Reviewerly/exHarmony huggingface dataset:
Deep Neural Networks for Time Series Classification in Human Activity Recognition

The field of Human Activity Recognition has been one of the most highly sought domains among professionals from academia and industry alike due to the rise in usage of mobile technologies and wearable motion-sensors in daily life. Due to its application in different fields such as healthcare, biometrics, and human-machine interaction, a plethora of research works proposing different neural networks have been conducted in the past with a majority of them focusing towards mobile sensor-based recognition due to significant advantages it holds over fixed sensor-based recognition. In this research, four neural network architectures - Bidirectional Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), ConvLSTM, and CNN-LSTM - were trained and tested on the WISDM Dataset independent of the subject for classifying different classes of human activities. The CNN-LSTM architecture was observed to have the highest efficiency among different models used in the research for classifying different types of hand-oriented and non-hand-oriented activities. It was also found that all the aforementioned models performed better in data from the accelerometer than the same from the gyroscope. Moreover, the difference in the efficiencies of the models in the two sensors was much more significant in watches than in the phones. Furthermore, in general, patterns in sensor data from watches were found to be more distinct from one another and thus could be captured more efficiently compared to the data from phones' sensors.
A Deep Reinforcement Learning Approach for Point Cloud Video Transmissions

The point cloud videos, thanks to the multi-view and immersive experiences, have recently attracted notable attentions from both academia and industry. Due to the high data volume, a point cloud video also raises the challenge of quality-of-experience (QoE), which is in terms of the balance between playback quality and buffering delay during the transmission under time-varying system conditions. In this paper, we propose a deep reinforcement learning (DRL) approach to optimize the expected long-term QoE for the client. Over the time horizon, the proposed approach learns to select the tiles of the corresponding video for transmissions in an iterative way. Under various settings, numerical experiments based on real throughput data traces are conducted to evaluate the proposed approach. Compared to the baselines, our approach not only enhances the video quality but also reduces the re-buffering time, obtaining an improvement of average QoE for the client by 9%&#x2013;14%.
Improved Face Detector on Fisheye Images via Spherical-Domain Attention

As one type of omnidirectional projection, fisheye images have been widely used in automatic driving and visual surveillance. However, they cannot be processed well by the traditional algorithms designed for the planar rectilinear images since they usually suffer from severe geometric distortion during image formation. In this paper, the conventional face detection algorithm is enhanced to fit the fisheye images via combining with the spherical convolution block by learning rotation-invariant features from the spherical domain. The learned features from both planar and spherical domains are subsequently mixed by the spatial attention mechanism. Consequently, the whole network can automatically learn the distorted features directly from different positions on the target image. Experimental results verify that our network can detect distorted faces on fisheye images effectively and maintain the performance on traditional planar images.
Retina: An open-source tool for flexible analysis of RTC traffic

Retina is an open-source command-line tool that produces rich and complex statistics from real-time communication (RTC) traffic. Starting from raw packet captures, it creates summaries of observed streams with flexible statistics and tracks the evolution of the stream over time. Retina is modular and highly configurable, providing the ability to configure output statistics, temporal resolution as well as many other parameters. Furthermore, if the packet captures are accompanied by application logs, it can reconcile the data and enrich its output with application and QoE-related statistics. Retina helps troubleshoot RTC applications and enables the use of Machine Learning models for traffic classification and Quality of Experience assessment. We believe Retina can be extremely useful for researchers studying RTC traffic and network professionals interested in effective traffic analysis.
Comparative Study of CNN and YOLOv3 in Public Health Face Mask Detection

Coronavirus Disease (COVID-19) is gaining special concern from entire world population. The transmission of the COVID-19 virus is spreading almost in whole the world, including Indonesia which undergoing a crisis, especially in the health and economic sector. In prevention, the government is implementing Large-Scale Social Restrictions which public services or public places require people to wear masks. During this time, the detection of masks is done manually with observations from security personnel, which is time consuming. This study will apply a mask detection system (Face Mask Detection) using deep learning image processing. This study apply the most popular deep learning model which consist Convolutional Neural Networks (CNN) and You Only Look Once (YOLOv3) method. In training step, the datasets taken vary with images of faces that using head attribute such as hijabs, hats, and not using attributes. In addition, the images were taken from various countries such as Asia including Indonesia mostly, Europe, and the Americas. The system used a combination of object detection classification, image, and object tracking to develop a system that detects using a mask or not using a mask faces in images or camera videos. From the comparative analysis which developed in training and deploying step with image and camera video stream, YOLOv3 can detect accurately and faster with 4.8 FPS than CNN.
Dataset Anonymization with Purpose: A Resource Allocation Use Case

