Reviewerly/exHarmony huggingface dataset:
Self‐supervised multimodal fusion transformer for passive activity recognition

The pervasiveness of Wi-Fi signals provides significant opportunities for human sensing and activity recognition in fields such as healthcare. The sensors most commonly used for passive Wi-Fi sensing are based on passive Wi-Fi radar (PWR) and channel state information (CSI) data, however current systems do not effectively exploit the information acquired through multiple sensors to recognise the different activities. In this study, new properties of the Transformer architecture for multimodal sensor fusion are explored. Different signal processing techniques are used to extract multiple image-based features from PWR and CSI data such as spectrograms, scalograms and Markov transition field (MTF). The Fusion Transformer, an attention-based model for multimodal and multi-sensor fusion is first proposed. Experimental results show that the Fusion Transformer approach can achieve competitive results compared to a ResNet architecture but with much fewer resources. To further improve the model, a simple and effective framework for multimodal and multi-sensor self-supervised learning (SSL) is proposed. The self-supervised Fusion Transformer outperforms the baselines, achieving a macro F1-score of 95.9%. Finally, this study shows how this approach significantly outperforms the others when trained with as little as 1% (2 min) of labelled training data to 20% (40 min) of labelled training data.
Real-time global action planning for unmanned ground vehicle exploration in Three-dimensional spaces

Autonomous exploration is a technical challenge in the field of mobile robots. In this paper, the problem of autonomous unmanned ground vehicle (UGV) exploration is formulated as a continuous action planning process and a novel method to achieve real-time action planning in 3D spaces is proposed. First, a traversability map and reduced approximated generalized Voronoi graph (RAGVG) are introduced as grid and topological map models, respectively. Then, an efficient and robust algorithm is presented for constructing RAGVGs, and a three-stage approach for fast path search is proposed. Furthermore, a fuzzy decision-making approach is employed to evaluate candidates and create action plans. The proposed method is tested in synthetic and real-world scenarios. The results of static experiments suggest that the RAGVG can achieve almost full coverage with significantly less redundancy (50 % less vertexes and 23 % less edges); moreover, collision-free action plans can be created in real time (less than 330 ms). Furthermore, the results of dynamic experiments indicate that the proposed method achieves effective, efficient, and stable performance with two exploration tasks in 3D spaces with static and moving obstacles, and it saves at least 10 % of the time required for full coverage exploration. The code related to this project is open-source and available at https://gitee.com/xinkaized/my_exploration.
A fractional coupled system for simultaneous image denoising and deblurring

Simultaneous image denoising and deblurring is a challenging issue because noise and edges are both high-frequency signals, and eliminating noise while enhancing edges counteracts each other. In this paper, we deal with this difficulty by designing a novel coupled time-fractional diffusion along with a fractional shock filter and developing a powerful discretization approach. The discretization process is based on the finite difference and L1 approximations for integer and fractional order derivatives, respectively. This scheme not only removes noise and sharpens edges efficiently, but also weakens the staircase effect. The stability analysis of the proposed scheme is also investigated. The experimental results show that the proposed approach outperforms other related approaches and can be applied to large-scale and chest X-Ray images.
E-Learning Online Platforms for Educational Approach

The research describes the e-learning online platforms such as MS Teams, BigBlueButton, Zoom and Rostov State Transport Online University which have the same characteristics of the content and functions for conducting the scientific conferences, designing the project, working in a team and consequently, providing educational process. The purpose of this research paper is to analyze different e-learning platforms, to compare their functions, to underline pros and cons of distant learning and to survey the students` online platform preferences. At the development of the distance technologies and appearance of the online platforms, the aim for teaching staff is to organize the educational process using e-learning online platforms as the additional tool for different purposes. Moreover, there are many questions related to methods of measuring online learning effectiveness, as it provides an opportunity for continuous learning and improvement of the professional activities. Convincingly, the education system is changing and developing according to the human needs. The application of the most suitable MS Teams, BigBlueButton, Zoom and Rostov State Transport Online University platforms for implementing the educational process and mastering the necessary competencies is considered in this paper. The benefits and drawbacks of using them in the educational process are described.
A self-supervised algorithm to detect signs of social isolation in the elderly from daily activity sequences

