Reviewerly/exHarmony huggingface dataset:
SIG: Towards More Personal Health Sensing

With the development of low-cost electronics, rapid prototyping techniques, as well as widely available mobile devices (e.g. mobile phones, smart watches), projects related to the design and fabrication of personal health sensing applications, either on top of existing device platforms (e.g. mHealth), or as stand-alone devices, have emerged in the last decade. In addition, recent advances in novel sensing and interface technologies, accessibility studies and system design open up new possibilities and can bring in different perspectives for personal health sensing. We believe that joining the forces in such interdisciplinary work is a key to moving the field of personal health sensing forward. This Special Interest Group aims to bring in researchers from different fields, identify the significance and challenges of the personal health sensing domain, discuss potential solutions and future research directions, and promote collaborative research opportunities.
Raman imaging of atoms inside a high-bandwidth cavity

High-bandwidth, fiber-based optical cavities are a promising building block for future quantum networks. They are used to resonantly couple stationary qubits such as single or multiple atoms with photons routing quantum information into a fiber network at high rates. In high-bandwidth cavities, standard fluorescence imaging on the atom-cavity resonance line for controlling atom positions is impaired since the Purcell effect strongly suppresses all-directional fluorescence. Here, we restore imaging of $^{87}\mathrm{Rb}$ atoms strongly coupled to such a fiber Fabry-P\'erot cavity by detecting the repumper fluorescence which is generated by continuous and three-dimensional Raman sideband cooling. We have carried out a detailed spectroscopic investigation of the repumper-induced differential light shifts affecting the Raman resonance, dependent on intensity and detuning. Our analysis identifies a compromise regime between imaging signal-to-noise ratio and survival rate, where physical insight into the role of dipole-force fluctuations in the heating dynamics of trapped atoms is gained.
WebTransceiVR

Increasing adoption of Virtual Reality (VR) systems in various fields has created the need for collaborative work and communication. Today, asymmetric communication between a VR user and other non-VR users remains a challenge. The VR user cannot see the external non-VR users, and the non-VR users are restricted to the VR user’s first-person view. To address this, we propose WebTransceiVR, an asymmetric collaboration toolkit which when integrated into a VR application, allows multiple non-VR users to share the virtual space of the VR user. It allows external users to enter and be part of the VR application’s space through standard web browsers on mobile and computers. These external users can explore and interact with the other users, the VR scene as well as the VR user. WebTransceiVR also includes a cloud-based streaming solution that enables many passive spectators to view the scene through any of the active cameras. We conduct informal user testing to gain additional insights for future work.
IDL-PMCFG, a Grammar Formalism for Describing Free Word Order Languages

Abstract We introduce Interleave-Disjunction-Lock parallel multiple context-free grammars (IDL-PMCFG), a novel grammar formalism designed to describe the syntax of free word order languages that allow for extensive interleaving of grammatical constituents. Though interleaved constituents, and especially the so-called hyperbaton, are common in several ancient (Classical Latin and Greek, Sanskrit...) and modern (Hungarian, Finnish...) languages, these syntactic structures are often difficult to express in existing formalisms. The IDL-PMCFG formalism combines Seki et al.’s parallel multiple context-free grammars (PMCFG) with Nederhof and Satta’s IDL expressions. We define the semantics of IDL-PMCFGs and study their expressivity, proving that IDL-PMCFG extends both PMCFG and IDL-CFG (context-free grammars equipped with IDL expressions) and that IDL-PMCFG parsing is $$\mathrm {NP}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>NP</mml:mi> </mml:math> -hard. We then introduce COMPĀ, a programming language extending Ranta’s Grammatical Framework (GF) and built as a high-level front-end formalism to IDL-PMCFG for practical grammar development. We present a parsing algorithm for IDL-PMCFG inspired by earlier Earley-style PMCFG parsing algorithms and Nederhof and Satta’s IDL graphs and give a worst-case estimate of its complexity as a function of several metrics on IDL expressions, the size of the input and a new notion of the G -density of a language.
If The Map Fits! Exploring Minimaps as Distractors from Non-Euclidean Spaces in Virtual Reality

With non-Euclidean spaces, Virtual Reality (VR) experiences can more efficiently exploit the available physical space by using overlapping virtual rooms. However, the illusion created by these spaces can be discovered, if the overlap is too large. Thus, in this work, we investigate if users can be distracted from the overlap by showing a minimap that suggests that there is none. When done correctly, more VR space can be mapped into the existing physical space, allowing for more spacious virtual experiences. Through a user study, we found that participants uncovered the overlap of two virtual rooms when it was at 100% or the overlapping room extended even further. Our results show that the additional minimap renders overlapping virtual rooms more believable and can serve as a helpful tool to use physical space more efficiently for natural locomotion in VR.
Analyzing the Evolution of Modern Tamil Script for Natural Language Processing

