Reviewerly/exHarmony huggingface dataset:
A New Approach for Evaluating the Topological Accuracy of Circuit Boards Made by 3D Printing

Present-day printed circuit boards (PCB) are quite complex products with a very laborious processes of development and production, while the requirements for them continue to harden. With a view to ensuring them, there is an active development and implementation of progressive production processes, including 3D printing . Due to the fact that a significant deviation in the dimensions and / or positions of the elements of the PCB conductive pattern can lead to a loss of functional properties of the product, one of the important parameters of technological processes is the provided topological accuracy (TA), which is affected by the manufacturing technology. On the basis thereof, to ensure the TA of PCB, it is necessary to conduct a set of studies, including an analysis of existing approaches to its assessment, with regard to the possibility of their application for 3D printing. There is a proposal of the approach, which is based on the assumption that Yu. B. Tsvetkov together with the hypothesis supposition that each single-type component of the alignment error (i.e., angular rotation, scale distortions (stretch-compression) and shape distortions (trapezoid, shift) could be represented as a repositioning and as a shape of a given geometric figure in two-dimensional space. The proposed approach could be applied not only for PCBs produced by 3D printing methods, but also for the boards, which are manufactured using standard technologies.
High-Precision Computing based on the CUDA Architecture in Residual Number Systems

Residual Number Systems (RNS) are recognized for their parallel arithmetic capabilities. In recent years, they have been employed in various demanding applications, including digital signal processing, high-precision computing, cryptography, and large neural networks. With the advancement of multiprocessor systems on general-purpose platforms, the utilization of RNS for the effective parallelization of arithmetic computational operations with a large dynamic range of up to several tens of bits becomes a crucial aspect. To this end, modern Graphics Processing Units (GPUs) with several thousands of computational cores are ideal platforms. This study evaluates the efficacy of using RNS for implementing multiple precision arithmetic on NVIDIA GPUs using the CUDA hardware and software parallel computing architecture. The results of the experiments demonstrate the high efficiency of RNS for the multiplication operation. A configuration of 256 modules providing a 6400-bit dynamic range results in a computation time that is 7.92 times faster compared to the implementation on the standard GMP MP variant.
Convolutional Neural Network to Detect Support and Suspend Insulators on Infrared Images

The thermographic evaluation method is widely uses in substations to detect electrical equipment and their faults. A lot of infrared images for each substation are analyzing by humans. To automate monitoring insulation, this paper proposes a convolutional neural network, which detects different supporting and suspending insulators from infrared images f. The technique may be used for online monitoring insulators in substations. The neural network model was tested on 2000 infrared images. Before testing the neural network, images were created from 900 infrared images of insulators belonging to 2 classes for training neural network using data augmentation methods. For evaluating the neural network model, were used statistical characteristics like metrics and loss functions to show minimizing and optimizing parameters of neural networks (weights) during the training. Results demonstrates that detection of support and suspended insulators reaches 98.81%. As a result, this proposed method with evaluated neural network can be used in monitoring systems to detect insulators before detecting their faults.
Accuracy of Feature Extraction Approaches in the Task of Recognition and Classification of Isolated Words in Speech

The paper presents the practical comparing of feature extraction methods in speech by the example of isolated words (commands) recognition and comparing the performance of a set of features using different classification algorithms. In this case, a consistently increasing set of keyword samples is used for the training set. Approaches based on cepstral coefficient extraction (including MFCC- and GFCC-based variations), linear prediction (LPCC), and wavelet transform (via Basilar-membrane Frequency-band) are considered. The obtained results indicate that in some cases (including recognition tasks) the use of features other than MFCC allows to increase the accuracy of recognition from 1 to 7 percent. At the same time, the statements of the authors of other approaches to the extraction of speech features about the significant superiority of their methods over MFCC are not confirmed.
Software and algorithmic support for online assessment of transformer substation technical condition 35/6(10) kV

Most equipment condition monitoring systems operate separately from each other and allow assessing the technical condition of each equipment individually. In this work, the software of online estimation of technical condition of the whole transformer substation 35/6 (10) kV has been developed. Based on the developed algorithms, statistical processing of data obtained from installed sensors at the transformer substation is carried out online. The technique of assessing the technical condition allows remote monitoring, monitoring the state of the existing equipment and predicting changes in the technical condition. The solution of the set tasks allows efficient operation of the substation equipment and comprehensive assessment of the technical condition of the equipment of the entire transformer substation.
Evaluation of Multiple Bioradar-based Fall Detection System’s Performance on a Test Sample including Recordings with Pets, moving Appliances and Household Activity

