Reviewerly/exHarmony huggingface dataset:
To change or not to change? Modeling software system interactions using Temporal Graphs and Graph Neural Networks: A focus on change propagation

Authors:
raw_author_name: Manuella Germanos
raw_affiliation_string: Department of Computer Science & Mathematics, Lebanese American University, Byblos, 1401, Lebanon
raw_author_name: Danielle Azar
raw_affiliation_string: Department of Computer Science & Mathematics, Lebanese American University, Byblos, 1401, Lebanon

Abstract: The world is quickly adopting new technologies and evolving to rely on software systems for the simplest tasks. This prompts developers to expand their software systems by adding new product features. However, this expansion should be cautiously tackled to prevent the degradation of the quality of the software product. One challenge when modifying code - whether to patch a bug or add a feature- is knowing which components will be affected by the change and amending possible misbehavior. In this context, the study of change propagation or the impact of introducing a change is needed. By investigating how changing one component may impact the functionality of a dependency (another component), developers can prevent unexpected behavior and maintain the quality of their system. In this work, we tackle the change propagation problem by modeling a software system as a temporal graph where nodes represent system files and edges co-changeability, i.e., the tendency of two files to change together. The graph representation is temporal so that nodes and edges can change with time, reflecting the addition of files in the system and changes in dependencies. We then employ a Temporal Graph Network and a Long Short-Term Memory model to predict which other files will be impacted by a modification performed on a file. We test our model on software systems of different functionality, size, and nature. We compare our results to other published work, and our model shows a significantly higher ability to predict files impacted by a change. The proposed approach effectively predicts change propagation in software systems and can guide developers and software engineers in planning the change and estimating the cost in terms of time and money.
Commutative encryption and watermarking algorithm based on compound chaotic systems and zero-watermarking for vector map

Authors:
raw_author_name: Liming Zhang
raw_affiliation_string: Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou 730070, China; Gansu Provincial Engineering Laboratory for National Geographic State Monitoring, Lanzhou 730070, China; National Local Joint Engineering Research Center of Technologies and Application for National Geographic State Monitoring, Lanzhou 730070, China
raw_author_name: Shuai Wang
raw_affiliation_string: Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou 730070, China; Gansu Provincial Engineering Laboratory for National Geographic State Monitoring, Lanzhou 730070, China; National Local Joint Engineering Research Center of Technologies and Application for National Geographic State Monitoring, Lanzhou 730070, China
raw_author_name: Lei Wang
raw_affiliation_string: Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou 730070, China; Gansu Provincial Engineering Laboratory for National Geographic State Monitoring, Lanzhou 730070, China; National Local Joint Engineering Research Center of Technologies and Application for National Geographic State Monitoring, Lanzhou 730070, China
raw_author_name: Ziyi Zhang
raw_affiliation_string: Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou 730070, China; Gansu Provincial Engineering Laboratory for National Geographic State Monitoring, Lanzhou 730070, China; National Local Joint Engineering Research Center of Technologies and Application for National Geographic State Monitoring, Lanzhou 730070, China

Abstract: Commutative encryption and watermarking (CEW) is an efficient and safe protection technology combining cryptography and digital watermarking. It features the dual functions of secure transmission and copyright safeguarding. In traditional CEW algorithms for vector map, the watermark is embedded by modifying the host data, which cannot meet the needs of high precision vector map, especially for vector maps applied in military and navigation positioning fields. To fill this gap, a CEW algorithm for vector map based on zero-watermarking and permutation encryption is proposed. Firstly, compound chaotic systems are applied to permutation encryption to encrypt the storage order and position of the vertex coordinates respectively. The key is generated by the SHA-256 hash method, which has higher security. Secondly, the number of vertex features is regarded as the feature invariants to establish the watermark information and watermark index. After that, the zero-watermarking image is constructed through the XOR operation of the feature matrix and the copyright information matrix. Experiments show that the combination of permutation encryption and zero watermark can better meet the needs of secure transmission and copyright protection of high-precision vector graphics. The security, robustness and watermark capacity of the algorithm have been improved, making it more practical.
DyFusion: Cross-Attention 3D Object Detection with Dynamic Fusion

Authors:
raw_author_name: Jiangfeng Bi
raw_affiliation_string: Hebei University of Science and Technology , Shijiazhuang, China
raw_author_name: Haiyue Wei
raw_affiliation_string: Hebei University of Science and Technology , Shijiazhuang, China
raw_author_name: Guoxin Zhang
raw_affiliation_string: Hebei University of Science and Technology , Shijiazhuang, China
raw_author_name: Kuihe Yang
raw_affiliation_string: Hebei University of Science and Technology , Shijiazhuang, China
raw_author_name: Ziying Song
raw_affiliation_string: Hebei University of Science and Technology , Shijiazhuang, China

