Reviewerly/exHarmony huggingface dataset:
Virtualization of Educational Environment in a Modern Tertiary School

The article is concentrated on the study of urgent problems of digitalization of education. The authors, based on the study of actual changes in the educational environment of a higher education institution in the face of digitalization, attempted to apply systematic approach to the review of technology and information ethics in the virtual educational environment, while usually these factors are considered separately. The authors believe that the virtual environment is a holistic system, which requires ethical review, evaluation, etc. within the framework of information ethics. Results: 1. Global educational trends were named: promoting a culture of innovation; expansion of cooperation between educational, scientific and business organizations; digital university model development; development of cooperative pedagogy; continuity of education; creation of individual educational trajectories. 2. Risks of virtualization of educational environment in a higher education institution identified: student identification problems, insufficiency of motivation in contactless education, infringement of communication privacy, asymmetry in information distribution, digital discrimination. 3. The authors pay special attention to the study of the educational environment individualization process, which includes three stages: management of educational content, management of educational process and emergence of personal educational environments. The authors conclude that in the complex space of virtual education, an effective means of ensuring information security is the formation of information literacy that provides for the possession of certain information skills and suggests such information behavior that would counteract the real and potential dangers of digitalization. The results of the paper can be used in further studies of ethical issues of dissemination of digital communication technologies in education, in the development of proposals and recommendations to managers in the education system to be used in practice.
Sentiment Analysis of Meeting Room

In the last decade, enormous data are being shared throughout the world. In many of today's big data world, the companies are trying to use some sentiment or emotion analysis techniques to analyze their customer moods and improve their efficiencies according to sentiments. As a different application we focused on the sentiment analysis of closed places in this research. It requires low noise environments obviously. Otherwise, system may be affected by distortion, and it may be contradiction for multiple sentiments. In this regard, an artificial neural network using meaningful voice features are proposed. Ryerson Audio Visual Database of Emotional Speech and Song (RAVDESS) dataset was used in this research. Normalization was applied to data. The artificial neural network was fed by training data and a classifier model was created. Estimation was made using the test data part and it was seen that accuracy of model is about 85%.
The Interpretability of Quantum-Inspired Neural Network

Recently, the mathematical framework of quantum mechanics has boosted interpretability and performance in a variety of NLP tasks. Since different linguistic units are explicitly weighted by this framework in density matrix, it is also often believed that the models inspired by quantum mechanics afford transparency. However, the relationship between weights of linguistic units and model outputs is uncertain. In this work we test whether this assumption holds by performing extensive experiments in question answering (QA) model to manipulating semantic weights and assessing the resulting differences in its output. Although we observe in some ways that higher weights do correlate with greater effect on model, but most of the data does not support it. i.e., the weight is independent of the prediction, and different weight distributions can produce equivalent result. Our results illustrate that standard quantum-inspired model has interpretable structure but does not accurately model feature importance and requires improvement in semantic representation and model interpretability.
Traceable Secret Sharing and Applications

Consider a scenario where Alice stores some secret data s on n servers using a t-out-of-n secret sharing scheme. Trudy (the collector) is interested in the secret data of Alice and is willing to pay for it. Trudy publishes an advertisement on the internet which describes an elaborate cryptographic scheme to collect the shares from the n servers. Each server who decides to submit its share is paid a hefty monetary reward and is guaranteed "immunity" from being caught or prosecuted in a court for violating its service agreement with Alice. Bob is one of the servers and sees this advertisement. On examining the collection scheme closely, Bob concludes that there is no way for Alice to prove anything in a court that he submitted his share. Indeed, if Bob is rational, he might use the cryptographic scheme in the advertisement and submit his share since there are no penalties and no fear of being caught and prosecuted. Can we design a secret sharing scheme which Alice can use to avoid such a scenario? We introduce a new primitive called as Traceable Secret Sharing to tackle this problem. In particular, a traceable secret sharing scheme guarantees that a cheating server always runs the risk of getting traced and prosecuted by providing a valid evidence (which can be examined in a court of law) implicating its dishonest behavior. We explore various definitional aspects and show how they are highly non-trivial to construct (even ignoring efficiency aspects). We then give an efficient construction of traceable secret sharing assuming the existence of a secure two-party computation protocol. We also show an application of this primitive in constructing traceable protocols for multi-server delegation of computation.
Re-shaping the future of identity through user-owned verifiable credentials

