Reviewerly/exHarmony huggingface dataset:
Fast and fully-automated histograms for large-scale data sets

G-Enum histograms are a new fast and fully automated method for irregular histogram construction. By framing histogram construction as a density estimation problem and its automation as a model selection task, these histograms leverage the Minimum Description Length principle (MDL) to derive two different model selection criteria. Several proven theoretical results about these criteria give insights about their asymptotic behavior and are used to speed up their optimisation. These insights, combined to a greedy search heuristic, are used to construct histograms in linearithmic time rather than the polynomial time incurred by previous works. The capabilities of the proposed MDL density estimation method are illustrated with reference to other fully automated methods in the literature, both on synthetic and large real-world data sets.
Reinforcement Learning and DEAR Framework for Solving the Qubit Mapping Problem

Quantum computing is gaining more and more attention due to its huge potential and the constant progress in quantum computer development. IBM and Google have released quantum architectures with more than 50 qubits. However, in these machines, the physical qubits are not fully connected so that two-qubit interaction can only be performed between specific pairs of the physical qubits. To execute a quantum circuit, it is necessary to transform it into a functionally equivalent one that respects the constraints imposed by the target architecture. Quantum circuit transformation inevitably introduces additional gates which reduces the fidelity of the circuit. Therefore, it is important that the transformation method completes the transformation with minimal overheads. It consists of two steps, initial mapping and qubit routing. Here we propose a reinforcement learning-based model to solve the initial mapping problem. Initial mapping is formulated as sequence-to-sequence learning and self-attention network is used to extract features from a circuit. For qubit routing, a DEAR (Dynamically-Extract-and-Route) framework is proposed. The framework iteratively extracts a subcircuit and uses A* search to determine when and where to insert additional gates. It helps to preserve the lookahead ability dynamically and to provide more accurate cost estimation efficiently during A* search. The experimental results show that our RL-model generates better initial mappings than the best known algorithms with 12% fewer additional gates in the qubit routing stage. Furthermore, our DEAR-framework outperforms the state-of-the-art qubit routing approach with 8.4% and 36.3% average reduction in the number of additional gates and execution time starting from the same initial mapping.
Optimal Resource Allocation for Containerized Cloud Microservices

One significant advantage of microservice cloud architectures is the agility with which microservices may be duplicated to enhance overall service quality and satisfy service-level contracts. However, the complex interactions within microservice meshes introduce new challenges in managing shared hardware resources and in identifying critical resource bottlenecks. In this paper, we formulate the efficient resource management of cloud microservice resources as a constrained optimization problem that is amenable to a solution in real time. A novel optimization-based reactive resource manager is implemented on an open-source platform and compared with the academic state of the art and existing cloud-provider solutions. The proposed optimal solution has resulted in an 268% improvement of the overall microservice deployment cost.
Integration of Ontology Transformation into Hidden Markov Model

The goal of this study is to suggest a method for turning an ontology into a hidden Markov model (HMM). Ontology properties (relationships between classes) and ontology classes are taken as HMM symbols and states, respectively. Knowledge is represented in many different fields using the central element of the Semantic Web dubbed ontology. The authors employed machine learning technologies like HMM to add knowledge to these ontologies or to extract knowledge from within them. The meaning obtained from ontologies is not described during this task. The ontology triples that were extracted using SPARQL queries are used in this paper to transform the ontology into an HMM in order to handle this semantic. The Pizza ontology has been used to implement this method, which is based on lightweight ontologies.
Acceleration of video stabilization using embedded GPU

Video stabilization is a technique used to eliminate the shakiness in video. It plays an important role to improve the quality of the videos captured by cameras mounted on drones and autonomous robots, and handheld cameras. In these cases, it is generally required to achieve real-time video stabilization using low-power and low-cost devices. In this research, we focus on software video stabilization, and propose a new implementation method using an embedded GPU. Our target device is Nvidia Jetson Nano, which is one of the smallest and least power consumption embedded GPUs. In our implementation, 1) multi-threads on the CPU on Jetson Nano and the GPU run asynchronously and in parallel to achieve high performance, 2) the size of the search area is minimized to reduce the amount of computation assuming that sufficiently fast processing speed is possible using the GPU, and 3) the data transfer from the global memory of the GPU is minimized to hide the high latency. Our implementation achieves 81.4 fps for full HD videos, and its quality is high enough for practical use.
Virtual reality as a tool for basic and vocational education