Abstract: In the realm of autonomous driving, LiDAR and camera sensors play an indispensable role, furnishing pivotal observational data for the critical task of precise 3D object detection. Existing fusion algorithms effectively utilize the complementary data from both sensors. However, these methods typically concatenate the raw point cloud data and pixel-level image features, unfortunately, a process that introduces errors and results in the loss of critical information embedded in each modality. To mitigate the problem of lost feature information, this paper proposes a Cross-Attention Dynamic Fusion (CADF) strategy that dynamically fuses the two heterogeneous data sources. In addition, we acknowledge the issue of insufficient data augmentation for these two diverse modalities. To combat this, we propose a Synchronous Data Augmentation (SDA) strategy designed to enhance training efficiency. We have tested our method using the KITTI and nuScenes datasets, and the results have been promising. Remarkably, our top-performing model attained an 82.52% mAP on the KITTI test benchmark, outperforming other state-of-the-art methods.
Robust Localization-Guided Dual-Branch Network for Camouflaged Object Segmentation

Authors:
raw_author_name: Chuanjiang Wang
raw_affiliation_string: College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao 266590, China
raw_author_name: Yuepeng Li
raw_affiliation_string: College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao 266590, China
raw_author_name: Guohui Wei
raw_affiliation_string: College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao 266590, China
raw_author_name: Xiujuan Sun
raw_affiliation_string: College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao 266590, China

Abstract: The existence of camouflage targets is widespread in the natural world, as they blend seamlessly or closely resemble their surrounding environment, making it difficult for the human eye to identify them accurately. In camouflage target segmentation, challenges often arise from the high similarity between the foreground and background, resulting in segmentation errors, imprecise edge detection, and overlooking of small targets. To address these issues, this paper presents a robust localization-guided dual-branch network for the recognition of camouflaged targets. Two crucial branches, i.e., a localization branch and an overall refinement branch are designed and incorporated. The localization branch achieves accurate preliminary localization of camouflaged targets by incorporating the robust localization module, which integrates different high-level feature maps in a partially decoded manner. The overall refinement branch optimizes segmentation accuracy based on the output predictions of the localization branch. Within this branch, the edge refinement module is devised to effectively reduce false negative and false positive interference. By conducting context exploration on each feature layer from top to bottom, this module further enhances the precision of target edge segmentation. Additionally, our network employs five jointly trained output prediction maps and introduces attention-guided heads for diverse prediction maps in the overall refinement branch. This design adjusts the spatial positions and channel weights of different prediction maps, generating output prediction maps based on the emphasis of each output, thereby further strengthening the perception and feature representation capabilities of the model. To improve its ability to generate highly confident and accurate prediction candidate regions, tailored loss functions are designed to cater to the objectives of different prediction maps. We conducted experiments on three publicly available datasets for camouflaged object detection to assess our methodology and compared it with state-of-the-art network models. On the largest dataset COD10K, our method achieved a Structure-measure of 0.827 and demonstrated superior performance in other evaluation metrics, outperforming recent network models.
V4D: Voxel for 4D Novel View Synthesis

Authors:
raw_author_name: Wanshui Gan
raw_affiliation_string: Graduate School of Frontier Sciences, The University of Tokyo, Chiba, Japan
raw_author_name: Hongbin Xu
raw_affiliation_string: South China University of Technology and Alibaba Group, Guangzhou, China
raw_author_name: Yi Huang
raw_affiliation_string: Shenzhen Key Lab of Computer Vision and Pattern Recognition, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China; University of Chinese Academy of Sciences, Beijing, China
raw_author_name: Shifeng Chen
raw_affiliation_string: Shenzhen Key Lab of Computer Vision and Pattern Recognition, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China; University of Chinese Academy of Sciences, Beijing, China
raw_author_name: Naoto Yokoya
raw_affiliation_string: Graduate School of Frontier Sciences, The University of Tokyo, Chiba, Japan

