Reviewerly/exHarmony huggingface dataset:
Collaborative software design and modeling in virtual reality

Authors:
raw_author_name: Ivan Polasek
raw_affiliation_string: Comenius University, Faculty of Mathematics, Physics and Informatics, Mlynská dolina F1, Bratislava, 842 48, Slovakia; Gratex International a.s., Galvaniho 17/C, Bratislava, 821 04, Slovakia
raw_author_name: Juraj Vincur
raw_affiliation_string: Comenius University, Faculty of Mathematics, Physics and Informatics, Mlynská dolina F1, Bratislava, 842 48, Slovakia
raw_author_name: Rodi Jolak
raw_affiliation_string: RISE Research Institutes of Sweden, Box 857, Borås, 501 15, Sweden
raw_author_name: Michel Chaudron
raw_affiliation_string: Eindhoven University of Technology, O.L. Vrouwestraat 1, Eindhoven, 5612 AW, Netherlands

Abstract: Context: Software engineering is becoming more and more distributed. Developers and other stakeholders are often located in different locations, departments, and countries and operating within different time zones. Most online software design and modeling tools are not adequate for distributed collaboration since they do not support awareness and lack features for effective communication. Objective: The aim of our research is to support distributed software design activities in Virtual Reality (VR). Method: Using design science research methodology, we design and evaluate a tool for collaborative design in VR. We evaluate the collaboration efficiency and recall of design information when using the VR software design environment compared to a non-VR software design environment. Moreover, we collect the perceptions and preferences of users to explore the opportunities and challenges that were incurred by using the VR software design environment. Results: We find that there is no significant difference in the efficiency and recall of design information when using the VR compared to the non-VR environment. Furthermore, we find that developers are more satisfied with collaboration in VR. Conclusion: The results of our research and similar studies show that working in VR is not yet faster or more efficient than working on standard desktops. It is very important to improve the interface in VR (gestures with haptics, keyboard and voice input), as confirmed by the difference in results between the first and second evaluation.
Universal adversarial backdoor attacks to fool vertical federated learning

Authors:
raw_author_name: Peng Chen
raw_affiliation_string: School of Computer Science, Fudan University, Shanghai, 200433, China
raw_author_name: Xin Du
raw_affiliation_string: School of Computer Science, Fudan University, Shanghai, 200433, China
raw_author_name: Zhihui Lu
raw_affiliation_string: School of Computer Science, Fudan University, Shanghai, 200433, China; Shanghai Blockchain Engineering Research Center, Shanghai, 200433, China
raw_author_name: Hongfeng Chai
raw_affiliation_string: School of Computer Science, Fudan University, Shanghai, 200433, China

Abstract: Vertical federated learning (VFL) is a privacy-preserving distribution learning paradigm that enables participants, owning different features of the same sample space to train a machine learning model collaboratively while retaining their data locally. This paradigm facilitates improved efficiency and security for participants such as financial or medical fields, making VFL an essential component of data-driven Artificial Intelligence systems. Nevertheless, the partitioned structure of VFL can be exploited by adversaries to inject a backdoor, enabling them to manipulate the VFL predictions. In this paper, we aim to investigate the vulnerability of VFL in the context of binary classification tasks. To this end, we define a threat model for backdoor attacks in VFL and introduce a universal adversarial backdoor (UAB) attack to poison the predictions of VFL. The UAB attack, consisting of universal trigger generation and clean-label backdoor injection, is incorporated during the VFL training at specific iterations. This is achieved by alternately optimizing VFL sub-problems' universal trigger and model parameters. Our work distinguishes itself from existing studies on designing backdoor attacks for VFL, as those require the knowledge of auxiliary information that is not accessible within the split VFL architecture. In contrast, our approach does not require additional data to execute the attack. On the real-world datasets, our approach surpasses existing state-of-the-art methods, achieving up to 100% backdoor task performance while maintaining the main task performance. Our results in this paper make a major advance in revealing the hidden backdoor risks of VFL, hence paving the way for the future development of secure VFL. Our results in this paper make a major advance in revealing the hidden backdoor risks of VFL, hence paving the way for the future development of secure VFL applications such as finance.
Managing the changing understanding of benefits in software initiatives