Considering the increasing aging of the population, multi-device monitoring of the activities of daily living (ADL) of older people becomes crucial to support independent living and early detection of symptoms of mental illnesses, such as depression and Alzheimer's disease. Anomalies can anticipate the diagnosis of these pathologies in the patient's normal behavior, such as reduced hygiene, changes in sleep habits, and fewer social interactions. These abnormalities are often subtle and hard to detect. Especially using non-intrusive monitoring devices might cause anomaly detectors to generate false alarms or ignore relevant clues. This limitation may hinder their usage by caregivers. Furthermore, the notion of abnormality here is context and patient-dependent, thus requiring untrained approaches. To reduce these problems, we propose a self-supervised model for multi-sensor time series signals based on Hyperbolic uncertainty for Anomaly Detection, which we dub HypAD. HypAD estimates uncertainty end-to-end, thanks to hyperbolic neural networks, and integrates it into the "classic" notion of reconstruction loss in anomaly detection. Based on hyperbolic uncertainty, HypAD introduces the principle of a detectable anomaly. HypAD assesses whether it is sure about the input signal and fails to reconstruct it because it is anomalous or whether the high reconstruction loss is due to the model uncertainty, e.g., a complex but regular signal (cf. this parallels the residual model error upon training). The proposed solution has been incorporated into an end-to-end ADL monitoring system for elderly patients in retirement homes, developed within a funded project leveraging an interdisciplinary consortium of computer scientists, engineers, and geriatricians. Healthcare professionals were involved in the design and verification process to foster trust in the system. In addition, the system has been equipped with explainability features.
The proposal of a modeling methodology for an industrial internet information model

With the large distributed, autonomous, diverse, and dynamic information sources generated in the Industrial Internet area, the information model becomes the critical technology for heterogeneous data interoperability. By establishing unified architecture, mutually agreed communication protocols and standardizing syntax and semantics, the potential of complex data can be released. However, most of the existing information models are isolated in the professional fields, and the interoperability and scope of standards are very limited. In this article, we design a uniform information model for the Industrial Internet, and present a general modeling method which aims to build a standardized organizational framework of information. Specifically, the Industrial Internet information model is first defined, where the seven key elements and value evaluation are devised for information extraction. Then, an optimization approach combining entropy and semantic distance theories is proposed that determines the information organization granularity. Next, as the cross-layer interaction of complex information is very tricky in a tree structure and its modeling cost is extremely high in a mesh topology, the underground root structure is invented for model representation. Finally, the modeling methodology is applied to the ordinary and precision machine tools demonstrating 18.75% and 18.18% modeling cost reduction, respectively, and these two information models are further implemented in a digital machining workshop to verify the effectiveness of the proposed modeling method.
1D Barcode Detection: Novel Benchmark Datasets and Comprehensive Comparison of Deep Convolutional Neural Network Approaches

Recent advancement in Deep Learning-based Convolutional Neural Networks (D-CNNs) has led research to improve the efficiency and performance of barcode recognition in Supply Chain Management (SCM). D-CNNs required real-world images embedded with ground truth data, which is often not readily available in the case of SCM barcode recognition. This study introduces two invented barcode datasets: InventBar and ParcelBar. The datasets contain labeled barcode images with 527 consumer goods and 844 post boxes in the indoor environment. To explore the influential capability of the datasets that affect recognition process, five existing D-CNN algorithms were applied and compared over a set of recently available barcode datasets. To confirm the model's performance and accuracy, runtime and Mean Average Precision (mAP) were examined based on different IoU thresholds and image transformation settings. The results show that YOLO v5 works best for the ParcelBar in terms of speed and accuracy. The situation is different for the InventBar since Faster R-CNN could allow the model to learn faster with a small drop in accuracy. It is proven that the proposed datasets can be practically utilized for the mainstream D-CNN frameworks. Both are available for developing barcode recognition models and positively affect comparative studies.
When BERT Started Traveling: TourBERT—A Natural Language Processing Model for the Travel Industry

In recent years, Natural Language Processing (NLP) has become increasingly important for extracting new insights from unstructured text data, and pre-trained language models now have the ability to perform state-of-the-art tasks like topic modeling, text classification, or sentiment analysis. Currently, BERT is the most widespread and widely used model, but it has been shown that a potential to optimize BERT can be applied to domain-specific contexts. While a number of BERT models that improve downstream tasks’ performance for other domains already exist, an optimized BERT model for tourism has yet to be revealed. This study thus aimed to develop and evaluate TourBERT, a pre-trained BERT model for the tourism industry. It was trained from scratch and outperforms BERT-Base in all tourism-specific evaluations. Therefore, this study makes an essential contribution to the growing importance of NLP in tourism by providing an open-source BERT model adapted to tourism requirements and particularities.
An IoT-based smart healthcare system using location-based mesh network and big data analytics

