Reviewerly/exHarmony huggingface dataset:
A MULTINOMIAL NAÏVE BAYES DECISION SUPPORT SYSTEM FOR COVID-19 DETECTION

Coronavirus disease 2019 termed COVID-19 is a highly infectious and pathogenic illness caused by severe acute respiratory syndrome. Symptoms of COVID-19 range from mild to severe, in some cases leading to death. Early detection could help to monitor progression of the disease, mitigate spread of the disease and possibly reduce mortality rate. Computer-aided diagnosis systems are designed to complement health care systems and assist in the early detection of diseases. Currently, as it is not possible to test all citizens especially in developing countries with very large populations due to financial constraints and the standard of their healthcare facilities, the problem of identifying suspected cases and deciding laboratory test priority among citizens is evident and more pressing. Therefore in this study, we introduce an interactive Artificial Intelligent web system using the Multinomial Naïve Bayes algorithm with the aim of detecting warning COVID-19 symptoms and to provide fitting suggestions. Furthermore, the study also evaluates the performance of the Multinomial Naïve Bayes based on the different holdout approaches experimented. The experimental results are promising as the Multinomial Naïve Bayes is shown to achieve high accuracy detection thus providing a reliable method to identify warning symptoms of COVID-19.
How well a multi-model database performs against its single-model variants: Benchmarking OrientDB with Neo4j and MongoDB

Digitalization is currently the key factor for progress, with a rising need for storing, collecting, and processing large amounts of data. In this context, NoSQL databases have become a popular storage solution, each specialized on a specific type of data. Next to that, the multi-model approach is designed to combine benefits from different types of databases, supporting several models for data. Despite its versatility, a multi-model database might not always be the best option, due to the risk of worse performance comparing to the single-model variants. It is hence crucial for software engineers to have access to benchmarks comparing the performance of multi-model and single-model variants. Moreover, in the current Big Data era, it is important to have cluster infrastructure considered within the benchmarks. In this paper, we aim to examine how the multi-model approach performs compared to its single-model variants. To this end, we compare the OrientDB multi-model database with the Neo4j graph database and the MongoDB document store. We do so in the cluster setup, to enhance state of the art in database benchmarks, which is not yet giving much insight into cluster-operating database performance.
Towards Fully-Synthetic Training for Industrial Applications

This paper proposes a scalable approach for synthetic image generation of industrial objects leveraging Blender for image rendering. In addition to common components in synthetic image generation research, three novel features are presented: First, we model relations between target objects and randomly apply those during scene generation (Object Relation Modelling (ORM)). Second, we extend the idea of distractors and create Object-alike Distractors (OAD), resembling the textural appearance (i.e. material and size) of target objects. And third, we propose a Mixed-lighting Illumination (MLI), combining global and local light sources to automatically create a diverse illumination of the scene. In addition to the image generation approach we create an industry-centered dataset for evaluation purposes. Experiments show, that our approach enables fully synthetic training of object detectors for industrial use-cases. Moreover, an ablation study provides evidence on the performance boost in object detection when using our novel features.
Side-Crossing Menus

Supporting many gestures on small surfaces allows users to interact remotely with complex environments such as smart homes, large remote displays, or virtual reality environments, and switching between them (e.g., AR setup in a smart home). Providing eyes-free gestures in these contexts is important as this avoids disrupting the user's visual attention. However, very few techniques enable large sets of commands on small wearable devices supporting the user's mobility and even less provide eyes-free interaction. We present Side-Crossing Menus (SCM), a gestural technique enabling large sets of gestures on %small interactive surfaces like a smartwatch. Contrary to most gestural techniques, SCM relies on broad and shallow menus that favor small and rapid gestures. We demonstrate with a first experiment that users can efficiently perform these gestures eyes-free aided with tactile cues; 95% accuracy after training 20 minutes on a representative set of 30 gestures among 172. In a second experiment, we focus on the learning of SCM gestures and do not observe significant differences with conventional Multi-stroke Marking Menus in gesture accuracy and recall rate. As both techniques utilize contrasting menu structures, our results indicate that SCM is a compelling alternative for enhancing the input capabilities of small surfaces.
Psychological Wellbeing through Health Information Behavior: Effects of the elderly’s digitigal literacy

