Reviewerly/exHarmony huggingface dataset:
A soft computing approach for software defect density prediction

Abstract Defect density is an essential software testing and maintenance aspect that determines the quality of software products. It is used as a management factor to distribute limited human resources successfully. The availability of public defect datasets facilitates building defect density prediction models using established static code metrics. Since the data gathered for software modules are often subject to uncertainty, it becomes difficult to deliver accurate and reliable predictions. To alleviate this issue, we propose a new prediction model that integrates gray system theory and fuzzy logic to handle the imprecision in software measurement. We propose a new similarity measure that combines the benefits of fuzzy logic and gray relational analysis. The proposed model was validated against defect density prediction models using public defect datasets. The defect density variable is frequently sparse because of the vast number of none‐defected modules in the datasets. Therefore, we also check our proposed model's performance against the sparsity level. The findings reveal that the developed model surpasses other defect density prediction models over the datasets with high and very high sparsity ratios. The ensemble learning techniques are competitive choices to the proposed model when the sparsity ratio is relatively small. On the other hand, the statistical regression models were the most inadequate methods for such problems and datasets. Finally, the proposed model was evaluated against different degrees of uncertainty using a sensitivity analysis procedure. The results showed that our model behaves stably under different degrees of uncertainty.
PHOTOCHAIN: A BLOCKCHAIN BASED SECURE PHOTO SHARING FRAMEWORK FOR CROSS-SOCIAL NETWORK

The popularity of online social networks has led to increased sharing of photos, but the privacy of these photos can be compromised when they are spread to other platforms.Existing solutions do not always provide efficient social network services while ensuring privacy.PhotoChain is a proposed blockchain based framework that allows for secure photo sharing across social networks with dissemination control, face masking, photo integrity verification , access control and dynamic privacy policy generation.Smart contracts ensure consistent consensus on dissemination control and the framework includes robust photo ownership identification mechanisms to prevent illegal reprinting.A prototype has been implemented and tested to demonstrate the security, efficacy, efficiency of the framework.
Modelling of piano string vibration using coupled mobilities and a state-space approach

In musical acoustics a double decay can occur if there are two decay slopes in the sound envelope with the second part of the time history having a less pronounced slope than the first. In piano strings the double decay can be caused by the differences between the decays of vibration in the two transverse directions of a single string: normal and parallel to the soundboard. Herein we aim to reproduce this phenomenon by coupling a stiff string model and the soundboard, in both frequency and time domain, using mobility coupling and state-space model approaches. The dynamics of a stiff string tensioned between the two end pins but disconnected from the soundboard bridge is first studied. The two transverse directions of vibration are assumed to be uncoupled due to symmetry. On the contrary, the soundboard mobilities at the point of connection with the string can be represented by a multidimensional (2x2 in this work) and fully populated matrix implying coupling between different directions. The string and the soundboard will be connected by means of frequency and time domain models to show the importance of the cross-mobility terms on the vibration response and double decay of the string.
Assessing manifestations of bossing in the context of personality traits of business managers

Abusive supervision or bossing represents a specific form of mobbing as a long-term, systematic negative action by superiors toward subordinates.From the point of view of the operationalization of this construct, the original BOSSm18 methodology is presented in the paper in the context of the B5 methodology, which enables specification of personality traits in terms of the original Big Five concept.Based on the research dataset of 636 business managers, the paper presents the results of the basic psychometric parameters of the methodology and the content specification of the extracted factors. The research findings support a multidimensional understanding of the bossing construct.The limiting factors of the interpretation and generalization of the results relate to the consideration of cultural contexts and situational conditions of perception of bossing manifestations.
ResAttn-recon: Residual self-attention based cortical surface reconstruction

Introduction: The accurate cerebral cortex surface reconstruction is crucial for the study of neurodegenerative diseases. Existing voxelwise segmentation-based approaches like FreeSurfer and FastSurfer are limited by the partial volume effect, meaning that reconstruction details highly rely on the resolution of the input volume. In the computer version area, the signed distance function has become an efficient method for 3D shape representation, the inherent continuous nature makes it easy to capture the fine details of the target object at an arbitrary resolution. Additionally, as one of the most valuable breakthroughs in deep learning research, attention is a powerful mechanism developed to enhance the performance of the encoder-decoder architecture. Methods: To further improve the reconstruction accuracy of the cortical surface, we proposed ResAttn-Recon, a residual self-attention based encoder-decoder framework. In this framework, we also developed a lightweight decoder network with skip connections. Furthermore, a truncated and weighted L1 loss function are proposed to accelerate network convergence, compared to simply applying the L1 loss function. Results: The intersection over union curve in the training process achieved a steeper slope and a higher peak (0.948 vs. 0.920) with a truncated L1 loss. Thus, the average symmetric surface distance (AD) for the inner and outer surfaces is 0.253 ± 0.051 and the average Hausdorff distance (HD) is 0.629 ± 0.186, which is lower than that of DeepCSR, whose absolute distance equals 0.283 ± 0.059 and Hausdorff distance equals 0.746 ± 0.245. Discussion: In conclusion, the proposed residual self-attention-based framework can be a promising approach for improving the cortical surface reconstruction performance.
Robust Graph Structure Learning with Virtual Nodes Construction

