Reviewerly/exHarmony huggingface dataset:
Graph-Query Suggestions for Knowledge Graph Exploration

We consider the task of exploratory search through graph queries on knowledge graphs. We propose to assist the user by expanding the query with intuitive suggestions to provide a more informative (full) query that can retrieve more detailed and relevant answers. To achieve this result, we propose a model that can bridge graph search paradigms with well-established techniques for information-retrieval. Our approach does not require any additional knowledge from the user and builds on principled language modelling approaches. We empirically show the effectiveness and efficiency of our approach on a large knowledge graph and how our suggestions are able to help build more complete and informative queries.
Data Protection Impact Assessment for the Corona App

Since SARS-CoV-2 started spreading in Europe in early 2020, there has been a strong call for technical solutions to combat or contain the pandemic, with contact tracing apps at the heart of the debates. The EU's General Daten Protection Regulation (GDPR) requires controllers to carry out a data protection impact assessment (DPIA) where their data processing is likely to result in a high risk to the rights and freedoms (Art. 35 GDPR). A DPIA is a structured risk analysis that identifies and evaluates possible consequences of data processing relevant to fundamental rights in advance and describes the measures envisaged to address these risks or expresses the inability to do so.Based on the Standard Data Protection Model (SDM), we present a scientific DPIA which thoroughly examines three published contact tracing app designs that are considered to be the most privacy-friendly: PEPP-PT, DP-3T and a concept summarized by CCC member Linus Neumann, all of which process personal health data. We show that even a decentralized architecture involves numerous serious weaknesses and risks, including larger ones left unaddressed. We also found that none of the proposed designs operates on anonymous data or ensures proper anonymization, that informed consent would not be a legitimate legal ground for the processing, that data subjects' rights are not sufficiently safeguarded, and that no design provides for sufficient purpose-binding.
Unnatural Language Processing: Bridging the Gap Between Synthetic and Natural Language Data

Large, human-annotated datasets are central to the development of natural language processing models. Collecting these datasets can be the most challenging part of the development process. We address this problem by introducing a general purpose technique for ``simulation-to-real'' transfer in language understanding problems with a delimited set of target behaviors, making it possible to develop models that can interpret natural utterances without natural training data. We begin with a synthetic data generation procedure, and train a model that can accurately interpret utterances produced by the data generator. To generalize to natural utterances, we automatically find projections of natural language utterances onto the support of the synthetic language, using learned sentence embeddings to define a distance metric. With only synthetic training data, our approach matches or outperforms state-of-the-art models trained on natural language data in several domains. These results suggest that simulation-to-real transfer is a practical framework for developing NLP applications, and that improved models for transfer might provide wide-ranging improvements in downstream tasks.
LPG-model: A novel model for throughput prediction in stream processing, using a light gradient boosting machine, incremental principal component analysis, and deep gated recurrent unit network

In recent years, the volume and velocity of streaming data have been increasing rapidly. Thus, real-time processing scenarios for streaming data have continued to increase. Stream processing tasks face huge challenges in areas such as load optimization, task scheduling, and resource management. Throughput prediction for stream processing tasks is a key technology in these areas. To predict the throughput of stream processing tasks accurately and efficiently, we propose a novel model named the LPG-model. It includes three main components: a light gradient boosting machine (LightGBM), incremental principal component analysis (IPCA), and an evolving deep gated recurrent unit (GRU) network. Unlike existing state-of-the-art models, the LPG-model not only offers a network structure adaptation mechanism (hidden layer adaptation mechanism), but also provides feature processing mechanisms for streaming data. Data preprocessing provides an interpolation method for missing values through an incremental interpolation mechanism and two normalization methods for features through incremental normalization mechanisms. An efficient dimensionality reduction mechanism provided by the LightGBM and IPCA is used to improve the prediction efficiency of the LPG-model. The hidden layer growing mechanism of the evolving deep GRU network is capable of learning new knowledge and maintaining previous knowledge from data streams. Moreover, it also has the ability to capture the temporal aspects of the data streams. The experimental results from four open-source benchmarks illustrate that the LPG-model is more accurate and efficient than state-of-the-art algorithms or networks, under the prequential test-then-train protocol. This proves the effectiveness of the LPG-model in throughput prediction scenarios for stream processing tasks. Furthermore, the numerical results from standard benchmark problems of data streams indicate that the LPG-model has potential to reduce the execution time of high-dimensional data streams with a high classification accuracy.
Head-Coupled Kinematic Template Matching: A Prediction Model for Ray Pointing in VR