Elderly people requiring care the entire day usually depend on the availability of their family members to give assistance. However, the family members might not provide appropriate help especially in an emergent situation. The application of Internet of Things (IoT) technology with a variety of interconnected devices provides the solution. We propose an IoT-based smart healthcare system comprising wearable devices, which integrates a variety of contact sensors with location-based mesh networks (LBMN) such as Wi-Fi and Bluetooth Low Energy (BLE) connections to continuously sense various parameters of aging people. The BLE-connected devices such as wearable sensors, fixed sensors, seat cushions, pedal mats, magnetic reed switches, and mobile devices are all involved in collecting, processing, and transmitting physiological data and their locations to the cloud. Through the utilization of convenient interfaces such as software applications on smartphones and web pages on computers, it provides real time monitoring of the elderly in terms of localization, activity pattern, and health status. Thus the system enables early detection of health risks to the elderly. We used Platform as a service (PaaS) to receive and store the health data generated from the interconnected devices and to perform analysis. The essential feature of this LBMN is to generate a complete 6W(Who, What,When,Where,Why and How)big data for policy, feed it to the PaaS analysis to easily and quickly obtain more accurate data, and then develop possible health strategy or preventive measures. The proposed healthcare system detected that, out of the 20 participants recruited, 2 persons (10%) were often restless. It was also able to detect abnormal daily activity patterns with more tag positioning and the historical data from the devices. More importantly, it can help to prevent potential physical and neuropsychiatric disorders based on the real-time monitoring information and analyzed historical data for the aging people.
UltraFuzz: Towards Resource-Saving in Distributed Fuzzing

Recent research has sought to improve fuzzing performance via parallel computing. However, researchers focus on improving efficiency while ignoring the increasing cost of testing resources. Parallel fuzzing in the distributed environment amplifies the resource-wasting problem caused by the random nature of fuzzing. In the parallel mode, owing to the lack of an appropriate task dispatching scheme and timely fuzzing status synchronization among different fuzzing instances, task conflicts and workload imbalance occur, making the resource-wasting problem severe. In this paper, we design UltraFuzz, a fuzzer for resource-saving in distributed fuzzing. Based on centralized dynamic scheduling, UltraFuzz can dispatch tasks and schedule power globally and reasonably to avoid resource-wasting. Besides, UltraFuzz can elastically allocate computing power for fuzzing and seed evaluation, thereby avoiding the potential bottleneck of seed evaluation that blocks the fuzzing process. UltraFuzz was evaluated using real-world programs, and the results show that with the same testing resource, UltraFuzz outperforms state-of-the-art tools, such as AFL, AFL-P, PAFL, and EnFuzz. Most importantly, the experiment reveals certain results that seem counter-intuitive, namely that parallel fuzzing can achieve ``super-linear acceleration'' when compared with single-core fuzzing. We conduct additional experiments to reveal the deep reasons behind this phenomenon and dig deep into the inherent advantages of parallel fuzzing over serial fuzzing, including the global optimization of seed energy scheduling and the escape of local optimal seed. Additionally, 24 real-world vulnerabilities were discovered using UltraFuzz.
Power Grid Data Monitoring and Analysis System based on Edge Computing

With the continuous accumulation of large-scale power grid data, the traditional centralized data analysis method is more and more expensive for data transmission. Based on this, we designed a grid big data monitoring and analysis system and transferred the computation process to the edge node close to the data source through an edge computing strategy. On the one hand, data processing and data analysis algorithms are encapsulated by container technology, and the algorithm is mirrored to the edge nodes of the power network through the system to complete the computation. On the other hand, the computing clusters are deployed at the edge nodes of the power network, which is responsible for the scheduling, execution, and status monitoring of computing tasks. Computing tasks can be flexibly managed in a cluster by extending user-defined resources. Through the reserved parameters, users can intervene in task execution policies, and tasks can be configured. The edge node sends the calculation result or early warning information to the central monitoring service through the asynchronous message. Compared with the traditional centralized data analysis system, the proposed method relieves the problem of the overhead of massive data transmission in the network, reduces the application cost, helps to apply the data analysis to more edge side nodes, and fully excavates the potential value of grid data.
A Survey on Machine Learning-based Automated Software Bug Report Classification