Graph neural networks (GNNs) have garnered significant attention for their ability to effectively process graph-related data. Most existing methods assume that the input graph is noise-free; however, this assumption is frequently violated in real-world scenarios, resulting in impaired graph representations. To address this issue, we start from the essence of graph structure learning, considering edge discovery and removal, reweighting of existing edges, and differentiability of the graph structure. We introduce virtual nodes and consider connections with virtual nodes to generate optimized graph structures, and subsequently utilize Gumbel-Softmax to reweight edges and achieve differentiability of the Graph Structure Learning (VN-GSL for abbreviation). We conducted a thorough evaluation of our method on a range of benchmark datasets under both clean and adversarial circumstances. The results of our experiments demonstrate that our approach exhibits superiority in terms of both performance and efficiency. Our implementation will be made available to the public.
MBDM: Multinational Banknote Detecting Model for Assisting Visually Impaired People

With the proliferation of smartphones and advancements in deep learning technologies, object recognition using built-in smartphone cameras has become possible. One application of this technology is to assist visually impaired individuals through the banknote detection of multiple national currencies. Previous studies have focused on single-national banknote detection; in contrast, this study addressed the practical need for the detection of banknotes of any nationality. To this end, we propose a multinational banknote detection model (MBDM) and a method for multinational banknote detection based on mosaic data augmentation. The effectiveness of the MBDM is demonstrated through evaluation on a Korean won (KRW) banknote and coin database built using a smartphone camera, a US dollar (USD) and Euro banknote database, and a Jordanian dinar (JOD) database that is an open database. The results show that the MBDM achieves an accuracy of 0.8396, a recall value of 0.9334, and an F1 score of 0.8840, outperforming state-of-the-art methods.
Bust Portraits Matting Based on Improved U-Net

Extracting complete portrait foregrounds from natural images is widely used in image editing and high-definition map generation. When making high-definition maps, it is often necessary to matte passers-by to guarantee their privacy. Current matting methods that do not require additional trimap inputs often suffer from inaccurate global predictions or blurred local details. Portrait matting, as a soft segmentation method, allows the creation of excess areas during segmentation, which inevitably leads to noise in the resulting alpha image as well as excess foreground information, so it is not necessary to keep all the excess areas. To overcome the above problems, this paper designed a contour sharpness refining network (CSRN) that modifies the weight of the alpha values of uncertain regions in the prediction map. It is combined with an end-to-end matting network for bust matting based on the U-Net target detection network containing Residual U-blocks. An end-to-end matting network for bust matting is designed. The network can effectively reduce the image noise without affecting the complete foreground information obtained by the deeper network, thus obtaining a more detailed foreground image with fine edge details. The network structure has been tested on the PPM-100, the RealWorldPortrait-636, and a self-built dataset, showing excellent performance in both edge refinement and global prediction for half-figure portraits.
On J-Diagrams for the One Groups of Finite Chain Rings

Let R be a finite commutative chain ring with invariants p,n,r,k,m. The purpose of this article is to study j-diagrams for the one group H=1+J(R) of R, where J(R)=(π) is Jacobson radical of R. In particular, we prove the existence and uniqueness of j-diagrams for such one group. These j-diagrams help us to solve several problems related to chain rings such as the structure of their unit groups and a group of all symmetries of {πk′}, where k′∣k. The invariants p,n,r,k,m and the Eisenstein polynomial by which R is constructed over its Galois subring determine fully the j-diagram for H.
Hybrid Attention Asynchronous Cascade Network for Salient Object Detection