The global trend towards the population aging makes the medical problems of the elderly, in particular falls, more significant from both moral and practical points of view. Machine learning models, the use of which is currently the main approach to the fall detection, are often trained and evaluated on data that differ from real conditions. In this paper, the performance of a previously developed radar-based fall detection system was evaluated on a test sample, which includes recordings with pet activity, appliance movements and household activity. Such movements presence a great issue because they can lead to false positive alarms.
First-Order Logical Equations with Parameter and their Exhaustive Search Solutions

The article deals with logical equations with a parameter and their algorithmic solutions on the example of the first-order logical tasks of the Unified State Exam (USE/EGE) in computer science. The introduction shows the theoretical part used in this paper to solve tasks: first-order logic, mathematical programming and optimization, features of tasks discussed. Programming language solutions and their features are shown for first-order logical tasks on intervals, discrete sets, divisibility, bitwise conjunction, and multiple variables. The discussed solutions of the tasks are presented for the first time in print.
Understanding Conversational Usage Patterns between English and Hindi

Conversational agents have exploded in popularity over the past three years around the world. This paper focuses on Amazon’s conversational agent, Alexa. Usage of conversational agents reached India four years ago and got adopted by the country with the Hindi speaking skills. This paper presents two studies researching the working of this conversational agent in English and in Hindi and the behavioral differences between the two. In the first study, we conducted basic surveys and interviews to obtain and understand the mental model of users. The device we used was Amazon Echo Dot third generation. For the second study, we gathered thirty-two participants who belong to different regions of India and had them interact with the device. We found interesting revelations about the working of the conversational agent, Alexa. We conclude with suggestions for improvements in future designs or redesigns of the product.
A Novel Disturbance Device for Aerial Manipulation Experiments

In the last decade, UAVs endowed with manipulators have increased in their ability to complete complex tasks such as manipulating doors and drawers. Very recent work also includes tasks with non-constant dynamics such as pushing a cart along a surface with a change in friction coefficient or pulling an electrical plug from a socket. These tasks are hard to design and compare controllers for because their dynamics are complex and they may not behave consistently. This paper proposes a tunable and repeatable mechanism for use in experiments that compare different controller designs. The proposed mechanism, called an overcentre mechanism, can provide a nonlinear resistive force and can be easily modified for different force magnitudes. Additionally, it can be quickly re-oriented to disturb altitude or position channels for vehicles with or without manipulators. This paper experimentally compares three traditional observer designs and a baseline controller in two different operating conditions.
Developing power-assisted two-wheeled luggage-carrying robot for stair-lifting using admittance control

We propose a control method for a two-wheeled robot that can carry a load in a staircase environment without increasing the burden on humans through power-assist control using admittance control. Considering that the motion of lifting the robot burdens a human in a staircase environment, it was necessary to reduce the peak value of the human force when pulling the robot upward. We evaluated the proposed, conventional, and the case without control (no control’’) methods by simulations and experiments on actual machines. Hence, the proposed method reduces the human workload to about 75% of that of the conventional method and about 50% of that without control. The proposed method can also enable the machine climb upstairs at a velocity of 1 step/s, which is about three times faster than the conventional method.
Explaining the Bag Gain in Batch Steganography

In batch steganography, the sender distributes the secret payload among multiple images from a “bag” to decrease the chance of being caught. Recent work on this topic described an experimentally discovered phenomenon, which we call the “bag gain”: for fixed communication rate, pooled detectors experience a decrease in statistical detectability for initially increasing bag sizes, providing an opportunity for the sender to gain in security. The bag gain phenomenon is universal in the sense of manifesting under a wide spectrum of conditions. In this paper, we explain this experimental observation by adopting a statistical model of detector response. Despite the simplicity of the model, it does capture observed trends in detectability as a function of the bag size, the rate, and cover source properties. Additionally, and surprisingly, the model predicts that in certain cover sources the sender should avoid bag sizes that are too small as this can lead to a bag loss.
Wearable sensing module for Table Tennis stroke detection

Sensor-based analytics is a science of capturing the essentials of human motion and has been extensively used in sports by attaching external sensors to players for deriving meaningful insights, thereby improving game-playing quality. This paper presents the work towards developing a wearable Body Sensor Network(BSN) for Table Tennis(TT) stroke detection through self-learned features. By capturing acceleration and rotation parameters using Inertial Measurement Unit (IMU) sensors mounted on the player forearm and shoulder while playing a TT stroke, a neural network has been trained to predict forehand and backhand shots. The model runs on Arduino Nano 33 BLE and achieves a real-time prediction accuracy of 90.7%. Also, a memory-efficient Tensorflow-based Quantization Aware Training(QAT) model, with up to 4 times reduction in model size, has been developed to demonstrate comparable prediction accuracy with size reduction.
PCCN: Progressive Context Comprehension Network for Oil Stains Detection of High-Speed Trains

