Reviewerly/exHarmony huggingface dataset:
Automatic Background Filtering for Cooperative Perception Using Roadside LiDAR

The vehicle-road cooperative perception needs high accuracy and real-time automatic background filtering to separate background objects from foreground objects in complex traffic scenes. Reducing the influence of foreground objects to improve accuracy, and introducing a new framework to improve real-time performance are two main challenges in automatic background filtering. This paper proposes an Automatic Background Filtering method with innovative Frame Selection and Background Matrix Extraction modules (ABF-FSBME) to address these challenges. Firstly, a new space division method with equal hitting probability is proposed to divide the 3D point cloud formed by roadside Light Detection and Ranging (LiDAR), which can reduce the influence of slight LiDAR vibrations. Secondly, the terminal-edge-cloud framework is introduced to balance delay-constrained tasks and computation-intensive tasks in automatic background filtering. Thirdly, a variance-based frame selection strategy with a sliding window mechanism is proposed to select candidate frames with fewer foreground objects. This strategy can reduce the influence of foreground objects in a coarse-grained way. Meanwhile, a new background matrix extraction method is proposed to construct the background matrix. This method can further reduce the influence of foreground objects in a fine-grained way. Finally, based on the extracted background matrix from a cloud server, the edge server can filter the raw frame in real-time. The experimental results show that the proposed ABF-FSBME method has better accuracy than other methods in error rate and integrity rate. Besides, the proposed ABF-FSBME can complete fame filtering within 10ms, and has almost no network delay, so it can satisfy the real-time requirement.
Behind Developer Contributions on Conflicting Merge Scenarios

Context: The success of Open Source Software (OSS) projects typically depends on simultaneous contributions of several developers. These contributions often affect the same changing source files and may lead to merge conflicts when integrated. Previous studies investigated the reduction of conflicting merge scenarios. However, empirical evidence on the involvement of OSS contributors in conflicting merge scenarios is scarce. Objective: We aim to fill this gap with a large-scale quantitative study with the goal of understanding: 1) the extent in which OSS contributors are involved in conflicting merge scenarios; 2) characteristics of these contributors; and 3) characteristics of changing source files. Method: We collect both contributor data and contribution data from 66 popular GitHub projects and analyze data of 2972 distinct contributors who were involved in at least one conflicting merge scenario. We rely on both descriptive and inferential statistics to address our research questions. Results: About 80% of the analyzed contributors are involved in only one or two conflicting merge scenarios. Additionally, 42 out of the 66 projects had its top-one contributor as the one mostly involved in conflicting merge scenarios. Finally, only a small set of changing source files are involved in conflicting merge scenarios. Conclusions: We advocate that training the typically small group of contributors involved in conflicting merge scenarios could significantly reduce the number of merge conflicts.
“Two Heads are Better than One”: Pair-Interviews for Visualization

Visualization research methods help us study how visualization systems are used in complex real-world scenarios. One such widely used method is the interview — researchers asking participants specific questions to enrich their understanding. In this work, we introduce the pair-interview technique as a method that relies on two interviewers with specific and delineated roles, instead of one. Pair-interviewing focuses on the mechanics of conducting semi-structured interviews as a pair, and complements other existing visualization interview techniques. Based on a synthesis of the experiences and reflections of researchers in four diverse studies who used pair-interviewing, we outline recommendations for when and how to use pair-interviewing within visualization research studies.
Is Performance of Object Storage Predictable for Serverless I/O Workloads? A Comparative Study

Serverless architectures abstract resource provisioning away from the user. However, this property may be at odds with performance. One example of this is Function as a Service (FaaS), where the lack of network addressability compels developers to resort to serverless storage services such as AWS S3 to share (intermediate) data between the functions. For IO-bound workflows, the literature has shown that the performance of parallel reads and writes highly depends on the level of parallelism. Simply put, both an excess or a deficiency in the number of functions may lead to longer IO times. The good news is that the provisioning of functions is fast. Consequently, it is feasible to auto-provision the serverless functions to the optimal number to minimize IO latency. For this, the performance of object storage must be predictable and consistent. We confirmed this in the past for IBM COS. And in this paper, we show that the same occurs to AWS S3. Concretely, we prove that the optimal level of parallelism for parallel reads and writes can be approximated analytically for AWS S3.
What Really Matters for Graph Contrastive Learning-Based Recommendations? A Unified Learning Strategy

