Reviewerly/exHarmony huggingface dataset:
A Survey of Facial Capture for Virtual Reality

The holograms in Star Wars have inspired many researchers to capture the whole human body in real-time and present it as an avatar into Virtual Reality (VR). Facial capture is important to achieve this because facial expressions are essential for social interaction. However, facial capture works well only when the face is not occluded. While a VR headset has been widely used as a display device for VR, it occludes half of the face and becomes an obstacle. This paper presents a systematic literature review on facial capture for VR. The survey aims to: review the current state-of-the-art facial capture technologies for VR; identify the types of technologies and context of the use, the methodologies, and theoretical groundings; identify research gaps in facial capture for VR; and identify solutions of facial capture for VR headsets. A realism index is defined to evaluate and compare the collected papers. The results show several technology trends in the facial capture for VR: tracking facial motion with markers, facial capture in headsets using cameras or sensors, facial performance capture, and hologram/volumetric capture. It is shown that the Modular Codec Avatar is the best facial capture method in a VR headset, whereas Metahuman has the best output effects. This paper also proposes that an open-design VR headset is an effective approach to lower the Total Cost of Ownership (TCO).
Extracting Walking Trajectories at Home From a Capacitive Proximity Sensing Floor

Walking at home can provide valuable information about locomotor efficiency, anticipation of daily hazards and general well-being. Here, we present a multidisciplinary method to reconstruct locomotor trajectories while walking at home with a capacitive proximity sensing device — the SensFloor — which was installed in a real occupied apartment in the city center of Montpellier in France. Our recognition method is based on the spatio-temporal statistical probability of body location corresponding to sensors’ activation. The results led to the localization of the inhabitant in the two-dimensional floor space, and their tracking over a 24-hour period. More precisely, our technique enabled us to distinguish human-related behavior from the location of static objects. It also allowed us to successfully identify locomotor trajectories in a highly confined space, including those from two simultaneously walking individuals in different rooms. It allowed us to obtain valuable information on spatial behavior (trajectory, stationarity) but also on temporal behavior (occupancy time, walking duration). As this technique compensates for the already established low accuracy of capacitive sensors, our method offers innovative possibilities to study locomotor metrics at home using relatively inexpensive sensing technology.
DRCE Maintainability Model for Component Based Systems using Soft Computing Techniques

Effective software maintainability is one of the most significant and challenging activity in the field of component based software. Several maintainability models are proposed by the researchers to reduce the maintenance cost, to improve the quality and life span of the software product. The proposed model will assist the software designers to develop maintainable softwares. This paper discusses a maintainability model, which selects four crucial factors that highly affect maintainability of component based software system. Soft computing techniques are employed to demonstrate strong correlation of these factors with maintainability. MATLAB’s Fuzzy logic toolbox is used for predicting the maintainability level of component (such as Excellent, Fair, Good, Bad and worst). Data generated by fuzzy model are provided as input to artificial neural network model. Experimental results shows mean absolute error (MAE) to be .028 and Relative Error (RE) to be .045.To further improve the performance of the model; neuro-fuzzy tool was employed. With the use of self learning capability of this tool, MAE and RE are now improved to the value .0029 and .039. It means that the model was sound enough to provide satisfactory outcomes in comparison to neural network.
Valha11a: Agency and Genre in Emergent Virtual Larp

When COVID-19 created barriers to sharing physical space, Otherworld Theatre and Moonrise Games created Valha11a, an emergent sci-fi larp performed using Zoom as an interactive platform.Three creators of Valha11a apply PAR methodology to interrogate the intended and unintended consequences of relocating larp performance online.We analyze the ways in which Zoom impacts players' agency in the performance; production management and facilitation of play; and genre verisimilitude.In this way, we establish a new nexus of ideas within and around larp performance, namely that mediatized larp can serve as a framework for a potentially more intimate, emotionally rich participant experience.
FDD-MEF: Feature-Decomposition-Based Deep Multi-Exposure Fusion

Multi-exposure image fusion is an effective algorithm for fusing differently exposed low dynamic range (LDR) images to a high dynamic range (HDR) images. In this study, a novel network architecture for multi-exposure image fusion (MEF) based on feature decomposition is proposed. The conventional MEF methods are weak for restoring detail and color, and they suffer from visual artifacts. To overcome these challenges, a feature of each LDR image is decomposed to the common and residual components at a feature level. Then, fusion is performed on the residual domain. It was found through diverse experiments that the proposed network could improve the MEF performance in three aspects; detail restoration in bright and dark regions, reduction of halo artifacts, and natural color restoration. In addition, an attempt was made to find the underlying principles of feature-decomposition-based MEF by visualizing the features through RGB channels.
A feminist server stack: co-designing feminist web servers to reimagine Internet futures

