Reviewerly/exHarmony huggingface dataset:
Radar Behavior Recognition Based on Radar Word Coding and Intelligent Dual Mode Recognition

In order to solve the problem of low recognition accuracy of radar working mode caused by the reuse of the same radar phrase by multiple working modes, this paper proposes a method based on mapping coding and intelligent dual-mode recognition. First, the physical entity of radar waveform is mapped to a digital code. Thirdly, the long and short term memory (LSTM) neural network embedded with attention mechanism is used to extract the digital code sequence to realize the automatic recognition of the working mode of radar emitter. Finally, an auxiliary recognition method based on the knowledge map is proposed, which inputs the double time sequence code into the central network through the knowledge map to assist the decision, so as to improve the recognition accuracy. The simulation test shows that the recognition method proposed in this paper can effectively improve the recognition accuracy of radar working mode compared with traditional methods.
Daily Living Human Activity Recognition Using Deep Neural Networks

In the provision of social health care and supporting older individuals, the impact of action recognition is substantial. Human Activity Recognition (HAR) refers to the challenge of identifying a specific human motion or movement. Recognizing action from video data involves identifying features in spatial position and defining the temporal variations across these extracted spatial parameters. Action recognition model accepts video data modal where frames are sampled and fed as image stack to the model. Although sampled frames contribute an imperative part, they are either randomly selected or skipped sequentially. Consequently, recognizing actions in a video requires meticulously selecting keyframes that significantly ensure low computational cost. Though different intriguing methods are available to recognize activity from video data modal, only limited research addresses the issue of keyframe selection. To this extent, we proposed an approach for selecting keyframes from activity videos which assist in constructing an acceptable synopsis by ex-tracting the most informative frames. Additionally, we represent a deep learning approach combining CNN and GRU, which exploits both the learning of the spatial information and the learning of the temporal dependencies from the instances of raw data. Such representation is tested on the MSRDaily Activity3D dataset. Our proposed model outperforms some recent works of different modalities of data in terms of accuracy. The mean accuracy of the proposed model is 94.22% for the RGB video modal. Our proposed CNN-GRU model achieves 94.87% precision, 94.25% recall, and 94.31 % Fl-score.
A2FL: Availability-Aware Selection for Machine Learning on Clients with Federated Big Data

Recent advances in Big Data Analytics are primarily driven by innovations in Artificial Intelligence and Machine Learning Methods. Due to the richness of data sources at the edge and with the increasing privacy concerns, Distributed privacy-preserving machine learning (ML) methods are increasingly becoming the norm for training ML models on federated big data. In a popular approach known as Federated learning (FL), service providers leverage end-user data to train ML models to improve services such as text auto-completion, virtual keyboards, and item recommendations. FL is expected to grow in importance with the increasing focus on big data, privacy and 5G/6G technologies. However, FL faces significant challenges such as heterogeneity, communication overheads, and privacy preservation. In practice, training models via FL is time-intensive and worse its dependent on client participation who may not always be available to join the training. Our empirical analysis shows that client availability can significantly impact the model quality which motivates the design of an availability-aware selection scheme. We propose A2FL to mitigate the quality degradation caused by the under-representation of the global client population by prioritizing the least available clients. Our results show that, compared to state-of-the-art methods, A2FL can improve the client diversity during the training and hence boost the trained model quality.
XAI-Assisted Radio Resource Management: Feature Selection and SHAP Enhancement

With the advancement of radio technologies, wireless systems have become more convoluted. This complexity, accompanied by an increase in the number of connections, is translated into a need for more parameters to analyze and decisions to take at each instant. Artificial Intelligence (AI) comes into play by automating these processes, particularly with Deep Learning techniques, that often show the best accuracies. However, the high performance of these methods comes at the cost of lower understandability and interpretability for humans. To this end, eXplainable AI (XAI) serves as a technique to better understand the decision process of these algorithms. This paper proposes an XAI framework that can be used in a Reinforcement Learning (RL) scenario. We focus on the use case of Radio Resource Management (RRM) for improved network energy efficiency. The featured framework presents a pre-model block using Concrete Autoencoders for feature reduction and a post model block using self-supervised learning to estimate feature importance. It also incorporates DeepSHAP to provide more detailed explanations. The explanations provided by the pipeline prove useful to reduce model complexity without loss of accuracy and to understand the usage of the input features by the AI model.
Classification With Dictionary Learning and A Distance Barrier Promoting Incoherence