&lt;p&gt;This article is a contribution to the study of the implementation of virtual reality as a tool for teaching and learning processes, especially in the field of basic and professional education. For this purpose, virtual reality is analyzed as an alternative to ensure the quality of the educational process, especially urgent in view of the current situation of physical distancing due to the pandemic. This research investigates already published works whose contributions can be considered adaptive, which have been developed and applied in basic and professional education environments, and which have managed to demonstrate effective results in the educational process. As a conclusion, the potential of virtual reality as an educational tool is evidenced, although it is not always the most appropriate, so its use must be carefully evaluated and defined.&lt;/p&gt;
Efficient and Complete Centralized Multi-Robot Path Planning

Multi-robot path planning is abstracted as the problem of computing a set of non-colliding paths on a graph for multiple robots. A naive search of the composite search space, although complete, has exponential complexity and becomes computationally prohibitive for problems with just a few robots. This work proposes an efficient and complete algorithm for solving a general class of multi-robot path planning problems, specifically those where there are at most n-2 robots in a connected graph of n vertices. The algorithm employs two primitives: a "push" operation where a robot moves toward its goal until no further progress can be made, and a "swap" operation that allows two robots to swap positions without altering the configuration of any other robot. Simulated experiments compare the proposed approach with several other centralized and decoupled planners, and show that the proposed technique has highly competitive computation time and easily scales to problems involving 100s of robots, solving them in under 5 seconds.
Overview and Analysis of Speech Recognition

In this era of rapid development of information, language is no longer a communication tool between people, nowadays it is possible to complete the natural communication between people and machines through the expression of language. In the field of human-computer interaction, speech recognition technology is very critical and has important research significance and practical value. This paper introduces the market prospect and application of speech recognition, outlines its development history, specifies the knowledge of speech recognition concepts and basic principles, introduces the key technologies adopted by major research vendors for speech recognition respectively and develops a series of comparisons as well as the current difficulties faced by this technology.
Physiotherapy Activities Classification with Deep Neural Networks

One of the most important factors affecting the success of the physiotherapy treatment is the participation of the patients in the treatment process. However, in this long process, it is very difficult to monitor the home exercises of the patients. In this study, as the first step of a system that will be developed for the monitoring of home physical therapy exercises, shoulder physiotherapy treatment activities has been classified. In this study, in which 9-axis inertia sensor data was used, the data were processed with three different deep neural networks and the classification performances of the models were compared. Also in this study, by giving time sequences of different lengths as input to the system, the effect of the history window size was also examined, and it was seen that the most successful classification for 11 activity classes was obtained with the 75-time step input sequence using the temporal convolutional networks model.
Effect of Dust Accumulation on the Performances of Solar Panels in Static and Tracking Systems in Bauchi Metropolis of Nigeria

The research aimed at comparing the effect of dust accumulation on the performances of solar PV modules in static position and on a tracking system within Bauchi metropolis of Nigeria , during the harmattan season –November to December. Dust deposition on the surface of PV panel reduces the conversion efficiency by absorbing and preventing the Solar radiation from reaching the cells of the panel. This necessitated the need to develop a reliable tool to relate energy production from solar cells with respect to dust deposition on both static and solar tracking PV modules. A test bed was designed for the field measurement of output current, voltage, temperature and dust weight data from the two PV modules.” Centsys” polycrystalline solarpanels () was used for the research. A statistical technique involving regression analysis in SPSS software was used to correlate between the various measured data from the test bed to predict the effect of dust accumulation on the surface of each module, which will enable solar power installers to take necessary measures for improving the PV module’s conversion efficiency during the period under review. The research result showed that the effect of dust on the tracking solar PV moduleis less significant compared to the static panel, giving the former a better conversion efficiency.
Tools for Fast Metric Data Search in Structural Methods for Image Classification

The article proposes a new classification method based on implementing the high-speed search tools for the indexed data structure created on the etalon set of features, which has significant advantages in processing speed compared to the traditional approaches.The classifier is represented as twostage processing, where at the first stage the class for the separate object descriptor is determined, and at the second stage, the resulting class of the object is determined based on the obtained set of local solutions.The developed method is based on the preliminary construction of the indexed hash structures for the set of descriptors of the base of the etalon images.Implementing the hash representation allows for increasing the speed of identification or classification of visual objects.A comparative experiment with the traditional method of voting has been conducted, where the linear search for the nearest descriptor has been implemented for the identification without the use of prior creation of the indexed hash representation of the etalons.In the experiment, we have gained in processing speed for the developed method compared to the traditional over 10 times.The gain in processing speed increases proportionally with the number of the etalons and the number of the descriptors in the descriptions.The experiment has shown that the efficiency of the method can be enhanced by varying the values of its parameters and adapting to the properties of the data.
Research on the Accuracy and Efficiency of Near-infrared Face Recognition