Currently credentials, such as one's academic accomplishments like degrees and transcripts as well as health information like COVID19 test results and vaccination proof, remain entirely paper entirely based, are susceptible to forgery and inefficient processing, presentation and verification. Trybe.ID provides a safe, easy and secure way to create digital COVID19 test results, educational certificates and identity documents.
A Case for Connecting SOLiD and Blockchains: Enforcement of Transparent Access Rights in Personal Data Stores

Decentralised Personal Data Store (PDS) systems, such as Social Linked Data (SOLiD), attempt to change the Web, currently dominated by a few large service providers, into a fully open and decentralized data sharing system. The aim is to provide the end-users total self-governance over their own data. While this enables certain degree of user empowerment, the explicit design principles of PDS architecture may prevent service providers' legitimate grounds for imposing certain restrictions on the data rights (e.g., for government regulations). In this paper, we propose a unique hybrid PDS architecture that extends SOLiD with blockchain and smart contract. The extension provides a neutral access control enforcement platform in which service providers can transparently define and operate application-wide data access policies, overriding the default user access rights in some cases, while still respecting the decentralised nature of Personal Data Store. We implemented an instance of the architecture using a university assignment management as a scenario to demonstrate the feasibility of the proposed framework.
A novel approach for exploring annotated data with interactive lenses

Abstract We introduce a novel approach for assisting users in exploring 2D data representations with an interactive lens. Focus‐and‐context exploration is supported by translating user actions to the joint adjustments in camera and lens parameters that ensure a good placement and sizing of the lens within the view. This general approach, implemented using standard device mappings, overcomes the limitations of current solutions, which force users to continuously switch from lens positioning and scaling to view panning and zooming. Navigation is further assisted by exploiting data annotations. In addition to traditional visual markups and information links, we associate to each annotation a lens configuration that highlights the region of interest. During interaction, an assisting controller determines the next best lens in the database based on the current view and lens parameters and the navigation history. Then, the controller interactively guides the user's lens towards the selected target and displays its annotation markup. As only one annotation markup is displayed at a time, clutter is reduced. Moreover, in addition to guidance, the navigation can also be automated to create a tour through the data. While our methods are generally applicable to general 2D visualization, we have implemented them for the exploration of stratigraphic relightable models. The capabilities of our approach are demonstrated in cultural heritage use cases. A user study has been performed in order to validate our approach.
Obstacle Avoidance for Line-Following AGV with Local Maps

Navigation of an Automatic Ground Vehicle in industrial environment is one of the key tasks for a such system and most commonly employs the guidance line following approach. Although, such systems were proven to be very reliable and robust, they are prone to interruptions due to obstacle being present on or even near the driving corridor of an AGV. Recently, improved navigation systems were introduced that allow the AGV to be localized globally, both eliminating the need for the guidance line and presenting dynamic path adaptation to avoid the obstacles. However, cost of such solutions are hard to justify if only the obstacle avoidance capability is needed. This paper presents lightweight environment mapping approach that is combined with trajectory planning and execution. The solution is implemented on an actual industrial AGV and is undergoing real-world tests.
Communication Complexity of Distributed High Dimensional Correlation Testing

We consider a two-party distributed hypothesis testing problem for correlated Gaussian random variables. For a d-dimensional random vector X and a scalar random variable Y, where X and Y are jointly Gaussian with an unknown correlation vector ρ, parties <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> and <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> observe independent copies of X and Y, respectively. The parties seek to test if their observations are correlated or not, namely they seek to test if ||ρ|| <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> exceeds τ or is it 0. To that end, they communicate interactively and declare the test output. We show that roughly order d/τ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> bits of communication are sufficient and necessary for resolving the distributed correlation testing problem above. Furthermore, we establish a lower bound of roughly d <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> /τ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> bits for the communication needed for distributed estimation of ρ, implying that distributed correlation testing requires less communication than distributed estimation. Both our lower bounds for testing and estimation hold for an arbitrary d and interactive communication with shared randomness, while our distributed test requires only one-way communication with shared randomness. For the one-dimensional case, with one-way communication and with probability of one of the error-types fixed, our bounds are more refined in the dependence on the other error-type and are tight even in the constant.
A Linkage Representation of the Human Hand Skeletal System Using CT Hand Scan Images

