Reviewerly/exHarmony huggingface dataset:
3D Virtual Reality Application with Design Approach for Introduction of Istano Basa Pagaruyung Tourist Attractions

This research is about the development of proper media for the tourist attraction, which in this case related to the Istano Basa Pagaruyung in order to increase the engagement during Covid-19 pandemic, which is the Android-based. It aims to produce interactivity and innovation product service by using Virtual Reality (VR) technology through design approach method as cognitive and practical process. For the purpose of testing, there are three aspects that were evaluated namely the functional conformity, the usability and the portability. In the first aspect, the results indicated almost 100% successful presentation, while the usability aspect provide the presentation value of 86.7% and the last aspects show zero errors result. Thus, it can conclude that the application can be one alternative solution to promote tourist attraction in the midst of the Covid-19 pandemic in term of marketing or learning mechanism. It also presents the useful practice on the utilization of design approach that can be aligned with multimedia development life cycle (MDLC) to have non-linear and iterative process in order to redefine the problem for designing the alternative solution
Research on The Architecture of an Intelligent Assistant System for Commanders

Battle commanders in modern war are facing new challenges such as massive information and short time windows. There is an urgent demand of intelligent assistant system to communicate with the commanders and staff officers, think about what they think, answer their questions, offer advises to their decision making, learn their experiences, and significantly improve their performance. Based on capability requirements analysis of such system, its system architecture was proposed, which shares data, knowledge, functions and computing power with the command information system currently in use. An AI framework based technical architecture was proposed, which has five characteristics: natural human-computer interaction, flexible function integration, knowledge system driven, continues ability evolution, and powerful computing support.
The Y-Model - Formalization of Computer Science Tasks in the Context of Adaptive Learning Systems

Tasks, understood as educational instructions to transform an initial state to a targeted state in the learner model, are central elements in (computer science) education, irrespective of the mode of delivery – online or classic face-to-face teaching. They appear in different roles; as learning objects that help acquire competencies, as assessment instruments for measuring learning success, or for practicing specific skills. However, as with any educational technology, proper handling and well-planned selection are crucial for successful teaching. Correspondingly, the requirements that tasks have to fulfill are wide-ranging: they should promote specific learning outcomes, be appropriate to address desired cognitive dimensions, and be tailored to the student to keep motivation and learning success high. To enable an on-demand, outcome-oriented, and individualized selection of tasks in adaptive learning systems, we need to formalize tasks in all dimensions. A detailed review of different models for cognitive processes, knowledge dimensions in education, tasks, and errors leads to the Y-Model, a model for formalizing tasks (in computer science). It forms the basis for an individual, competence-oriented integration of tasks in educational systems. Finally, we present the first implementation of the model.
A Routing Algorithm of Blockchain Network Based on Reinforcement Learning

Aiming at the problems of the current methods of accelerating block propagation by optimizing the blockchain network topology: (1) these methods are based on a fixed network topology, however, the network topology of the blockchain network dynamically changes; (2) these methods take use of the local centralized characteristics, which contradict the decentralized characteristics of the blockchain network, we propose a new blockchain network routing algorithm which changes the blockchain network routing problem to a reinforcement learning problem. The algorithm reacts to the change of network environment and optimizes the route based on the feedback. Therefore, a better propagation path can be obtained by reacting to network change timely, which can ultimately reduce the total propagation delay. The experimental results show that: (1) compared with the Gossip algorithm of the bitcoin network, our proposed algorithm can reduce the total propagation delay by 38% when the number of nodes exceeds 10,000 and the degree of network variation is 10%; (2) compared with Gossip, when the number of nodes reaches 10,000, and the degree of network variation increases from 1% to 10%, our proposed algorithm can reduce the variation range of the total propagation delay from 73% to 14%.
Semantics‐guided generative diffusion model with a 3DMM model condition for face swapping