The highlighted area or object is defined as the salient region or salient object. For salient object detection, the main challenges are still the clarity of the boundary information of the salient object and the positioning accuracy of the salient object in the complex background, such as noise and occlusion. To remedy these issues, it is proposed that the asynchronous cascade saliency detection algorithm based on a deep network, which is embedded in an encoder–decoder architecture. Moreover, the lightweight hybrid attention module is designed to obtain the explicit boundaries of salient regions. In order to effectively improve location information of salient objects, this paper adopts a bi-directional asynchronous cascade fusion strategy, which generates prediction maps with higher accuracy. The experimental results on five benchmark datasets show that the proposed network HACNet is on a par with the state of the art for image saliency datasets.
Intelligent Music Interfaces: When Interactive Assistance and Augmentation Meet Musical Instruments

The interactive augmentation of musical instruments to foster self-expressiveness and learning has a rich history. Over the past decades, the incorporation of interactive technologies into musical instruments emerged into a new research field requiring strong collaboration between different disciplines. The workshop "Intelligent Music Interfaces" covers a wide range of musical research subjects and directions, including (a) current challenges in musical learning, (b) prototyping for improvements, (c) new means of musical expression, and (d) evaluation of the solutions.
LUNAChair: Remote Wheelchair System Linking Users to Nearby People and Assistants

With the growing need for wheelchairs, much wheelchair research has focused on wheelchair control, such as fully automatic driving and remote control. For wheelchair users, conversations with caregivers and others around the wheelchair are also important and must be considered. Therefore, we propose a system that connects the users to remote caregivers and others by using omnidirectional displays. Our system allows remote caregivers to observe both the users and their surroundings and to control the wheelchair, while the users and others feel the existence of remote caregivers. We conducted a system evaluation and user interviews to demonstrate that the proposed system augments wheelchair users’ experience.
The Effect of Posture on Virtual Walking Experience Using Foot Vibrations

The virtual walking systems that do not involve physical movement of the legs has the advantage of being able to be experienced while seated or supine. The sensation of virtual walking can be effectively elicited through the combination of optic flow and rhythmic foot vibrations. However, the effects of posture have yet to be fully understood. In light of this, the present study sought to investigate the effects of posture (standing, sitting, and lying supine) on the virtual walking experience utilizing foot vibrations and optic flow. Our hypothesis posited that the synchronization of foot vibrations would augment the walking sensation even in a seated or supine position. Our findings indicate that synchronized foot vibrations produced the sensation of virtual walking in all three postures, with the standing posture eliciting the strongest virtual walking sensation. No significant differences were observed between the sitting and supine postures. These results suggest that virtual walking systems utilizing foot vibrations have the potential to provide a certain degree of walking experience even for individuals unable to leave their bed.
Real‐time coloring method of laser surgery video based on generative adversarial network

Abstract Lasers have become an important technology for the medical treatment of cancer. However, the high power of visible laser knives makes conventional endoscopic imaging light pollution severe. This phenomenon can cause the physician to be unable to determine the condition of the lesion site. In this article, a colorization method for laser surgery videos is proposed. The method performs real‐time colorization of the laser surgery video under black‐and‐white imaging. The main contribution of this work is to propose a new idea for solving laser contamination in medical surgery videos, along with real‐time generative adversarial networks (RTGANs). The network takes advantage of generative adversarial networks and residual networks for fast colorization of black and white images. To evaluate the method, we conducted experiments using three datasets. The experimental results show that the RTGAN network takes only 0.026 s to colorize a single frame image. Coloring speed up to 67 times faster than advanced methods, and the Peak Signal to Noise Ratio reached a maximum of 35.736.
LocatAR: An AR Object Search Assistance System for a Shared Space

Item-finding tasks due to memory lapse are costly activities commonly experienced by many people. However, conventional systems are not suitable for use in a collaborative environment. Therefore, we propose a multi-functional, pre-registration-free, and 3D location-based item management system. The system has two main functions: registration and search. The automatic registration is performed by image-based item movement recognition from the user’s grasping and placing motions. The registered item movement data comprises the item category, and the start and end locations. We ensure privacy protection by storing item movement data without images. Also, we provide a user interaction to refuse to share the items with other users. The search is based on the item list or item location. The location-based search is performed by specifying where the user last saw the item. To optimize and test the performance of the system, we first performed parameter optimization and then conducted a user study investigating the performance of a search task. The parameter optimization performed in the registration system led to the discovery of optimal values that are difficult to reach empirically. The search experiment showed that the proposed system’s search and guidance functions are effective as an assistance system for finding items, both in terms of search time and user experience. Overall, our system demonstrated the potential to be a useful assistance system for managing items in a shared space. We further discuss the possibility of further exploiting the limited registered information by treating item location as an identifier of the moved item.
Tablet Cutting Board: Tablet-based Knife-control Support System for Cookery Beginners

