, thanks to binary ledgers distributed amongst the system nodes, and what role they play in validating the succession of blocks. The work analyses how blockchain can be applied to innovative fields and investigates related Privacy issues. Indeed, the chain can certify the time, the parties and the object included in a 'block' but cannot guarantee the legal validity, the veracity or correctness of the content. Furthermore, its immutability is in direct conflict with the right to be forgotten. In addition, due to the distributed nature of the system, it does not allow identification of data controllers and, consequentially, the accountable subject for the personal data processed within the digital ledger. The aim of the study is to highlight the characteristics of the proposed solution, i.e. supporting centralised governance of blockchain infrastructures to ensure control over the distributed nodes, as well as having the capability to intervene in modifying the chain when the law requires it. This set of interventions would also render publicly available the personal information within the blockchain with different levels of accessibility ('Privacy by Layers' (PbL)) and, therefore, provide log control that can ensure compliance with the Data Protection regulatory framework. To provide complete analysis on the matter, the study also addresses how Intelligent Systems running on a blockchain-based infrastructure that holds pieces of personal information can clash with Article 22 of the GDPR on automated decisions when it affects the fundamental rights of individuals. Finally, the conclusions crystallise the legal remarks by stressing the essential elements of the analysis that emerged during the study and framing them within the bigger picture of how the Law addresses social or technological phenomena.
Adaptive weighted dynamic differential evolution algorithm for emergency material allocation and scheduling

Abstract Emergency material allocation and scheduling is a combination optimization problem, which is essentially a Non‐deterministic Polynomial (NP) problem. Aiming at the problems such as slow convergence, easy prematurely falling into local optimum, and parameter constraints to solve high‐dimensional and multi‐modal combination optimization problems, this article proposes an adaptive weighted dynamic differential evolution (AWDDE) algorithm. The algorithm uses a chaotic mapping strategy to initialize the population. By weighting the standard differential evolution (DE) mutation strategy, a new weighted mutation operator is proposed. The scaling factor and cross probability can be adaptively adjusted. A disturbance operator is introduced to randomly generate the perturbation mutation and to accelerate the premature individuals to jump out of the local optimum. The algorithm is applied to the problem of emergency material allocation and scheduling, and a two‐stage emergency material allocation and scheduling model is established. Compared with the standard DE algorithm and the chaos adaptive particle swarm algorithm, the results show that the AWDDE algorithm has the characteristics of stronger global optimization ability and faster convergence speed compared with other optimization algorithms, which provide assistance for smart cities research, including smart city services, applications, case studies, and policymaking considerations for emergency management.
Multi-Point Prediction of Aircraft Motion Trajectory Based on GA-Elman-Regularization Neural Network

Aiming at the problem that the traditional algorithm has large prediction error on motion trajectory and short prediction distance, this paper proposes a GA-Elman-Regularization based neural network method. The GA algorithm has the characteristics of parallel search global optimal solution, which makes up for the shortcomings of static property given by neural network model and the tendency of training algorithm to fall into partial optimal solution, and introduces regularization terms to improve the generalization ability of the network, also improves the prediction accuracy of the network. Comparison of experimental results of motion trajectory prediction by Elman neural network, GA-Elman neural network and GA- Elman-Regularization neural network on semi-physical dataset, the predicted average errors are 1.37%, 0.82% and 0.556%. Experiments show that the optimized algorithm improved the generalization ability of the network and the accuracy of prediction.
A Vocabulary Recommendation System based on Knowledge Graph for Chinese Language Learning

In order to solve the problem of vocabulary learning confusion of primary Chinese language learners as well as effectively meet the current situation of learning resources overload, this study constructs a Chinese vocabulary learning resources recommendation system based on knowledge graph. First of all, the research on computer-supported vocabulary learning and learning resources recommendation based on knowledge graph is systematically reviewed. Secondly, this study designs the learning resources and 10 kinds of relations of Hanyu Shuiping Kaoshi (HSK) three-level vocabulary, and gives symmetry or transitivity to each relation, creates learning resources ontology, and constructs learning resources recommendation system based on knowledge graph by using Protégé, Apache Jena and Python, namely CiHai. Finally, through the trial of primary Chinese language learners, the semi-structured interview is used to evaluate the results. The results showed that learners are satisfied with the learning experience of the vocabulary recommendation system. CiHai is helpful in improving the learning effect of Chinese vocabulary.
Selecting Features for Breast Cancer Analysis and Prediction

Breast Cancer (BC) is the second most common cancer in women after skin cancer and has become a major health issue. As a result, it is very important to diagnose BC correctly and categorizing the tumors into malignant or benign groups. We know that Machine Learning (ML) techniques have unique advantages and that is why they are widely used to analyze complex BC dataset and predict the disease. Wisconsin Diagnosis Breast Cancer (WDBC) dataset has been used to develop predictive models for BC by researchers in this field. The dataset has 573 instances and 32 features. In this paper, we have proposed a method for analyzing and predicting BC on the same dataset using Apache Spark. This big data framework is a very powerful tool for working on huge volume of data, such as healthcare data [4]. Principle Component Analysis (PCA) has been applied on the dataset for selecting the most important features. We have run experiments with top 6 and 10 features. The experiments are executed on Hadoop cluster, a cloud platform provided by the Electrical Engineering and Computer Science (EECS) department of University of Cincinnati. We have also made a comparison between the performance of different machine learning techniques: Decision Tree and Random Forest Classifier. We have set the performance of Decision Tree with top 10 features as a benchmark in our work. Random forest Classifier performs better than Decision Tree algorithm with top 6 as well as top 10 features. Random Forest achieves 97.52 % accuracy using top 10 features. Our results show that selecting the right features significantly improves accuracy in predicting BC.
Machine Learning-based Spoofing Attack Detection in MmWave 60GHz IEEE 802.11ad Networks

Spoofing attacks pose a serious threat to wireless communications. Exploiting physical-layer features to counter spoofing attacks is a promising solution. Although various physical-layer spoofing attack detection (PL-SAD) techniques have been proposed for conventional 802.11 networks in the sub-6GHz band, the study of PL-SAD for 802.11ad networks in 5G millimeter wave (mmWave) 60GHz band is largely open. In this paper, we propose a unique physical layer feature in IEEE 802.11ad networks, i.e., the signal-to-noise-ratio (SNR) trace obtained at the receiver in the sector level sweep (SLS) process, to achieve efficient PL-SAD. The SNR trace is readily extractable from the off-the-shelf device, and it is dependent on both transmitter location and intrinsic hardware impairment. Therefore, it can be used to achieve an efficient detection no matter the attacker is co-located with the legitimate transmitter or not. The detection problem is formulated as a machine learning classification problem. To tackle the small sample learning and fast model construction challenges, we propose a novel neural network framework consisting of a backpropation network, a forward propagation network, and generative adversarial networks (GANs). It can tackle small sample learning and allow for quick model construction. We conduct experiments using off-the-shelf 802.11ad devices, Talon AD7200s and MG360, to evaluate the performance of the proposed PL-SAD scheme. Experimental results confirm the effectiveness of the proposed PL-SAD scheme, and the detection accuracy can reach 98% using small sample sizes under different scenarios.