Reviewerly/exHarmony huggingface dataset:
Cryptocurrency Adoption in Malaysia: Does Age, Income and Education Level Matter?

Dated back in 2008, the first blockchain-powered cryptocurrency-Bitcoin was introduced by Satoshi Nakamoto. Over the years, the types of cryptocurrencies available in the market amounted to more than 2,000. With the disruptive potential to revolutionize the traditional financial services, cryptocurrencies become a topic of interest among scholars, global regulators, investors, business operators, information technology enthusiasts and consumers. Nevertheless, the negative activities associated with cryptocurrencies such as money laundering and illicit trading, have resulted in the legality of cryptocurrencies remain controversial in the global context.As the worldwide regulators expressed different stance towards cryptocurrency acceptance and adoption, this study sought to gauge the individual’s behavioral intention to use cryptocurrency. Recognizing the dearth of study in Asian countries, especially the developing country, this study addresses the literature gap by focusing the case of Malaysian individuals. Specifically, this study investigated the effects of age, education level and income level differences in an individual’s behavioral intention to use cryptocurrency.The sample is made up of 176 Malaysian individuals who are equipped with cryptocurrency knowledge. The empirical data were gathered using online survey questionnaire via Google form. Subsequently, the data were analyzed using one-way analysis of variance (ANOVA) to understand the demographics effect on the intention to adopt cryptocurrency.Finally, the results revealed that the role of age, education and income level are not significant in influencing an individual’s behavior towards cryptocurrency adoption.
Blockchain-Based Secure Credit Card Storage System for E-Commerce

Recently, serious damages have occurred in e-commerce applications due to rapidly increasing data leaks and end-user vulnerabilities. Although the source of the vulnerabilities is different, attacks result in the theft of unsafe data. In particular, the theft of credit card information reveals a financial loss. In this study, a blockchain-based secure storage model has been developed in order to prevent the theft of credit card information in e-commerce applications as a result of a possible data leak. In the sample e-commerce application developed with ASP.NET, data other than credit cards are stored. Credit card data is transmitted to the blockchain over the API with SSL protection in the e-commerce application. The blockchain model was developed using MongoDB with the BigchainDB framework. The data in each block of the blockchain is encrypted with Advanced Encryption Standard (AES) 256 bits. The data integrity of the block is provided by the SHA256 algorithm. it is aimed to protect credit card data from a possible data leak with the proposed BigchainDB-based blockchain model.
Ontology-Based Model for Security Assessment: Predicting Cyberattacks Through Threat Activity Analysis

The prediction of attacks is essential for the prevention of potential risk. Therefore, risk forecasting contributes a lot to the optimization of the information security budget. This article focuses on the ontology and stages of a cyberattack. It introduces the main representatives of the attacking side and describes their motivation.
Cyclostationary Signals Analysis Methods Based on High-Dimensional Space Transformation Under Impulsive Noise

Cyclostationary signals analysis is a highly widespread tool for non-stationary signals processing. In practical transmission, signals may be contaminated by non-stationary non-Gaussian impulse noises. However, cyclostationary is weak in dealing with such non-Gaussian distributions. In this paper, a cyclic mean kernel function is proposed for cyclostationary signal analysis under impulse noise, based on a high-dimensional space transformation. Then, the generalized cyclic mean kernel function is proposed based on the constructed generalized Hankel matrix. The simulation results show that the cyclostationary signal frequency estimation methods based on these two functions have advantages in performance and robustness over the existing methods under impulsive noise.
Evidential Undersampling Approach for Imbalanced Datasets with Class-Overlapping and Noise

The class imbalance issue involves many real-world domains such as fraud detection, medical diagnosis, intrusion detection, etc. Most classification algorithms tend to perform poorly when the training dataset is class-imbalanced. This problem gets more challenging in the presence of other factors such as class-overlapping and noise. Among many methods, undersampling is a simple and efficient approach which re-balances the imbalanced dataset by removing majority samples. In this paper, we propose a novel method named Evidential Undersampling (EVUS), which is a re-sampling approach based on the theory of evidence. To avoid removing meaningful samples, each majority object is assigned a soft evidential label to gain more information about its location, then majority samples which are considered ambiguous or noisy by our framework, are eliminated from the training set. The conducted results with CART and SVM show that our proposal outperformed other well-known undersampling methods according to the AUC metric.
Gamma Precorrection and Phase Error Compensation Methods Based on Three-Frequency with Three-Phase Shift