In this article, we reimagine the Internet as a feminist cosmos called a Feminist Server Stack. By scrutinising how webserver infrastructure is normalised based on imperial conventions and distorted knowledge systems, we suggest alternatives. This embrace of ‘post-patriarchal futures’ (Bardzel 2019, 20) offers counter perspectives on how we may holistically embody the Internet as a situated ecology. When experiencing the Internet materiality, it leads us to imagine beyond the known and possible. We focus on enchanting affirmations and tacit hands-on computational methods from the frontiers of feminist digital literacy and server communities. These acts make visible the seams and fissures of concealed undercurrents and schemata of infrastructures. The findings reveal utterly different relationships between webserver providers and clients. Drawing from extensive fieldwork and observational analysis, we show how the choreography of happenings is seeding the ground for participatively co-designing the transfiguration of planetary computing into one that is scalable, visceral, and restorative. We welcome these nascent pathways to guide us to an enigmatic realm upon which theories of co-design could dance in communion with a realm that is to-come.
New Model of Heteroasociative Min Memory Robust to Acquisition Noise

Associative memories in min and max algebra are of great interest for pattern recognition. One property of these is that they are one-shot, that is, in an attempt they converge to the solution without having to iterate. These memories have proven to be very efficient, but they manifest some weakness with mixed noise. If an appropriate kernel is not used, that is, a subset of the pattern to be recalled that is not affected by noise, memories fail noticeably. A possible problem for building kernels with sufficient conditions, using binary and gray-scale images, is not knowing how the noise is registered in these images. A solution to this problem is presented by analyzing the behavior of the acquisition noise. What is new about this analysis is that, noise can be mapped to a distance obtained by a distance transform. Furthermore, this analysis provides the basis for a new model of min heteroassociative memory that is robust to the acquisition/mixed noise. The proposed model is novel because min associative memories are typically inoperative to mixed noise. The new model of heteroassocitative memory obtains very interesting results with this type of noise.
Cyberthreats to Hospitals: Panacea, a Toolkit for People-Centric Cybersecurity

Healthcare organizations are an attractive target for cyber-attacks, because the digitization of health processes is emerging as a necessity. Healthcare is a rich source of valuable data and its defences are weak. The particular weakness of this domain is due to the high complexity and dynamism of the healthcare technological environment and to the fact that healthcare working environment has many characteristics that make human behaviour a cybersecurity hazard. Cyberattacks may have significant effects on the provision of health services. Concrete measures strengthening a healthcare setting must take into account number and diversity of hospital basic components and existing security policies. The purpose of this work is to present a cybersecurity toolkit for connected devices and people. Panacea toolkit supports hospitals performing preparedness activities for example: assessment of the nature and severity of a threat, identification of mitigation measures and adoption of mitigation strategies.
Numerical Approach of Symmetric Traveling Salesman Problem Using Simulated Annealing

The aim of this paper is to elaborate the performance of Simulated Annealing (SA) algorithm for solving traveling salesmen problems. In this paper, SA algorithm is modified by using the interaction between outer and inner loop of algorithm. This algorithm produces low standard deviation and fast computational time compared with benchmark algorithms from several research papers. Here SA uses a certain probability as indicator for finding the best and worse solution. Moreover, the strategy of SA as cooling to temperature ratio is still given. Thirteen benchmark cases and thirteen square grid symmetric TSP are used to see the performance of the SA algorithm. It is shown that the SA algorithm has promising results in finding the best solution of the benchmark cases and the squared grid TSP with relative error 0 - 7.06% and 0 – 3.31%, respectively. Further, the SA algorithm also has good performance compared with the well-known metaheuristic algorithms in references.
Mining Simple Path Traversal Patterns in Knowledge Graph

The data mining has remained a subject of unfailing charm for research. The knowledge graph is rising and showing infinite life force and strong developing potential in recent years, where it is observed that acyclic knowledge graph has capacity for enhancing usability. Though the development of knowledge graphs has provided an ample scope for appearing the abilities of data mining, related researches are still insufficient. In this paper, we introduce path traversal patterns mining to knowledge graph. We design a novel simple path traversal pattern mining framework for improving the representativeness of result. A divide-and-conquer approach of combining each path is proposed to discover the most frequent traversal patterns in knowledge graph. To support the algorithm, we design a linked list structure indexed by the length of sequences with handy operations. The correctness of algorithm is proven. Experiments show that our algorithm reaches a high coverage with low output amounts compared to existing frequent sequence mining algorithms.
Study of the Expectations of Employers-Partners of Educational Institutions as a Necessary Element of Strategic Development and Creation of the University Image