This paper presents a new technique to predict the ray pointer landing position for selection movements in virtual reality (VR) environments. The technique adapts and extends a prior 2D kinematic template matching method to VR environments where ray pointers are used for selection. It builds on the insight that the kinematics of a controller and Head-Mounted Display (HMD) can be used to predict the ray's final landing position and angle. An initial study provides evidence that the motion of the head is a key input channel for improving prediction models. A second study validates this technique across a continuous range of distances, angles, and target sizes. On average, the technique's predictions were within 7.3° of the true landing position when 50% of the way through the movement and within 3.4° when 90%. Furthermore, compared to a direct extension of Kinematic Template Matching, which only uses controller movement, this head-coupled approach increases prediction accuracy by a factor of 1.8x when 40% of the way through the movement.
Approach for accurate calibration of RGB-D cameras using spheres

RGB-D cameras (or color-depth cameras) play key roles in many vision applications. A typical RGB-D camera has only rough intrinsic and extrinsic calibrations that cannot provide the accuracy required in many vision applications. In this paper, we propose a novel and accurate sphere-based calibration framework for estimating the intrinsic and extrinsic parameters of color-depth sensor pair. Additionally, a method of depth error correction is suggested, and the principle of error correction is analyzed in detail. In our method, the feature extraction module can automatically and reliably detect the center and edges of the sphere projection, while excluding noise data and outliers, and the projection of the sphere center on RGB and depth images is used to obtain a closed solution of the initial parameters. Finally, all the parameters are accurately estimated within the framework of nonlinear global minimization. Compared to other state-of-the-art methods, our calibration method is easy to use and provides higher calibration accuracy. Detailed experimental analysis is performed to support our conclusions.
Keeping Covid-19 emergency legislation socially distant from ordinary legislation: principles for the structure of emergency legislation

Domestic and international jurisprudence indicate the principles for law making in a public health emergency. These are that emergency laws should be limited, time-bounded and proportionate to the nature of the emergency. One way to give effect to these principles is to socially distance emergency legislation from ordinary legislation. This makes emergency laws separate and distinct from ordinary laws, and reduces the chances of them being used for periods and purposes beyond their initial remit. Specific structural techniques to do this are: to use sunset clauses, to use a single legislative vehicle for emergency laws, to use non-textual amendments, to avoid the standard mosaic approach to making new laws, to expressly state their temporary nature, to specifically limit their use to the emergency and to give them a title which indicates their emergency nature.
Performance Evaluation of Mixed Reality Display for Guidance During Transcatheter Cardiac Mapping and Ablation

Cardiac electrophysiology procedures present the physician with a wealth of 3D information, typically presented on fixed 2D monitors. New developments in wearable mixed reality displays offer the potential to simplify and enhance 3D visualization while providing hands-free, dynamic control of devices within the procedure room. Objective: This work aims to evaluate the performance and quality of a mixed reality system designed for intraprocedural use in cardiac electrophysiology. Method: The Enhanced Electrophysiology Visualization and Interaction System (ĒLVIS) mixed reality system performance criteria, including image quality, hardware performance, and usability were evaluated using existing display validation procedures adapted to the electrophysiology specific use case. Additional performance and user validation were performed through a 10 patient, in-human observational study, the Engineering ĒLVIS (E2) Study. Results: The ĒLVIS system achieved acceptable frame rate, latency, and battery runtime with acceptable dynamic range and depth distortion as well as minimal geometric distortion. Bench testing results corresponded with physician feedback in the observational study, and potential improvements in geometric understanding were noted. Conclusion: The ĒLVIS system, based on current commercially available mixed reality hardware, is capable of meeting the hardware performance, image quality, and usability requirements of the electroanatomic mapping display for intraprocedural, real-time use in electrophysiology procedures. Verifying off the shelf mixed reality hardware for specific clinical use can accelerate the adoption of this transformative technology and provide novel visualization, understanding, and control of clinically relevant data in real-time.
TS-GWO: IoT Tasks Scheduling in Cloud Computing Using Grey Wolf Optimizer

The most critical action in the Internet of things (IoT), particularly in the cloud computing (CC) ecosystem, is job/task scheduling. The rule of the task scheduling (TS) means that how to arrange/schedule the jobs over the given virtual machines (VMs) by decreasing the Makespan measured (Mm) values and the cost need. Various scholars propose several scheduling algorithms for solving this problem of scheduling the tasks in cloud computing ecosystems. In this chapter, a task scheduling method is proposed using a multi-objective design model and the Gray Wolf Optimizer (GWO), called TS-GWO. Firstly, the multi-objective criterion measures the fitness function by determining the cost value of the CPU ratio and memory size (Me). The fitness function is determined by calculating the Makespan value and demand value. The proposed method (TS-GWO) can expertly arrange the given jobs to the offered VMs while keeping the smallest Makespan value and cost value. Conclusively, the performance of the proposed method (TS-GWO) is investigated and matched with the basic GWO for the testing measures: Makespan ratio and cost value. From the empirical results, we concluded that the introduced scheduling method (TS-GWO) schedule the given tasks to the offered VMs effectively by producing the smallest Makespan values and the smallest average cost values.
A pruning feedforward small-world neural network based on Katz centrality for nonlinear system modeling