Authors:
raw_author_name: Sinan Sigurd Tanilkan
raw_affiliation_string: Simula Metropolitan Center for Effective Digitalization of the Public Sector, Pb. 4, St. Olavs plass, Oslo, 0130, Norway
raw_author_name: Jo Erskine Hannay
raw_affiliation_string: Simula Metropolitan Center for Effective Digitalization of the Public Sector, Pb. 4, St. Olavs plass, Oslo, 0130, Norway

Abstract: When deciding to develop new software, that decision should be based on a clear understanding of the intended benefits the system can lead to. But what if the understanding of those benefits fluctuates. In this article, we investigate the understanding that stakeholders have or build of the benefits of the system under development, and what role that understanding plays. We conducted four studies: an exploratory study based on 22 interviews on nine projects that had explicit incentives to conduct benefits management, a confirmatory study based on a survey (n=110) of concepts of benefits understanding developed in the first study, and two explanatory studies arranged as focus groups (five participants in each) on issues that arouse during analysis of the first two studies. We find that it is common for stakeholders' understanding of benefits to change during and after development (reported by 96% of respondents from their latest digitalization efforts). Predicting and managing the changes to understanding of benefits is not straightforward. This leads to uncertainty, both for investment decisions and how to manage benefits. We conclude that, rather than focusing on cookbook recipes for benefits management, more emphasis should be focused on helping practitioners embrace changes to understanding of benefits.
Evolutionary multi-objective design of autoencoders for compact representation of histopathology whole slide images

Authors:
raw_author_name: Davood Zaman Farsa
raw_affiliation_string: Computer Science, Ontario Tech University, Oshawa, ON, Canada
raw_author_name: Shahryar Rahnamayan
raw_affiliation_string: NICI Lab, Brock University, St. Catharines, ON, Canada
raw_author_name: Azam Asilian Bidgoli
raw_affiliation_string: Faculty of Science, Wilfrid Laurier University, Waterloo, ON, Canada
raw_author_name: H.R. Tizhoosh
raw_affiliation_string: Rhazes Lab, Artificial Intelligence and Informatics, Mayo Clinic, Rochester, MN, USA

Abstract: The recent success of deep learning coincides with the surge of digital pathology as an effect of the pandemic. However, processing a massive volume of gigapixel histopathology images is daunting. Hence, establishing compact image representations for efficient computational pathology becomes paramount. Deep autoencoders have been frequently used to compress feature vectors extracted from pre-trained networks. However, the topology of autoencoders is generally set heuristically. In this paper, we propose a multi-objective evolutionary framework as a bi-level optimization scheme in which the outer level evolves autoencoders for the goal of dimension reduction while the inner level is to optimize their weights for training. Throughout the evolution process, multiple minimization objectives such as complexity, classification error, and code size are considered. These objectives can obtain compressed models to generate a very small set of deep features with the highest classification accuracy. We use images from The Cancer Genome Atlas (TCGA) repository provided by the National Cancer Institute to train and validate our optimization framework. We increased the classification accuracy by 8% while the image representation is 46,000 times shorter. We apply the framework to demonstrate higher accuracy, substantial memory reduction and faster processing for image retrieval compared to SOTA.
Dual-DIANet: A sharing-learnable multi-task network based on dense information aggregation

Authors:
raw_author_name: Kejie Lyu
raw_affiliation_string: College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, Zhejiang, China
raw_author_name: Yingming Li
raw_affiliation_string: College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, Zhejiang, China
raw_author_name: Zhongfei Zhang
raw_affiliation_string: Computer Science Department, Binghamton University, Binghamton, NY, USA

Abstract: Designing neural networks that balance shared features and task-specific ones is a major challenge in multi-task learning. To solve this issue, we aggregate information from both multi-scale and multi-level perspectives for a more comprehensive understanding of the multi-task features, which enables the network to learn a better sharing way. Specifically, we first introduce a basic Multi-scale-focused Dense Information Aggregation Network, where learnable fusion modules are used to connect two task-biased subnets and control the feature sharing. The fusion modules, consisting of densely cascaded dilated convolutions and scale-wise attention blocks, adaptively aggregate multi-scale information for each task. To further exploit multi-level information, the modules at different levels are mutually connected in a dense manner and guided by auxiliary supervision. By combining these two aspects of information aggregation, a Dual Dense Information Aggregation Network with a strong ability to learn appropriate sharing is finally proposed. Comprehensive experiments are reported on NYUDv2, SUN RGB-D, and Mini-Taskonomy to show the effectiveness of our method.
A markless 3D human motion data acquisition method based on the binocular stereo vision and lightweight open pose algorithm