The present study proposed a method for establishing a linkage representation of the human hand skeletal system. Hand skeletons of 15 male subjects were reconstructed from computed tomography (CT) scans in 10 different postures selected from a natural hand-closing motion. The wrist joint center was estimated as the intersection of the centerline of the metacarpal of the middle finger and the distal wrist crease. The remaining joint centers were kinematically estimated based on the relative motion between the distal bone segment and the proximal bone segment of a given joint. A hand linkage representation was then formed by connecting the derived joint centers. Regression models for predicting internal hand link lengths using hand length as the independent variable were established. In addition, regression models for predicting the joint center coordinates of the thumb carpometacarpal (CMC) and finger metacarpophalangeal (MCP) joints using hand length or hand breadth were established. Our models showed higher R2 values and lower maximum standard errors than the existing models. The findings of the present study can be applied to hand models for ergonomic design and biomechanical modeling.
A Knowledge-Based Cognitive Architecture Supported by Machine Learning Algorithms for Interpretable Monitoring of Large-Scale Satellite Networks

Cyber-physical systems such as satellite telecommunications networks generate vast amounts of data and currently, very crude data processing is used to extract salient information. Only a small subset of data is used reactively by operators for troubleshooting and finding problems. Sometimes, problematic events in the network may go undetected for weeks before they are reported. This becomes even more challenging as the size of the network grows due to the continuous proliferation of Internet of Things type devices. To overcome these challenges, this research proposes a knowledge-based cognitive architecture supported by machine learning algorithms for monitoring satellite network traffic. The architecture is capable of supporting and augmenting infrastructure engineers in finding and understanding the causes of faults in network through the fusion of the results of machine learning models and rules derived from human domain experience. The system is characterised by (1) the flexibility to add new or extend existing machine learning algorithms to meet the user needs, (2) an enhanced pattern recognition and prediction through the support of machine learning algorithms and the expert knowledge on satellite infrastructure, (3) the ability to adapt to changing conditions of the satellite network, and (4) the ability to augment satellite engineers through interpretable results. An industrial real-life satellite case study is provided to demonstrate how the architecture could be used. A single blind experimental methodology was used to validate the results generated by our approach.
Two-step calibration method for extrinsic parameters of an airborne camera

In order to meet the accuracy requirements of target geo-location of a wide-area reconnaissance camera, it is necessary to calibrate the extrinsic parameters of the camera. A novel calibration method is proposed for the orientation relationship between the camera coordinate system (CCS) and the frame coordinate system (FCS). First, the calibration between the roll axis of the FCS and the CCS is carried out based on the method of the extended Kalman filter. Second, the calibration between the pitch axis of the FCS and the CCS is deduced based on the least mean square combined with the particle swarm optimization method. Then, the calibration accuracy of the proposed method is quantitatively analyzed by numerical simulation. Finally, a calibration experiment is conducted on verifying the effectiveness of the method.
Quadruplet-Based Deep Cross-Modal Hashing

Recently, benefitting from the storage and retrieval efficiency of hashing and the powerful discriminative feature extraction capability of deep neural networks, deep cross-modal hashing retrieval has drawn more and more attention. To preserve the semantic similarities of cross-modal instances during the hash mapping procedure, most existing deep cross-modal hashing methods usually learn deep hashing networks with a pairwise loss or a triplet loss. However, these methods may not fully explore the similarity relation across modalities. To solve this problem, in this paper, we introduce a quadruplet loss into deep cross-modal hashing and propose a quadruplet-based deep cross-modal hashing (termed QDCMH) method. Extensive experiments on two benchmark cross-modal retrieval datasets show that our proposed method achieves state-of-the-art performance and demonstrate the efficiency of the quadruplet loss in cross-modal hashing.
Detecting SIMBox Fraud Using CDR Files And Neo4j Technology

Voice traffic termination fraud; often referred to Subscriber Identity Module box (SIMBox) fraud, is a common illegal practice on mobile networks, in which cellular operators around the world incur billions loss annually. Fraudulent SIMBoxes seize international voice calls to be transferred over the Internet to a cellular device network, where it is re-injected into the cellular network. Therefore, calls do not appear local at the destination network, and cellular operators of intermediate and destination networks do not receive payments for routing and terminating calls. Recently, data mining techniques have gained great popularity as a robust and inevitable technique among fraud prevention approaches. The aim of this research is to detect simBox fraud using CDR files related to the Almadar Aljadid operator. The analysis of the CDR files was performed over a period of four months using the Neo4j technology to discover SIM cards used in SIMBox devices. We examine users' natural behavior to define any suspicious behavior in order to detect fraudulent cards. The data preparation process was performed using Python language to be ready for the analysis using Neo4j technology, where Cypher queries for Neo4j technology were written based on four features: users' numbers making calls without receiving any, those who received text messages without sending any, numbers related to users making calls from a fixed location and numbers that their calls exceeded the predefined duration. A graphical user interface was designed to facilitate the application of the practical side of the study. Our result confirms the efficiency of the neo4j technology to analyze the CDR and thus recognizing SIMBox frauds.
Mobile Friendly Fully Decentralized Voting System using Blockchain Technology and IPFS