History is essential for understanding the society's former state. It is beneficial to understand people's historical status, culture, and how we view the present. The society benefits from the society's diversified culture and unparalleled civilization. The limited sources available to learn about our history are books, epigraphs, and inscriptions. Only around half of all Indian inscriptions are in Tamil, and only half of those are published. The name India is derived from the Indus River, and the Indus Valley Civilization is the most active and progressive civilization in the ancient world. Hinduism, Buddhism, Sikhism, and Jainism are four well-known religions that emerged from this culture. Epigraphs are the most reliable source of information about ancient India's life, culture, religion, and politics. This study examines the evolution of India's many scripts and languages. Discovering the incremental changes in scripts and languages that demonstrate the linguistic relationship between distinct populations is critical. Natural language processing benefits from knowledge of numerous scripts, languages, and techniques for letter recognition.
NLU++: A Multi-Label, Slot-Rich, Generalisable Dataset for Natural Language Understanding in Task-Oriented Dialogue

We present NLU++, a novel dataset for natural language understanding (NLU) in task-oriented dialogue (ToD) systems, with the aim to provide a much more challenging evaluation environment for dialogue NLU models, up to date with the current application and industry requirements. NLU++ is divided into two domains (BANKING and HOTELS) and brings several crucial improvements over current commonly used NLU datasets. 1) NLU++ provides fine-grained domain ontologies with a large set of challenging multi-intent sentences, introducing and validating the idea of intent modules that can be combined into complex intents that convey complex user goals, combined with finer-grained and thus more challenging slot sets. 2) The ontology is divided into domain-specific and generic (i.e., domain-universal) intent modules that overlap across domains, promoting cross-domain reusability of annotated examples. 3) The dataset design has been inspired by the problems observed in industrial ToD systems, and 4) it has been collected, filtered and carefully annotated by dialogue NLU experts, yielding high-quality annotated data. Finally, we benchmark a series of current state-of-the-art NLU models on NLU++; the results demonstrate the challenging nature of the dataset, especially in low-data regimes, the validity of `intent modularisation', and call for further research on ToD NLU.
Continual Learning for Peer-to-Peer Federated Learning: A Study on Automated Brain Metastasis Identification

Due to data privacy constraints, data sharing among multiple centers is restricted. Continual learning, as one approach to peer-to-peer federated learning, can promote multicenter collaboration on deep learning algorithm development by sharing intermediate models instead of training data. This work aims to investigate the feasibility of continual learning for multicenter collaboration on an exemplary application of brain metastasis identification using DeepMedic. 920 T1 MRI contrast enhanced volumes are split to simulate multicenter collaboration scenarios. A continual learning algorithm, synaptic intelligence (SI), is applied to preserve important model weights for training one center after another. In a bilateral collaboration scenario, continual learning with SI achieves a sensitivity of 0.917, and naive continual learning without SI achieves a sensitivity of 0.906, while two models trained on internal data solely without continual learning achieve sensitivity of 0.853 and 0.831 only. In a seven-center multilateral collaboration scenario, the models trained on internal datasets (100 volumes each center) without continual learning obtain a mean sensitivity value of 0.699. With single-visit continual learning (i.e., the shared model visits each center only once during training), the sensitivity is improved to 0.788 and 0.849 without SI and with SI, respectively. With iterative continual learning (i.e., the shared model revisits each center multiple times during training), the sensitivity is further improved to 0.914, which is identical to the sensitivity using mixed data for training. Our experiments demonstrate that continual learning can improve brain metastasis identification performance for centers with limited data. This study demonstrates the feasibility of applying continual learning for peer-to-peer federated learning in multicenter collaboration.
Applications of augmented reality as a blended learning tool for architectural education

Augmented reality (AR) is an innovative learning tool that is gaining popularity in educational settings as it has the potential to significantly enhance interactive learning. In addition, AR has been a significant contributor to blended learning because it can be used in addition to conventional learning aids like text, images, video tutorials and the internet. However, there is a lack of research on the application of AR in the context of programmes with technical and practical components particularly with reference to the possibilities it may offer in terms of Architecture, Engineering and Construction (AEC). This paper reports on the first phase of an action research study done in an architecture department in a higher education institution that offers a module on Construction Technology. The learning unit for which AR is particularly explored is Construction Methods, Assembly, and Materials. A qualitative research approach was applied where principles of the Delphi method were used after selecting a group of eight experts who were purposively sampled. The participants were exposed to the AR technology, viewed a simulation, and were encouraged to explore three-dimensional model building for themselves by using the AR tool. Themes that emerged from group discussion were recorded to illustrate the possibilities of the application of AR in the context of the module as well as possible limitations or concerns. Curriculum outcomes were then proposed to enhance an interdisciplinary component, incorporating the AR project outcomes with wider applicability than architecture only. The implications shared should be applicable to skill development in engineering and construction education in learning units that deal with construction methods, assembly and materials.
Feature Selection using Information Gain Method for Building Classification Model DDoS Attack at Application Layer