Abstract Face swapping is a technique that replaces a face in a target media with another face of a different identity from a source face image. Currently, research on the effective utilisation of prior knowledge and semantic guidance for photo‐realistic face swapping remains limited, despite the impressive synthesis quality achieved by recent generative models. In this paper, we propose a novel conditional Denoising Diffusion Probabilistic Model (DDPM) enforced by a two‐level face prior guidance. Specifically, it includes (i) an image‐level condition generated by a 3D Morphable Model (3DMM), and (ii) a high‐semantic level guidance driven by information extracted from several pre‐trained attribute classifiers, for high‐quality face image synthesis. Although swapped face image from 3DMM does not achieve photo‐realistic quality on its own, it provides a strong image‐level prior, in parallel with high‐level face semantics, to guide the DDPM for high fidelity image generation. The experimental results demonstrate that our method outperforms state‐of‐the‐art face swapping methods on benchmark datasets in terms of its synthesis quality, and capability to preserve the target face attributes and swap the source face identity.
Exploring the Use of Social Virtual Reality Conferences in Higher Education

Sparked by the recent growth of online and remote teaching formats, social virtual reality (Social VR) applications are being employed in higher education teaching. This eliminates the need for physical presence in one classroom and allows for increased accessibility of classes. More engaging distant virtual classrooms and extracurricular activities are made possible by Social VR applications like Mozilla Hubs (MH). We conducted a virtual conference in web-based MH as a cross-university collaboration for two game development courses for undergraduate and graduate students. We report on our organizational strategy and subsequent online survey evaluation of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$N=29$</tex> attendees. We present solutions to problems that are frequently encountered, specifically in MH, while organizing virtual conferences.
Path planning with obstacle avoidance for soft robots based on improved particle swarm optimization algorithm

Soft-bodied robots have the advantages of high flexibility and multiple degrees of freedom and have promising applications in exploring complex unstructured environments. Kinematic coupling exists for the soft robot in a problematic space environment for motion planning between the soft robot arm segments. In solving the soft robot inverse kinematics, there are only solutions or even no solutions, and soft robot obstacle avoidance control is tough to exist, as other problems. In this paper, we use the segmental constant curvature assumption to derive the positive and negative kinematic relationships and design the tip self-growth algorithm to reduce the difficulty of solving the parameters in the inverse kinematics of the soft robot to avoid kinematic coupling. Finally, by combining the improved particle swarm algorithm to optimize the paths, the convergence speed and reconciliation accuracy of the algorithm are further accelerated. The simulation results prove that the method can successfully move the soft robot in complex space with high computational efficiency and high accuracy, which verifies the effectiveness of the research.
Pseudocide: dying to get away with it

Purpose When an individual leaves evidence to suggest that they are dead to mislead others, they are committing “pseudocide.” This study aims to examine pseudocides performed for the purpose of committing or concealing a financial crime. Design/methodology/approach This manuscript summarizes information from both academic and nonacademic publications. To provide evidence related to the conclusions made by prior authors and identify the risk factors that are often indicative of pseudocide, this manuscript examines the characteristics of pseudocides that received media coverage. Findings Pseudocides that receive media coverage often involve a prominent figure or a compelling story. These stories are not representative of the average pseudocide, which receives no publicity because it occurs in a less developed nation and is committed by a nonpublic figure. Common characteristics include the absence of a corpse and paperwork procured through bribery. Originality/value One of the only academic papers focused on pseudocide, this manuscript provides readers with information related to the scope of the issue, common methodologies and factors indicative of pseudocide. This should be of interest to several parties including forensic accountants, insurers, regulators and academics.
FPC-BTB detection and positioning system based on optimized YOLOv5

With the aim of addressing the visual positioning problem of board-to-board (BTB) jacks during the automatic assembly of flexible printed circuit (FPC) in mobile phones, an FPC-BTB jack detection method based on the optimized You Only Look Once, version 5 (YOLOv5) deep learning algorithm was proposed in this study. An FPC-BTB jack real-time detection and positioning system was developed for the real-time target detection and pose output synchronization of the BTB jack. On that basis, a visual positioning experimental platform that integrated a UR5e manipulator arm and Hikvision industrial camera was built for BTB jack detection and positioning experiments. As indicated by the experimental results, the developed FPC-BTB jack detection and positioning system for BTB target recognition and positioning achieved a success rate of 99.677%. Its average detection accuracy reached 99.341%, the average confidence of the detected target was 91%, the detection and positioning speed reached 31.25 frames per second, and the positioning deviation was less than 0.93 mm, which conforms to the practical application requirements of the FPC assembly process.
A smartphone accelerometer data-driven approach to recognize activities of daily life: A comparative study