Oil plays an important role in the gearbox, traction transformer, hydraulic damper, and other key components of high-speed trains. Once oil leakage occurs, it indicates that some faults may happen and even affect the normal operation of high-speed trains, thus posing a threat to the safety of the train. Therefore, we can detect the oil stains to discover leaks or find some faults in these components to eliminate potential threats in time. Oil stains have the properties of irregular shapes and huge differences in size, and the internal environment of the train is complex and composed of various parts so it is difficult to detect oil stains. This paper proposes a novel methodology to detect oil stains, named Progressive Context Comprehension Network(PCCN). The Upsampling Augmentation Module(UAM) and Feature Refinement Module(FRM) are proposed to augment features by strengthening the long short-term contextual relationship of features from multi-levels. The Pyramid Context Fusion Module(PCFM) is proposed to fuse features by progressively integrating adjacent features, which makes features of each level richer. Moreover, a loss function is applied to instruct the network to learn the characteristics of oil stains in the case of extremely uneven distribution of oil stains and background. Experiment results show that our network achieves better performance such as mIoU and F1-Score with a minimal number of parameters and FLOPs than advanced semantic segmentation methods. Besides, we apply our proposed network to five public saliency datasets and achieve better performance, which demonstrates the adaptability and effectiveness of our method as well.
Efficient Prediction of Segment Kinematics and Dynamics from Motion Capture Data Using Deep Learning

In recent years, motion capture systems have emerged, allowing for a broad and ever-expanding range of practical applications in a myriad of domains, including healthcare and biomechanics, sports, and artificial reality technologies. These concise and deterministic systems, however, suffer from limitations of prohibitive costs, and the practical infeasibilities of being used en-masse for round-the-clock day-to-day applications. This paper investigates the possibility of drastically reducing the number of sensors required to satisfactorily predict, within acceptable bounds of error, a range of targeted kinematic and dynamic particulars endemic to a specific domain of work, and determining the optimal placements of these sensors on the human body. These are achieved through a judicious application of data-analysis techniques, followed by the training and testing of artificial neural networks to establish a foundational proof of concept that may serve as a basis for further research.
Prism: Real-Time Privacy Protection Against Temporal Network Traffic Analyzers

Traffic analysis is widely used in network monitoring. However, the attackers can sometimes infer sensitive information from the patterns of the encrypted network traffic, which poses a threat to network security. Most existing countermeasures are proposed to obfuscate traffic flows using adversarial examples. However, there are two challenges when adding perturbations to live network traffic. Firstly, the perturbations imposed on the feature space cannot be conveniently projected to original traffic flows in feature-space based methods. Secondly, it is laborious and impractical to apply symmetrical framework to encode/decode the adversarial traffic in traffic-space based approaches. To address the above issues, in this paper, we propose an asymmetric defending scheme, namely <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Prism</i> , to protect the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">live</i> connection privacy against attacks of temporal network traffic analyzers. Specifically, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Prism</i> first extracts standardized temporal features via Power-Law Division (PLD) algorithm, and then employs Time-stacked State Transition Model (TSTM) to obtain the fingerprint of each application. Finally, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Prism</i> defends against the analyzers with online traffic perturbation. Since the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Prism</i> is designed as a traffic-space based defender with asymmetric defending structure, the deployment is lightweight and efficient. Experimental results on two real-world datasets demonstrate the effectiveness and generalization of our adversarial perturbations. In particular, it is encouraging to see that our proposed defending scheme outperforms the advanced countermeasures, such as adversarial training and traffic filter.
Embedded Machine Learning on Accelerometer Data for Exercise Classification

This paper describes the use of embedded machine learning (ML) on raw accelerometer data to classify three lower-limb exercises. The developed model, which uses supervised ML can accurately classify the exercises. The model performed with an accuracy of 99.41 % on the test data and F1 score of 0.99. The trained model is deployed on the microcontroller board, Arduino Nano 33 BLE Sense (ARM Cortex M4F processer). The system has been developed using open-source platforms. The developed wearable device, PhysioSens has the potential to enhance limb exercise monitoring in home-based physical therapy.
RS-Aug: Improve 3D Object Detection on LiDAR With Realistic Simulator Based Data Augmentation

