Reviewerly/exHarmony huggingface dataset:
On the Differential Privacy in Federated Learning based on Over-the-Air Computation

The federated learning is a promising machine learning technique to bring about advanced services and application for future industries. It has been known that the federated learning secures the privacy of the participants well so far. However, various attacks appear recently which are possible to extract the private information of them in the federated learning systems. Consequently, development of privacy preserving schemes for the federated learning is paramount. In this paper, we consider the over-the-air computation based federated learning system, and adopt the concept of differential privacy to prevent the private information leakage. During the training process, when a sum of local gradients is received via over-the-air computation, they conceal each other and appear to be random to the parameter server. Motivated by this fact, the differential privacy of the over-the-air computation based federated learning is analyzed by considering the inherent randomness of the local gradients. We analytically quantify required amount of the artificial noise to be added to preserve privacy. Furthermore, a parameter estimation based algorithm is proposed which is applicable in real scenarios. The simulation results show the efficacy of the proposed algorithm for preserving privacy.
Analyzing Public Sentiment Towards the Covid-19 Pandemic: A Twitter-Based Sentiment Analysis and Machine Learning Approach

The Covid-19 pandemic has significantly reshaped societies, prompting an unprecedented surge in digital interactions and communication via social media platforms. Amidst this evolving landscape, the analysis of public sentiment towards pandemic management strategies has emerged as a critical avenue for understanding societal responses. This paper delves into the intricate landscape of sentiment dynamics surrounding the Covid-19 pandemic, with a focus on sentiments expressed in Twitter posts. Leveraging sentiment analysis techniques, the study provides nuanced insights into societal attitudes, concerns, and perceptions across distinct phases of the pandemic. The investigation not only employs traditional lexicon-based sentiment analysis but also explores the intersection of sentiment analysis and machine learning algorithms. The research contributes to the discourse by presenting a comprehensive analysis of public sentiment dynamics during various pandemic stages, shedding light on evolving emotional responses and offering insights into the effectiveness of measures.
Examining the Correlations Between Teacher Profiling, ICT Skills, and the Readiness of Integrating Augmented Reality in Education

This study explores the relationship between respondent demographic profiling and the factors affecting teachers' readiness in adopting augmented reality (AR) for teaching and learning. While AR holds promise for transforming educational practices, the readiness of teachers to adopt this technology remains a critical factor for successful implementation. However, there is a lack of research examining the specific relationship between respondent demographics and the factors influencing teachers' readiness in AR adoption. This study aims to fill this gap by employing a quantitative research approach and survey methodology. By analyzing the data collected from primary school teachers, including demographic information and factors affecting readiness, the study seeks to identify any significant correlations or patterns. The findings will contribute to a better understanding of how respondent demographics influence teachers' readiness in AR integration, addressing a crucial problem in the field of educational technology. The insights gained from this study will inform policymakers and educational institutions in developing targeted strategies to enhance teachers' readiness and support the successful integration of AR in primary school classrooms.
Security In The Clouds

Leading experts provide advice on how aerospace companies can protect themselves from the growing threat posed by hackers
Seasonal Influence on the Microbiology and Physicochemistry of Crude Oil Polluted Wetlands

Pollutants such as crude oil spills in an environment, affect the physical, chemical and biological properties of that environment. This study was conducted to determine the effect of seasonal variations on the microbiological and physicochemical characteristics of three crude oil-polluted wetlands in Rivers State, Nigeria. Soil samples were collected at 3 depths (0-15cm, 15-30cm and 30-45cm) with the aid of a hand auger, sampling was done for a period of twelve months covering the rainy (March to August) and dry seasons (September to February). Samples were analyzed for physicochemical characteristics, using standard analytical and microbiological methods. The data obtained was analyzed using the statistical package for social science (SPSS) version 22 and Duncan’s multiple range test was used to separate means where differences occurred. Microbiological analyses showed that significantly higher microbial counts were observed in the dry season than in the rainy season in the wetland samples which could be due to favourable environmental conditions during the period of study. Wetland soil total heterotrophic bacterial (THB) count in the wet season ranged from 1.09x107cfu/g to 10.45x107cfu/g and from 2.43x107cfu/g to 18.56x107cfu/g in the dry season. Total heterotrophic bacteria had the highest population in all soil depths (0-15cm, 15-30cm and 30-45cm) in both seasons. Hydrocarbon-utilizing bacteria such as Bacillus subtilllis, B. rigui, B. flexus, Lysinibacillus macrolides, Staphylococcus aureus, Proteus penneri and fungi such as Aspergillus species, Fusarium species, Mucor species and Rhizopus species were genetically identified in this study. Temperature and other physicochemical parameters in the wetlands were altered due to the variation in season. Higher temperature ranges were observed in the dry season than in the rainy season as seen in Iwofe, temperature ranged from 26.500C (0-15cm) to 28.500C (30-45cm) in the rainy season while in the dry season it ranged from 31.500C (0-15cm) to 33.500C (30-45cm) which was above FEPA permissible limit of 300C for soil temperature. The study showed that season of study as well as crude oil spillage have significant effects on the microbiology and physicochemical characteristics of wetlands and therefore, contribute to microbiological degradation in wetlands.
Revolutionizing Healthcare industry 5.0: Exploring the Potential of Blockchain Technology for Medical Applications