Smartphones have become an indispensable part of our everyday life, influencing various aspects of our routines, from wake-up alarms to managing daily life activities. Nowadays, almost every smartphone has a built-in accelerometer sensor. Motivated by the notable increase in smartphone usage in our everyday life, in this research, we focus on harnessing the potential of smartphone accelerometers to recognize human daily life activities, aiming to leverage the usability and convenience of smartphones. We used smartphone accelerometer data from data collection to daily life activity recognition. To accomplish this, we first collected the smartphone's accelerometer data while performing five activities of daily living (ADLs) namely: moving downstairs, upstairs, running, standing, and walking, from 25 volunteers through a mobile application. After this, we extracted 15 statistical features from the smartphone's accelerometer data to efficiently classify the five referred ADLs. We then applied data pre-processing techniques, i.e., data cleaning and feature extraction. Afterward, we trained nine commonly used machine learning models to recognize five ADLs. Finally, we evaluated and compared the performance of all nine ML models to recognize each activity and analyzed the performance of these trained ML models to identify all five ADLs. The evaluated results revealed that the Adaboost (AB) classifier outperformed all other ML models with 100% area under the curve (AUC), precision, recall, accuracy, and F1-score for recognizing the five ADLs.
Evaluating the velocity of ships from low-resolution SAR images

Synthetic aperture radar (SAR) is a fundamental tool in maritime surveillance for its weather and daylight insensitivity and large swath. Position, size and bearing of a target ship can be extracted with high accuracy from a SAR image. Additionally, those images contain several cues to estimate the target velocity. However, dealing with spatial resolutions of tens of meters, the applicability of some approaches is questionable. We formerly experimented two velocity estimation methods based on the features extracted from the wake of a sailing ship [1]: one is based on the wavelength of the cusp waves characterizing the Kelvin wake pattern; the other is based on the typical azimuth shift that affects moving targets imaged by a SAR system. Both methods directly return the radial component of the ship speed; the azimuthal speed, and thus the complete velocity vector, can finally be reconstructed using the estimated ship track. The mentioned techniques rely on the detection of significant parts of the wake in the SAR image. This is not always possible, especially with low-resolution images, due to a combination of unfavorable incidence angles and rough status of the sea.
Deep Learning-Based Vehicle Classification

Vehicle classification is an essential part of intelligent transportation systems (ITS). This work proposes a model based on transfer learning, combining data augmentation for the recognition and classification of local vehicle classes in Canada. It takes inspiration from contemporary deep learning (DL) achievements for image classification. This makes use of the Dataset named Stanford AI of Vehicles, which has 16185 images. The images in this section are divided into 196 types of vehicles. To increase performance further, additional classification blocks are added to the residual network (ResNet-50)-based model which is being used. In this case, vehicle type details are automatically extracted and classified. A number of measures like accuracy, precision, recall, etc. are used during the analysis to evaluate the results. The proposed model exhibits increased accuracy despite vehicles' different physical characteristics. In comparison to the current baseline method and the two pre-trained DL systems, AlexNet and VGG-16, our suggested method outperforms them all. The suggested ResNet-50 pre-trained model achieves an accuracy of 90.07% in the classification of native vehicle types, according to the outcome comparisons. We also compare this by running VGG-16 where we are getting an accuracy of 82.5%. Along with this vehicle classification, we have implemented number plate detection and smart vehicle counter systems which all together makes our transport system better than ever before.
Indoor self-supervised monocular depth estimation based on level feature fusion

