Reviewerly/exHarmony huggingface dataset:
How Real Do You See Yourself in VR? The Effect of User-Avatar Resemblance on Virtual Reality Experiences and Behaviour

The aim of this study was to gain better insight into the effects of high and low avatar- owner resemblance on spatial presence, engagement, naturalness and negative effects regarding the user experience in an immersive virtual environment. Participants, between 18 and 32 years old, needed to walk on a 60 m high broken pathway in either a condition in which the shoes in VR matched the shoes they were wearing in reality (high resemblance condition) or not (seeing only generic shoes in a low resemblance condition). The results showed no differences between the conditions, despite that participants within the high avatar- owner resemblance perceived a higher similarity between themselves and their digitally displayed shoes and all liked to customize their shoes in VR. This results indicates that it is not a requirement to develop high avatar-owner resemblance in highly immersive virtual environments in which extreme situations are presented which trigger psychological arousal such as stress.
Abnormal Activity Detection in Edge Computing: A Transfer Learning Approach

Detecting real-time abnormal human activities require rapid and accurate computations. However, cloud computing can not meet the highly responsive requirement. Thus, the appearance of edge computing is promised to become a data processing solution for abnormal activity detection. On the other hand, artificial intelligence (AI), as a rapidly emerging force, is leading-edge computing to a new level. Edge computing embedded AI enables IoT devices to become smarter. In this work, we first propose a light deep learning framework using SMOTE to solve the imbalance label problem and implement a Convolutional Neural Network-Embedding-Feature (CNNEF) to recognize abnormal human activities through the sensor data in edge nodes. Then we feed the extracted high-level embedding features from CNNEF to the classical machine learning algorithms, such as logistic regression(LR), K-Nearest Neighbors (KNN), decision tree (DT), Naive Bayes (NB), Random Forests (RF) and support vector machines (SVM). Compare with previous research methods, and our comprehensive empirical results demonstrate the robustness of accuracy and notable time-saving of our algorithms. The highest AUC is 99.53%, and the best time reduction is 97.93%. The proposed method provides a new solution of edge computing-based AI for time-series IoT data applications.
Many-out-of-Many Proofs and Applications to Anonymous Zether

Anonymous Zether, proposed by Bünz, Agrawal, Zamani, and Boneh (FC’20), is a private payment design whose wallets demand little bandwidth and need not remain online; this unique property makes it a compelling choice for resource-constrained devices. In this work, we describe an efficient construction of Anonymous Zether. Our protocol features proofs which grow only logarithmically in the size of the "anonymity sets" used, improving upon the linear growth attained by prior efforts. It also features competitive transaction sizes in practice (on the order of 3 kilobytes).Our central tool is a new family of extensions to Groth and Kohlweiss’s one-out-of-many proofs (Eurocrypt 2015), which efficiently prove statements about many messages among a list of commitments. These extensions prove knowledge of a secret subset of a public list, and assert that the commitments in the subset satisfy certain properties (expressed as linear equations). Remarkably, our communication remains logarithmic; our computation increases only by a logarithmic multiplicative factor. This technique is likely to be of independent interest.We present an open-source, Ethereum-based implementation of our Anonymous Zether construction.
Immersive Training: Outcomes from Small Scale AR/VR Pilot-Studies

From paper-based and face-to-face workshops to electronic and digital simulated educational programs, vast amounts are spent to train workers to perform assembly-tasks, especially in manufacturing. This need for robust, cost-effective, adaptable learning environments supported the rise of Immersive Training. The phenomenon of utilizing the immersive capabilities of Augmented and Virtual Reality (AR/VR) to construct learning environments, i.e. Immersive Training, has flourished, mainly due to the rise and availability of commercial headsets and applications. In this paper, we describe and analyze two such scenarios, a VR lab experiment created to test the VR capabilities in training and a field study exploring the use of AR in a large manufacturing company. Our results provide a) guidelines on how to set up such experiments, b) indicate the temporal demand, and accuracy as significant improvements in VR and c) indicate the effort, physical demand and the time performance in AR.
Segment boundary detection directed attention for online end-to-end speech recognition