Compared with RGB images, which are easily affected by light, near-infrared imaging face recognition has gradually received widespread attention. Current research mainly focuses on traditionally handcrafted feature extraction algorithms, which have low efficiency and accuracy in near-infrared face recognition. There are also some studies using deep learning methods, but these studies mainly focus on the case of large-size images as input. This paper designed a convolutional neural network (CNN) method for smaller near-infrared images. The experimental results on CBSR NIR Face Dataset show that our proposed CNN method has surpassed other recognition methods such as the Histogram of Oriented Gradient (HOG) and Triplet-Loss in recognition accuracy. Additionally, this paper reduced the size of face images while ensuring high accuracy to improve training efficiency and usage efficiency.
HueCode2: An Illumination-Robust Meta-Marker Overlaying Multiple Fiducial Markers using Optimal Color Scheme

We studied HueCode, i.e., a metamarker with multiple fiducial markers overlaid in different colored layers. HueCode consists of relative pose markers (e.g., AR markers) and additional information markers (e.g., QR codes) through overlaying within the area of a single marker. Robots can simultaneously recognize the relative pose as well as additional information required for movement and recognition from a single HueCode. However, conventional coloring schemes and recognition methods deal with degradation of the recognition performance by illumination. Thus, we propose HueCode2 to solve this problem. The new coloring scheme allows the use of any color that is easy to distinguish. The new recognition method uses support vector machines trained under various illumination conditions to identify colors. The experimental results show that these methods improve recognition rates over conventional HueCode under various illumination conditions including indoor and outdoor environment.
Analytical Algorithm for Capacities of Classical and Classical-Quantum Channels

We derive an analytical algorithm for the channel capacity of a classical channel without any iteration, while its existing algorithms require iterations and the number of iterations depends on the required precision level. Hence, our algorithm is its first analytical algorithm for this task without any iteration, while this algorithm needs several conditions for the channel. We apply the obtained algorithm to examples, and see how the obtained algorithm works in these examples. Then, we extend it to the channel capacity of a classical-quantum (cq-) channel. Many existing studies proposed algorithms for a cq-channel and all of them require iterations. Our extended analytical algorithm has also no iteration, and outputs the exactly optimum value.
Parallelizing Optical Flow Estimation on an Ultra-Low Power RISC-V Cluster for Nano-UAV Navigation

Optical flow estimation is crucial for autonomous navigation and localization of unmanned aerial vehicles (UAV). On micro and nano UAVs, real-time calculation of the optical flow is run on low power and resource-constrained microcontroller units (MCUs). Thus, lightweight algorithms for optical flow have been proposed targeting real-time execution on traditional single-core MCUs. This paper introduces an efficient parallelization strategy for optical flow computation targeting new-generation multicore low power RISC-V based microcontroller units. Our approach enables higher frame rates at lower clock speeds. It has been implemented and evaluated on the eight-core cluster of a commercial octa-core MCU (GAP8) reaching a parallelization speedup factor of 7.21 allowing for a frame rate of 500 frames per second when running on a 50 MHz clock frequency. The proposed parallel algorithm significantly boosts the camera frame rate on micro unmanned aerial vehicles, which enables higher flight speeds: the maximum flight speed can be doubled, while using less than a third of the clock frequency of previous single-core implementations.
A Semantic-Based Smart Contract Defect Detection General Platform

The emergence of smart contracts has brought blockchain into a new era. Smart contract has the feature that cannot be modified once deployed, which can ensure the credibility of smart contract. Security incidents on blockchain platforms in recent years have been linked to smart contracts. If the code of the smart contract contains defects, it may bring unpredictable risks and losses to the contract participants. Therefore, it is very necessary for smart contract to detect defects before deployment. To solve the above problems, this paper proposes a general platform for smart contract defect detection. The platform generates the abstract syntax tree of smart contracts, obtains the semantic description of corresponding functions, variables. Generate assertions by knowledge of security model libraries and semantic description of abstract syntax trees, and expressions, and then detects the defects of smart contracts. This paper introduces the design and implementation of the platform. Taking the variables parsed by the abstract syntax tree as an example, it explains in detail how the platform detects the delegatecall dangerous call vulnerability.
A Study on Feature Selection for Gender Detection in Speech Processing for Assamese Language