Authors:
raw_author_name: Bo Sheng
raw_affiliation_string: Shanghai Key Laboratory of Intelligent Manufacturing and Robotics, Shanghai University, 99 Shangda Road, Baoshan District, Shanghai, China
raw_author_name: Jian Cheng
raw_affiliation_string: Shanghai Rural Commercial Bank Co., Ltd., 1045 Laian Road, Pudong District, Shanghai, China
raw_author_name: Yanxin Zhang
raw_affiliation_string: Department of Exercise Sciences, The University of Auckland, 4703906 Newmarket, Auckland, New Zealand
raw_author_name: Zikai Hua
raw_affiliation_string: School of Mechatronic Engineering and Automation, Shanghai University, 99 Shangda Road, Baoshan District, Shanghai, China
raw_author_name: Jing Tao
raw_affiliation_string: School of Mechatronic Engineering and Automation, Shanghai University, 99 Shangda Road, Baoshan District, Shanghai, China

Abstract: This study aimed to achieve precise 3D human motion data acquisition without the need for markers. Specifically, the proposed system utilized two affordable cameras to capture human motion from diverse angles. It developed a lightweight Open Pose algorithm to extract 2D human body joints from recorded videos. Subsequently, a stereo-matching method was applied to derive accurate 3D human body joint data. Comprehensive experiments with 10 healthy participants showcased the system's exceptional performance, demonstrating superior measurement accuracy compared to established standards like the NOKOV system (RMSE values consistently below 9°) and commonly used Microsoft Kinect devices (V2 and Azure). This innovative binocular vision system presents a new approach to motion capture, potentially offering a cost-effective and precise tool for practical applications across various scenarios (e.g., motor functional assessment, virtual avatar streaming), contributing significantly to both scientific and academic communities.
Attention driven CWT-deep learning approach for discrimination of Radar PRI modulation

Authors:
raw_author_name: Purabi Sharma
raw_affiliation_string: Department of Electronics and Communication Engineering, Gauhati University, Guwahati, Assam, India
raw_author_name: Kandarpa Kumar Sarma
raw_affiliation_string: Department of Electronics and Communication Engineering, Gauhati University, Guwahati, Assam, India

Abstract: With the proliferation of radio frequency (RF) systems and radar applications, Electronic Warfare (EW) is receiving increasing importance. The analysis of the radar signals is a critical EW task that decides the nature of counter employments. In an Electronic Support (ES) system, the challenge is to detect hostile radiation sources efficiently and trigger a counter response. Detection of types of Pulse Repetition Interval (PRI) modulation of radar signal significantly facilitates the manifestation of RF emitters during recognition which is difficult in a dense EW environment. Recent developments in artificial intelligence (AI) methods suggest that this emerging technology can be effective for such purposes. In this direction, an automatic approach for recognizing several kinds of complex PRI modulation based on Continuous Wavelet Transform (CWT) and a combination of the vanilla Convolutional Neural Network (CNN), a multi-head self-attention (MHSA) mechanism and the popular Long Short-Term Memory (LSTM) is proposed. The CWT is used to decompose the PRI modulation sequence and obtain different time–frequency components. Further, aided by the proposed CNN-MHSA-LSTM combination, the features extracted from the CWT 2D-scalograms are used to execute PRI modulation discrimination. In this method, the vanilla CNN is employed for the extraction of deep features to figure out the class details while capturing the spatial attributes. Thereafter, to improve the discriminative power of the entire framework a MHSA mechanism is used. The temporal attributes are acquired by the LSTM which works in concert with the CNN for executing the detection of the PRI classes based on the extracted features. Also to assess the effectiveness of the proposed method, three models based on ResNet, popular CNN and SqueezeNet are implemented for benchmark comparison in terms of overall performance and complexity. The simulation results show that the proposed method enhances performance and achieves robustness in the noise-filled and imperfect channel knowledge environment. The best recognition accuracy is 98.3% with 50% spurious pulses in the environment which fluctuates with imperfect channel knowledge cases.
Enhanced radial base deep learning algorithm for predicting multimedia security issues