Graph contrastive learning (GCL) has become increasingly popular in recommendation due to its remarkable ability to reduce reliance on labels. Typically, GCL employs data augmentation methods, e.g., structure perturbation and representation perturbation, and CL loss to enhance performance. Recent studies have shown that structural perturbation plays a minor role in GCL, but there is still a lack of exploration on the representation perturbation. Therefore, we compare the two data perturbations in detail and reveal that both of them have a limited impact on performance. Simply combining recommendation loss and CL loss can produce comparable improvements. Besides, we identify a shared principle between the designs of recommendation loss and CL loss: both aim to optimize representation by increasing similarity between a target node and its positive samples while decreasing similarity with negative samples. Based on these findings, we propose Compact Graph Contrastive Learning (CGCL), a streamlined strategy that eliminates the data augmentation and deeply unifies recommendation loss and CL loss by elegantly incorporating their respective contributions. Leveraging the benefits of the unified strategy, we discover that our model can learn a concentrated mode length distribution of representation, which can enhance the ability to debias and thus improve the performance of the recommendation. This is a novel perspective on representation learning, and we also validate its rationality through rigorous experiments. Our comprehensive study on multiple benchmark datasets demonstrates that CGCL outperforms existing GCL methods.
An Unsupervised Vision-related Keywords Retrieval and Fusion Method for Visual Storytelling

Visual storytelling is a multi-modal generation task aiming to generate a coherent story for a sequence of images. Previous visual storytelling models utilize task-beneficial non-visual features, e.g., emotion, sentiment, or knowledge graph, as additional supplements for visual features to further improve the generation quality of stories. However, these appropriate non-visual features require to be carefully designed and selected by specialized researchers, moreover, high-quality external knowledge sources are not readily available. This increases the development cost of the VST model. To alleviate the above problem, this paper explores mining the knowledge existing in the multi-modal pre-trained models (MM-PTMs). First, we propose an Unsupervised Keywords Retrieval module (UKR), which takes an MM-PTM as the expert to select image-related keywords from a prepared task-related text corpus. The retrieved keywords not only complement and illustrate the visual features of the images but also provide more explicit generative signals to improve the interpretability and controllability of the generation process. Furthermore, we propose a Local Multi-modal Adaptive Fusion module (LMAF) to better fuse the textual and visual features and avoid noise brought by irrelevant keywords. LMAF dynamically aggregates features from both modalities through finer-grained correlation matching. The experimental results on the VST dataset, VIST, show that our proposed method achieves competitive results on several automatic metrics. Comparable results to previous methods can be achieved even if the model does not refer to visual features during story generation.
Fall Detection and Notification System for the Elderly using a fusion of Geophone and Thermal Imaging Camera sensors

Falls among elderly individuals living alone pose significant risks to their well-being. However, conventional fall detection devices often compromise privacy or are easily forgotten or rejected by the users. To address this issue, we present a combined fall detection device utilizing a Geophone as the primary sensor and a Thermal Imaging camera as the secondary sensor. The device aims to provide timely alerts to caregivers while respecting the privacy concerns of the elderly. Our approach utilizes the ESP32 microcontroller, Raspberry Pi, and incorporates a Thermal Sensor (MLX90640) and Geophone (SM-24 10Hz). This paper describes the design, implementation, and evaluation of our non-wearable fall detection device, highlighting its potential to enhance the safety and privacy of elderly individuals living alone. Our novelty is implementing a non-wearable device with sensor fusion. Wearable devices can cause discomfort and inconvenience for elderly individuals. Moreover, there are instances where elderly individuals may wake up during the night and forget to put on the wearable device, leaving them vulnerable to fall incidents without proper monitoring or assistance.
Blended Teaching Reform and Assessment of Petrochemical Technology

In view of the problems existing in the course teaching, blended teaching reform was used in the Petrochemical Technology with six-step teaching method, which based on “problem-oriented” and “quality expansion project” with the help of information technology such as “rain class” and “MOOC”. During the teaching process, “students’ feedback” was paid attention to. The content and process of “teaching” and “learning” were reconstructed according to the refinery enterprises technology. The “quality dextension project” were focused on before and after class. The teaching activities was paid attention to the process before, during and after the class, so the students’ classroom participation was improved to 100%. At the same time, the online evaluation system of curriculum was innovated. During the two teaching cycles, the percentage of excellent students increased from 32% to 70%.
Overcoming the Triple Challenges of Digital Contact Tracing in Japan