Abstract: Neural radiance fields have made a remarkable breakthrough in the novel view synthesis task at the 3D static scene. However, for the 4D circumstance (e.g., dynamic scene), the performance of the existing method is still limited by the capacity of the neural network, typically in a multilayer perceptron network (MLP). In this article, we utilize 3D Voxel to model the 4D neural radiance field, short as V4D, where the 3D voxel has two formats. The first one is to regularly model the 3D space and then use the sampled local 3D feature with the time index to model the density field and the texture field by a tiny MLP. The second one is in look-up tables (LUTs) format that is for the pixel-level refinement, where the pseudo-surface produced by the volume rendering is utilized as the guidance information to learn a 2D pixel-level refinement mapping. The proposed LUTs-based refinement module achieves the performance gain with little computational cost and could serve as the plug-and-play module in the novel view synthesis task. Moreover, we propose a more effective conditional positional encoding toward the 4D data that achieves performance gain with negligible computational burdens. Extensive experiments demonstrate that the proposed method achieves state-of-the-art performance at a low computational cost.
A Progressive UNDML Framework Model for Breast Cancer Diagnosis and Classification

Authors:
raw_author_name: G. Meenalochini
raw_affiliation_string: School of Computing, Kalasalingam Academy of Research and Education, Krishnankoil, Tamil Nadu
raw_author_name: Ramkumar Sivasakthivel
raw_affiliation_string: Department of Computer Science, School of Sciences, CHRIST (Deemed to be University), Bangalore, India
raw_author_name: Manikandan Rajagopal
raw_affiliation_string: Lean Operations and Systems, School of Business and Management, CHRIST (Deemed to be University), Bangalore, India

Abstract: According to recent research, it is studied that the second most common cause of death for women worldwide is breast cancer. Since it can be incredibly difficult to determine the true cause of breast cancer, early diagnosis is crucial to lowering the disease's fatality rate. Early cancer detection raises the chance of survival by up to 8%. Radiologists look for irregularities in breast images collected from mammograms, X-rays, or MRI scans. Radiologists of all levels struggle to identify features like lumps, masses, and micro-calcifications, which leads to high false-positive and false-negative rates. Recent developments in deep learning and image processing give rise to some optimism for the creation of improved applications for the early diagnosis of breast cancer. A methodological study was carried out in which a new Deep U-Net Segmentation based Convolutional Neural Network, named UNDML framework is developed for identifying and categorizing breast anomalies. This framework involves the operations of preprocessing, quality enhancement, feature extraction, segmentation, and classification. Preprocessing is carried out in this case to enhance the quality of the breast picture input. Consequently, the Deep U-net segmentation methodology is applied to accurately segment the breast image for improving the cancer detection rate. Finally, the CNN mechanism is utilized to categorize the class of breast cancer. To validate the performance of this method, an extensive simulation and comparative analysis have been performed in this work. The obtained results demonstrate that the UNDML mechanism outperforms the other models with increased tumor detection rate and accuracy
Update on the use of gamified educational resources in the development of cognitive skills

Authors:
raw_author_name: Javier Gonzalez-Argote

Abstract: Introduction: the use of game elements and game design techniques in content unrelated to the same is known as gamification. It is likely that gamified environments generate a fruitful scenario to evoke complex processes within higher nervous activity and thus develop cognitive skills. This facilitates its application for educational purposes. The objective was to characterize the use of gamified educational resources in the development of cognitive skills. Method: a total of 27 articles in Spanish and English were reviewed, from Pubmed, Scielo and Scopus; using as keywords: gamification, game, cognition, being more than 50% of the last five years.Result: gamification improves the services in which it is applied through playful experiences, it can positively influence aspects of the student experience such as level of interest, intellectual intensity and intrinsic motivation, by providing opportunities for autonomy, relationship and competition. There is a strong need for further exploration and experimentation to provide a gamified design that meets user preferences, tailored to individual characteristics. Conclusions: Gamified educational environments facilitate learning like a game. Through gamification it seems to increase motivation, commitment, active interaction, level of interest, critical thinking, intellectual intensity and intrinsic motivation. The effectiveness of gamification largely depends on the use of different game features and how they are implemented
Constrained Nonnegative Matrix Factorization Based on Label Propagation for Data Representation

Authors:
raw_author_name: Junmin Liu
raw_affiliation_string: School of Mathematics and Statistics, Xi&#x0027;an Jiaotong University, Xi&#x0027;an, China
raw_author_name: Yicheng Wang
raw_affiliation_string: School of Mathematics and Statistics, Xi&#x0027;an Jiaotong University, Xi&#x0027;an, China
raw_author_name: Jing Ma
raw_affiliation_string: School of Mathematics and Statistics, Xi&#x0027;an Jiaotong University, Xi&#x0027;an, China
raw_author_name: D. Han
raw_affiliation_string: School of Credit Management, Guangdong University of Finance, Guangdong, China
raw_author_name: Yifan Huang
raw_affiliation_string: School of Mathematics and Statistics, Xi&#x0027;an Jiaotong University, Xi&#x0027;an, China