We present a new approach to the incoherent dictionary learning problem using a barrier function that promotes incoherence. This function has a context-dependent quadratic term and a distance barrier term that can be used in both local and global structures. This strategy achieves better results in terms of error representation and incoherence of the dictionary, compared with the standard problem. We demonstrate on several datasets that this function can improve the performance of dictionaries in classification problems.
Joint Transmission and Understanding of Semantics with Edge Intelligence

Edge devices in existing systems mainly rely on cloud-based services to conduct the semantic understanding, which brings in unnecessary communication overhead and slower response. This paper proposes a novel semantic communication framework for joint semantic transmission and understanding at the network edge, wherein the structured semantic information such as the user intent and slot values that can be understood by machines are extracted directly from the received features without recovering the explicit user input. To achieve this target, we propose to use the slot name as the initial information to generate slot values by a lightweight recurrent neural network. An additional attention mechanism is adopted to integrate the semantic information in the slot value generation process and a hybrid loss is proposed to train the intent prediction and the slot value generation in parallel. Experimental results show that, compared to the conventional separate bit transmission and se-mantic understanding technologies, the proposed system achieves a much better performance under low SNR channels, and greatly reduces the computing overhead at the receiver end and thus lowers the threshold for the local semantic understanding at the edge.
Multipartite Entanglement for the Quantum Internet

Multipartite entanglement plays a crucial role in the Quantum Internet design, due to its potentiality of significantly increasing the network performance. In this paper, we identify the four key network functionalities for managing multipartite entanglement among remote nodes. And we discuss each functionality by considering - as case study - a specific multipartite state, which exhibits an attractive computing feature. Specifically, the designed state allows an arbitrary entangled node to calculate - in a distributed way - the sum of a set of values arbitrarily selected by the remaining entangled nodes.
ATOM: An Efficient Topology Adaptive Algorithm for Minor Embedding in Quantum Computing

Quantum annealing (QA) has emerged as a powerful technique to solve optimization problems by taking advantages of quantum physics. In QA process, a bottleneck that may prevent QA to scale up is minor embedding step in which we embed optimization problems represented by a graph, called logical graph, to Quantum Processing Unit (QPU) topology of quantum computers, represented by another graph, call hardware graph. Existing methods for minor embedding require a significant amount of running time in a large-scale graph embedding. To overcome this problem, in this paper, we introduce a novel notion of adaptive topology which is an expandable sub graph of the hardware graph. From that, we develop a minor embedding algorithm, namely Adaptive TOpology eMbedding (ATOM). ATOM iteratively selects a node from the logical graph, and embeds it to the adaptive topology of the hardware graph. Our experimental results show that ATOM is able to provide a feasible embedding in much smaller running time than that of the state-of-the-art without compromising the quality of resulting embedding. 1
Mobile Application as a Learning Material in Ecotourism

Mobile games play significant roles in today's education. The teaching and learning process becomes more active and attractive by using mobile games. EcoMode application is created for Fostering Eco – Innovative Business Model Development in SMEs in Hospitality Industry and offers three different study models. The EcoMode application focuses on giving theoretical knowledge to beginners in ecotourism. It is based on the best practice of EU countries and consists of three different examination models. This study emphasizes trainers who are likely to use today's technology in ecotourism education. This study designed and developed an EcoMode application that allowed trainers to use mobile technology in ecotourism.
Authorship Attribution in Bahasa Indonesia Using Twitter Dataset on Political Topic

Fake news so called made-up news intended to cause misinformation is identified as Hoax. In Indonesia, hoaxes could not be ignored, fairly low literacy rate is the cause of hoaxes could spread quickly in Indonesia, to make it even worse, there are so many people would believe in hoaxes without any intention to make sure of the integrity of the news. even the media in Indonesia are competing with one another to provide content with provocative names in the hopes of increasing their rating and reaching as many people as possible. In the course of this research, a method for identifying authors on the basis of their writings will be developed to reduce the spread of hoaxes and encourage people to re-evaluate their decision to create harmful content and hoax news in the future. In this investigation, a transformer-based methodology focusing on IndoBERTbase, IndoBERTbase-uncased, and IndoBERTtweet will be employed in conjunction with the self-collected Indonesian tweet data that will be crawled from Indonesian Social Actor Focused on politicians in Twitter and will be combined with another topic in the future study. This research has shown a promising 98,93% in Training Accuracy using IndoBERTtweet.
FedNaWi: Selecting the Befitting Clients for Robust Federated Learning in IoT Applications

