Reviewerly/exHarmony huggingface dataset:
Access-based Lightweight Physical Layer Authentication for the Internet of Things Devices

Physical-layer authentication is a popular alternative to the conventional key-based authentication for internet of things (IoT) devices due to their limited computational capacity and battery power. However, this approach has limitations due to poor robustness under channel fluctuations, reconciliation overhead, and no clear safeguard distance to ensure the secrecy of the generated authentication keys. In this regard, we propose a novel, secure, and lightweight continuous authentication scheme for IoT device authentication. Our scheme utilizes the inherent properties of the IoT devices’ transmission model as its source for seed generation and device authentication. Specifically, our proposed scheme provides continuous authentication by checking the access time slots and spreading sequences of the IoT devices instead of repeatedly generating and verifying shared keys. Due to this, access to a coherent key is not required in our proposed scheme, resulting in the concealment of the seed information from attackers. Our proposed authentication scheme for IoT devices demonstrates improved performance compared to the benchmark schemes relying on physical channels. Our empirical results find a near threefold decrease in the misdetection rate of illegitimate devices and close to zero false alarm rate in various system settings with varied numbers of active devices up to 200 and signal-to-noise ratio from 0 dB to 25 dB. Our proposed authentication scheme also has a lower computational complexity of at least half the computational cost of the benchmark schemes based on support vector machine and binary hypothesis testing in our studies. This further corroborates the practicality of our scheme for IoT deployments.
FAFNs: Frequency-Aware LiDAR–Camera Fusion Networks for 3-D Object Detection

The 3-D object detection is a crucial task for autonomous driving and robotics. However, it faces various challenges attributed to the complexity and sparsity of 3-D data. Light detection and ranging (LiDAR) and camera are two common sensors that provide complementary information for 3-D object detection. LiDAR provides accurate depth and shape information, while camera captures rich semantic and texture information. However, most existing methods for LiDAR–camera fusion either ignore the frequency-domain information or use simple concatenation or elementwise operations to fuse the features, which may not fully realize the potential of both modalities. This article proposes frequency-aware LiDAR–camera fusion networks (FAFNs) that leverage the frequency-domain information to enhance the LiDAR and camera feature maps. Specifically, we design a frequency-aware feature enhancement module (FAFEM) that adaptively learns the frequency importance of each modality and assigns different weights to different frequency bands. We also design a frequency-aware multiscale query initialization strategy that enriches the object query with frequencywise information and multiscale variations. We conduct the evaluation of our FAFNs using two well-established benchmarks for 3-D object detection, nuScenes and Waymo, which demonstrates its competitive performance over existing state-of-the-art methods.
Practices of De-Motivators in Adopting Agile Software Development Methods at Large Scale Development Teams From Management Perspective

During the last few decades, agile methodologies have become the most widely used software development methods. Agile methodologies provide great customer satisfaction and fast product delivery at the low cost. The low cost and customer satisfaction make agile methods highly attractive. Agile methods were designed and used, in its early days, for small-scale projects. There are many challenges in applying agile methods for large-scale projects as determined by some previous studies. The objective of this research is to identify the practices of De-motivators in adopting agile projects at a large scale from management point of view by employing a systematic literature review (SLR). In the review process, a total of 72 practices were discovered.. De-motivator factors have been identified and ranked, through the analytical hierarchical process, through SLR in our previous study [1]. Analytical hierarchical processing (AHP) is a well-known method used for multi-criteria decisions implemented by Satty [69]. In this study AHP is used to prioritize de-motivators and their categories based on their relative importance. The prioritized de-motivators and their categories provide a strong background to the software project manager and practitioners for adopting Agile Software Development in large-scale projects.
Video Display Field Communication: Practical Design and Performance Analysis

Display field communication (DFC) is a spectral domain-based, unobtrusive display-to-camera (D2C) communication method, in which a commercially available digital display serves as the transmitter and an off-the-shelf camera serves as the receiver. The data are transmitted through video and image frame sequences over D2C links in such a way that it does not obstruct the normal viewing experience. In this study, we propose a Video-DFC approach to detect data embedded in running videos. First, we propose the design of the frame packet structure that is used to facilitate encoding input data as a specific frame sequence. Then, we design a color code-based 4-point block pattern, that is inserted into the four vertices of the transmitted image frames. This block pattern enables frame-to-frame synchronization and accurate extraction of the data-embedded region from the received frames. Our proposed Video-DFC approach demonstrates the potential of transmitting a larger amount of data and achieving a data transfer by embedding distinct data in each frame of the running video. To evaluate the robustness of Video-DFC, we conducted extensive experiments varying the frame packet structure, the input video, and the camera resolution. Depending on the frame packet structure, the results showed a maximum achievable data rate of 27 kbps. Overall, our findings suggest that Video-DFC has the potential to be a next-generation, high-capacity D2C communication method.
Hierarchical Dynamic Masks for Visual Explanation of Neural Networks