Distributed Denial of Services (DDoS) is one of the digital attacks that often occurred, the record for DDoS attacks in the second quartal of 2018 reaches 5.7Gbps. The application layer becomes one of the targets for this attack type; this type of DDoS attack always mimicks the user's request, making it harder to detect than DDoS attack at the network and transport layer. The classification has been offered as one method to overcome this problem. Before classification, the selection feature becomes important due to some features that lead to error classification and make the process classification longer. This research uses information gain as a selection feature method and using CICIDS 2017 as the dataset. The CICIDS2017 has 692.704 records consist of 78 features and five classes. The result of feature selection using the information gain method reduces the numbers of features from 78 to 5. To prove that these five features can classify DDoS attacks correctly, we use a randomForest method as a classification method. The randomForest was used to classify the data into five classes: normal, DDoS Goldeneye, DDoS Hulk, DDoS Slowhttptest, and DDoS Slowloris. The result of performance for accuracy is 99.43%, for recall of each class are 99.48%, 99.81%, 99.41%, 96.01%, 99.97% respectively. Besides the result of performance for precision each class are 99.65%, 96.04%, 99.90%, 98.63%, 71.37%, respectively. The results of performance for classification time using five features are decreasing execution time 3.1 seconds.
The “Regulatory Guillotine” Mechanism in Education: Problems and Prospects of Practical Implementation in the Work of the Federal Service for Supervision in the Field of Education and Science

The paper analyzes the control-supervisory and administrative practices of the Federal Service for Supervision in the Field of Education and Science (Rosobrnadzor of Russia) in relation to the activities of educational institutions of higher education. In the context of the review of verification measures, the paper provides an assessment of the current level, as well as the prospects for the effectiveness of the implementation of the regulatory concept of deregulation and reducing the administrative burden on controlled entities within the framework of the conceptual (main) provisions of the administrative reform. It is concluded that control and supervision activities in the field of education should be consistent with the principle of encouraging conscientious compliance by educational organizations with mandatory requirements. When forming the practice of sustainable conscientious behavior by educational organizations within the framework of their statutory activities, Rosobrnadzor has the opportunity to use incentive measures for such entities aimed at solving the tasks of ensuring the socioeconomic development of individual territories without reducing the quality of educational services provided by them.
The Uses and Gratifications Model of Voice Shopping

This research investigated the voice shopping&nbsp;phenomenon, specifically&nbsp;by examining a model that delineates four-gratification dimensions (i.e., life efficiency, entertainment, social presence, affordance), one mediating variable (i.e., satisfaction), and one dependent variable (i.e., purchase intention). Based on the uses and gratifications theory, the research conducted a survey method by recruiting participants from Pollfish. A total of 166 responses was collected. Partial Least Squares (PLS) modeling was used for data analysis. The measurement model was tested by assessing Cronbach’s alpha, AVE, CR, discriminant validity, and the results&nbsp;were all&nbsp;satisfactory. The evaluation of structural model was tested by estimating the effect sizes (f2),&nbsp;predictive relevance (q2), R2 (satisfaction = .533, purchase =.315), goodness of fit (srmr = .079).&nbsp;The study results revealed that H1, H2, H5, and H6a, H6b were supported. The research finding indicates the importance of incorporating utilitarian and hedonic gratification in using voice assistants.
NCDSearch: Sliding Window-Based Code Clone Search Using Lempel-Ziv Jaccard Distance

Software developers may write a number of similar source code fragments including the same mistake in software products. To remove such faulty code fragments, developers inspect code clones if they found a bug in their code. While various code clone detection methods have been proposed to identify clones of either code blocks or functions, those tools do not always fit the code inspection task because a faulty code fragment may be much smaller than code blocks, e.g. a single line of code. To enable developers to search code clones of such a small faulty code fragment in a large-scale software product, we propose a method using Lempel-Ziv Jaccard Distance, which is an approximation of Normalized Compression Distance. We conducted an experiment using an existing research dataset and a user survey in a company. The result shows our method efficiently reports cloned faulty code fragments and the performance is acceptable for software developers.
Job Acceptance in Money Mule Recruitment: Theoretical View on the Rewards