본 연구는 건강 커뮤니케이션을 통한 심리적 건강 증진의 효과, 특히 건강정보행동의 매개효과와 디지털 리터러시의 조절효과를 살펴보았다. 만 20세부터 79세까지 비노인과 노인 집단을 포함한 1000명을 대상으로 일대일 대면 설문조사를 실시하였다. 연구결과, 노인 집단과 비노인 집단간 심리적 건강, 건강정보행동, 디지털리터러시에 있어 전반적인 수준 차이가 확인되었다. 무엇보다연령 증가가 심리적 건강에 미치는 영향에 있어 건강정보행동의 매개효과와 디지털리터러시의 조절 효과가 통계적으로 유의미한것으로 검증되었다. 노인의 경우 디지털리터러시가 높을 경우 비노인에 비해 건강정보행동이 크게 증가하여 심리적 건강의 향상으로연결되는 것이 발견되었다. 연구 결과는 노인 집단에 있어 능동적인 건강 정보 추구와 디지털리터러시의 중요성을 보여준다.This study investigated psychological wellbeing through health information behavior. In particular, the mediating role of health information behavior and the moderating effect of digital literacy were examined. A total of 1,000 South Koreans aged 20 - 79, including an elderly group and a non-elderly group, participated in face to face interviews. ROverall, results confirmed differences in the level of psychological wellbeing, health information behavior and digital literacy between the elderly and non-elderly groups. More importantly, both the mediating role of health information behavior and the moderating effect of digital literacy turned out be statistically significant. That is, the elderly with high levels of digital literacy showed significantly increased health information behavior, while the non-elderly with high levels of digital literacy did not. The results of this study highlight the importance of active health information seeking and digitial literacy for the elderly.
Approximations With Polynomial, Trigonometric, Exponential Splines of the Third Order and Boundary Value Problem

This paper is devoted to the construction of localapproximations of functions of one and two variables using thepolynomial, the trigonometric, and the exponential splines. Thesesplines are useful for visualizing flows of graphic information.Here, we also discuss the parallelization of computations. Someattention is paid to obtaining two-sided estimates of theapproximations using interval analysis methods. Particularattention is paid to solving the boundary value problem by usingthe polynomial splines and the trigonometric splines of the thirdand fourth order approximation. Using the considered splines,formulas for a numerical differentiation are constructed. Theseformulas are used to construct computational schemes for solvinga parabolic problem. Questions of approximation and stability ofthe obtained schemes are considered. Numerical examples arepresented.
Privately Connecting Mobility to Infectious Diseases via Applied Cryptography

Recent work has shown that cell phone mobility data has the unique potential to create accurate models for human mobility and consequently the spread of infected diseases [74]. While prior studies have exclusively relied on a mobile network operator’s subscribers’ aggregated data in modelling disease dynamics, it may be preferable to contemplate aggregated mobility data of infected individuals only. Clearly, naively linking mobile phone data with health records would violate privacy by either allowing to track mobility patterns of infected individuals, leak information on who is infected, or both. This work aims to develop a solution that reports the aggregated mobile phone location data of infected individuals while still maintaining compliance with privacy expectations. To achieve privacy, we use homomorphic encryption, validation techniques derived from zero-knowledge proofs, and differential privacy. Our protocol’s open-source implementation can process eight million subscribers in 70 minutes.
Comparison of VR and Desktop Game User Experience in a Puzzle Game: “Keep Talking and Nobody Explodes”

Since the contemporary game production process is based on the Integrated Development Environment (IDE) applications, it is easier for developers to create multiple versions of their game for both VR and desktop platforms. This provided a great opportunity for researchers to conduct comparative studies to explore the user experience of the relatively novel virtual reality applications In this study, we evaluated a puzzle game through a within-subjects experiment design using objective measures of game success and gameplay duration, as well as plenty of subjective measures in order to assess game user experience, comparing desktop and VR. In addition to selected dimensions of GUESS (Game User Experience Satisfaction Scale), we employed MEC-SPQ (Measurement Effects Conditions - Spatial Presence Questionnaire) to measure presence. Furthermore, we employed NASA-TLX (NASA Task Load Index) to compare the perceived task complexity of the same task executed in VR and desktop gaming environments. Results revealed that there is not a significant difference in objective measures of player performance, comparing the VR and desktop gameplay. The Game User Experience Satisfaction Scale did not reveal any significant difference between the mean scores of VR and desktop experiences. The spatial presence related dimensions of MEC-SPQ revealed significantly higher scores of VR, for Possible Actions and Self Location dimensions. NASA-TLX weighted scores were significantly higher for VR in physical load and for desktop in frustration. Our results show that a puzzle-based game experienced in VR does not lead to a higher level of satisfaction in terms of game user experience but triggers a sense of spatial presence. Due to the different control schemes, players perceive that HMD based gameplay demands more physical task load. However, the gameplay duration and game success rate are not significantly different. The failure in desktop gameplay might have led to higher frustration, since the experience seems more familiar to players. Since the results are partially concordant with previous studies, it is not possible to make a strict conclusion on the effect caused by different immersive technologies on game user experience. Further studies are required through a more consistent methodology with a focus on game design components rather than game genre.
Research and Application of Computer Technology in the Transformation Mode of Scientific and Technological Achievements in Chinese Universities