Authors:
raw_author_name: V. Haripriya
raw_affiliation_string: Department of Computer Science and Information Technology, Jain (Deemed to be University), Bangalore, India
raw_author_name: Mohan Vishal Gupta
raw_affiliation_string: College of Computing Science and Information Technology, Teerthanker Mahaveer University, Moradabad, Uttar Pradesh, India
raw_author_name: Suraj Malik
raw_affiliation_string: Department of Computer Science & Engineering, IIMT University, Meerut, Uttar Pradesh, India
raw_author_name: Aditya Yadav
raw_affiliation_string: Maharishi School of Business Management, Maharishi University of Information Technology, Uttar Pradesh, India
raw_author_name: Apoorva Joshi
raw_affiliation_string: Masters in Computer Applications, Noida Institute of Engineering and Technology, Greater Noida, Uttar Pradesh, India

Abstract: From online social networks to life-or-death security systems, multimedia files (photos, movies, and audio recordings) have grown common in today’s digital culture. Protecting people, businesses and infrastructure requires strict adherence to the encryption and decryption of multimedia data. We suggested an Ensemble Whale Optimized Recurrent Neural Network (EWO-RNN)used in this study to overcome these issues. With the help of this study, multimedia security will be evaluated in more accurate and comprehensive manner. Smarter decisions and proactive security measures may follow as a result of this. To increase the system quality and the overall performance, the collected data is pre-processed for normalized data by using Min-Max Normalization. Pre-processed data is extracted by using Kernel Principle Component Analysis (K-PCA). The EWO-RNN evaluates the effectiveness and efficiency of an approach by analyzing the performance of Accuracy (97.85%), Precision (92.2%), F1-score (96.1%), Mean Square Error (MSE) (0.086), Root Mean Square (RMSE) (0.12%) and Sensitivity (95%). The Enhanced Radial Base Deep Learning Algorithm for Predicting Multimedia Security Issues proposes a solution with improved resilience, accuracy, generalization, and decision-making capabilities. In a dynamic and evolving digital environment, this makes the algorithm a viable tool for multimedia security assessments.
Automated code-based test case reuse for software product line testing

Authors:
raw_author_name: Pilsu Jung
raw_affiliation_string: Department of Aerospace and Software Engineering, Department of AI Convergence Engineering, Gyeongsang National University, 501 Jinju-daero, Jinju-si, Gyeongsangnam-do, Republic of Korea
raw_author_name: Seonah Lee
raw_affiliation_string: Department of Aerospace and Software Engineering, Department of AI Convergence Engineering, Gyeongsang National University, 501 Jinju-daero, Jinju-si, Gyeongsangnam-do, Republic of Korea

Abstract: A software product line (SPL) grows in size as a new product is developed. A new product in an SPL should be tested extensively for quality assurance. For the efficient testing, previous studies suggested reusing the existing test cases of a product family. However, either their methods were not efficient because interventions from human experts, specifications, architecture and/or traceabilities for test cases were required. To address these limitations, we propose an Automated Code-based Test case reuse for SPLs (ActSPL). ActSPL automatically identifies reusable test cases for new products of a product family using source code and test cases. ActSPL automatically constructs a hash-based traceability links between test cases and source code of a product family. Using the traceability links, ActSPL selects reusable test cases for a given new product from existing test cases of the product family. We evaluated ActSPL in terms of the effectiveness and cost reduction of reusing test cases with five open-source SPLs. The evaluation results showed that ActSPL, on average, achieved 100 % precision and 62 % recall. In addition, ActSPL, on average, saved 47.5 % of time required for testing a new product from scratch. Our study shows the feasibility of ActSPL reusing SPL test cases based on source code and test cases. Our results can be a basis for successive studies for automated code-based SPL testing.
Pixel-wise annotated and high-quality synthesized image datasets for semi-supervised truck segmentation with limited raw images