Abstract Attention-based encoder-decoder models have recently shown competitive performance for automatic speech recognition (ASR) compared to conventional ASR systems. However, how to employ attention models for online speech recognition still needs to be explored. Different from conventional attention models wherein the soft alignment is obtained by a pass over the entire input sequence, attention models for online recognition must learn online alignment to attend part of input sequence monotonically when generating output symbols. Based on the fact that every output symbol is corresponding to a segment of input sequence, we propose a new attention mechanism for learning online alignment by decomposing the conventional alignment into two parts: segmentation —segment boundary detection with hard decision—and segment-directed attention —information aggregation within the segment with soft attention. The boundary detection is conducted along the time axis from left to right, and a decision is made for each input frame about whether it is a segment boundary or not. When a boundary is detected, the decoder generates an output symbol by attending the inputs within the corresponding segment. With the proposed attention mechanism, online speech recognition can be realized. The experimental results on TIMIT and WSJ dataset show that our proposed attention mechanism achieves comparable online performance with state-of-the-art models.
Chronic gastritis classification using gastric X-ray images with a semi-supervised learning method based on tri-training

High-quality annotations for medical images are always costly and scarce. Many applications of deep learning in the field of medical image analysis face the problem of insufficient annotated data. In this paper, we present a semi-supervised learning method for chronic gastritis classification using gastric X-ray images. The proposed semi-supervised learning method based on tri-training can leverage unannotated data to boost the performance that is achieved with a small amount of annotated data. We utilize a novel learning method named Between-Class learning (BC learning) that can considerably enhance the performance of our semi-supervised learning method. As a result, our method can effectively learn from unannotated data and achieve high diagnostic accuracy for chronic gastritis.
Forecasting and Avoiding Student Dropout Using the K-Nearest Neighbor Approach

All India School Education Survey of 2018–2019 has put a total of 13,06,992 schools and enrollment figure as 22,67,19,283 number of students in these schools in India, and the problem is continuing studies at school or college for many students is difficult due to reasons such as financial problem, domestic problems at home, being unable to cope up with studies, language or medium of education and many other problems like this. The dropout ratio in 2018–2019 for secondary level classes IX and X is at 17% as compared to 4% in primary classes I to V, and to minimize the dropout ratio, the technique of prediction is proposed; in the field of machine learning and data mining, the most widely used prediction method is K-nearest neighbors. This method is more versatile and simple and can handle different types of data in the process of prediction. The prediction of students is classified into dropout or no-dropout category and hence enables the teacher to counsel the students who are at risk of dropping out.
Heterogeneous Tailoring Approach Using the Spotify Model

Organisations tailor agile methods to fit their specific needs. There are two recognised approaches to agile method tailoring: Contingency Factors and Method Engineering. Spotify has developed its own agile culture, which has become influential among agile proponents. The Spotify model has formed the basis of agile methods used in numerous other organisations. We have identified a lack of research into the tailoring approach used in the Spotify model.
Blockchain Secured Auction-Based User Offloading in Heterogeneous Wireless Networks

This letter presents a secure user offloading mechanism in heterogeneous wireless networks (HWNs), where a macrocell base station (MBS) offloads its users to small cell access points (SCAs) using Vickrey auction. Additionally, a user-in-the-loop (UIL) strategy is exploited to encourage the unserved users to move to desired locations for connections. As the participants in the conventional auction-based trading may collude or take selfish actions, we employ Ethereum framework for trustless, secure and distributed auctioning. Simulation results are presented to demonstrate the advantages of the proposed user offloading methodology. The security aspects of the blockchain framework are also discussed.
Discriminative Power of Typing Features on Desktops, Tablets, and Phones for User Identification

Research in Keystroke-Dynamics (KD) has customarily focused on temporal features without considering context to generate user templates that are used in authentication. Additionally, work on KD in hand-held devices such as smart-phones and tablets have shown that these features alone do not perform satisfactorily for authentication. In this work, we analyze the discriminatory power of the most-used conventional features found in the literature, propose a set of context-sensitive or word-specific features, and analyze the discriminatory power of proposed features using their classification results. To perform these tasks, we use the keystroke data consisting of over 650K keystrokes, collected from 20 unique users during different activities on desktops, tablets, and phones, over a span of two months. On an average, each user made 12.5K, 9K, and 10K keystrokes on desktop, tablet, and phone, respectively. We find that the conventional features are not highly discriminatory on desktops and are only marginally better on hand-held devices for user identification. By using information of the context, a subset (derived after analysis) of our proposed word-specific features offers superior discrimination among users on all devices. We find that a majority of the classifiers, built using these features, perform user identification well with accuracies in the range of 90% to 97%, average precision and recall values of 0.914 and 0.901, respectively, on balanced test samples in 10-fold cross validation. We also find that proposed features work best on hand-held devices. This work calls for a shift from using conventional KD features to a set of context-sensitive or word-specific KD features that take advantage of known information such as context.
A Study on Innovation in University Education: Focusing on 5G Mobile Communication