éå¯¹ç®åèªçç£åç®æ·±åº¦ä¼°è®¡ç½ç»å¨å æ¥çå¤§éä½çº¹çãä½å ç §åºåçå®¤å å¤æåºæ¯ä¸­å­å¨é¢æµæ·±åº¦ä¿¡æ¯ä¸ç²¾ç¡®ãç©ä½è¾¹ç¼æ¨¡ç³ä»¥åç»èä¸¢å¤±ä¸¥éç­é®é¢ï¼æ¬ææåºä¸ç§åºäºå±çº§ç¹å¾èåçå®¤å èªçç£åç®æ·±åº¦ä¼°è®¡ç½ç»æ¨¡åãé¦å ï¼éè¿æ å°ä¸è´æ§å¾åå¢å¼ºæ¨¡åæ¥å¤çå®¤å å¾åï¼æåä½å ç §åºåå¯è§æ§å¹¶ä¸ä¿æäº®åº¦ä¸è´æ§ï¼ä¸°å¯çº¹çç»èï¼ä¸å®ç¨åº¦ä¸è§£å³äºè®­ç»ç½ç»æ¶åºç°æ¨¡ç³åå¹³é¢æ¶åæ¨¡åçé®é¢ãç¶åï¼è®¾è®¡ç»ååºäºæ³¨æåæºå¶çè·¨å±çº§ç¹å¾è°æ´æ¨¡åçæ·±åº¦ä¼°è®¡ç½ç»ï¼å åèåç¼ç å¨ä»¥åç¼-è§£ç å¨å¤å±çº§ç¹å¾ä¿¡æ¯ï¼æåç½ç»çç¹å¾å©ç¨è½åï¼ç¼©å°é¢æµæ·±åº¦ä¸çå®æ·±åº¦çè¯­ä¹å·®è·ãæåï¼è®¾è®¡åºäºå¾åé£æ ¼ç¹å¾çæ ¼æå§ç©éµç¸ä¼¼æ§æå¤±å½æ°ä½ä¸ºé¢å¤çèªçç£ä¿¡å·çº¦æç½ç»æ¨¡åï¼æåç½ç»é¢æµæ·±åº¦çè½åï¼è¿ä¸æ­¥æé«äºé¢æµæ·±åº¦çç²¾åº¦ãå¨NYU Depth V2 åScanNetå®¤å æ°æ®éä¸è¿è¡è®­ç»ä¸æµè¯ï¼æ­£ç¡®é¢æµæ·±åº¦åç´ çæ¯ä¾è½å¤åå«è¾¾å°81.9%å76.0%ãå®éªç»æè¡¨æï¼ç¸æ¯ç°æä¸»è¦çå®¤å èªçç£åç®æ·±åº¦ä¼°è®¡ç½ç»ï¼æ¬æç½ç»æ¨¡åå¾å¥½å°ä¿æäºç©ä½è¾¹ç¼åç»èä¿¡æ¯ï¼ææå°æé«äºé¢æµæ·±åº¦çç²¾åº¦ã
Classical Capacity of Arbitrarily Distributed Noisy Quantum Channels

With the rapid deployment of quantum computers and quantum satellites, there is a pressing need to design and deploy quantum and hybrid classical-quantum networks capable of exchanging classical information. In this context, we conduct the foundational study on the impact of a mixture of classical and quantum noise on an arbitrary quantum channel carrying classical information. The rationale behind considering such mixed noise is that quantum noise can arise from different entanglement and discord in quantum transmission scenarios, like different memories and repeater technologies, while classical noise can arise from the coexistence with the classical signal. Towards this end, we derive the distribution of the mixed noise from a classical system’s perspective, and formulate the achievable channel capacity over an arbitrary distributed quantum channel in presence of the mixed noise. Numerical results demonstrate that capacity increases with the increase in the number of photons per usage.
Image Caption Generation for Dai Ethnic Clothing Based on ViT-B and BertLMHeadModel