Approaching to the biological neural network, small-world neural networks have been demonstrated to improve the generalization performance of artificial neural networks. However, the architecture of small-world neural networks is typically large and predefined. This may cause the problems of overfitting and time consuming, and cannot obtain an optimal network structure automatically for a given problem. To solve the above problems, this paper proposes a pruning feedforward small-world neural network (PFSWNN), and applies it to nonlinear system modeling. Firstly, a feedforward small-world neural network (FSWNN) is constructed according to the rewiring rule of Watts–Strogatz. Secondly, the importance of each hidden neuron is evaluated based on its Katz centrality. If the Katz centrality of a hidden neuron is below the predefined threshold, this neuron is considered to be an unimportant node and then merged with its most correlated neuron in the same hidden layer. The connection weights are trained using the gradient-based algorithm, and the convergence of the proposed PFSWNN is theoretically analyzed in this paper. Finally, the PFSWNN model is tested on some problems for nonlinear system modeling, including the approximation for a rapidly changing function, CATS missing time-series prediction, four benchmark problems of UCI public datasets and a practical problem for wastewater treatment process. Experimental results demonstrate that PFSWNN exhibits superior generalization performance by small-world property as well as the pruning algorithm, and the training time of PFSWNN is shortened owning to a compact structure.
Quantum Device Emulates the Dynamics of Two Coupled Oscillators

Our quantum device is a solid-state array of semiconducting quantum dots that is addressed and read by 2D electronic spectroscopy. The experimental ultrafast dynamics of the device is well simulated by solving the time-dependent Schrödinger equation for a Hamiltonian that describes the lower electronically excited states of the dots and three laser pulses. The time evolution induced in the electronic states of the quantum device is used to emulate the quite different nonequilibrium vibrational dynamics of a linear triatomic molecule. We simulate the energy transfer between the two local oscillators and, in a more elaborate application, the expectation values of the quantum mechanical creation and annihilation operators of each local oscillator. The simulation uses the electronic coherences engineered in the device upon interaction with a specific sequence of ultrafast pulses. The algorithm uses the algebraic description of the dynamics of the physical problem and of the hardware.
Study of Various Methods for Tokenization

Tokenization is the mechanism of splitting or fragmenting the sentences and words to its possible smallest morpheme called as token. Morpheme is smallest possible word after which it cannot be broken further. As the tokenization is initial phase and as well very crucial phase of Part-Of-Speech (POS) tagging in Natural Language Processing (NLP). Tokenization could be sentence level and word level. This paper analyzes the possible tokenization methods that can be applied to tokenize the word efficiently.
Modeling and Path Planning for Persistent Surveillance by Unmanned Ground Vehicle

This article proposes a novel modeling and path planning framework to tackle a new problem, named road-network persistent surveillance problem (RPSP), in which the occurrence location and probability of the events are unknown. To capture such events, an unmanned ground vehicle (UGV) with certain detection ability must move along the road-network with different monitoring priorities to persistently monitor them. First, we presented an event-oriented modeling method to formulate the new problem. The monitoring effect of the road-network is quantified by uncertainty, which takes the detection ability of the sensor, the detection interval, and the monitoring weight into account. Based on the proposed model, we then designed a heuristic path planning algorithm from a decision-making perspective, which enables a UGV to persistently monitor the viewpoints of the road-network without traversing them. The simulation results and analysis demonstrate the feasibility and superiority of the proposed approach. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This article was motivated by developing an effective solution for unmanned ground vehicle (UGV) to execute road-network persistent surveillance tasks under outdoor circumstances. To the best of our knowledge, there is no other consideration dealing with this problem. To capture spatiotemporal random events, we proposed a new approach that enables UGV to persistently monitor an area with road-network performing in the weighted mode. Distinguished from the traditional path planning methods in the literature, the proposed approach considered the detection ability of the UGV. Consequently, the so-called “area-to-area” planning approach does not require traversing all the viewpoints. The approach can be used in road-network with any topology. Moreover, the difficulty in modeling road-network from the actual environment is degraded, and compared with the existing patrolling methods, the monitoring efficiency of the proposed approach is improved obviously.
JSUT and JVS: Free Japanese voice corpora for accelerating speech synthesis research