Authors:
raw_author_name: Benyang Song
raw_affiliation_string: State Key Laboratory of Hydraulic Engineering Simulation and Safety, Tianjin University, 135 Yaguan Rd., Tianjin 300350, China
raw_author_name: Jiajun Wang
raw_affiliation_string: State Key Laboratory of Hydraulic Engineering Simulation and Safety, Tianjin University, 135 Yaguan Rd., Tianjin 300350, China
raw_author_name: Xiaoling Wang
raw_affiliation_string: State Key Laboratory of Hydraulic Engineering Simulation and Safety, Tianjin University, 135 Yaguan Rd., Tianjin 300350, China
raw_author_name: Tuocheng Zeng
raw_affiliation_string: State Key Laboratory of Hydraulic Engineering Simulation and Safety, Tianjin University, 135 Yaguan Rd., Tianjin 300350, China
raw_author_name: Dongze Li
raw_affiliation_string: State Key Laboratory of Hydraulic Engineering Simulation and Safety, Tianjin University, 135 Yaguan Rd., Tianjin 300350, China

Abstract: Deep-learning-based segmentation methods have emerged as effective paradigms for intelligent monitoring, such as the loading status and locations of trucks at construction sites. However, the time-consuming and labor-intensive data preparation hinders the implementation of these methods. We propose a novel semi-supervised method to automatically synthesize high-quality and pixelwise annotated datasets for trucks using limited raw images. Ada-StyleGAN2 is initially employed to synthesize high-quality images from limited raw images. Subsequently, a label generator derived from DatasetGAN is integrated with atrous spatial pyramid pooling (ASPP) and a positional embedding module to generate pixel-level masks. Finally, a "crop and paste" module is proposed to address the scale variations. With only 5000 raw images and 18 annotated samples, the method can synthesize high-quality and pixelwise annotated images with a Fréchet inception distance (FID) of 5.40 in 30 min. Other annotations, such as pose estimation and activity recognition, have potential for future research.
Differential privacy scheme using Laplace mechanism and statistical method computation in deep neural network for privacy preservation

Authors:
raw_author_name: G. Sathish Kumar
raw_affiliation_string: Centre for Computational Imaging and Machine Vision, Department of Artificial Intelligence and Data Science, Sri Eshwar College of Engineering, Coimbatore, Tamil Nadu, India
raw_author_name: K. Premalatha
raw_affiliation_string: Department of Computer Science and Engineering, Bannari Amman Institute of Technology, Erode, Tamil Nadu, India
raw_author_name: G. Uma Maheshwari
raw_affiliation_string: Department of Computer Science and Engineering, Dr. Mahalingam College of Engineering and Technology, Coimbatore, Tamil Nadu, India
raw_author_name: P. Rajesh Kanna
raw_affiliation_string: Department of Computer Science and Engineering, Bannari Amman Institute of Technology, Erode, Tamil Nadu, India
raw_author_name: M. Nivaashini
raw_affiliation_string: Department of Computer Science and Engineering, Sri Ramakrishna Engineering College, Coimbatore, Tamil Nadu, India

Abstract: Mountainous amounts of information are now available in hospitals, finance, counter-terrorism, education and many other sectors. Those information can offer a rich source for analysis and decision making. Such information contains user's sensitive and personal data as well. This emanates direct conflict with the user's privacy. Individual's privacy is their right. The existing privacy preserving algorithms works mainly on the numerical data and doesn't care about the categorical data. In addition, there is a heavy trade-off between privacy preservation and data utility. To overcome these issues, a deep neural network - statistical differential privacy (DNN−SDP) algorithm is proposed as the solution to disguise the individual's private and sensitive data. Both the numerical and categorical based human-specific data are considered and fed to the input layer of the neural network. The statistical methods weight of evidence and information value is applied in the hidden layer along with the random weight (wi) to get the initial perturbed data. This initially perturbed data is taken by Laplace computation based differential privacy mechanism as the input and provides the final perturbed data. Census income, bank marketing and heart disease datasets are used for experimentation. While comparing with the state-of-the-art methods, DNN−SDP algorithm provides 97.4% of accuracy with 98.2% of precision, 99% of recall rate and 98.7% of F-measure value. In addition, the fall-out rate, miss rate and false omission rate of the proposed algorithm are less than 4.1%. The DNN−SDP algorithm guarantees the privacy preservation along with data utility.