Despite the remarkable accomplishments of deep neural networks in computer vision tasks, the inherent opacity of their operations remains a pressing concern. Attribution methods generating visual explanatory maps representing the importance of image pixels for model classification are popular for explaining neural network decisions. However, the small and diverse decision regions in fine-grained or medical images limit the precision and comprehensiveness of the existing attribution methods when explaining decisions made for such a data type. This paper introduces a novel attribution method called hierarchical dynamic masks (HDM) to overcome these concerns to generate saliency maps with high recognition reliability and localization capability. Specifically, we suggest dynamic masks (DM), which enable multiple small-sized benchmark mask vectors to learn the image's critical information roughly through an optimization method. The benchmark mask vectors guide the learning of the large-sized combination mask vectors so that their overlay mask accurately learns detailed pixel importance information. Additionally, we construct the HDM by hierarchically concatenating DM modules. These DM modules search and combine the regions of interest in the remaining neural network classification decisions within the masked image in a learning-based way. Since HDM forces DM to perform importance analysis in different areas, it makes the fused saliency map more comprehensive. The experiments reveal that the proposed method outperforms existing approaches significantly regarding recognition credibility and positioning ability when qualitatively and quantitatively tested on CUB-200-2011 and iChallenge-PM datasets.
Contact Part Detection From 3D Human Motion Data Using Manually Labeled Contact Data and Deep Learning

Research on the interaction between users and their environment has been conducted in various fields, including human activity recognition (HAR), human-scene interaction (HSI), computer graphics (CG), and virtual reality (VR). Typically, the interaction process commences with a human body part’s movement and involves contact with a target object or the environment. The choice of the body part to make contact depends on the interaction’s purpose and affordance, making contact a fundamental aspect of interaction. However, detecting the specific body parts in contact, especially in the context of 3D motion and complex environments, poses computational challenges. To address this challenge, this study proposes a method for contact detection using motion data. The motion data utilized in this study are limited to actions feasible in an office environment. Since contact states of different body parts are independent, the proposed method comprises two distinct models: a feature model generating common features for each body part and a part model recognizing the contact state of each body part. The feature model employs a bidirectional long-short term memory(Bi-LSTM) structure to capture the sequential nature of motion data, ensuring the incorporation of continuous data characteristics. In contrast, the part model employs separate weights optimized for each body part within the deep neural network. Experimental results demonstrate the proposed method’s high accuracy, recall, and precision, with values of 0.99, 0.97, and 0.95, respectively.
Data-level Hybrid Strategy Selection for Disk Fault Prediction Model Based on Multivariate GAN

Data class imbalance is a common problem in classification problems, where minority class samples are often more important and more costly to misclassify in a classification task. Therefore, it is very important to solve the data class imbalance classification problem. The SMART dataset exhibits an evident class imbalance, comprising a substantial quantity of healthy samples and a comparatively limited number of defective samples. This dataset serves as a reliable indicator of the disc's health status. In this paper, we obtain the best balanced disk SMART dataset for a specific classification model by mixing and integrating the data synthesised by multivariate generative adversarial networks (GAN) to balance the disk SMART dataset at the data level; and combine it with genetic algorithms to obtain higher disk fault classification prediction accuracy on a specific classification model.
ANALISIS KEAMANAN SISTEM INFORMASI PADA EMAIL CORPORATE UNIVERSITAS TEKNOLOGI SUMBAWA UNTUK IMPLEMENTASI SISTEM KERJA PAPERLESS ( INOVASI SMART CAMPUS )

University is an educational unit whose role is to organize higher education in advancing science and technology. In order to realize this role, the University must have an adequate data center. This study aims to analyze the security of information systems on corporate email, in order to implement a paperless-based work system at the Sumbawa Technology University, in realizing Smart Campus innovation by the entire Sumbawa Technological University academic community. This study uses a qualitative descriptive research type. By making direct observations about the information system security audit using COBIT 4.1. The results of the research are in the form of a 'Term of Agreement' pact on the integrity of corporate email rules and the implementation of the university's paperless work system innovation.
Implementing Low-cost Immersive 360° Video Technology to Promote Core Skills in Journalism Courses