In software development processes, classifying software bugs is a vital step since it helps grasp the nature, implications, and causes of software failures. Further, categorization enables reacting to software bugs appropriately and faster. However, manual classification of software bugs is inefficient and costly, especially in large-scale software projects, since one must deal with extensive bug reports from multiple sources. Hence, many studies have addressed this problem by automated software bug classification with the help of machine learning techniques. Researchers used various machine learning-based algorithms and techniques to obtain better classification performance. Furthermore, many researchers used open source bug repositories to compare their results with previous studies. In this paper, we aimed to report the main studies in machine learning-based automated software bug report classification by highlighting the recent improvements and indicating the key steps in this process. So, this survey can benefit the researchers and practitioners working in automated software bug report classification and other related domains.
Accelerometer Based Fall Detection and Location Tracking System Of Elderly

Falls are a major sign of critical states in the elderly who live independently and can cause serious further injuries. In this report, an accelerometer-based elderly fall detection system is proposed with the main objective of providing timely medical assistance to an elderly person in the event of a fall. The system monitors human body movements by detecting falls during normal daily activities. It uses an efficient algorithm to analyze the input data from an accelerometer and automatically sends the coordinates of the fall location to the caregivers. The algorithm detects falls by tracking accelerations from an accelerometer. To improve performance and accuracy, post-fall posture is also recognized based on the inclination of the body relative to the earth's surface. The results of the stimulation experiments show that the combination of threshold changes in angle and acceleration can be an effective fall detection solution.
Face Masked and Unmasked Humans Detection and Tracking in Video Surveillance

Due to the spread of COVID-19, people wearing face masks became a regular occurrence worldwide. Moreover, there are nations where covering one's face is done for religious or cultural reasons, or even wear face masks for convenience. However, current face detection and tracking systems are hindered by face masks as the full facial features are no longer visible and therefore became less effective. In this paper, it is proposed to improve current face detection and long-term tracking technology by extracting the facial features of the top regions of the face, taking into account the eye, eyebrow, and forehead. The methodology contains two models, the face detector and the long-term object tracker. The face detection model uses a joint dataset from ISL-UFMD and MaskedFace-Net. The dataset is used to train a Keras sequential model. The object detection model uses pre-trained YOLOv4 weights and DeepSORT to identify people and uses the tracking-by-detection method to perform long-term tracking throughout the surveillance video. The final face detection model results show a testing accuracy of 93.33% and a loss of 26.92%, which are up to par and comparable with other state-of-the-art models.
A Systematic Comparison Between Flutter and React Native from Automation Testing Perspective

In recent years, mobile applications have attempted to significantly improve our lives. Android and iOS are the two most popular mobile platforms, yet they have varied settings (OS versions, screens, manufacturers, etc.) as a result creating a mobile application might be difficult. In recent years, several cross-platform frameworks, such as Flutter and React Native, have shown to be pioneers in this field. For developers, however, choosing cross-platform can be an intimidating task. In this research, we examine the automated testing capabilities of the Flutter and React Native frameworks in terms of automation testing. We concentrate on automation testing reusability, integration, and compatibility. We created a To-Do List mobile app in Flutter and React-Native by using Testproject.io as an automation testing platform. As a result of this research, the experiment demonstrates that React Native outperforms Flutter in terms of reusability and compatibility, with no significant difference in terms of integration,
Comparative analysis between the traditional K-Nearest Neighbor and Modifications with Weight-Calculation

This paper describes our experimental study and comparison of the standard k-nearest neighbor (KNN) method with modifications of the standard KNN, based on the calculated weights for every neighbor with some well-known and researched methods for weight-calculation, such as Weighted K- Nearest Neighbor, Distance- Weighted K-Nearest Neighbor, Dual Weighted K-Nearest Neighbor, etc. Experiments were conducted with 18 different datasets, in which the results were compared and shown graphically for further analyzation. It can be seen that the nearest neighbor method, KNN, without additional calculation of neighbor weights, performs the worst in the majority of the experiments. Depending on the used dataset and the classification problem, the most accurate method varies. A drop in the accuracy rate is also observed as the number of neighbors increases.
Lip Segmentation using Haar Cascade: A Smart Cosmetic Selector

The pandemic has brought touchless technology in every walk of our lives. Our proposed project, Smart Cosmetic Selector (SCS), brings this touch less technology to people who want to try different lipstick shades without the actual application. This method is implemented by capturing the image of person by a high-resolution camera in real time. The open computer vision and Haar cascade files are used to analyze facial characteristics and change the color of the lips according to the chosen lipstick shade. The proposed method is compared with several existing methods like the MTCNN (Multi-Task Cascaded Convolutional Neural Networks), tiny face tensor flow, face recognition models. It is faster than these by 60%, 90% and 25% respectively. The proposed method takes less time to process grayscale images than color images.