During the recent COVID-19 pandemic, various countries developed and deployed digital contact tracing tools as game-changers for infection control. As the pandemic approaches its final stages, evaluations of these efforts are progressing in different countries. This paper examined COCOA, a contact tracing app developed in Japan, to identify the reasons for its failure to function effectively. To address these issues, we proposed a solution that leverages technologies studied in Japan prior to the pandemic, and introduced an ongoing study at campus-level to demonstrate their potential. Digital contact tracing represents a significant technological innovation in public health, and it is desirable to expand the scope of validation to regional and national levels, in the future, for further development.
User-Oriented Qualitative Communication for JPEG/MPEG Packet Transmission

The existing Internet treats all packets equally, which means that a packet can be dropped entirely due to network congestion. To address this issue, a new packetization scheme and transport paradigm called Qualitative Communication, along with its associated packet wash operation, have been proposed. Qualitative Communication is particularly beneficial for transmitting multimedia content in the presence of network congestion. This paper specifically focuses on the transmission of JPEG/MPEG packets and investigates the characteristics of the Discrete Cosine Transform (DCT) used in the JPEG/MPEG encoding process. The proposed encoding scheme organizes the quantization coefficients of all image blocks in a packet based on their significance for image decoding and reconstruction. Additionally, the paper explores the idea that end users may be willing to accept a certain level of quality degradation rather than receiving nothing at all within an expected time frame. The benefits provided by the proposed mechanisms are discussed, and simulation results validate the improvements in the successful packet delivery ratio and the end user's level of satisfaction.
Robustness Assessment of Wind Power Prediction Under Cyber Security Attacks and its Impacts on Power System Operations

Wind power prediction accuracy is closely related to the quality of wind power data. However, most of traditional prediction methods do not consider the cybersecurity of raw data. So, it would be important to study the prediction accuracy under false data injection attack (one type of cybersecurity attacks) and its influence on the economy and security of power system. In this paper, very short-term forecasting approach of wind power generation is studied for choosing the optimal prediction model. Different scenarios of false data injection attack are then simulated upon wind power data. Wind power prediction accuracy after being affected by false data injection is observed. The robustness of wind power prediction model is evaluated. Finally, wind power prediction information is applied to day-ahead unit commitment to evaluate the impact of cybersecurity of wind power forecasting on power system economy and security.
Blockchain-based Edge-assisted Knowledge Base Management for Semantic Communication in Remote Driving

Remote driving, an emergent technology enabling remote operation of vehicles, presents a significant challenge due to the necessity of transmitting substantial volumes of image data from the vehicle to a central server. This requirement outpaces the capacity of traditional communication methods, emphasizing the need for efficient data communication. We propose a framework using semantic communication, specifically through a semantic segmentation-based method, which reduces the communication cost by transmitting meaningful semantic information rather than bit-wise data. Addressing the challenge of inconsistencies across knowledge bases in semantic communication, we present a blockchain-based, edge-assisted knowledge base management system. This system leverages edge nodes to manage multiple, geographically and contextually diverse knowledge bases while ensuring security through blockchain's tamper-resistant nature. Furthermore, blockchain sharding is employed to manage different knowledge bases for varying tasks, thereby enhancing the blockchain's throughput. Experimental results showed a great reduction in latency by sharding and an increase in model accuracy, confirming our framework's effectiveness.
CLEVER: A Framework for Connecting Lived Experiences with Visualisation of Electronic Records

The disconnect between insights generated from data and real-life practices of decision makers presents a number of open questions for visual analytics (VA). In public service planning, routine data are often perceived as unavailable, biased, incomplete and inconsistent across services. Decision makers often rely on qualitative data - sometimes collected through co-production - to understand the lived experience of communities before formulating a decision. We followed a subjectivist case study approach and immersed ourselves in ongoing co-production activities over the course of one year, to capture how VA can support the dialogue between population health decision-makers and the communities they serve. We present a framework for Connecting Lived Experiences with Visualisation of Electronic Records (CLEVER). The framework regards visualisation as a central component in a complex adaptive decision-making ecosystem and highlights the need to structure domain knowledge across decision contexts in Population Health Management (PHM) at clinical-, service- and district-levels. Our process for developing an initial framework comprised three steps: (i) we elicited decision-making tasks through a series of qualitative data collection activities; (ii) we developed a preliminary domain model to capture data views and a subjective view of the world through human stories; and (iii) we developed a series of visualisation prototypes to instantiate the framework and demonstrated them regularly to stakeholders. In future work, we will conduct ‘deep dives’ to systematically study the role of VA in individual stages of the framework.
Засвоєння студентами вокальних професійних навичок у процесі дистанційного навчання: актуальні проблеми та шляхи їх вирішення