The inspiring digital innovation has gained traction from criminals who have perfected their modus operandi in targeting victims with cybercrimes and fraudulent activities with the financial motive being the common denominator. The money trail had been the evidence of the crime, as such the illicit funds enter a money-laundering scheme to disguise the money trail from the unlawful activities and transform the funds into legitimate sources. Criminals recruit money mules and position them in the money laundering cycle to transfer funds that have been credited into their bank accounts. Criminals strategize recruitment by promoting job packages with persuasive job vacancy advertisements and attractive rewards. The hidden criminal agenda has been blurred in the decision making and job acceptance process by the rewards offered. This study will explore the underlying psychological aspect which relates to rewards and job acceptance using the theory of heuristics, social influence, and individual differences. Based on the examination, victims were captivated by attractive rewards and scrutinizing the job package became less attentive in deciding the job acceptance.
A Deep Neural Network for Coarse-to-Fine Image Dehazing with Interleaved Residual Connections and Semi-Supervised Training

In this letter, we propose a deep neural network and semi-supervised learning based dehazing algorithm. The dehazing network uses a pyramidal architecture to recover the haze-free scene from a single hazy image in a coarse-to-fine order. To faithfully restore the objects with different scales, we incorporate cascaded multi-scale convolutional blocks into each level of the pyramid. Feature fusion and transfer in the network are achieved using the paths constructed by interleaved residual connections. For better generalization to the complicated haze in real-world environments, we also devise a discriminator that enables semi-supervised adversarial training. Experimental results demonstrate that the proposed work outperforms comparative ones with higher quantitative metrics and more visually pleasant outputs. It can also enhance the robustness of object detection under haze.
Teachers’ Challenges in Teaching Speaking Through Online Learning During Covid-19 Pandemic

This research aims at at finding out 1) the challenges of the teachers in teaching speaking during online learning in SMAN 2 Abiansemal, 2) the teachers’ ways to cope with the challenges in teaching speaking during online learning in SMAN 2 Abiansemal and 3) the strengths and the weaknesses of the strategy used by the investigated teachers during online learning in teaching speaking. The subjecs of this research were three English teachers in SMAN 2 Abiansemal. To collect the data, two instruments were needed, observation sheet and interview guide. The obtained data were analyzed qualitative by using Interactively Data Analysis Model by Miles and Huberman (1994). This research discovers that there were 9 challenges in online learning. Teachers also had implemented strategies to cope the challenges. This study also discussed the weaknesses and strengths of the strategies.
Technological trends of Industry 4.0 in education: opportunity navigator

Introduction. This article aims to comprehend the possibilities of application of the technological trends in education initiated by Industry 4.0. The technological trends announced for application in education are understood as digital technologies included in the list of strategic programs for national development (in the context of Industry 4.0): the big data technologies, virtual and augmented reality, robotics and sensorics components, artificial intelligence, new production technologies, industrial internet and wireless communication technologies, quantum technologies and distributed ledger systems. Problem Statement. The article is aimed to identify the specifics and potential of digital technologies of the Industry 4.0 era, both separately and in their integration, which are provided to education in the context of its digital transformation and acquisition of its new quality as Education 4.0. Methodology. The methodology of the study is based on the author’s approach to the formation of the source base for the subsequent tasks of its analysis and synthesis. The work analyzes more than 50 foreign studies published in international citation databases over the past decade, i.e. since the moment when Industry 4.0 began to emerge. Research results. The conducted multilateral analysis of frontier foreign sources showed that not all digital technologies are equally applicable in education, but have a character ranging from “actively applied” to “potentially applicable” in this area. Significant applied potential of the digital technologies is fixed in the technologies of big data, virtual and augmented reality, robotics and sensorics, as well as artificial intelligence in the context of creating data warehouses, introducing new models of learning and educational agents, implementation of customized educational solutions. Conclusions. The vector and nature of the digital transformation of the branches of industries, including education, are set by digital technologies, which the national programs (in the context of Industry 4.0) have placed at the heart of this process. The ongoing digital metamorphosis of education, based on the active implementation and effective use of the digital technologies, allows the latter to acquire new forms and quality on the way to Education 4.0.
Using Blockchain Technology to Manage Hajj and Umrah

Blockchain is a disruptive technology, particularly because it provided the basis for Bitcoin, followed by other cryptocurrencies. The use of a blockchain is not limited to cryptocurrencies only. The technology can be used for creating and maintaining records of all sorts of financial, business, economic, and marketing transactions. Some of the most appealing properties of blockchain are transparency, disintermediation, and security. In this paper, we have reviewed and provided different properties and applications provided by blockchain to manage some important functions and activities of the Hajj and Umrah pilgrimages. These pilgrimages take place in Makkah, Saudi Arabia, and performed by millions of pilgrims from different parts of the world. In doing so, we have also provided a brief background, properties, and applications of blockchain in different domains. The findings, after analyzing various aspects of blockchain provided in this paper, suggest that the technology is highly desirable and bernificial in improving several management tasks of Hajj and Umrah.