Gender identification is an integral part of a Speech recognition system. Specifically, for the low resource languages, it is a challenging task. For any speech recognition system, finding a suitable feature plays an essential role in the system's performance. In this paper, we have done a comparative analysis of gender identification from formant frequencies F1 and F2 of speech data set collected from the speakers of Assamese language (a low recourse language of North-East India). The objective is to explore different classification techniques for developing a gender identification module for Assamese language. We have used four supervised classification techniques kNN, Logistic Regression, decision tree, and SVM, and found that when F1 and F2 are used together, the methods give the best result. One unsupervised method Gaussian Mixture Model (GMM) is also applied and found that the best result is given by formant frequency F1.
Development of CRF and CTC Based End-To-End Kazakh Speech Recognition System

Architecture end-to-ends are commonly used methods in many areas of machine learning, namely speech recognition. The end-to-end structure represents the system as one whole element, in contrast to the traditional one, which has several independent elements. The end-to-end system provides a direct mapping of acoustic signals in a sequence of labels without intermediate states, without the need for post-processing at the output, making it easy to implement. Combining several end-to-end method types perform better results than applying them separately. Inspired by this issue, in this work we have realized a method for using CRF and CTC together to recognize a low-resource language like the Kazakh language. In this work, architectures of a recurrent neural network and a ResNet network were applied to build a model using language models. The results of experimental studies showed that the proposed approach based on the ResNet architecture with the RNN language model achieved the best CER result with a value of 9.86% compared to other network architectures for the Kazakh language.
A Multi-Task Model for Multi-Attribute Fashion Recognition and Retrieval

We present a multi-task model to perform multiple fashion attribute recognition and retrieval together. Although classical computer vision problems, they still lack good performance in multiple classes. In the fashion domain, the recognition task to capture the attributes of fashion clothing is always challenging, as well as the retrieval task to find similar items of fashion clothing. To handle the first challenge, we built a recognition model to parallelly and independently output a set of labels, each for a pre-defined single attribute notion. To handle the second challenge, we embedded the fashion clothing image into a feature vector, where different subspaces capture different notions of similarities. These two sub-tasks were then connected to be trained jointly in a multi-task learning framework. Their losses were also well aligned, combined, and optimized collectively.
MENTORING AS A MANIFISTATION OF HUMANE ATTITUDE TO PRACTICE-ORIENTED TRAINING OF FUTURE TEACHERS IN HIGHER EDUCATION

The article deals with the issue of humanization of higher education in the light of the realities of the twenty­first cen­ tury based on modern general scientific ethodological approaches: civilization, culturology, axiology, personality and activity. The author considers manifestation of humanity as a generic human quality, as well as principles of its affirmation and value orientations of its implementation in the course of teaching training of postgraduate students in higher educational establishments.
JointContrast: Skeleton-Based Mutual Action Recognition with Contrastive Learning

Skeleton-based action recognition relies on skeleton sequences to detect certain categories of human actions. In skeleton-based action recognition, it is observed that many scenes are mutual actions characterized by more than one subject, and the existing works deal with subjects independently or use the pooling layer for feature fusion leading to ineffective learning and fusion of different subjects. In this paper, we propose a novel framework, JointContrast, for Skeleton-based action recognition to deal with these challenges. Our JointContrast includes two innovative components. One is the pre-training process with a fine-grained contrastive loss that effectively enhances the representation ability of the model, and the other is an Interactive Graph (IG) representation for skeletal sequences that contributes to the fusion of features between subjects. We validate our JointContrast in the popular SBU and NTU RGB-D datasets, and experimental results show that our model outperforms other baseline methods in terms of recognition accuracy.
Joint Discriminative and Metric Embedding Learning for Person Re-identification

Person re-identification is a challenging task because of the high intra-class variance induced by the unrestricted nuisance factors of variations such as pose, illumination, viewpoint, background, and sensor noise. Recent approaches postulate that powerful architectures have the capacity to learn feature representations invariant to nuisance factors, by training them with losses that minimize intra-class variance and maximize inter-class separation, without modeling nuisance factors explicitly. The dominant approaches use either a discriminative loss with margin, like the softmax loss with the additive angular margin, or a metric learning loss, like the triplet loss with batch hard mining of triplets. Since the softmax imposes feature normalization, it limits the gradient flow supervising the feature embedding. We address this by joining the losses and leveraging the triplet loss as a proxy for the missing gradients. We further improve invariance to nuisance factors by adding the discriminative task of predicting attributes. Our extensive evaluation highlights that when only a holistic representation is learned, we consistently outperform the state-of-the-art on the three most challenging datasets. Such representations are easier to deploy in practical systems. Finally, we found that joining the losses removes the requirement for having a margin in the softmax loss while increasing performance.