Abstract Colleges and universities are the important gathering place of high-end talents in China. Every year, colleges and universities undertake many national scientific research projects, so there will be a large number of patented technologies and scientific and technological achievements. But in the end, over 90% of these achievements were "locked away" and the achievements were not transferred, not allowed to transfer, or there was no benefit after transformation. Therefore, how to apply computer technology effectively transform and apply scientific and technological achievements in universities is of great significance to promote mass entrepreneurship and innovation. Therefore, this paper discusses the transformation mode of scientific and technological achievements in colleges and universities in China.At present, the scientific and technological achievements of foreign universities account for the mainstream of the national transformation activities while the transformation of scientific and technological achievements in China is still in its infancy. Through years of effort and based on our country launching a series of related policies that give corresponding support, encourage, guide and make unremitting efforts to innovation and exploration, we have obtained some experience in the field of transformation of scientific and technological achievements and achieved a certain economic and social benefits. Up to now, a number of universities in China have established scientific and technological achievements transformation centers, which can be seen that China attaches great importance to the transformation of scientific and technological achievements in universities. Therefore, in the current era, how to transform the scientific and technological achievements of colleges and universities into practical social application more efficiently is a problem that must be deeply studied.
A New Approach to Land Registry System in Turkey: Blockchain-Based System Proposal

Blockchain is an emerging technology whose extraordinary advantages make it popular. Blockchain applications are not limited to cryptocurrencies; it can be used in many areas, including smart ownership. Blockchain-based land registry systems have great potential thanks to their advantages of high-level security and no intermediary transactions. Although the requirements of land registry systems vary by country, common to all of them are the eye-catching advantages and conveniences that a blockchain-based system brings. We examined the process used in Turkey, which consists of eight steps, identified the participants of the system, and generated a blockchain-based land registry system for Turkey. This article explains that proposed blockchain-based land registry system, configured for the local requirements of Turkey.
The Data Visualisation and Immersive Analytics Research Lab at Monash University

This article reviews two decades of research in topics in Information Visualisation emerging from the Data Visualisation and Immersive Analytics Lab at Monash University Australia (Monash IA Lab). The lab has been influential with contributions in algorithms, interaction techniques and experimental results in Network Visualisation, Interactive Optimisation and Geographic and Cartographic visualisation. It has also been a leader in the emerging topic of Immersive Analytics, which explores natural interactions and immersive display technologies in support of data analytics. We reflect on advances in these areas but also sketch our vision for future research and developments in data visualisation more broadly.
Multi-Platform Expansion of the Virtual Human Toolkit: Ubiquitous Conversational Agents

We present an extension of the Virtual Human Toolkit to include a range of computing platforms, including mobile, web, Virtual Reality (VR) and Augmented Reality (AR). The Toolkit uses a mix of in-house and commodity technologies to support audio-visual sensing, speech recognition, natural language processing, nonverbal behavior generation and realization, text-to-speech generation and rendering. It has been extended to support computing platforms beyond Windows by leveraging microservices. The resulting framework maintains the modularity of the underlying architecture, allows re-use of both logic and content through cloud services, and is extensible by porting lightweight clients. We present the current state of the framework, discuss how we model and animate our characters, and offer lessons learned through several use cases, including expressive character animation in seated VR, shared space and navigation in room-scale VR, autonomous AI in mobile AR, and real-time user performance feedback leveraging mobile sensors in headset AR.
TSDG: Content-aware Neural Response Generation with Two-stage Decoding Process

Neural response generative models have achieved remarkable progress in recent years but tend to yield irrelevant and uninformative responses.One of the reasons is that encoderdecoder based models always use a single decoder to generate a complete response at a stroke.This tends to generate high-frequency function words with less semantic information rather than low-frequency content words with more semantic information.To address this issue, we propose a content-aware model with two-stage decoding process named Two-stage Dialogue Generation (TSDG).We separate the decoding process of content words and function words so that content words can be generated independently without the interference of function words.Experimental results on two datasets indicate that our model significantly outperforms several competitive generative models in terms of automatic evaluation and human evaluation.
Circuit Transformations for Quantum Architectures.

