Reviewerly/exHarmony huggingface dataset:
A Comparative Study of the User Experience of Controller and Hand-Tracking Interactions in a Virtual Environment

In this study, we compared controller and hand-tracking interaction conditions for both browsing and object manipulation tasks to explore the relationship between the interaction method and user experience in a virtual environment (VE), as measured by their sense of agency (SoA), body ownership (BO), interaction fidelity (IF), immersion, usability, and workload. In the first of two experiments, a browsing task was investigated, with the participants in the controller condition experiencing greater SoA, IF, immersion, and usability than those in the hand-tracking condition, while the overall workload was higher for the hand-tracking condition. In experiment 2, we tested an object manipulation task and found a significant difference between the controller and hand-tracking conditions. Participants in the hand-tracking condition experienced higher BO, IF, immersion, and usability, though no meaningful difference was found for SoA and overall workload between the two conditions. Overall, this research confirmed the importance of the association between the interaction method and user experience in a VE.
Themis: An Equal, Unpredictable, and Scalable Consensus for Consortium Blockchain

Consensus algorithm is the core component of consortium blockchains. Equality, Unpredictability and Scalability are three important demands for the consensus algorithms of consortium blockchain. Existing deterministic consensus algorithms (e.g. PBFT) can ensure Equality, but cannot meanwhile meet Unpredictability and Scalability; probabilistic consensus algorithms (e.g. PoW) can achieve Scalability and guarantee a decent Unpredictability, but cannot meet the Equality requirement. In this paper, we propose a new consensus algorithm, namely Themis, which takes the three properties into account. Themis independently adjusts the block-producing difficulty of each node through a self-adaptive node election mechanism, effectively reducing the correlation between the block-producing frequency and the invested computing power of each node. Besides, a GEOST main chain consensus rule is proposed to handle forks and further improve the performance of the algorithm. If a fork occurs, consensus nodes will choose the sub-chain with the highest Equality to join the main chain. Evaluations show that Themis achieves outstanding performance in Equality and Unpredictability while ensuring Scalability, compared with the existing algorithms.
Intelligent Teaching Mode of Chinese Language and Literature Based on Artificial Intelligence

The important role of AI (Artificial Intelligence) in global development has aroused great international attention, and many countries have upgraded AI to a national strategy. Under the influence of AI, the education field is also undergoing a profound innovation. In the field of Chinese language and literature, the intelligent teaching model can effectively link theory and practice. In view of this, this article focuses on the exploration of the intelligent teaching model of Chinese language and literature based on AI to analyze the deep influence of AI on teaching design. It not only breaks the historical and gradual research on Chinese language and literature teaching design, but also provides a new research perspective for the design and development of Chinese language and literature intelligent teaching mode, and promotes the deep integration of AI technology and education. This article proposes an intelligent teaching mode of Chinese language and literature suitable for students. And carry out the research on the strategy of integrating the Chinese language and literature intelligent teaching mode into AI, analyze and summarize the related AI-based Chinese language and literature intelligent teaching mode. Studies have shown that the practice of AI classroom teaching is verified in the smart classroom environment, and the teaching effect of the Chinese language and literature intelligent teaching mode under AI is analyzed. Through the analysis of learner data, the intelligence of Chinese language and literature based on AI is discovered. The model of teaching can make students more involved and achieve better teaching performance.
Analysis of Classification Algorithms for the Prediction of Purchase Intention in Electronic Commerce

The analysis focuses on data from an e-commerce retailer to identify the classification techniques for obtaining the best performances of validated classification algorithms. It is essential to highlight that this type of prediction is used in the commerce sector since it is a sector in constant development and allows to identify the consumer’s behaviour to variables such as average time on the website, transactions completed, pages rated, among others. The research focuses on Business to Consumer commerce, collecting data from the purchase sessions for 12 months. This allowed the comparison of the Naive Bayes model of Gaussian type, Random Forest and Extra Trees Classifier, which are finally validated against the results of Dummy classifier, highlighting the efficiency of Extra Trees Classifier. This type of study and the selection of the variables provide a higher correlation with the Revenue attribute, which is the one used as a prediction class. The efficiency of Extra Trees Classifier stands out in this type of study, together with the selection of the variables that provide the highest correlation with the Revenue attribute, which is the one used as the prediction class.
Instance-level and Class-level Contrastive Incremental Learning for Image Classification