Light detection and ranging (LiDAR) is an essential sensor for three dimensional (3D) object detection via generating 3D point cloud of the surroundings, and it has been widely used in the various visual applications, especially autonomous driving. However, limited numbers of labeled LiDAR datasets brutally restrain the development of 3D object detector, and this situation breeds an urgent demand on data augmentation in this field. By far, most of the traditional methods reuse the labeled samples, while those unlabeled are hastily untaken. Motivated by this, we propose a <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</u> ealistic <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</u> imulator based data augmentation (RS-Aug). It aims to construct augmented real scenes to enrich the diversity of training dataset. To train 3D object detector in a supervised learning way, the first step of RS-Aug is auto-annotation. Time-continuous LiDAR frames are used to construct the dense scene, which is beneficial to annotation and the subsequent rendering augmentation. However, 3D points with incorrect semantic labels are naturally gathered during multi-view reconstruction, causing the negative effect on auto-annotation. We propose an algorithm of cluster guided <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> -nearest neighbor (c- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> NN). It emphasizes on de-nosing semantic labels of clustered points using distance and intensity constraints. Then, the next step of RS-Aug is rendering augmentation on the real scene. To enhance the rendering quality using collision and distance constraints with the less computation complexity, we propose a scheme of heuristic search (HS) based object insertion. It estimates the proper position of the inserted object from 2D bird’s eye view (BEV). Experiments demonstrate the de-noising accuracy of c- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> NN, rendering quality of HS based object insertion, and improvement of RS-Aug on object detection.
Decomposition and Reorganization of Phonetic Information for Speaker Embedding Learning

Speech content is closely related to the stability of speaker embeddings in speaker verification tasks. In this paper, we propose a novel architecture based on self-constraint learning (SCL) and reconstruction task (RT) to remove the influence of phonetic information on speaker embedding generation. First, SCL is used to reduce the divergence of frame-level features, which can avoid ambiguity between the resulting embeddings of the two utterances being compared. Second, RT is used to further remove phonetic information in frame-level layers, focusing on speaker-discriminative feature transformation. In our experiments, the speaker embedding models were trained on the VoxCeleb2 dataset and evaluated on the VoxCeleb1, Librispeech, SITW and VoxMovies datasets. Experimental results on VoxCeleb1 show that the proposed DROP-TDNN system reduced the EER by 7.5%, compared to the state-of-the-art ECAPA-TDNN system. Furthermore, the proposed DROP-TDNN system also outperformed the ECAPA-TDNN system in the experiments on SITW, Librispeech and VoxMovies under cross-dataset conditions. In the experiments on SITW, the proposed system reduced the EER by 3.4% compared to the ECAPA-TDNN system. In the experiments on Librispeech, the proposed system demonstrated the advantage of removing phonetic information under the clean speech condition, with a significant reduction of 25.5% in EER compared to the ECAPA-TDNN system. In the experiments on VoxMovies, the proposed system reduced the EER by up to 7.9% compared to the ECAPA-TDNN system under different pronunciation and background conditions.
Dap-FL: Federated Learning Flourishes by Adaptive Tuning and Secure Aggregation

Federated learning (FL), an attractive and promising distributed machine learning paradigm, has sparked extensive interest in exploiting tremendous data stored on ubiquitous mobile devices. However, conventional FL suffers severely from resource heterogeneity, as clients with weak computational and communication capabilities may be unable to complete local training using the same local training hyper-parameters. In this article, we propose Dap-FL, a deep deterministic policy gradient (DDPG)-assisted adaptive FL system, in which local learning rates and local training epochs are adaptively adjusted by all resource-heterogeneous clients through locally deployed DDPG-assisted adaptive hyper-parameter selection schemes. Particularly, the rationality of the proposed hyper-parameter selection scheme is confirmed through rigorous mathematical proof. Besides, due to the thoughtlessness of security consideration of adaptive FL systems in previous studies, we introduce the Paillier cryptosystem to aggregate local models in a secure and privacy-preserving manner. Rigorous analyses show that the proposed Dap-FL system could protect clients’ private local models against chosen-plaintext attacks and chosen-message attacks in a widely used honest-but-curious participants and active adversaries security model. More importantly, through ingenious and extensive experiments, the proposed Dap-FL achieves higher model prediction accuracy than two state-of-the-art RL-assisted FL methods, i.e., 6.03% higher than DDPG-based FL and 7.85% higher than DQN-based FL. In addition, experimental results also show that the proposed Dap-FL achieves higher global model prediction accuracy and faster convergence rates than conventional FL, and the comprehensiveness of the adjusted local training hyper-parameters is validated.