The healthcare industry is experiencing a paradigm shift from an industrial era (Industry 1.0) to a digital era (Industry 4.0), and now towards a more patient-centered, value-driven era (Industry 5.0). The advent of Blockchain Technology (BCT) has provided a significant opportunity to transform the healthcare industry towards Industry 5.0 by enabling secure and decentralized data sharing, improving patient privacy and data security, and streamlining healthcare processes. This paper explores the potential of BCT for medical applications within the context of Industry 5.0. We provide a comprehensive overview of the current state of the healthcare industry and highlight the challenges faced by patients, providers, and researchers in accessing and sharing medical data. We then discuss how BCT can address these challenges by providing secure and decentralized data storage, sharing, and exchange, as well as enhancing patient control over their data. We conclude that BCT has significant potential for revolutionizing the healthcare industry towards Industry 5.0 by improving data security, privacy, and interoperability, reducing costs and improving efficiency, and enabling new models of patient-centered care. However, realizing this potential will require overcoming significant challenges and a collaborative effort from all stakeholders in the healthcare ecosystem. The integration of BCT in the healthcare industry will facilitate the transition towards Industry 5.0 and ensure that patients receive high-quality, value-driven care.
Cut Elimination for Extended Sequent Calculi

We present a syntactical cut-elimination proof for an extended sequent calculus covering the classical modal logics in the \(\mathsf{K}\), \(\mathsf{D}\), \(\mathsf{T}\), \(\mathsf{K4}\), \(\mathsf{D4}\) and \(\mathsf{S4}\) spectrum. We design the systems uniformly since they all share the same set of rules. Different logics are obtained by “tuning” a single parameter, namely a constraint on the applicability of the cut rule and on the (left and right, respectively) rules for \(\Box\) and \(\Diamond\). Starting points for this research are 2-sequents and indexed-based calculi (sequents and tableaux). By extending and modifying existing proposals, we show how to achieve a syntactical proof of the cut-elimination theorem that is as close as possible to the one for first-order classical logic. In doing this, we implicitly show how small is the proof-theoretical distance between classical logic and the systems under consideration.
IMPROVING ABILITY WRITING AROUND DESCRIPTION WITH THE STUDENT'S FIELD TRIP METHOD

The low Skills write essay description caused by several factors, namely: 1) less students understand the essence of descriptive essay; 2) student Not yet describe object which is clear with involve impression sense;students have not been able to use the structure good and correct language; 4) students have not used capital letters correctly; 5) students have difficulty expressing ideas. Study This aim describe Enhancement Ability Write Essay Description with Field Trip Method Grade IV students at SDN 2 Rappang, Pancarijang District, Sidrap Regency . Study This use method study action class (PTK). subject this study were fourth grade students of SDN 2 Rappang, Pancarijang, Sidrap, while the object writes a descriptive essay using the field trip method. Technique collection data Which used is observation And test ability student. This study uses a qualitative descriptive analysis technique. From the results research can be concluded that the field trip method can improve ability write essay description student on cycle I after apply the field trip method of students who scored &gt; 70 as many as 6 students with an average score class average of 65.93. After being given action cycle II students who scored&gt; 70 as much 14 students with an average value as much 77,26.&#x0D;
Blockchain in the Healthcare Sector

After the COVID-19 pandemic, the healthcare sector has faced many challenges as telehealth increased and the need for a secure and efficient healthcare record system became essential. Nowadays, the healthcare sector suffers from many problems, such as security, trust, availability of data, and drug traceability. Blockchain technology is a recent technology that has proven its efficiency in many sectors, such as finance, banking, bitcoin, and healthcare sectors. This chapter describes blockchain integration in the healthcare sector to cover these problems. The authors will start by introducing blockchain technology. After that, they will present the healthcare sector challenges. Then, they focus on advantages of blockchain usage to resolve existing problems in the healthcare sector, especially in the electronic health record and drug traceability.
Human Video Instance Segmentation and Tracking via Data Association and Single-stage Detector