Nowadays, companies are collecting huge amounts of data. Applying the collected data to optimize the business activities can significantly improve profit margins. In this context, companies often want to enhance their models by enriching the data with data from external sources. Increasingly, companies are also considering selling data as an additional source of income. Governments are also willing to share citizen data with businesses. The GDPR regulation, introduced in May 2018 provides a framework for different parties (commercial, governmental, academic) to share and sell data provided that the data is anonymized. The effect of this anonymization step on the quality of the data (and the resulting business optimization conclusions) are still unclear. Utility and quality metrics that exist are purely theoretical, and do not grasp the purpose of the anonymized data, resulting in discrepancies between the expected and the actual utility of an anonymized dataset. This work studies the practical utility of anonymized datasets. It assesses the effect of applying the K-anonymity metric and dataset sampling on the utility of the data by conducting experiments on a resource allocation use case. Practical guidelines are presented for anonymizing datasets while maintaining a high degree of practical utility.
Intelligent Data Management System and Performance Joint Blockchain Model for Commercial Bank Management Accounting

The management accounting system of commercial banks is an effective tool for commercial banks to improve their management level and enhance their core competitiveness. Building an intelligent data management system and performance management system for commercial bank management accounting based on blockchain technology has become a major topic of commercial bank information technology innovation. This paper takes software engineering as the main line and focuses on the commercial bank management accounting system based on the intelligent data management and performance combined blockchain model.
Model-Validation and Implementation of a Path-Following Algorithm in an Autonomous Underwater Vehicle

This article studies the design, modeling, and implementation of a path-following algorithm as a guidance, navigation, and control (GNC) architecture for an autonomous underwater vehicle (AUV). First, a mathematical model is developed based on nonlinear equations of motion and parameter estimation techniques, including the model validation based on field test data. Then, the guidance system incorporates a line-of-sight (LOS) algorithm with a combination of position PID controllers. The GNC architecture includes a modular and multi-layer approach with an LOS-based, path-following algorithm in the AUV platform. Furthermore, the navigation used in the path-following algorithm is developed based on a predefined coverage area. Finally, this study addresses simulation and field test control scenarios to verify the developed GNC architecture.
A Healthcare Data Management System on Blockchain Framework

Blockchain is a distributed ledger technology that is commonly employed in the development of new digital currencies. Many new successes in industry and academia have resulted from the rapid development of blockchain technology. This architecture is currently widely employed in e-finance as a generally acknowledged electronic money. It has crucial qualities like immutability, decentralization, and transparency that could help with critical healthcare challenges also. These issues may include incomplete records at the point of care, restricted access to patients’ own health information, etc. Multiple copies of a single data are stored in various locations and on several devices of a blockchain architecture. In spite of lose or damage of one copy of data, multiple copies remain safe and secure. The connection between the blocks prevents any of the information from being modified or a block from being put between two existing blocks. In this paper, a healthcare data management system based on blockchain technology has been proposed. The proposed model is a multilayer architecture where different entities related to healthcare system will be different components. The entities are patients, doctors, clinic or hospitals, medical records etc. Effective medical data sharing is one of the important aspect of this research work.
Lightweight Online Semi-Supervised Learning Algorithm for Ultrasonic Gesture Recognition

This paper presents a lightweight online semi-supervised learning algorithm that utilizes the sequentially arrived unlabeled user gesture samples to improve recognition performance for ultrasonic gesture recognition systems. For each class of gesture, a binary extreme random forest is pre-trained in the offline manner, by which the newly arrived unlabeled samples are given pseudo-labels before they are filtered according to probability and then fed to an initially unexpanded forest to grow its trees incrementally. To limit complexity, feature cache and index pool are introduced. Experiments show that the incrementally grown forests identify 8 gestures and 4 micro gestures with average accuracies of 95.8% and 93.6%, respectively exceeding its nearest offline supervised competitor by 2.1% and 4.1%.
Efficient feature subset selection and classification using levy flight‐based cuckoo search optimization with parallel support vector machine for the breast cancer data