Dai ethnic clothing is unique and distinct subset of minority apparel within China, impressing with their rich patterns and unique styles. Yet, due to the complexity and diversity of Dai clothing, providing accurate captions of these outfits is a formidable challenge. In an effort to address this issue, this study utilizes ViT-B from the pre-trained model BLIP to perform feature extraction on images of Dai clothing, thereby converting them into a sequence of feature vectors. Subsequently, these vectors are decoded using BertLMHeadModel to generate corresponding text captions, effectively producing captions for the Dai clothing. For the training of our model, we constructed Dai clothing dataset consisting of Dai clothing image dataset and image text annotation dataset. With images and textual descriptions serving as inputs, model parameters are optimized using cross-entropy loss and AdamW. In employing this strategy, our model is capable of extracting key features from images and generating accurate captions, providing detailed captions of the characteristics of Dai clothing with a high degree of semantic accuracy.
Online subject searching of humanities PhD students at a Swedish university

Purpose This exploratory study aims to help contribute to the understanding of online information search behaviour of PhD students from different humanities fields, with a focus on subject searching. Design/methodology/approach The methodology is based on a semi-structured interview within which the participants are asked to conduct both a controlled search task and a free search task. The sample comprises eight PhD students in several humanities disciplines at Linnaeus University, a medium-sized Swedish university from 2020. Findings Most humanities PhD students in the study have received training in information searching, but it has been too basic. Most rely on web search engines like Google and Google Scholar for publications' search, and university's discovery system for known-item searching. As these systems do not rely on controlled vocabularies, the participants often struggle with too many retrieved documents that are not relevant. Most only rarely or never use disciplinary bibliographic databases. The controlled search task has shown some benefits of using controlled vocabularies in the disciplinary databases, but incomplete synonym or concept coverage as well as user unfriendly search interface present hindrances. Originality/value The paper illuminates an often-forgotten but pervasive challenge of subject searching, especially for humanities researchers. It demonstrates difficulties and shows how most PhD students have missed finding an important resource in their research. It calls for the need to reconsider training in information searching and the need to make use of controlled vocabularies implemented in various search systems with usable search and browse user interfaces.
SimpleG: A Design of a Beginner-Friendly Procedural Content Generator for the Unity Tool

This study covers how uncommon Procedural Content Generation (PCG) is in today's industry and why there are only a limited number of people that use it. The game design proposal in this study contains a summary regarding a Unity Extension geared towards an easier gateway for new developers to get started with Procedural Content Generation. This extension was tested to collect feedback about their experiences with PCG and their opinions for how the extension could be improved.
Detecting Phishing Attacks Using Feature Importance-Based Machine Learning Approach

Phishing is a fraudulent technique that involves creating a malicious website or link to trick people into revealing sensitive information such as passwords, financial details, or personal information. It is a common form of cybercrime that can lead to identity theft and other harmful consequences. To address this serious threat, researchers have explored various approaches to detect it, including heuristic, list-based, and machine learning approaches. In our study, we developed an automated phishing detection approach that selects important features through feature selection based on the feature importance method. Once the important features were identified, we built models using Random Forest, KNN, and Logistic Regression algorithms. For our study, we utilized the ISCXURL-2016 dataset and employed a feature selection process to identify and select only significant features. By doing so, our approach achieves a remarkable accuracy of 98.85% with the Random Forest algorithm. Notably, the scalability of our method is enhanced because our method automatically extracts the important features from the data and eliminates the need for a human to select the features using domain knowledge and experience. As a result, our approach's scalability makes it a feasible solution for detecting phishing attacks in real-world scenarios.
Cybersecurity in the Metaverse: Challenges and Approaches

A network of 3D virtual worlds centered on social interaction is known as a metaverse. It is frequently characterized as a hypothetical version of the Internet as a single, worldwide virtual environment in science fiction and futuristic literature, made possible by the usage of virtual reality (VR) and augmented reality (AR) headgear. The basic blocks of the metaverse's architecture are mostly already in advanced stages of development. On the other hand, many others are still several years away from being useful. We begin by examining its structures before moving on to the ingenious protection and security challenges brought up by this new viewpoint. The goal is to layout the ground for an insightful discussion about user privacy, integrity, and authentication in the metaverse, as well as countermeasures and security perspectives with humans in and out of the loop. Finally, we highlight some of the broad yet recurring effects on a variety of settings in terms of data explosion and exploitation, distributed denial of service, device vulnerability, and polarization and radicalization in a singleton world.