The article reveals the issues of professional vocal skills formation in students of music area of expertise and the ways and approaches to their solution. It has been established that distance learning is a modern brand and one of the ways to reduce risks arising during martial law. At the same time, it has been emphasized that its use for forming professionally essential abilities and skills of future musical performers causes several specific problems related to the peculiarities of performing and pedagogical activities in the academic vocal class. It has been concluded that compliance with certain conditions and terms can ensure the effectiveness of such training. Among them, material and technical conditions: high-quality acoustic and video equipment, made comfortable workplace for the teacher and student, specialized software for training vocal skills and remote interaction through communicators, video communication, learning management systems and learning content management systems. The organizational terms include establishing visual contact between the teacher and the student during classes, introducing an individual approach to practising professional vocal skills based on a specially thought-out system of vocal work, implementing group forms of learning distance interaction via group sessions, online competitions, guest master classes, creation, and provision of access to educational content (video lessons, tutorials, author’s master classes, sound libraries, and music collections) and auxiliary materials (accompanying phonograms, and exercise collections). The psychological and pedagogical terms for the formation of professional vocal skills using distance learning cover the readiness of the teacher (motivational, technical-technological, and cognitive) to organize and competently manage such activities, constant feedback between the teacher and the student, the creation of an atmosphere of emotional well-being, purposeful formation of motivation and self-discipline in students, individual and practice-oriented approach.
Strengthening the Value of Digital Citizenship through the Podcasters Application for Students Citizenship Education in South Kalimantan

Education and technological progress are 2 things that cannot be separated. Digital citizenship is one of the studies that is highly relevant to the demands of the 21st century learners. Technological advances aimed at education need to balanced with strengthening the values of digital citizenship,
DRIP: Segmenting individual requirements from software requirement documents

Abstract Numerous academic research projects and industrial tasks related to software engineering require individual requirements as input. Unfortunately, according to our observation, several requirements may be packed in one paragraph without explicit boundaries in specification documents. To understand this problem's prevalence, we performed a preliminary study on the open requirement documents widely used in the academic community over the last 10 years, and found that 26% of them include this phenomenon. Several text segmentation approaches have been reported; however, they tend to identify topically coherent units which may contain more than one requirement. What is more, they do not take the constitutions of semantic units of requirements into consideration. Here we report a two‐phase learning‐based approach named DRIP to segment individual requirements from paragraphs. To be specific, we first propose a Requirement Segmentation Siamese framework, which models the similarity of sentences and their conjunction relations, and then detects the initial boundaries between individual requirements. Then, we optimize the boundaries heuristically based on the semantic completeness validation of the segments. Experiments with 1132 paragraphs and 6826 sentences show that DRIP outperforms the popular unsupervised and supervised text segmentation algorithms with respect to processing different documents (with accuracy gains of 57.65%–187.53%) and processing paragraphs of different complexity (with average accuracy gains of 54.46%–158.68%). We also show the importance of each component of DRIP to the segmentation.
TRANSITIVITY ANALYSIS OF SYSTEMIC FUNCTIONAL GRAMMAR ON INDONESIAN STATE SPEECH OF JOKO WIDODO

This research aimed to analyze the transitivity in political speech by using systemic functional grammar in each sentence. The data in this research were fragments of clauses which identified the type of transitivity (process, participant, and circumstance), logical meaning, and context of the situation while the source of the data was Jokowi's presidential campaign speech to his volunteer in the 2019 election. This research was carried out using recording and note-taking techniques. The result showed five types of process is used by Joko Widodo, such as material process, relational process, mental process, verbal process, and existential process. The material process in ideational metafunction was the most dominant with the choice of the word "work" as an interpretation of an invitation or call for volunteers to work even harder. Besides, the most dominant theme used in President Jokowi's speech text was the Marked Theme thematic structure, namely the thematic structure that was in the phrase that occupied the subject function indicated that Jokowi gave more emphasis on the things that were meant.