In this paper, we develop two corpora for speech synthesis research. Thanks to improvements in machine learning techniques, including deep learning, speech synthesis is becoming a machine learning task. To accelerate speech synthesis research, we aim at developing Japanese voice corpora reasonably accessible from not only academic institutions but also commercial companies. In this paper, we construct the JSUT and JVS corpora. They are designed mainly for text-to-speech synthesis and voice conversion, respectively. The JSUT corpus contains 10 hours of reading-style speech uttered by a single speaker, and the JVS corpus contains 30 hours containing three styles of speech uttered by 100 speakers. This paper describes how we designed the corpora and summarizes the specifications. The corpora are available at our project pages.
MemoryPath: A deep reinforcement learning framework for incorporating memory component into knowledge graph reasoning

Knowledge Graph (KG) is identified as a major area in artificial intelligence, which is used for many real-world applications. The task of knowledge graph reasoning has been widely used and proven to be effective, which aims to find these reasonable paths for various relations to solve the issue of incompleteness in KGs. However, many previous works on KG reasoning, such as path-based or reinforcement learning-based methods, are too reliant on the pre-training, where the paths from the head entity and the target entity must be given to pre-train the model, which would easily lead the model to overfit on the given paths seen in the pre-training. To address this issue, we propose a novel reasoning model named MemoryPath with a deep reinforcement learning framework, which incorporates Long Short Term Memory (LSTM) and graph attention mechanism to form the memory component. The well-designed memory component can get rid of the pre-training so that the model doesn't depend on the given target entity for training. A tailored mechanism of reinforcement learning is presented in this proposed deep reinforcement framework to optimize the training procedure, where two metrics, Mean Selection Rate (MSR) and Mean Alternative Rate (MAR), are defined to quantitatively measure the complexities of the query relations. Meanwhile, three different training mechanisms, Action Dropout, Reward Shaping and Force Forward, are proposed to optimize the training process of the proposed MemoryPath. The proposed MemoryPath is validated on two datasets from FB15K-237 and NELL-995 on different tasks including fact prediction, link prediction and success rate in finding paths. The experimental results demonstrate that the tailored mechanism of reinforcement learning make the MemoryPath achieves state-of-the-art performance comparing with the other models. Also, the qualitative analysis indicates that the MemoryPath can store the learning process and automatically find the promising paths for a reasoning task during the training, and shows the effectiveness of the memory component.
Survey on Zero-Trust Network Security

As a promising tool to cope with the current phishing attacks, zero-trust is gradually being taken seriously while the situation of network security becoming more and more severe. In zero trust, no unit is fully trusted even if it passes the certification. They only have the least privilege, access to data requires authentication, these processes need to be logged. This paper performs a typical survey of the composition and key technologies of zero trust, and combines the application of this technology in some scenarios to introduce the advantages of zero trust, such as big data function, cloud network and IoT. Moreover, this paper also discusses some of the challenging research issues in the field that can provide ideas for future researchers.
ANALYSIS OF THE CONSEQUENCES OF THE TRANSITION TO ONLINE LEARNING ON THE EXAMPLE OF MOOC PHILOSOPHY DURING THE COVID-19 PANDEMIC

Purpose of the study: The situation of a mass transition of Universities to online education in the period of the COVID-19 pandemic allowed us to see the challenges of distance e-learning in practice. In this unique situation, the same students studying the same course changed only the form of education, which allows us to see the consequences of such a transition. The purpose of the study is the analysis of changes in students' educational activities in the transition to online learning.&#x0D; Methodology: The article provides a quantitative statistical analysis of changes in the behaviour of first-year students of Peter the Great St. Petersburg Polytechnic University (N=3122) in the framework of studying the mass open online course “Philosophy” on the platform open.edu when switching to fully e-learning in March 2020. The authors have applied data mining MOOCs from students’ learning portfolios.&#x0D; Main Findings: Existing technological solutions and educational technologies made it possible to quickly adapt the education system to the distance format. However, the transition to fully e-learning has led to a sharp increase (by 16-17%) in the number of students who do not participate in intermediate tests and not doing homework in the e-course and later did not return to normal learning.&#x0D; Applications of this study: Since modern higher education is increasingly using e-learning, it is necessary to anticipate the consequences of the implementation of e-learning. The study helps to see general trends in this area.&#x0D; Novelty/Originality of this study: The study provides an analysis of students’ learning when switching to online education based on data taken directly from students’ learning portfolios, which allows us to see a completely objective picture of changes in students' behaviour.