The problems of image are now becoming a strategic management concern for higher educational institutions since they affect the level of satisfaction and loyalty of all parties interacting with the institutions. Efficient image management can be critical to the survival and success of an organization in the context of government funding reduction and a highly competitive environment. The article analyzes the results of a survey on the Anketolog.ru platform which involved 52 respondents and was aimed at collecting the expert commentaries of potential employers engaged as tutors in the educational process at Baikal State University (BSU). The survey revealed assessments of the quality of training provided by the system of higher education, assessment of the current BSU’s graduates employment efficiency, as well as the level of BSU’s graduates’ satisfaction with the quality of their training and skill level. The «word cloud» method enabled the authors to determine what skills (competencies) obtained by the graduates the potential employers consider the most important, and to find the right wording for them and for the relevant courses to be included in the academic programs. Therefore, the analysis of the survey results provided the data on the demand for specialists trained at the university and the requirements for their skills and characteristics expected by potential employers, which helps to determine possible strategic prospects for BSU’s cooperation with potential employers in the 5-year period to come. Though the results of the study are of a practical value for Baikal State University, they might be useful for any university to build up its image.
A Fast and Accurate Spatial Target Snapping Method for 3D Scene Modeling and Mapping in Mobile Augmented Reality

High-performance spatial target snapping is an essential function in 3D scene modeling and mapping that is widely used in mobile augmented reality (MAR). Spatial data snapping in a MAR system must be quick and accurate, while real-time human–computer interaction and drawing smoothness must also be ensured. In this paper, we analyze the advantages and disadvantages of several spatial data snapping algorithms, such as the 2D computational geometry method and the absolute distance calculation method. To address the issues that existing algorithms do not adequately support 3D data snapping and real-time snapping of high data volumes, we present a new adaptive dynamic snapping algorithm based on the spatial and graphical characteristics of augmented reality (AR) data snapping. Finally, the algorithm is experimented with by an AR modeling system, including the evaluation of snapping efficiency and snapping accuracy. Through the experimental comparison, we found that the algorithm proposed in this paper is substantially improved in terms of shortening the snapping time, enhancing the snapping stability, and improving the snapping accuracy of vector points, lines, faces, bodies, etc. The snapping efficiency of the algorithm proposed in this paper is 1.6 times higher than that of the traditional algorithm on average, while the data acquisition accuracy based on the algorithm in this paper is more than 6 times higher than that of the traditional algorithm on average under the same conditions, and its data accuracy is improved from the decimeter level to the centimeter level.
ROTed: Random Oblivious Transfer for embedded devices

Oblivious Transfer (OT) is a fundamental primitive in cryptography, supporting protocols such as Multi-Party Computation and Private Set Intersection (PSI), that are used in applications like contact discovery, remote diagnosis and contact tracing. Due to its fundamental nature, it is utterly important that its execution is secure even if arbitrarily composed with other instances of the same, or other protocols. This property can be guaranteed by proving its security under the Universal Composability model. Herein, a 3-round Random Oblivious Transfer (ROT) protocol is proposed, which achieves high computational efficiency, in the Random Oracle Model. The security of the protocol is based on the Ring Learning With Errors assumption (for which no quantum solver is known). ROT is the basis for OT extensions and, thus, achieves wide applicability, without the overhead of compiling ROTs from OTs. Finally, the protocol is implemented in a server-class Intel processor and four application-class ARM processors, all with different architectures. The usage of vector instructions provides on average a 40% speedup. The implementation shows that our proposal is at least one order of magnitude faster than the state-of-the-art, and is suitable for a wide range of applications in embedded systems, IoT, desktop, and servers. From a memory footprint perspective, there is a small increase (16%) when compared to the state-of-the-art. This increase is marginal and should not prevent the usage of the proposed protocol in a multitude of devices. In sum, the proposal achieves up to 37k ROTs/s in an Intel server-class processor and up to 5k ROTs/s in an ARM application-class processor. A PSI application, using the proposed ROT, is up to 6.6 times faster than related art.
Fuzzy rule based sentiment analysis for finding University Student Satisfaction in Yemen