Recently, people pay more attention to catastrophic forgetting problem, that is, the ability of the model to recognize old tasks decreases dramatically when new tasks are added incrementally. Previous studies focused on making the outputs or intermediate features of the new model as similar as possible the old model but ignored the inner-class assignment information. We consider that the inner-class information can effectively reflect the association pattern and intrinsic nature of the samples with each other, so that maintaining the inner-class relationship among task data is helpful to alleviate the negative impact of catastrophic forgetting. Contrastive learning exhibits excellent performance under self-supervising tasks, which can enhance robustness and make representation more compact. We propose an Incremental Learning algorithm with Instance-level and Class-level Contrastive loss and Knowledge Distillation (IL-ICCKD) as common constraints. Specifically, we encourage our model to maintain the knowledge learned in the past from perspectives of instance characteristics and inner-class assignment distribution. At the same time, our model uses a spatial group-wise enhanced attention mechanism to make the learned representations grasp the spatial distribution of subfeatures. We extensively evaluate our framework on three popular benchmark datasets and demonstrate the performance beyond other models.
Mask-Net: A Hardware-efficient Object Detection Network with Masked Region Proposals

Object detection on embedded systems is challenging because it is hard to achieve real-time inference with low energy consumption and limited hardware resources. Another challenge is to find hardware-friendly methods to avoid redundant computation. To address these challenges, in this work, we propose Mask-Net, a hardware-efficient object detection network with masked region proposals in regular shapes. First, we propose a hardware-friendly region proposal method to avoid redundant computation as much as possible and as early as possible, with slight or no accuracy loss. Second, we demonstrate that our method is generalizable by applying it to several detection backbones including SkyNet, ResNet-18 and UltraNet. Our method performs well in different scenarios, including DAC-SDC dataset, UAV123 dataset and OTB100 dataset. We choose SkyNet as our base model to design an accelerator and verify our design on Xilinx ZCU106 FPGA. We observe a speedup of 1.3× and about 30% energy consumption reduction when the FPGA runs at different frequencies from 124 MHz to 214 MHz with only a slight accuracy loss. We also conduct a design space exploration and demonstrate that our accelerator can achieve a theoretical speedup of 1.76× with masked region proposals. This is achieved by optimally allocating DSPs to different parts of the accelerator to balance the computations before and after the mask.
Gamification in teaching EFL through computer games and augmented reality

The assimilation of modern technologies for educational purposes is one of the urgent applied tasks of modern culture, which can serve as an irreplaceable assistant to the teacher, in particular, to make up for the lack of a linguistic environment within the framework of a traditional foreign language lesson. Gamification in education is focused on increasing the factor of student involvement in the educational process by integrating additional entertainment elements into the curriculum. The article examines a computer game and augmented reality technology as examples of gamification of the educational process and experimentally demonstrates their positive effect on the development of motivation for learning English as a foreign language. The immersion in the digital environment is amazing and leads to increased levels of learning motivation, which in turn is reflected in higher academic performance. Students realize the importance of the learning process and result, see the practical value in learning a foreign language, feel confident in their abilities to learn and apply new knowledge, are confident in the relevance of the results obtained and the applicability of knowledge in practice.
VRDoc: Gaze-based Interactions for VR Reading Experience

Virtual reality (VR) offers the promise of an infinite office and remote collaboration, however, existing interactions in VR do not strongly support one of the most essential tasks for most knowledge workers, reading. This paper presents VRDoc, a set of gaze-based interaction methods designed to improve the reading experience in VR. We introduce three key components: Gaze Select-and-Snap for document selection, Gaze MagGlass for enhanced text legibility, and Gaze Scroll for ease of document traversal. We implemented each of these tools using a commodity VR headset with eye-tracking. In a series of user studies with 13 participants, we show that VRDoc makes VR reading both more efficient (p < 0.01) and less demanding (p < 0.01), and when given a choice, users preferred to use our tools over the current VR reading methods.
Three Sources of Blockchain Technology Vulnerabilities - how to Deal with them?