Human video instance segmentation plays an important role in computer understanding of human activities and is widely used in extensive artificial intelligence applications for consumer electronics. In this paper, we develop a new method for human video instance segmentation based on the one-stage detector. To track the instance across the video, we have adopted a data association strategy for matching the same instance in the video sequence, where we jointly learn target instance appearances and their affinities in a pair of video frames in an end-to-end fashion. We have also adopted the centroid sampling module for enhancing the embedding extraction ability of instances, so that the problem that the same instance is represented by two different instances, which tends to be confronted in the one-stage detector, can be alleviated. Finally, we collect PVIS dataset by assembling several video instance segmentation datasets to fill the gap of the current lack of datasets dedicated to human video segmentation. Extensive simulations based on the dataset have been conducted and the simulation results show that the proposed method can achieve better performance of human video instance segmentation than other SOTA methods by approximately 3% improvements in DetA, AssA, and IDS measurements. The inference time can also achieve 18.9 FPS, which is much more efficient than most of other SOTA methods. This illustrates the efficiency and effectiveness of the proposed work.
Students' Capabilities In Determining The Theme Of Bugis Folklore Texts With Intrinsic Elements Approach

This study aims to describe the ability to determine the theme of Bugis folklore. Based on the table data, it can be concluded that the highest score obtained by students on the ability to understand the theme of intrinsic elements in the story text La Maddukkelleng is 4 and gets a score of 100 while the lowest score obtained by students is 2 so that they get a value of 50. The research design used in this research is a descriptive quantitative research design. The population of this research is the entire class VIII SMP, totaling 81 students and divided into four classes. Based on the results of this study, it was found that the Ability to Understand Intrinsic Elements in Bugis folklore texts with seven assessment indicators found the level of ability in the theme aspect was categorized as capable as much as 75% who got a score of 75-100.
Engineering Runtime Root Cause Analysis of Detected Anomalies

The main objective of this work is to provide a unified, easy to configure and extensible end-to-end system that performs root cause analysis (RCA) methods on top of anomaly detection (AD) methods in an online setting. AD-focused RCA for online settings has not been investigated so far; therefore our work can be seen as an initial approach to this end. Inspired by the solutions developed in the ThirdEye project, which is coupled with the Apache Pinot data warehousing system, we re-engineer ThirdEye’s RCA components/techniques so that they operate in a manner that they can directly ingest input records from Apache Kafka and continuously compute aggregates at different level of granularity in a principled manner for both OLAP queries and provision of baselines to support RCA. To attain scalability, we build our solution in the Apache Flink stream processing engine. This work presents the main design choices when applying ThirdEye’s concepts on data streams and presents indicative examples and scalability experiments. Our solution is provided in open-source.
Simulation System of College Physical Education Based on Support Vector Machine Algorithm

Teaching simulation system can not only assist traditional teaching and training methods, but also stimulate athletes' interest in learning and training, and improve their ability to recognize the main role. Therefore, physical education needs the help of high technology to meet its own needs. Based on this, it is an urgent and necessary work to build a physical education simulation system. In this paper, a simulation system of college physical education based on SVM(support vector machine) algorithm is designed. This physical education simulation system can finally realize the effects of real-time control, all-angle switching and multi-angle display of teaching contents in physical education through kinect equipment. And it can be displayed from all directions and angles according to the three-dimensional model. In this paper, a new idea based on hierarchical classification is used. According to the motion description, the motion is separated in advance, and then the SVM-based method is used for training. It is transmitted frame by frame in the way of retraining, which provides more and more complete historical information for reference for moving target tracking. The experimental results show that the algorithm has strong robustness in some video image sequences with drastic target changes. The recognition rate of human body movements in sitting position is higher, and the physical education simulation system designed in this paper can improve the quality of physical education.
GestureSet: Public Domain Dataset for Human Gesture Recognition using Wrist-worn Devices: A Preliminary Version

Human Gesture Recognition (HGR) has become an increasingly important research area in recent years, driven by the need for more natural and intuitive ways of interacting with machines. Wearable smart devices, such as smartwatches, smart wristbands, and other wrist-worn devices have the potential to enable accurate and efficient gesture recognition in real-world settings. However, equivalent problems, such as object detection in computer vision and audio identification in audio analysis, have obtained enormous advantages from comprehensive datasets over the last years, for instance, ImageNet and AudioSet, accordingly. This paper proposes the structure and the creation of a preliminary version of GestureSet, an HGR large-scale multitask dataset, which includes manually annotated data of gesture events that were collected from wrist-worn devices. In particular, it proposes a hierarchy abstraction schema for the proper dataset structure and instantiates the basis of the dataset through the first HGR task that is related to the collection and annotation of gestures related to allergic rhinitis identification. Thus, through this preliminary version, we describe how the proper dataset structure is divided into different groups, each one including sessions, individual gestures, and pilot data that could be used for evaluating the learning algorithms' performance in real-world scenarios. To the best of our knowledge, this is the first attempt that envisions a unified dataset for various HGR tasks that can find applications in different domains. Our vision, specifically, is for this work to set the basis for the dataset enrichment in a wide range of various tasks, and its data to be used in multiple learning and identification settings, providing circularity in different domains, and enabling reproducibility of the dataset's gestures events.
A BSN-Enabled Collaborative Edge-Cloud Architecture for Multi-User Activity Recognition