Objectives:To analyze the sentiment and understand Student's opinions and satisfaction about university, and the services which introduce by it to reach accuracy in evaluation of the university.Methods: The sentiment of students was analyzed and quantified the satisfaction towards the University by building an algorithm using a set of fuzzy rules.This study proposes an integrated optimization method: applying a set of fuzzy rules with two lexicons (SentiWordNet and AFINN lexicon) besides to other parameter.The lexicons in sentiment anlysis are individual words that can be considered as a unit of opinion information.The proposed fuzzy system integrates Natural Language Processing techniques to analyze and quantify student's satisfaction towards the University by classify the comments into very positive, positive, very negative, negative, or neutral sentiment classes.Findings: with this approach, the accuracy is more 0.891 compared to Support Vector Machines (SVM), Naïve Bayes, The Fuzzy Inference to analyze the sentiment with their own lexicon Opinion Words Lexicon and classified the sentiment into 2 classpositive, negative and The Fuzzy Inference to analyze the sentiment with only SentiWordNet lexiconin evaluation obtained for University evaluation, the evaluation has been verified by simulation results on MATLAB.Novelty: The novelty of this study lies in the formulation of few fuzzy rules to evaluate the sentiment class of tweets, and the proposed model can be adapted to any lexicon.
On Reducing Repetition in Abstractive Summarization

Repetition in natural language generation reduces the informativeness of text and makes it less appealing.Various techniques have been proposed to alleviate it.In this work, we explore and propose techniques to reduce repetition in abstractive summarization.First, we explore the application of unlikelihood training and embedding matrix regularizers from previous work on language modeling to abstractive summarization.Next, we extend the coverage and temporal attention mechanisms to the token level to reduce repetition.In our experiments on the CNN/Daily Mail dataset, we observe that these techniques reduce the amount of repetition and increase the informativeness of the summaries, which we confirm via human evaluation.
EFL Students’ Attitudes Towards the Ease of Use Mobile Technology to Learn English at a University in Vietnam

Learning through mobile devices (Mobile learning) appears as the significant breakthrough of technology 4.0.Among the sharp progress of up-to-date technology, it has been seen that mobile devices' smart functions are able to support learners' learning wherever and whenever.Hence, mobile-assisted language learning (MALL) has been believed to serve learners' English skills and knowledge well.Although mobile learning is expected to apply appropriately in English teaching and learning, students' attitudes towards the use of mobile devices for their learning under wireless networks need to be assessed seriously.This writing is to report EFL learners' behaviors and awareness on using mobile technology in their English learning at a University in Ho Chi Minh City, Vietnam, through a survey on 100 university EFL students by a questionnaire including 13 English-Vietnamese statements within 15 minutes.The survey lasted two weeks in the second semester of the school year 2018-2019.Quantitative data were analyzed with SPSS version 20.On the one hand, the findings indicated that students have positive constructive and acceptable towards using mobile technology in English learning.On the other hand, mobile learning applications in the future at the current University were able to be accepted firmly.
SCAN: A shared causal attention network for adverse drug reactions detection in tweets

Twitter is a popular social media site on which people post millions of Tweets every day. As patients often share their experiences with drugs on Twitter, Tweets can also be considered as a rich alternative source of adverse drug reaction (ADR)-related information. This information can be useful for health authorities and drug manufacturing companies to monitor the post-marketing effectiveness of drugs. However, the automatic detection of ADRs in Tweets is challenging, as Tweets are informal and prone to grammatical errors. The existing approaches to automatically detecting ADRs do not consider the cause-effect relationships between a drug and an ADR. In this paper, we propose a novel shared causal attention network that exploits such cause-effect relationships to detect ADRs in Tweets. In our approach, we split a Tweet into the prefix, midfix, and postfix segments based on the position of the drug name in the Tweet and separately extract causal features from the segments. We then share these separate causal features with both word and parts-of-speech features, and apply the multi-head self-attention mechanism. We run extensive experiments on three publicly available benchmark datasets to illustrate the effectiveness of the proposed approach.
Towards A Cloud Computing Success Model For Hospital Information System In Jordan

Cloud computing technology success has yet to develop from the infancy stage of implementation in the context of hospitals. This study primarily aimed to minimize the literature gap by examining the critical factors influencing cloud computing success in hospitals, particularly in Jordan. The study made use of online survey method for data collection, and the survey were distributed to chief information officers (CIOs) in the hospitals. Based on the obtained results, the inclusion of trust, networking quality and perceived risk into the information system model will contribute to the explanatory power of cloud computing success in Jordanian hospitals. In added, system quality, information quality, networking quality, service quality and trust were found to have a direct influence on cloud computing satisfaction, while risk was found to indirectly influence satisfaction via trust. Trust mediated the relationship between perceived risk and satisfaction. The proposed cloud computing success model is expected to assist hospitals in their evaluation and achievement of success following their cloud computing adoption in healthcare services.