Elections play an important role in a healthy democracy. Government officials come together at the will of the people. As already known, traditional voting systems are not safe, as in India, the United States still has a flawed voting system. Voting fraud, EVM hacking (electronic voting machine), electoral manipulation in the current electoral system, and ballot box arrests are the biggest problems. Nowadays, vote rigging is a big problem for society. This problem can be overcome by utilizing an e-voting system based on Aadhaar verification. Blockchain can be considered as the next big revolution after the Internet. Every paradigm fills one gap for each. What is the gap that fills the blockchain? In the 1400s, the printing press filled the gap of knowledge, in 1800 the engine filled the power gap, in the 1900s the internet filled the gap, now the next difference that exists in the 2000s, created a gap in our society. Is happening and there is an invention that fills this gap called blockchain. Blockchain is something that is very fundamental to us. Blockchain is a localized information refining and information sharing platform which permits several authoritative domains, who do not have conviction with each other, to collaborate, synchronizes in a relational decision-making process.
A Study of OpenStack Networking and Auto-Scaling Using Heat Orchestration Template

OpenStack is a cloud computing paradigm comprising of a number of open-source software tools called projects, which are responsible for some specific function and expose some application programming interface (API). The most widely used core projects are compute, storage, network, identity, and image that collaborate with other dozens of projects to deliver the requisite cloud to the end users. OpenStack (Ussuri) was released on May 13, 2020, and is freely available under Apache license. In this paper, we demonstrate the full-fledged neutron networking using Ussuri OpenStack along with its auto-scaling features. Then, we explain how heat orchestration template (HOT) is used to define the networking infrastructure in a YAML format for enhanced scalability.
Incentivising the Adoption of COVID-19 Contact-Tracing Apps: A Randomised Controlled Online Experiment on the German Corona-Warn-App

Contact-tracing apps constitute a key pillar of many nations' public health strategy to contain the COVID-19 pandemic. However, the effectiveness of digital contact-tracing depends crucially on widespread app installation and usage among citizens. Despite the large social benefits, evidence on the effectiveness of incentives to stimulate adoption of contact-tracing apps is scarce. Whereas monetary compensations for app installation or usage provide users with a direct economic benefit, research in related health domains has found that extrinsic incentives may crowd out intrinsic motivation and pro-social behaviour. Thus, we conduct a randomised controlled online experiment to evaluate the effectiveness of different incentive mechanisms in promoting the installation and usage of the German Corona-Warn-App (CWA). We find that monetary incentives are effective in significantly increasing verified app installations and app usage 14 days after installation, up to three times the usage without compensation. Deferred monetary compensation for verified usage yields lower installation rates than immediate monetary compensation for installation, but sustains the highest retention rate among adopters. Non-monetary compensation in the form of optional charity donations is found to be ineffective in promoting installations or usage beyond outcomes of monetary incentives, suggesting that there is no substantial crowding-out of intrinsic motives. This suggests that monetary incentives can serve as an important policy instrument to encourage the adoption of contact-tracing apps and contain the spread of COVID-19.
Digital Gimbal: End-to-end Deep Image Stabilization with Learnable Exposure Times

Mechanical image stabilization using actuated gimbals enables capturing long-exposure shots without suffering from blur due to camera motion. These devices, however, are often physically cumbersome and expensive, limiting their widespread use. In this work, we propose to digitally emulate a mechanically stabilized system from the input of a fast unstabilized camera. To exploit the trade-off between motion blur at long exposures and low SNR at short exposures, we train a CNN that estimates a sharp high-SNR image by aggregating a burst of noisy short-exposure frames, related by unknown motion. We further suggest learning the burst’s exposure times in an end-to-end manner, thus balancing the noise and blur across the frames. We demonstrate this method’s advantage over the traditional approach of deblurring a single image or denoising a fixed-exposure burst on both synthetic and real data.