Federated Learning (FL) is an important privacy-preserving learning paradigm that is expected to play an essential role in the future Intelligent Internet of Things (IoT). However, model training in FL is vulnerable to noise and the statistical heterogeneity of local data across IoT clients. In this paper, we propose FedNaWi, a “Go Narrow, Then Wide” client selection method that speeds up the FL training, achieves higher model performance, while requiring no additional data or sensitive information transfer from clients. Our method first selects reliable clients (i.e., going narrow) which allows the global model to quickly improve its performance and then includes less reliable clients (i.e., going wide) to exploit more IoT data of clients to further improve the global model. To profile client utility, we introduce a unified Bayesian framework to model the client utility at the FL server, assisted by a small amount of auxiliary data. We conduct extensive evaluations with 5 state-of-the-art FL methods, on 3 IoT tasks and under 7 different types of label and feature noise. We build an FL testbed with 38 IoT nodes (20 nodes run on Raspberry Pi 4B and 18 nodes run on Jetson Nano) for the evaluation. Our results show that FedNaWi improves the FL accuracy substantially and significantly reduces energy consumption. In particular, FedNaWi improves the accuracy from 35% to 75% in the non-IID Dirichlet setting, and reduces the average energy consumption by 55%.
Performance Investigations on Integrating Federated Learning with Future Networks

For future networks, communications and computing will converge to provide services; Federated Learning (FL), as one of the typical distributed computing technologies, needs to be integrated with networking. For such integration, FL suffers from the straggler effect that the entire learning speed can be lowered down, because of the existence of the devices taking more time to complete their tasks. There are many existing works targeting at reducing straggler effects; However, they lacks the detailed investigations on the reasons and the impact of each cause when integrating FL with networking. To carefully investigate those aspects, we classify the reasons of such effects into 3 categories, computing power, communication capability and data distributions, and conduct the extensive experiments with carefully designs. After investigations, it is observed that learning completion time cannot be estimated by formulation with FLoating-point Operations Per second (FLOPs) if the device's computing capability is low. Also the communication time can be reduced by intentionally selecting appropriate devices when the computing powers of devices are heterogeneous, and the model parameters can be discarded if the device holds independent and identically distributed (i.i.d.) dataset.
Under the Spotlight! Facial Recognition Applications in Prison Security: Bayesian Modeling and ISO27001 Standard Implementation

This article highlights the importance of using Bayesian models and adhering to the ISO27001 standard in developing a web application to enhance prison security through facial recognition techniques. The proposed approach includes several key stages: 1. Identify the functional and non-functional requirements of the application, ensuring alignment with the desired objectives. 2. Design the application architecture and carefully select facial recognition techniques and Bayesian models that best suit the intended purpose. 3. Implement the application and perform thorough unit and integration testing to ensure functionality and compatibility. 4. Performed an experimental evaluation of the application in a controlled test environment, using performance and security metrics as benchmarks. The results demonstrate that using a web application integrated with a Bayesian model, in conjunction with adherence to the standardized practices outlined in ISO27001, enables the proactive identification of risks and threats. As a result, it serves as a valuable tool for mitigating prison insecurity.
DE-VSNet: Dense Efficient Vehicle State Classification Using Drone Traffic Dataset

This paper proposes a vehicle state classification model utilizing drones for achieving highly reliable autonomous driving vehicles. The model offers a wide field of view, providing improved data for the autonomous driving assistance system by considering the relationships with existing vehicles in the video. The object detection model, based on Convolutional Neural Networks, is trained using image data collected by drones. The YOLOv5 model is employed for detecting vehicles on the road. Detected vehicles are cropped into five candidate regions based on proximity, which serve as input data for the vehicle state classification model. The proposed model introduces a depthwise CSP block to effectively learn high-density layers and facilitate efficient weight learning. Additionally, a method that leverages deformable residual block to set receptive fields relying on features extracted from the fixed receptive field of the conventional convolutional kernel is proposed, enabling faster identification of critical features. Experiments are conducted using directly collected drone data to evaluate the performance of the trained model and compare it with a model based on dense layers. The approach aims to provide a more reliable and efficient vehicle state model for a robust autonomous driving system.
Design and Validation of a Virtual Reality Scenery for Learning Radioactivity: HalDron Project