Intentional training in storytelling is fundamental in journalism education, and effective storytelling requires empathy and perspective taking. Emerging media technologies, such as virtual reality and 360° video, are noteworthy for their ability to enhance user sense of presence and to build empathy. Using cardboard virtual reality viewers, this qualitative study explored how immersive 360° videos are relevant to journalism students’ core (i.e., emotional) skill acquisition in a classroom on crisis and trauma coverage. In this pilot study on applied instructional design, twenty-three student journal entries were analyzed, supported by the Cognitive and Affective Model of Immersive Learning (CAMIL). Key emerging themes included emotional impact, sense of presence, appeal of immersive experiences, and novelty. Pedagogical recommendations are presented to educators, instructional designers, and other audiences interested in integrating usable, existing, and affordable immersive content in journalism education.
Slides from #LISBIB21 Lightning Talk: Using the Altmetric Explorer for Responsible Metrics as a Researcher, Author and Journal Editor [28 April 2021]

I have now deposited my deck of slides from my presentation yesterday in the LIS Bibliometrics 2021 conference (28 April 2021).
Now Deposited: SketCHI 4.0: Hands-On Special Interest Group on Remote Sketching in HCI

CHI2021 extended abstract now deposited.
Cognitive Robotics: Integrating Artificial Intelligence and Embodied Intelligence for Advanced Problem Solving

Artificial intelligence (AI) and embodied intelligence are combined in cognitive robotics, which marks a paradigm change in highly developed problem-solving skills. This study offers a comprehensive review of the theoretical underpinnings, practical difficulties, practical applications, and moral implications of cognitive robots. The study explores machine learning methods, natural language processing tools, and computer vision technologies after introducing the fundamental concepts. It clarifies embodied intelligence further by emphasizing the important roles that sensory data fusion, robotic actuation, and adaptive behaviours play in the cognitive capabilities of robots. The study then moves on to discuss cognitive robotics' integration difficulties, including issues in sensory data fusion, cognitive dissonance in AI-driven robotics, and human-robot interaction. Applications in autonomous cars, healthcare, manufacturing, and assistive technologies demonstrate how cognitive robotics is transforming many different industries. The ethical obligations involved in the creation and use of cognitive robotic systems are highlighted, along with ethical issues relating to employment, privacy, and ethical AI practises. In-depth analysis is done of the present and foreseeable difficulties in cognitive robotics. Along with cognitive difficulties like contextual comprehension and moral conundrums, technical obstacles linked to sensor limits and processing restrictions are also studied. The study highlights cutting-edge research projects and multidisciplinary partnerships that provide answers to these problems, paving the path for the development of cognitive robots in the future. The article envisions a future where cognitive robots would play a part in space exploration, disaster response, and personalised robotics. It also surveys integration possibilities with cutting-edge technologies like augmented reality and the Internet of Things. The study summarises the key findings and presents the implications for future research, and emphasises the important part cognitive robotics plays in resolving difficult issues. To ensure that cognitive robots continue to have an impact on a future that is smarter, more connected, and understanding, ethical issues, multidisciplinary collaboration, and responsible development practises are necessary.
Analyzing the Role of AI in Cyber Security Threat Detection &amp; Prevention

Abstract: This research paper aims to analyze the role of artificial intelligence (AI) in cyber security threat detection and prevention. With the increasing sophistication and frequency of cyber-attacks, traditional security measures have become insufficient. AI has emerged as a promising solution to enhance cyber security by leveraging its capabilities in data analysis, pattern recognition, and anomaly detection. This paper explores the potential of AI in addressing cyber security challenges, examines existing literature, defines the problem statement, proposes a methodology, discusses the results, and concludes with future scope and recommendations.
College Admission Prediction using Machine Learning

Students struggle mightily to get accepted into the college of their choice. The present engineering admission process is little challenging when it comes to picking the right college based on test results and areas of interest. The candidates' ability to complete the application form accurately depends on their selection, which may differ depending on their academic performance and entrance exam results. Numerous colleges provide a variety of engineering courses. Students find it difficult to arrange and list the appropriate institutions of their choice for courses based on their performance score. The historical college cut-off data used by the college admission predictor to determine the most likely colleges.
Creating Face Transition using Deep Fake Technology with its Architecture