Abstract The complexity of breast cancer classification is a drastic concern for a long time. Therefore, different algorithms for data mining have been developed and used to effectively classify the dataset of breast cancer. But, due to inefficient subset choice of this operation, these algorithms have problems in accuracy of classification. In this article, levy flight–based cuckoo search optimization (LFCSO) with parallel support vector machine (PSVM) is proposed for enhancing the classification accuracy of breast cancer. The proposed LFCSO‐PSVM technique includes three key steps: (i) preprocessing, (ii) feature selecting subset, and (iii) classification. Preprocessing is carried out using k‐means clustering that is required to eliminate the values. To acquire maximum detailed feature, these attributes are transmitted to the process the feature selecting subset, which is executed by LFCSO algorithm, and the aspiration function is used to determine the essential feature on the basis of proper fitness values. Then, the PSVM algorithm is implemented to classify the model for training and testing. This is used to increase the accuracy of the classification for the breast cancer dataset. The experimental outcomes demonstrate that the proposed LFCSO‐PSVM algorithm provides higher efficiency of classification than the existing algorithms in terms of precision, recall, f‐measurement, and reliability.
Correcting Arabic Soft Spelling Mistakes Using Transformers

Spelling mistakes are a common issue in the Arabic language; several techniques have been used to solve this issue such as confusion matrices, language models, and neural networks. In recent years, a neural network called the Transformer has been introduced as a machine translation model. Since then, the transformer and its variants has become a very popular solution for most of the Natural Language Processing (NLP) tasks. In this paper, we aim to use the transformer to correct four types of Arabic soft spelling mistakes, namely, confusion among various shapes of hamza, shapes of alef at the end of the word, insertion and omission of alef after waw aljamaea, and confusion among teh, teh marbuta, and heh at the end of the word. We used artificial errors for training and evaluation; these errors were generated using an approach called stochastic error injection. The best model we trained was able to correct 97.37% of the artificial errors that were injected into the test set and achieved a character error rate (CER) of 0.86% on a set that contains real soft spelling mistakes.
Debris Object Detection Caused by Vehicle Accidents Using UAV and Deep Learning Techniques

The road debris clean-up process can be improved by utilizing drones, Deep Learning, and object detection to optimize the operation and re-open roads for traffic. Common debris is unsecured items that fly out from vehicles after a vehicle accident. The cleaning procedure of the road debris after an accident is cumbersome and sensitive. It demands much workforce and a time-consuming process to haul debris properly. The paper aims to detect debris on the road using a drone to minimize the time cleaning procedure. Object detection API with the pre-trained model of SSD and Faster R-CNN is used for object detection. The accuracy graphs, evaluation matrix, and detection box score determine the efficient model for debris detection.
BEV-Net: A Bird’s Eye View Object Detection Network for LiDAR Point Cloud

LiDAR-only object detection is essential for autonomous driving systems and is a challenging problem. For the representation of a bird’s eye view LiDAR point-cloud, this paper proposes a single-stage object detector. The detector can output classification information and accurate positioning information for multi-category objects. In this paper, the detector’s design methods are detailed from a bird’s eye view LiDAR point-cloud encoding, network design, data augmentation, etc. The detector was evaluated on three challenging datasets: KITTI, nuScenes and Waymo. The experimental results demonstrated that the proposed detector can accurately achieve object detection tasks and the detection speed can reach 26.9 FPS. Both the precision and the speed can meet the requirements of most autonomous driving scenarios.
Improvement of Practical Byzantine Fault Tolerant Consensus Algorithm for Blockchain

The PBFT (Practical Byzantine Fault Tolerant) is a consensus algorithm widely used in alliance blockchain, but there are some problems of high communication overhead and high latency. Therefore, the original PBFT consensus algorithm is improved in the paper. Firstly, the voting mechanism is introduced for the original PBFT algorithm. The nodes are divided into two classes: consensus node and ordinary node. The primary node is selected from the consensus nodes to lead the consensus process to ensure the reliability of the consensus results. Secondly, the consensus process of PBFT algorithm was optimized. The consensus process is simplified into three phases, which reduces the algorithm complexity and improves the consensus efficiency. The experiment shows that our improved PBFT algorithm has better performance than the original PBFT algorithm in communication overhead and consensus delay.
FastTune: Timely and Precise Congestion Control in Data Center Network

Modern data center networks (DCNs) exhibit high dynamics in both time and space dimensions, which poses challenges for congestion control protocols to achieve low latency, fast convergence, and high throughput. Existing methods have leveraged fine-grained link load information to achieve precise congestion control, but it still suffers from untimely control in highly dynamic DCNs. In this paper, we propose a timely and precise congestion control method called FastTune. FastTune employs fine-grained network status to achieve accurate feedback, uses switch feedback to control the first RTT, and leverages ACK-padding to shorten the feedback path and regulate congestion in time. FastTune develops a multiplicative increase/decrease (MI/MD) algorithm to achieve fast convergence based on timely and precise feedback. Large-scale evaluations show that, compared with state-of-the-art work, FastTune significantly reduces the feedback delay by up to 87%, reduces the average flow completion time by 40%, and the 99th percentile flow completion time by 51%. Besides, FastTune maintains near-zero queueing and reasonable throughput.
An IT platform for the management of a Power Cloud community leveraging IoT, data ingestion, data analytics and blockchain notarization