Immersive technologies could be used to teach more complex concepts, particularly those which are hard to conceptualize related to science. The present paper presents how a team comprised of a Physics professor and some undergraduate Physics and Mathematics students with basic knowledge of C#/C + + design developed a Virtual Reality (VR) experience by using Blender and Unity to create an immersive experience through the use of Oculus Quest 2 headset, the objective was teaching the basic concepts of natural radioactivity and nucleosynthesis to undergraduate students with the help of a 3D modeled Virtual Learning Companion to test if immersive technologies would help to better understand and retain the information through an experience not available to them in experimental laboratories. An intervention was conducted with 143 undergraduate students to validate the system as a learning method. The students who participated in the testing received a prior knowledge assessment through a Google Forms test which was emailed to them, then experienced a quick virtual reality immersion (less than 10 min) and finally received a new theoretical assessment the following day after the VR experience along a user experience test. The results show that the student’s average grades went from 4.9 to 9.8 (of 15 points) and a high completion rate, even among those unfamiliar with VR or head-mounted displays.
Joint Source-Channel Coding for Wireless Image Transmission: A Neural Architecture Search Approach

In this paper, we propose a joint source-channel coding (JSCC) scheme for wireless image transmission, where the encoder and decoder of the transmission system are implemented by convolutional neural networks. A neural architecture search (NAS) method is introduced to find promising network structures aiming at minimizing the distortion of the target image. Experimental results demonstrate that our proposed scheme, referred to as NAS-JSCC, significantly outperforms the conventional manually designed ones in terms of peak-signal-to-noise ratio in nearly all tested signal-to-noise ratio regions.
Auralization of Three-Dimensional Sound Field in an Acoustic Scale Model Using Laser-Induced Sound Sources

This study presents a system for auralizing a three-dimensional (3D) sound field in a scale-model using laser-induced air breakdown (LIB) of acoustic waves for concert hall acoustic design. LIB solves sound source problems such as pressure level, directivity, duration, and frequency response. The experimental setup uses a YAG laser to generate ultrasonic pulses by LIB. The characteristics of the laser-induced acoustic waves are analyzed, and signal processing techniques for auralization, such as dynamic range extrapolation and air absorption correction, are highlighted. The auralization process of a 3D sound field encompasses microphone placement and encoding/decoding methods using ambisonics. By implementing these techniques in an acoustic scale-model of a concert hall, high-quality auralization of an orchestral performance can be achieved, thus facilitating concert hall acoustic design.
Offensive Language Detection in Code-Mixed Bambara-French Corpus: Evaluating machine learning and deep learning classifiers

In this paper, we deal with offensive and abusive language detection on Bambara language, which is an under-resourced language mainly spoken in Mali and some other African countries. As a first work on this language, we aim to release OBAM v1.0 corpus compiling 4k of texts that are labeled as normal, offensive and abusive. In addition, we have carried out a set of experiments with different configurations using various machine learning and deep learning classifiers. In overall, the classifiers produced acceptable results (0.80 and 0.73 of F-score in two-category and three-category classification, respectively), but they could be improved by expanding the corpus and proposing new features.
Low-Count Time Series Anomaly Detection

Low-count time series describe sparse or intermittent events, which are prevalent in large-scale online platforms that capture and monitor diverse data types. Several distinct challenges surface when modelling low-count time series, particularly low signal-to-noise ratios (when anomaly signatures are provably undetectable), and non-uniform performance (when average metrics are not representative of local behaviour). The time series anomaly detection community currently lacks explicit tooling and processes to model and reliably detect anomalies in these settings. We address this gap by introducing a novel generative procedure for creating benchmark datasets comprising of low-count time series with anomalous segments. Via a mixture of theoretical and empirical analysis, our work explains how widely-used algorithms struggle with the distribution overlap between normal and anomalous segments. In order to mitigate this shortcoming, we then leverage our findings to demonstrate how anomaly score smoothing consistently improves performance. The practical utility of our analysis and recommendation is validated on a real-world dataset containing sales data for retail stores.
Team Productivity Factors in Agile Software Development: An Exploratory Survey with Practitioners

Agile software development (ASD) has favored the software industry thanks to the early delivery of value to customers and for providing certain advantages for their work teams, including increased productivity. Productivity in ASD is a relevant concept, it is still under study and is composed of a set of factors that allow to determine the performance of each of the members of a team. The purpose of this article is to compare professionals’ perceptions of team productivity in ASD with productivity factors identified in a preliminary Systematic Literature Mapping (SMS). The study is oriented under the protocol for the construction of surveys in Software Engineering by Kitchenham and Pfleeger. As a result, the perceptions of 82 professionals working with agile methods were obtained, who associate productivity as an indicator of improvement within the team’s processes and in the fulfillment of objectives to a client, this last aspect being also recurrent in the SMS. Finally, for the professionals only 22 factors are relevant for the evaluation of productivity highlighting Velocity, Communication, Work Capacity, Commitment, Team Leader, and Quality which are categorized into Meaning, Impact, Flexibility, and Socio-Human.