Digital fringe projection measurement technology has been widely used in computer vision and optical three-dimensional (3D) measurement. Considering the phase error caused by the gamma distortion and nonlinear error, the active gamma precorrection and phase error compensation methods based on the three-frequency with three-phase shifts are designed to reversely solve the initial phase and accurately compensate phase error. On the one hand, the gamma coefficient of the measurement system depends on precoding two groups of fringe sequences with different gamma coefficients to calculate the corresponded proportional coefficient of harmonic component. On the other hand, the phase error compensation method is designed to compensate the phase error and improve the accuracy and speed of phase calculation after gamma correction. Experiments show that the proposed precalibration gamma coefficient method can effectively reduce the sinusoidal error in nearly 80 percent which only needs fewer fringe patterns. Compared with the traditional three-frequency with four-phase shift method, the proposed method not only has higher phase accuracy and better noise resistance but also has good robustness and flexibility, which is not limited to the gamma distortion model.
Small CSI Samples-Based Activity Recognition: A Deep Learning Approach Using Multidimensional Features

With the emergence of tools for extracting CSI data from commercial WiFi devices, CSI-based device-free activity recognition technology has developed rapidly and has been widely used in security monitoring, smart home, medical monitoring, and other fields. However, the existing CSI-based activity recognition algorithms need a large number of training samples to obtain the ideal recognition accuracy. To solve the problem, an attention-based bidirectional LSTM method using multidimensional features (called MF-ABLSTM method) is proposed. In this method, the signal preprocessing and continuous wavelet transform algorithms are used to construct time-frequency matrix, the sample entropy is used to characterize the statistical feature of CSI amplitudes, the energy difference at a fixed time interval is used to characterize the time-domain feature of activities, and the energy distribution of different frequency components is used to characterize the frequency-domain feature of activities. By expanding the training samples with the proposed tensor prediction algorithm, the accurate activity recognition can be realized with only a few samples. A large number of experiments verify the good performance of MF-ABLSTM method.
Unscented Particle Filter Approach for Underwater Target Tracking

This research aims to find an appropriate approach to improve system accuracy in the Doppler-bearing tracking (DBT) problem for target estimation. The topic of DBT problem is to achieve a target trajectory using bearing and frequency measurements. The difficulty of DBT problem comes from the nonlinearity terms exposed in the measurement equations. The unscented particle filter approach is proposed to estimate the accuracy in the target motion parameters (TMP). This approach requires the observer maneuver so that the target trajectory is observable. Although in recent research papers, DBT has been proven to work efficiently without observer maneuver, TMP is unknown to the observer, and consequently, there is a need for observer maneuver. So, the algorithm is simulated with observer following s-maneuver and without any maneuver executed by the observer, and results are compared. The effectiveness of the solution and results are determined by using MATLAB simulation. It is shown that the truthfulness of the outcome is superior when the observer performs s-maneuver while compared to that without observer maneuver.
Learning Sparse Graph with Minimax Concave Penalty under Gaussian Markov Random Fields

This paper presents a convex-analytic framework to learn sparse graphs from data. While our problem formulation is inspired by an extension of the graphical lasso using the so-called combinatorial graph Laplacian framework, a key difference is the use of a nonconvex alternative to the ℓ1 norm to attain graphs with better interpretability. Specifically, we use the weakly-convex minimax concave penalty (the difference between the ℓ1 norm and the Huber function) which is known to yield sparse solutions with lower estimation bias than ℓ1 for regression problems. In our framework, the graph Laplacian is replaced in the optimization by a linear transform of the vector corresponding to its upper triangular part. Via a reformulation relying on Moreau's decomposition, we show that overall convexity is guaranteed by introducing a quadratic function to our cost function. The problem can be solved efficiently by the primal-dual splitting method, of which the admissible conditions for provable convergence are presented. Numerical examples show that the proposed method significantly outperforms the existing graph learning methods with reasonable computation time.
Astrolabous: A Universally Composable Time-Lock Encryption Scheme

In this work, we study the Time-Lock Encryption (TLE) cryptographic primitive. The concept of TLE involves a party initiating the encryption of a message that one can only decrypt after a certain amount of time has elapsed. Following the Universal Composability (UC) paradigm introduced by Canetti [IEEE FOCS 2001], we formally abstract the concept of TLE into an ideal functionality. In addition, we provide a standalone definition for secure TLE schemes in a game-based style and we devise a hybrid protocol that relies on such a secure TLE scheme. We show that if the underlying TLE scheme satisfies the standalone game-based security definition, then our hybrid protocol UC realises the TLE functionality in the random oracle model. Finally, we present Astrolabous, a TLE construction that satisfies our security definition, leading to the first UC realization of the TLE functionality.Interestingly, it is hard to prove UC secure any of the TLE construction proposed in the literature. The reason behind this difficulty relates to the UC framework itself. Intuitively, to capture semantic security, no information should be leaked regarding the plaintext in the ideal world, thus the ciphertext should not contain any information relating to the message. On the other hand, all ciphertexts will eventually open, resulting in a trivial distinction of the real from the ideal world in the standard model. We overcome this limitation by extending any secure TLE construction adopting the techniques of Nielsen [CRYPTO 2002] in the random oracle model. Specifically, the description of the extended TLE algorithms includes calls to the random oracle, allowing our simulator to equivocate. This extension can be applied to any TLE algorithm that satisfies our standalone game-based security definition, and in particular to Astrolabous.
Semantic Parsing in Task-Oriented Dialog with Recursive Insertion-Based Encoder