This work aims to describe an IT platform for the management of an energy community, where prosumagers (users capable of consuming, producing and storing electric energy) cooperate to maximize the self-consumption within the community, to increase efficiency of energy use and reduce energy costs.
Blockchain-based Smart Decentralized Energy Trading for Grids with Renewable Energy Systems

The rise of renewable energy sources and smart grids and the almost simultaneous appearance of blockchain technology has, through their synergy, opened a path to a fundamental shift in the very nature of energy systems as we know them. Traditionally, production, distribution, and trading of electrical energy has been a centralized process based on a limited number of regional, vertically integrated monopolies. Today, the share of energy produced using renewable energy systems by former consumers, now termed ‘prosumers,’ is constantly increasing, leading to a decentralized environment in which the line between producers and consumers is getting ever blurrier. In this paper, we first discuss the current state-of-the-art in applications of distributed ledgers and blockchain in the energy sector. For addressing novel challenges which appear in such a decentralized setting, we propose a system based on a lean distributed ledger with aggregator nodes and intelligent-agent trading founded on the notion of a shared supraordinal grid and a common carrier/producer that serves as the medium of exchange and supplier of last resort. We also show how such a pragmatic solution may be extended to allow for a futuristic fully decentralized grid while still respecting the here-and-now.
Usability Evaluation of iPekalonganKota with Different End-user Groups Using Usability Testing and USE Questionnaire Methods

iPekalonganKota is a social media-based digital library mobile app from the Archives and Library Office of Pekalongan City, which more than 1000 users have downloaded. This application can be used by all circles of society, both pupils, college students, workers, and housewives. However, the number of active users of the application itself is still not in line with the target and expectations. The average user log is only one to two users who perform activities in one week. There were many shortcomings both in terms of appearance and ease of application flow. Moreover, with a lot of competition with other digital library applications, it can threaten the iPekalonganKota application to be abandoned by users. So, the usability evaluation for the iPekalonganKota application must be carried out to find out the values of the usability aspect from the perspectives of various user groups. Application evaluation was conducted using usability testing methods and USE questionnaires to measure effectiveness, efficiency, usefulness, ease of use, ease of learning, and satisfaction. The results showed that the average value of each aspect was 67.30%, with some input from the respondents. Improvements must be made using a high-fidelity prototype and retesting the prototype so that the results of each aspect have increased. The average value on the second test is 94.40%. The results of the recommendations can be used as a reference for sustainable application development.
Blockchain-Based Double Auction for Edge Cloud Resource Trading with Differential Privacy

present, edge cloud is becoming more and more important as it provides computing resources required by mobile terminals. The online edge cloud resource trading mechanism has become one of the most important ways for edge cloud resource sharing, attracting much attention. In this paper, we have proposed a Blockchain-based Privacy-preserving Combinatorial Double Auction (BPCDA) mechanism, which combines trustworthiness, truthfulness and privacy protection for the first time. Firstly, BPCDA avoids dependence on third-party brokers by adopting blockchain. Secondly, we draw on the economic theories, and then adopt double auction to ensure the truthfulness of bids while participants in the auction are composed of multiple sellers and multiple buyers. Lastly, we make fell use of the differential privacy mechanism on the blockchain to avoid privacy leakage. Through simulated experiments, we verify the superior performance and practicality of our proposed edge cloud resource trading mechanism.
Enterprise Performance Management following Big Data Analysis Technology under Multisource Information Fusion

With the development of the Internet, big data collection, analysis, and processing are flourishing. The study aims to explore the performance management of power enterprises based on multisource information fusion and big data. First, the application of big data to enterprise management is analyzed. Second, the multisource information fusion method is introduced, and the multisource information fusion model is implemented. Finally, the fuzzy language algorithm is used to evaluate the performance management of power enterprises. The results show that the proposed multisource information fusion algorithm has high efficiency in evaluating enterprise performance management. The evaluation result is closer to the actual value than other algorithms, and the maximum acceleration ratio can reach 7, indicating that the algorithm is suitable for processing big data. The performance evaluation shows that enterprises pay most attention to the quality of their products; the weight reached 0.414; and the index weight difference is large. This study promotes the reform of the performance management mode and improves the management efficiency of enterprises through the proposed enterprise performance management strategy. It provides a great reference for the application of big data and information fusion technology.