Commercialization of 5G will enable transformation of university education. This study identifies five attributes (device type, learning place, learning content, learning field and expense payment) and corresponding levels to study the impact of 5G in the future of university education. The attributes and the levels are then combined into few 5G education service alternatives for respondents to rank. 102 students ranked the alternatives based on their preferences and intent to use. Results indicate that the intent to use 5G-based education service was high with 86% and the most important factor was expense payment (37%), followed by learning field (26 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> ), learning content (24 %), device type (8%) and learning place 95 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> ). Specifically, students preferred smart device, practical and experiential content, ubiquitous (no limitation of space and time) learning, practical education and free rate when adopting 5G-based education service. These will provide implications to accelerate adoption of and exploitation of 5G for innovating university education.
Graph Games and Logic Design

Graph games are interactive scenarios with a wide range of applications. This position paper discusses old and new graph games in tandem with matching logics and identifies general questions behind this match. Throughout, we pursue two strands: logic as a way of analyzing existing graph games, and logic as an inspiration for designing new graph games. Our aim is modest: we propose a perspective that complements existing game-theoretic and computational ones, we raise questions, make observations, and suggest research directions—technical results are left to future work. But frankly, our main aim with this survey paper is to show that graph games are concrete, fun, easy to grasp, and yet challenging to study.
Predicting Stock Market Trends for Japanese Candlestick Using Cloud Model

Cloud model covers the randomness gap in fuzzy logic model and represents the uncertainty transformation between two different concepts. First concept is the linguistic term that represent the qualitative mean. While the second is the crisp term which represent the quantitative mean. The proposed work presents promising model which combines cloud model, fuzzy time series, and Heikin-Ashi candlestick to predict and confirm accurate stock trend. The model solves several challenging such as: nonlinearity, uncertainty and noises in stock market trend. Heikin-Ashi Candlesticks are an extended branch of Japanese candlesticks, such candlestick filters out stock noise and effort to highlight the trend. Heikin-Ashi Candlestick is constructed by calculating averages of the previous and current period prices. Cloud model handle the ambiguous and uncertainty in the Japanese candlestick definitions (qualitative information) and actual stock prices (quantitative data). It is applied to build membership functions by handling the uncertainty and vagueness of the stock historical data. Then the suggested model constructs dynamic weighted fuzzy logical relationships based on the membership functions to predict the next open and close prices of the stock as well as the high and low values. Finally it constructs the next Heikin-Ashi Japanese candlestick pattern that clarify the trend direction based on the patterns sequence. The imperial evaluation proves that the proposed model has high forecasting accuracy and is feasible to be implemented.
An Empirical Analysis on Load Balancing and Service Broker Techniques using Cloud Analyst Simulator

Cloud computing technology provides sophisticated IT services that are presented to users in different ways based on their business requirements. Both services and assets are provided automatically at a specified time. Load balancing is one of important approaches that is used in a cloud environment for endorsing the performance level of the cloud resources. The load is allocated in all resources that contribute to the architecture of the virtualized data center to 1) minimize the time of user requests in the data center and to 2) maximize the system throughput. Many load balancing algorithms were developed by researchers in recent years. Those algorithms are classified into two main categories (static algorithms and dynamic algorithms). The main target is to optimize the cloud environment performance by reducing the overall cost by transmitting the user requests to prime nodes. In this paper, an empirical analysis is presented on both (Load Balancing and Service Broker) techniques using cloud analyst simulator. The analysis target is to study the behavior of three different load balancing algorithms (Round Robin, Throttled and Active Monitoring). Those algorithms contain several service broker techniques in the virtualized cloud data centers.
PHYAlert: identity spoofing attack detection and prevention for a wireless edge network

Abstract Delivering service intelligence to billions of connected devices is the next step in edge computing. Wi-Fi, as the de facto standard for high-throughput wireless connectivity, is highly vulnerable to packet-injection-based identity spoofing attacks (PI-ISAs). An attacker can spoof as the legitimate edge coordinator and perform denial of service (DoS) or even man-in-the-middle (MITM) attacks with merely a laptop. Such vulnerability leads to serious systematic risks, especially for the core edge/cloud backbone network.In this paper, we propose PHYAlert, an identity spoofing attack alert system designed to protect a Wi-Fi-based edge network. PHYAlert profiles the wireless link with the rich dimensional Wi-Fi PHY layer information and enables real-time authentication for Wi-Fi frames. We prototype PHYAlert with commercial off-the-shelf (COTS) devices and perform extensive experiments in different scenarios. The experiments verify the feasibility of spoofing detection based on PHY layer information and show that PHYAlert can achieve an 8x improvement in the false positive rate over the conventional signal-strength-based solution.
Automatic Helmet Detection in Real-Time and Surveillance Video