We introduce a Recursive INsertion-based Encoder (RINE), a novel approach for semantic parsing in task-oriented dialog. Our model consists of an encoder network that incrementally builds the semantic parse tree by predicting the non-terminal label and its positions in the linearized tree. At the generation time, the model constructs the semantic parse tree by recursively inserting the predicted non-terminal labels at the predicted positions until termination. RINE achieves state-of-the-art exact match accuracy on low- and high-resource versions of the conversational semantic parsing benchmark TOP, outperforming strong sequence-to-sequence models and transition-based parsers. We also show that our model design is applicable to nested named entity recognition task, where it performs on par with state-of-the-art approach designed for that task. Finally, we demonstrate that our approach is 2-3.5 times faster than the sequence-to-sequence model at inference time.
Locating Identities in Time: An Examination of the Formation and Impact of Temporality on Presentations of the Self through Location-Based Social Networks

Studies of identity and location-based social networks (LBSN) have tended to focus on the performative aspects associated with marking one's location. Yet these studies often present this practice as being an a priori aspect of locative media. What is missing from this research is a more granular understanding of how this process develops over time. Accordingly, we focus on the first 6 weeks of 42 users beginning to use an LBSN we designed and named GeoMoments . Through our analysis of our users' activities, we contribute to understanding identity and LBSN in two distinct ways. First, we show how LBSN users develop and perform self-identity over time. Second, we highlight the extent these temporal processes reshape the behaviors of users. Overall, our results illustrate that although a performative use of GeoMoments does evolve, this development does not occur in a vacuum. Rather, it occurs within the dynamic context of everyday life, which is prompted, conditioned, and mediated by the way the affordances of GeoMoments digitally organize and archive past locational traces.
ESPS: Energy Saving Power Spectrum-Aware Scheduling to Leverage Differences in Power Ratings of Physical Hosts in Datacenters

Cloud Computing has seen massive growth over the past couple of decades, leading to exponential growth in energy consumption at data centres. Data centres consuming high amounts of energy leave a carbon footprint of the same scale, hence Cloud Service Providers (CSPs) have been looking for energy-efficient solutions to task scheduling in cloud to reduce the amount of carbon dioxide emission. Saving energy not only helps reduce the carbon footprint datacentres have on the environment, but also helps cover the costs of running multiple datacentres on the CSP’s end. In this paper, we propose an energy saving task scheduling heuristic for heterogeneous cloud systems which selects the optimal physical host containing virtual machines with the additional consideration of the utilization of any incoming task on that particular virtual machine. We compare the energy efficiency of our proposed heuristic with recent algorithms including ECTC, MaxUtil, Random, and FCFS on several benchmark and synthetic datasets to display its superiority in energy-efficient task scheduling in heterogeneous cloud environments. Our proposed heuristic, namely Energy Saving Power Spectrum-Aware Scheduling (ESPS) minimizes energy consumption in a heterogeneous cloud environment by about 38.65%, 33.59%, 53.02%, and 46.96% when compared to FCFS, MaxUtil, Random and ECTC respectively.
High Performance Post-Quantum Key Exchange on FPGAs

Lattice-based cryptography is a highly potential candidate that protects against the threats of quantum attack. At Usenix Security 2016, Alkim, Ducas, Popplemann, and Schwabe proposed a post-quantum key exchange scheme called NewHope, based on a variant of lattice problem, the ring-learning-with-errors (RLWE) problem. In this work, we propose a high performance hardware architecture for NewHope. Our implementation requires 6,680 slices, 9,412 FFs, 18,756 LUTs, 8 DSPs and 14 BRAMs on Xilinx Zynq-7000 equipped with 28mm Artix-7 7020 FPGA. In our hardware design of NewHope key exchange, the three phases of key exchange costs 51.9, 78.6 and 21.1 μs, respectively. It achieves more than 4.8 times better in terms of area-time product compared to previous results of hardware implementation of NewHope-Simple from Oder and Guneysu at Latin-crypt 2017.
Reinforcement Learning for Load-Balanced Parallel Particle Tracing