It is difficult for novice cooks to cut food evenly and quickly with a knife. In this study, we propose a learning support system for kitchen knife skills using a tablet terminal. The system aims to help novice cooks cut food evenly and vertically. The system has a function to display guide lines for cutting food, and a function to detect the movement of the knife using a touch panel and provide real-time feedback on whether the width of the cut food is appropriate or not. In addition, an inertial sensor was attached to the knife to estimate the tilt of the knife based on acceleration data, and a function was implemented to provide feedback on whether the knife is cutting at an appropriate angle or not. A prototype system was implemented, and the accuracy of estimating the width of the food and the accuracy of tilt detection were measured when the prototype system was used to cut the food. It was found that the prototype system was able to estimate the width of the food with an average error of 0.5 mm and the tilt of the knife with an average error of 0.5 degrees.
Comparative Evaluation of Joystick and Leaning-Based Locomotion in Immersive Telepresence with Body Motion Feedback

Some previous studies have focused on the effects of leaning-based methods and motion feedback for reducing cybersickness and improving usability during locomotion in virtual environments. However, it is actually challenging to combine of these technologies for practical use in the real environments using telepresence robots. In this study, we developed an immersive telepresence system with body motion feedback and comparatively evaluated the effectiveness of using a joystick and a leaning-based method in terms of reducing cybersickness and improving usability during locomotive tasks. The results of the user study (N=8) showed that leaning-based method with body motion feedback is effective for improving comfort and reducing cybersickness compared with the cases of using the joystick. Moreover, the use of body motion display to generate walking like sensation was shown to be effective for improving the telepresence experience.
Super-immersive Remote Working via Virtual Reality Controlled Robotics

In this paper, we briefly describe a novel VR-robot interface developed (on top of the eNetReality [2] authoring platform by eCom Scotland) to provide users with an enhanced learning experience by enabling them to operate the robot remotely through the VR device. We implemented and tested this interface to control the Franka Emika Panda robotic arm. The main objective of this work is to provide an immersive experience to the user by enabling them to interact with the digital twin of the Franka robot and visualize its actions in the VR device. To achieve this objective, we integrated a haptic feedback device to enable the user to receive feedback while the robotic arm performs the pick and place task. We have extensively tested this interface with a cohort of Electronics and Electrical engineering graduates from Glasgow College, UESTC, China. Our ultimate goal is to extend our interface to support multiple robots.
Investigating Effects of Facial Self-Similarity Levels on the Impression of Virtual Agents in Serious/Non-Serious Contexts

Recent technological advances have enabled the use of AI agents to assist with human tasks and augment human cognitive abilities in a variety of contexts, including decision making. It is critical that users trust these AI agents in order to use them effectively. Given that people tend to trust other people who are similar to themselves, incorporating features of one’s own face into the AI agent’s face may improve one’s trust in the AI agent. However, it is still unclear how impressions differ when comparing agents with the same appearance as one’s own and some similarities under the same conditions. Recognizing the appropriate level of similarity when using a self-similar agent is important for establishing a trustworthy agent relationship between people and the AI agent. Therefore, we investigated the effect of the degree of self-similarity of the face of the AI agent on the user’s trust in the agent. We examined users’ impressions of four AI agents with different degrees of face self-similarity in different scenarios. The results showed that the AI agent, whose similarity to the user’s facial feature was slightly recognizable but not obvious, received higher ratings on the feeling of closeness, attractiveness, and facial preferences. These self-similar AI agents were also more trustworthy in everyday non-serious decisions and were more likely to improve people’s trustworthiness in such situations. Finally, we discuss the potential applications of our findings to design real-world AI agents.
Phantom Undulations: Remote Physiological Sensing in Abstract Installation Works

Phantom Undulations is a mixed-media work in which an artist’s physiological data is being used remotely to manipulate the sounds and visuals of an abstract artifact in a gallery setting. This work relies heavily on the concept of showing the artist’s presence or liveness in an abstract and remote manner through changes in the harmony, rhythm, and timbre of a loosely structured soundscape as well as the physical appearance of the artifact. We propose a method of utilizing real time physiological sensing data through a custom built sensing wristband and accompanying software. This system reads the physiological data of the artist and sends it to the Internet, where it can be received by the artifact anywhere on Earth. In addition to the artist’s physiological data, we also offer a way for the audience to incorporate their own data into the work via several sensing wristbands which will accompany the artifact. Through this collaborative process, we wish to invite the audience to join the artist in manipulating the sonic and visual characteristics of this artifact and create a contrapuntally fluid and responsive musical experience.