In current world, number of vehicles growing day by day results in higher accidents. So, "helmet" is the one key element for ensuring safety of bike riders. Knowing the fact, people tend to avoid wearing helmet. Government had imposed various rules making it compulsory to wear helmet and fine being levied on offenders. But it is not possible to track each rider in current manual tracking and video surveillance system. A model for detection and classification of bike riders who are wearing the helmet as well as those who are not wearing it is proposed in this paper. The proposed model trained on COCO dataset uses only one neural network per image and that is quicker than R-CNN and Fast R-CNN as they use multiple neural networks.
Convolutional Graph Neural Networks

Convolutional neural networks (CNNs) restrict the, otherwise arbitrary, linear operation of neural networks to be a convolution with a bank of learned filters. This makes them suitable for learning tasks based on data that exhibit the regular structure of time signals and images. The use of convolutions, however, makes them unsuitable for processing data that do not exhibit such a regular structure. Graph signal processing (GSP) has emerged as a powerful alternative to process signals whose irregular structure can be described by a graph. Central to GSP is the notion of graph convolutional filters which can be used to define convolutional graph neural networks (GNNs). In this paper, we show that the graph convolution can be interpreted as either a diffusion or aggregation operation. When combined with nonlinear processing, these different interpretations lead to different generalizations which we term selection and aggregation GNNs. The selection GNN relies on linear combinations of signal diffusions at different resolutions combined with node-wise non-linearities. The aggregation GNN relies on linear combinations of neighborhood averages of different depth. Instead of node-wise nonlinearities, the nonlinearity in aggregation GNNs is pointwise on the different aggregation levels. Both of these models particularize to regular CNNs when applied to time signals but are different when applied to arbitrary graphs. Numerical evaluations show different levels of performance for selection and aggregation GNNs.
"Taking Care of a Fruit Tree": Nurturing as a Layer of Concern in Online Community Moderation

Care in communities has a powerful influence on potentially disruptive social encounters. Practising care in moderation means exposing a group's core values, which, in turn, has the potential to strengthen identity and relationships in communities. Dissent is as inevitable in online communities as it is in their offline counterparts. However, dissent can be productive by sparking discussions that drive the evolution of community norms and boundaries, and there is value in understanding the role of moderation in this process. Our work draws on an exploratory analysis of moderation practices in the MetaFilter community, focusing on cases of intervention and response. We identify and analyse MetaFilter moderation with the metaphor: "taking care of a fruit tree", which is quoted from an interview with moderators on MetaFilter. We address the relevance of care as it is evidenced in these MetaFilter exchanges, and discuss what it might mean to approach an analysis of online moderation practices with a focus on nurturing care. We consider how HCI researchers might make use of care-as-nurture as a frame to identify multi-faceted and nuanced concepts characterising dissent and to develop tools for the sustainable support of online communities and their moderators.
Comparison of Naive Bayes and K-NN Method on Tuition Fee Payment Overdue Prediction

Attending basic education is an obligation for all Indonesian citizens. The financial cost is one of the input components to implement an education or even can be considered as the main requirement in achieving the goal of education. For a private education institution in Indonesia, the financial cost is mainly covered by students' tuition payments. SMK Al-Islam Surakarta is a private school that manages all its students to pay school tuition fees monthly. According to its last year's administrative report, the number of students who are late in paying school tuition fee is around 60%. Since the school's operational costs are heavily depended on their income from tuition fees, this considered an essential problem and has to be managed and predicted as well. This research will discuss techniques in predicting the late payment of tuition fees. From many popular methods available in this area, we observed two of them namely Naive Bayes and K-Nearest Neighbor (K-NN). This study will compare the accuracy between those two methods. The data used for the lab work is the official education basic data of Al-Islam Surakarta Vocational School in 2017/2018 totaling 236 data. To increase its accuracy, this study also combines the prediction methods with feature selection technique Information Gain which is commonly used to select an optimal parameter for the prediction process. In the end, the system is tested using the Confusion Matrix method. The results showed that the Naive Bayes Method with Information Gain attribute selection produced the highest accuracy of 69%.