The authors consider the problem of ensuring the security of smart contracts. The relevance of research is associated with a growing field of threats, an increase in economic risks, and the absence or insufficiency of the necessary tools to ensure the security of smart contracts. The authors begin the study by classifying the sources of vulnerabilities in smart contracts. The authors distinguish three groups: the disadvantages and limitations of the programming languages of smart contracts, the weaknesses of the blockchain technology itself, and the penalties associated with the misunderstanding or incorrect application of accepted programming methods. The authors provide a detailed classification of the vulnerabilities of smart contracts. Further, the authors show the need to use static code analyzers of smart contracts and their automation using static code analyzers of smart contracts. The final part discusses the results of an experiment to study the effectiveness of using static analyzers to improve the security of smart contracts.
Corruption in the Nigeria Police Force: A Response to a Hostile State

Over the years, Nigerians have been confronted with police brutality and highhandedness. It is becoming a common happenstance to witness extra-judicial killings, disappearance of suspects and detainees from custody, rebellions and revolts among the rank and file, and of course, the usual giving and taking of bribes in all the nooks and crannies of the country wherever a policeman is sighted either at the police stations, checkpoints or public offices. Very few researchers have taken due care to examine the root causes of police malfeasances. A closer look at the state and condition of service of an average Nigerian police officer will reveal that he is working and operating under the most inhumane and vicious circumstance. The implication of this is that he becomes frustrated, disillusioned, resigns to fate and takes his destiny in his own hands. In his daily conducts, he manifests acts of corruption, lawlessness, violent and aggressive behaviours to the State. Qualitative method of data collection was adopted in this study. Data for the study was generated from secondary sources such as textbooks, journals, newspapers, the internet, etc., while frustration-aggression theory was deployed as the theoretical framework for the study. This research revealed that corruption and other unethical attitudes exhibited by the Nigerian Police Force are a response to the hostile milieu in which they are subjected to. The paper, therefore, recommends that the welfare of the police officers must be prioritized.
A Prediction Method for Dimensional Sentiment Analysis of the Movie and TV Drama based on Variable-length Sequence Input

Time continuous emotion prediction problem has always been one of the difficulties in affective video content analysis. The current research mainly designs a temporally continuous long video emotion prediction method by dividing the long video into short video segments of fixed duration. These methods ignore the time dependencies between short video clips and the mood changes in short video clips. Therefore, combined with the related concepts of film and television narrative structure in cinematic language, this paper defines a prediction method for dimensional sentiment analysis of the movie and TV drama based on variable sequence length inputs. First, this paper defines a method for partitioning variable-length audiovisual sequences that set subunits of dimensional emotion prediction as variable sequence-length inputs. Then, a method for extracting and combining audio and visual features of each variable-length audiovisual sequence is proposed. Finally, a prediction network for dimensional emotion is designed based on variable sequence length inputs. This paper focuses on dimensional sentiment prediction and evaluates the proposed method on the extended COGNIMUSE dataset. The method achieves comparable performance to other methods while increasing the prediction speed, with the Mean Square Error (MSE) reduced from 0.13 to 0.11 for arousal and from 0.19 to 0.13 for valence.
Sustainable Health Care System for Cognitive Breast Cancer Detection using Machine Learning

Cancer of the breast is a common cause of death in women. It is the second most origin of cancer deaths in women. For classifying breast tumors using image processing techniques, the researchers had developed image classification systems. Breast cancer disease becomes complicated as the causes remain unspecified. The regular indication of this disease masses and micro calcifications which is appeared on mammograms. They can be used to improve initial diagnosis approaches over time, which is important for women’s lives. Mammography is an x-ray of the breasts to execute in the absence of symptoms. X-ray mammography is the major test used for screening and processing is the key to developing breast cancer. The first method comprises determining the cells present with various processes. An abnormal cell growth that serves no purpose is a tumor. In this work machine learning technique-SVM is used to detect the breast cancer and this work also predict the various stages of breast cancer. Proposed model results in 97% accuracy which is better than previous work.
Design for Positive UX: From Experience Categories to Psychological Needs

One challenge students or other novices face when designing for positive User Experience can be taking the step from finding out all about users’ needs and then identifying them before turning it into concepts that reflect those needs rather than simply developing concepts and retroactively mapping them to needs in general. In this late breaking work, we describe an approach to familiarizing students with psychological needs and their identification. The sections in the paper are accompanied by a classification in the design process to give an overview of how we applied the methods and in which order. In the process, needs were identified and worked with in all further process phases. The personas were expanded to include needs. All described use cases were developed from the needs. Or rather, a mapping of the functions to the identified needs was created. In this article we described the transfer from Experience categories directly to the needs. This simplifies the work of identifying the needs.
Public-Coin 3-Round Zero-Knowledge from Learning with Errors and Keyless Multi-Collision-Resistant Hash