Deep fake videos are videos where the features and expressions of a person are replaced with the features and expressions of another person. Videos can be converted or manipulated using powerful Deep Learning techniques. This technology may be used in wrong way or maliciously as a means of spreading misinformation of any activity, manipulation, and persuasion. Currently there are not many solutions to identify products of Deep fake technology, although there is significant research being conducted to tackle or handle with this problem. Generative Adversarial Network (GAN) is the one often researched deep learning technology. These networks preferred to develop or generate the non-existing patterns or creations. In this work, we’re working on the development of first order motion model for image animation using Dense motion network. Using key point detectors as a baseline, we train a GAN and extract the facial landmarks from the driving video and building the embedding model to create the synthesized video using the dedicated module to prepare the Deep fakes. At the end, we shows a model to get the efficacy of a group of GAN generators using dense motion networks. Our results generate the augmented animation video using the sequel driving combination of driving video with source image. This project can be used in many areas like multiplying the dataset counts with minimum number source, CG platforms where gaming industry animation industry using to create real-time backgrounds characters, Cloth translations, 3D object generation, etc
Aggregating distributed KOSs: enriching with meta information and linking to the multilingual KOS registry

Purpose This study aims to collect distributed knowledge organization systems (KOSs) from various domains, enrich each with meta information and link them to the multilingual KOS registry, facilitating integrated search alongside KOSs from various languages and regions. Design/methodology/approach This research involved collecting and organizing KOS information through three primary steps. The initial phase involved finding KOSs from Web search results, supplemented by the Korea ON-line E-Procurement System (KONEPS) and the National R&amp;D Integrated Notification Service. After obtaining these KOSs, they were enriched by structuring contextual meta information using Basic Register of Thesauri, Ontologies and Classification (BARTOC) metadata elements and established dedicated media wiki pages for each. Finally, the KOSs were linked to the multilingual KOS registry, BARTOC, ensuring seamless integration with KOSs from various languages and regions and creating connections between each registry entry and its associated KOS wiki page. Findings The research findings revealed several insights, as follows: (1) importance of a stable source for collecting KOS: no national body currently oversees KOS registration, underscoring the need for a systematic approach to collect dispersed KOSs. For Korean KOSs (K-KOSs), KONEPS and National R&amp;D Integrated Notification Service are effective data sources. (2) Importance of enhanced metadata: merely collecting KOSs were not enough. Enhanced metadata bridges access gaps and dedicated wiki pages aid user identification and understanding. (3) Observations from multilingual registry uploads: When adding KOSs to a multilingual registry, similarities were observed across languages and regions. Recognizing this, the K-KOSs were linked with their international counterparts, fostering potential global collaboration. Research limitations/implications Due to the absence of a dedicated KOS registry agency, the study might have missed KOSs from certain fields or potentially over-collected from others. Furthermore, this study primarily focused on K-KOSs and their integration into the BARTOC registry, which might influence the methods and perspectives on collecting and establishing links among analogous KOSs in the registry. Originality/value This research pursued a stable method to detect KOS development and revisions across various fields. To facilitate this, we used the integrated e-procurement and R&amp;D notification system and added meta information to aid in the identification and understanding of KOSs, which includes media wiki pages. Furthermore, link information was provided between the BARTOC registry and the Korean KOS websites and media wiki pages.
The Economy and The Digital: Investments to Improve the Student Experience

For several decades, universities have been trying to implement new technologies in their teaching methods, intending to create skills for the twenty-first century. In the literature, this process is called digital transformation. This chapter is based on an integrative revision and solid work of the authors in their university, providing students with technological devices, such as laptops, tablets, and other gadgets to invest in digital education skills. Concluding that investing in digital education is crucial for improving the student experience and preparing students for the future workforce.
Multilingual legal terminology databases

This chapter focuses on multilingual legal terminology databases (MLTDBs) and their specific features. We address the aspects influencing the structure of MLTDBs, i.e., the number of legal systems, the different usage scenarios, and target users. We describe the steps within a typical workflow for creating MLTDBs, i.e., needs analysis, design, documentation, term extraction, compilation of terminological entries, revision and quality assurance, maintenance, and dissemination, highlighting differences with other domains. We list the roles involved in legal terminology work, considering not only human users but also the increasing role played by machines as a new type of user of terminological data for various applications. Finally, we discuss aspects of quality management, planning, assurance, and control in MLTDBs, including relevant standards.
The role of Semantic Web technologies in legal terminology

With the rise of Artificial Intelligence driven services and Natural Language Processing techniques, high quality terminological resources are more needed than ever. Still, substantial manual effort is involved in the generation of such resources and many of them are published in non-machine-readable formats, hindering their reuse. In this chapter, we analyse how the Semantic Web can help represent legal language resources and how its publication in open formats can contribute to its interoperability. Throughout the sections, we give an overview of the Semantic Web, review existing legal language resources and the semantic models used to represent them. We also offer a series of practical examples to show the advantages, encouraging users to adopt these conventions.