The expansion of Internet if Things (IoT) technology has led to the widespread use of sensors in various everyday environments, including healthcare. Body Sensor Networks (BSNs) enable continuous monitoring of human physiological signals and activities, benefiting healthcare and well-being. However, existing BSN systems primarily focus on single-user activity recognition, disregarding multi-user scenarios. Therefore, this paper introduces a collaborative BSN-based architecture for multi-user activity recognition to identify group collaborations among nearby users. We discuss first the general problem of multi-user activity recognition, the associated challenges along with potential solutions (such as data processing, mining techniques, sensor noise, and the complexity of multi-user activities) and, then, the software abstractions and the components of our architecture. This represents an innovative solution of collective intelligence and it holds significant potential for enhancing healthcare and well-being applications by enabling real-time detection of group activities and behaviors.
A Stronger Stitching Algorithm for Fisheye Images Based on Deblurring and Registration

The fisheye lens, which is suitable for panoramic imaging, has the prominent advantage of a large field of view and low cost. However, the fisheye image has a severe geometric distortion, which may interfere with the stage of image registration and stitching. Aiming to resolve this drawback, we devise a stronger stitching algorithm for fisheye images by combining the traditional image processing method with deep learning. In the stage of fisheye image correction, we propose the attention-based nonlinear activation free network (ANAFNet) to deblur fisheye images corrected by Zhang's calibration method. Specifically, ANAFNet adopts the classical single-stage U-shaped architecture based on convolutional neural networks with soft-attention technique, and it can restore a sharp image from a blurred image effectively. In the part of image registration, we propose the ORB-FREAK-GMS (OFG), a comprehensive image matching algorithm, to improve the accuracy of image registration. Experimental results demonstrate that panoramic images of superior quality stitching by fisheye images can be obtained through our method.
An Interactively Motion-Assisted Network for Multiple Object Tracking in Complex Traffic Scenes

Multiple object tracking plays a crucial role in intelligent transportation systems. Due to the varying size, fast motion, and occlusion of traffic objects, multiple object tracking in complex traffic scenes is prone to low tracking accuracy and tracking fragmentation. To solve these problems, numerous tracking methods have been proposed based on object discrimination feature. However, these methods neglect the guiding prior role of historical tracking information for detection, making them not applicable to more complex scenes, such as extremely small, fast-moving, and severely obscured objects. In this paper, we propose an Interactively Motion-assisted Network (IMANet) for multiple object tracking in complex traffic scenes. First, to capture the object motion patterns from historical frames, an object motion modeling module considering camera movement is proposed, which particularly has prominent advantages on videos obtained by moving cameras. Next, a multi-scale fusion detection and embedding module is designed to incorporate historical motion information, thereby improving detection performance. Finally, multiple object tracking can be achieved by associating objects detected in different frames based on detection and embedding results. The proposed method combines the detection and tracking in an interactive way, where detection performance is facilitated using historical information provided by the tracking module, and better detection in turn enhances the tracking. Several real-world traffic examples are used to illustrate the performance of the proposed method in both detecting and tracking traffic objects. The results demonstrate that the proposed method outperforms the state-of-the-art methods, especially in complex surveillance videos with varying sizes and occlusions.
Feature selection in text classification: Identifying spurious words with causal inference methods

As has been scrutinized by many, non-causal model may contain spurious correlations that act like shortcuts during the prediction phase, undermining cross-domain accuracy. This can be caused by biased training data that contains spurious words with neutral meanings yet can induce the model to predict wrongly. Based on this assumption, we propose a series of methods to detect these spurious words before feeding the model with the training data. We used advanced causal inference methods which are arising novas in recent studies, such as propensity score matching and inverse propensity score weighting to facilitate the feature selection before training. We experimented with multiple approaches to estimate propensity scores and got profound improvements. We further experimented with BERT model to evaluate the effectiveness of feature selection and find that the model performance with in-domain and out-of-domain testing samples is boosted after we remove the spurious words detected by our methods in the training data.