Quantum computer architectures impose restrictions on qubit interactions. We propose efficient circuit transformations that modify a given quantum circuit to fit an architecture, allowing for any initial and final mapping of circuit qubits to architecture qubits. To achieve this, we first consider the qubit movement subproblem and use the ROUTING VIA MATCHINGS framework to prove tighter bounds on parallel routing. In practice, we only need to perform partial permutations, so we generalize ROUTING VIA MATCHINGS to that setting. We give new routing procedures for common architecture graphs and for the generalized hierarchical product of graphs, which produces subgraphs of the Cartesian product. Secondly, for serial routing, we consider the TOKEN SWAPPING framework and extend a 4-approximation algorithm for general graphs to support partial permutations. We apply these routing procedures to give several circuit transformations, using various heuristic qubit placement subroutines. We implement these transformations in software and compare their performance for large quantum circuits on grid and modular architectures, identifying strategies that work well in practice.
Exotic Quantum States of Circuit Quantum Electrodynamics in the Ultra‐Strong Coupling Regime

Abstract The quantum coherent behaviors of superconducting devices at macroscopic scales and recent technical advances in fine tunability have brought the conventional cavity quantum electrodynamics (QED) to superconducting circuits. With an ultra‐strong cavity photon–artificial atom coupling and the inherent nonlinearity of the Josephson junctions, the so‐called circuit QED offers new opportunities to explore new realms of physics that have remained a challenge for conventional cavity QED. Circuit QED has even attracted public attention as the leading architecture for quantum computation. In this article, a pedagogical review of recent studies and activities on exotic quantum states that can be attributed to ultra‐strong coupling is provided. A progress report on attempts to seek the smallest unit of the topological matter and its fundamental interaction with light is also provided.
Information-Theoretic Active Learning for Content-Based Image Retrieval

We propose Information-Theoretic Active Learning (ITAL), a novel batch-mode active learning method for binary classification, and apply it for acquiring meaningful user feedback in the context of content-based image retrieval. Instead of combining different heuristics such as uncertainty, diversity, or density, our method is based on maximizing the mutual information between the predicted relevance of the images and the expected user feedback regarding the selected batch. We propose suitable approximations to this computationally demanding problem and also integrate an explicit model of user behavior that accounts for possible incorrect labels and unnameable instances. Furthermore, our approach does not only take the structure of the data but also the expected model output change caused by the user feedback into account. In contrast to other methods, ITAL turns out to be highly flexible and provides state-of-the-art performance across various datasets, such as MIRFLICKR and ImageNet.
An Effective and Efficient Technique for Supporting Privacy-Preserving Keyword-Based Search over Encrypted Data in Clouds

Nowadays, cloud providers offer to their clients the possibility of storage of emails and files on the cloud server. To avoid privacy concerns, encryption should be applied to data. Unlike searching plaintext documents by keywords, encrypted documents cannot be retrieved in the same manner. As keyword searches on encrypted data are in demand, this paper describes an effective and efficient technique to support privacy-preserving keyword-based search over encrypted outsourced data. With this technique, encrypted data are first searched with the keyword, support for dynamic operations is then checked, and all relevant data documents are finally sorted based on the number of keywords matching the user query. To evaluate the technique, precision and recall are measured. The results reveal the effectiveness and efficiency of the technique in supporting privacy-preserving keyword-based search over encrypted outsourced data.
Pseudo-Bidirectional Decoding for Local Sequence Transduction

Local sequence transduction (LST) tasks are sequence transduction tasks where there exists massive overlapping between the source and target sequences, such as grammatical error correction and spell or OCR correction. Motivated by this characteristic of LST tasks, we propose Pseudo-Bidirectional Decoding (PBD), a simple but versatile approach for LST tasks. PBD copies the representation of source tokens to the decoder as pseudo future context that enables the decoder self-attention to attends to its bi-directional context. In addition, the bidirectional decoding scheme and the characteristic of LST tasks motivate us to share the encoder and the decoder of LST models. Our approach provides right-side context information for the decoder, reduces the number of parameters by half, and provides good regularization effects. Experimental results on several benchmark datasets show that our approach consistently improves the performance of standard seq2seq models on LST tasks.
Corpora Evaluation and System Bias Detection in Multi-document Summarization

Multi-document summarization (MDS) is the task of reflecting key points from any set of documents into a concise text paragraph. In the past, it has been used to aggregate news, tweets, product reviews, etc. from various sources. Owing to no standard definition of the task, we encounter a plethora of datasets with varying levels of overlap and conflict between participating documents. There is also no standard regarding what constitutes summary information in MDS. Adding to the challenge is the fact that new systems report results on a set of chosen datasets, which might not correlate with their performance on the other datasets. In this paper, we study this heterogeneous task with the help of a few widely used MDS corpora and a suite of state-of-theart models. We make an attempt to quantify the quality of summarization corpus and prescribe a list of points to consider while proposing a new MDS corpus. Next, we analyze the reason behind the absence of an MDS system which achieves superior performance across all corpora. We then observe the extent to which system metrics are influenced, and bias is propagated due to corpus properties. The scripts to reproduce the experiments in this work are available at https://github.com/LCS2-IIITD/summarization_bias.git