We explore an online reinforcement learning (RL) paradigm to dynamically optimize parallel particle tracing performance in distributed-memory systems. Our method combines three novel components: (1) a work donation algorithm, (2) a high-order workload estimation model, and (3) a communication cost model. First, we design an RL-based work donation algorithm. Our algorithm monitors workloads of processes and creates RL agents to donate data blocks and particles from high-workload processes to low-workload processes to minimize program execution time. The agents learn the donation strategy on the fly based on reward and cost functions designed to consider processes’ workload changes and data transfer costs of donation actions. Second, we propose a workload estimation model, helping RL agents estimate the workload distribution of processes in future computations. Third, we design a communication cost model that considers both block and particle data exchange costs, helping RL agents make effective decisions with minimized communication costs. We demonstrate that our algorithm adapts to different flow behaviors in large-scale fluid dynamics, ocean, and weather simulation data. Our algorithm improves parallel particle tracing performance in terms of parallel efficiency, load balance, and costs of I/O and communication for evaluations with up to 16,384 processors.
A Survey on Skeleton-Based Activity Recognition using Graph Convolutional Networks (GCN)

Skeleton-Based Activity recognition is an active research topic in Computer Vision. In recent years, deep learning methods have been used in this area, including Recurrent Neural Network (RNN)-based, Convolutional Neural Network (CNN)-based and Graph Convolutional Network (GCN)-based approaches. This paper provides a survey of recent work on various Graph Convolutional Network (GCN)-based approaches being applied to Skeleton-Based Activity Recognition. We first introduce the conventional implementation of a GCN. Then methods that address the limitations of conventional GCN's are presented.
Discriminable Multi-Label Attribute Selection for Pre-Course Student Performance Prediction

The university curriculum is a systematic and organic study complex with some immediate associated steps; the initial learning of each semester's course is crucial, and significantly impacts the learning process of subsequent courses and further studies. However, the low teacher-student ratio makes it difficult for teachers to consistently follow up on the detail-oriented learning situation of individual students. The extant learning early warning system is committed to automatically detecting whether students have potential difficulties-or even the risk of failing, or non-pass reports-before starting the course. Previous related research has the following three problems: first of all, it mainly focused on e-learning platforms and relied on online activity data, which was not suitable for traditional teaching scenarios; secondly, most current methods can only proffer predictions when the course is in progress, or even approaching the end; thirdly, few studies have focused on the feature redundancy in these learning data. Aiming at the traditional classroom teaching scenario, this paper transforms the pre-class student performance prediction problem into a multi-label learning model, and uses the attribute reduction method to scientifically streamline the characteristic information of the courses taken and explore the important relationship between the characteristics of the previously learned courses and the attributes of the courses to be taken, in order to detect high-risk students in each course before the course begins. Extensive experiments were conducted on 10 real-world datasets, and the results proved that the proposed approach achieves better performance than most other advanced methods in multi-label classification evaluation metrics.
On the Importance of Capturing a Sufficient Diversity of Perspective for the Classification of Micro-PCBs

Byerly, Adam Kalganova, Tatiana Grichnik, Anthony J.We present a dataset consisting of high-resolution images of 13 micro-PCBs captured in various rotations and perspectives relative to the camera, with each sample labeled for PCB type, rotation category, and perspective categories. We then present the design and results of experimentation on combinations of rotations and perspectives used during training and the resulting impact on test accuracy. We then show when and how well data augmentation techniques are capable of simulating rotations versus perspectives not present in the training data. We perform all experiments using CNNs with and without homogeneous vector capsules (HVCs) and investigate and show the capsules’ ability to better encode the equivariance of the sub-components of the micro-PCBs. The results of our experiments lead us to conclude that training a neural network equipped with HVCs, capable of modeling equivariance among sub-components, coupled with training on a diversity of perspectives, achieves the greatest classification accuracy on micro-PCB data.
Kamchatka seismic monitoring and Earthquake prediction system and its evolution. Main results of observations in 2016-2020

In this paper we present brief review of results of Kamchatka Seismic Monitoring and Earthquake Prediction System operations in the last five years. In addition, the retrospective of development of hardware, equipment and software of the System performed. The main direction in the System evolution in this period concerned the creation and modernization of data acquiring and pro-cessing methods. One of main results is creation basic informational space, that includes all pro-cesses if seismic observations, from data acquiring till exchange (including external users) of da-ta processing results. In particular, the system of data storage was deeply modernized, high-speed access to the data archive was provides, high-performance computing clusters were deployed, all seismic stations were combined in the unified network. Development algorithms and software for data processing and seismic regime controlling was continued. Creation and development of the Seismological Data Informational System (SDIS) provide the access to seismic observations re-sults for research community. The service of automatic data exchange with external users was created and incorporated in SDIS. Kamchatka Seismic Monitoring and Earthquake Prediction System in 2016-2020 allowed registering and processing over 83 thousand tectonic and volcanic earthquakes. The complex studies for seven the strongest ones were conducted. Detailed analysis showed, that magnitude of completeness for regional scale is MLc=2.5, and for local scale (for example – volcano seismic monitoring) – MLc=–0.2.