We construct a public-coin 3-round zero-knowledge argument for NP assuming (i) the sub-exponential hardness of the learning with errors (LWE) problem and (ii) the existence of keyless multi-collision-resistant hash functions against slightly super-polynomial-time adversaries. These assumptions are almost identical to those that were used recently to obtain a private-coin 3-round zero-knowledge argument [Bitansky et al., STOC 2018]. (The difference is that we assume sub-exponential hardness instead of quasi-polynomial hardness for the LWE problem.)
Adaptive Federated Learning via Mean Field Approach

In order to solve the problem of "data island" and preserve individual’s privacy, federated learning, as a distributed machine learning technology, has emerged recently. In federated learning, the model training is distributed over edge clients and coordinated by a central server. Each client only needs to send the updated model parameter to the central server for aggregation without sharing its private data. However, due to the data divergence of heterogeneous clients, the convergence rate of the global model training may be very slow, especially for non-IID data case. To deal with this issue and achieve fast convergence, we propose an adaptive learning rate strategy for each client by considering the deviation of the local model parameter from the global model parameter at each global training iteration. To enable decentralized learning rate design for each client, a mean-field scheme is introduced to estimate the global model parameters over time, which does not even require many clients to communicate frequently. Finally, we run numerical experiments to validate our results.
Agents Assembly: Domain Specific Language for Agent Simulations

Researchers studying group behavior, social dynamics, or epidemiology lack an easy-to-use tool to run large-scale simulations. This contribution introduces a domain specific language (Agents Assembly; AASM) and a toolset for creating and running scalable simulations, using containerized environment. The proposed language supports describing abstract concepts, such as agent, message, and behavior. Its structure, resembling assembly mnemonics, is simple to understand. The language is supported by a code generation module with a graphical user interface, which allows defining simulations using instruction blocks. The AASM code is translated to Python and runs in a distributed containerized ecosystem, utilizing a slightly modified SPADE agent framework. The developed toolset includes also data storage and live visualization.
Deepfake Detection using Multi-path CNN and Convolutional Attention Mechanism

Image and video forgery using cutting-edge deep learning techniques has become one of the major issues in the social networking era. Media manipulation in which one person’s face is swapped out for another’s or has additional features added is referred to as deepfakes. Despite the fact that it has many beneficial purposes, fraudsters generally utilise it to create celebrity porn, revenge porn, and fake news, among other things. One of the biggest risks that deepfake presents is that people’s belief in the reality of many things may decline. The motivation behind deepfake detection is the need to prove that the real thing is real and the fake thing is fake. In this paper a multi-CNN approach for detecting deepfakes is being proposed. Here, a multipath convolutional neural network (CNN) with three modules is used, each of which is stacked with a convolutional block attention mechanism. The first two modules in the dual-path paradigm are a Resnet module and a Densenet module. The Resnet component enables for feature reuse while Densenet allows for the investigation of new features. The parallel InceptionResnet module contains a one-dimensional feature reduction module with residual connections. When the performance of the proposed model is compared with that of four deep learning based approaches, it is found that the proposed method gave the best outcomes, with an accuracy and F1-score of 0.940 and 0.939, respectively.
Towards Open Set Video Anomaly Detection

Open Set Video Anomaly Detection (OpenVAD) aims to identify abnormal events from video data where both known anomalies and novel ones exist in testing. Unsupervised models learned solely from normal videos are applicable to any testing anomalies but suffer from a high false positive rate. In contrast, weakly supervised methods are effective in detecting known anomalies but could fail in an open world. We develop a novel weakly supervised method for the OpenVAD problem by integrating evidential deep learning (EDL) and normalizing flows (NFs) into a multiple instance learning (MIL) framework. Specifically, we propose to use graph neural networks and triplet loss to learn discriminative features for training the EDL classifier, where the EDL is capable of identifying the unknown anomalies by quantifying the uncertainty. Moreover, we develop an uncertainty-aware selection strategy to obtain clean anomaly instances and a NFs module to generate the pseudo anomalies. Our method is superior to existing approaches by inheriting the advantages of both the unsupervised NFs and the weakly-supervised MIL framework. Experimental results on multiple real-world video datasets show the effectiveness of our method.
Caption and Observation Based on the Algorithm for Triangulation (COBALT): Preliminary Results from a Beta Trial

We report on the development progress of a closed beta trial of the COBALT ecosystem, a suite of instruments and software developed to make psychophysiological research accessible to the NeuroIS community and beyond. We provide an overview of the ecosystem and early feedback derived from interview data. The current synthesis of these data indicates that the COBALT ecosystem is utile, easy to use and understand, and can be implemented quickly within a range of experimental contexts to provide vastly improved time-to-insight through workflow efficiency gains.