Abstract: <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Nonnegative matrix factorization</i> (NMF) algorithms are a series of dimensional reduction techniques widely used in data preprocessing. To improve the performance of clustering and the discrimination of the low-dimensional representation in NMF, we proposed a novel semi-supervised <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">constrained nonnegative matrix factorization based on label propagation</i> (LpCNMF). Specifically, the proposed LpCNMF adopts graph and label propagation as regularization terms, then makes use of a small amount of labeled data to predict the label information of the unlabeled data and finally obtains a predictive membership matrix with more label information. At the same time, we introduce an efficient alternating iterative algorithm to solve the optimization problem of the objective function in the LpCNMF. Unlike other NMF algorithms that only update the basis and coefficient matrices, the LpCNMF algorithm increases the update of the predictive membership matrix obtained by label propagation. Experimental results on various benchmark datasets demonstrate the superiority of our algorithm over existing state-of-the-art NMF algorithms. The code is publicly available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/ycwang-libra/LpCNMF_github.git.</uri>
A new class of generalized almost perfect nonlinear monomial functions

Authors:
raw_author_name: Lijing Zheng
raw_affiliation_string: School of Mathematics and Physics, University of South China, Hengyang, Hunan, 421001, China
raw_author_name: Haibin Kan
raw_affiliation_string: School of Computer Sciences, Fudan University, Shanghai, 200433, China
raw_author_name: Jie Peng
raw_affiliation_string: Mathematics and Science College of Shanghai Normal University, Shanghai, 200234, China
raw_author_name: Yanjun Li
raw_affiliation_string: Institute of Statistics and Applied Mathematics, Anhui University of Finance and Economics, Bengbu, Anhui, 233030, China
raw_author_name: Yanbin Zheng
raw_affiliation_string: School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou 510665, China; School of Mathematical Sciences, Qufu Normal University, Qufu 273165, China

Abstract: In this short note, we present a new class of GAPN power functions of the type xk2p2i+k1pi+k0 over finite fields Fpn with p odd and gcd(n,i)=1 (up to EA-equivalence).
A Multi-vocal Literature Review on challenges and critical success factors of phishing education, training and awareness

Authors:
raw_author_name: Orvila Sarker
raw_affiliation_string: Centre for Research on Engineering Software Technologies (CREST), The University of Adelaide, Australia; Cyber Security Cooperative Research Centre, Australia; School of Computer and Mathematical Sciences, The University of Adelaide, Australia
raw_author_name: Asangi Jayatilaka
raw_affiliation_string: Centre for Research on Engineering Software Technologies (CREST), The University of Adelaide, Australia; School of Computer and Mathematical Sciences, The University of Adelaide, Australia
raw_author_name: Sherif Haggag
raw_affiliation_string: School of Computer and Mathematical Sciences, The University of Adelaide, Australia
raw_author_name: Chelsea Liu
raw_affiliation_string: Adelaide Business School, The University of Adelaide, Australia
raw_author_name: M. Ali Babar
raw_affiliation_string: Centre for Research on Engineering Software Technologies (CREST), The University of Adelaide, Australia; Cyber Security Cooperative Research Centre, Australia; School of Computer and Mathematical Sciences, The University of Adelaide, Australia

Abstract: Phishing is a malicious attempt by cyber attackers to steal personal information through deception. Phishing attacks are often aided by carefully crafted phishing emails, which can go undetected by automated anti-phishing tools due to their limited accuracy. Studies found that user education, training, and awareness can thwart phishing attacks. Understanding diverse interconnected challenges and critical success factors of phishing education, training, and awareness (PETA) approaches can help improve organizations’ defense against phishing. This study presents a comprehensive, structured view of the challenges and critical success factors of the design, implementation, and evaluation stages of PETA. We have conducted a Multi-vocal Literature Review (MLR) by systematically collecting 53 academic studies and 16 grey studies from popular databases by following a well-known MLR guideline. We identified 20 challenges and 23 critical success factors, some of which involve human-centric and socio-technical factors in PETA. Our findings point out the need for designing explainable anti-phishing systems and developing automated tools and platforms to conduct real-world phishing studies. Our systematic analysis of 69 studies has enabled us to highlight the need for addressing human-centric issues, incorporating users’ knowledge gaps, and adopting personalized approaches in PETA.
Probabilistic Programming with Exact Conditions

Authors:
raw_author_name: Dario Stein
raw_affiliation_string: Radboud University Nijmegen, The Netherlands
raw_author_name: Sam Staton
raw_affiliation_string: University of Oxford, United Kingdom

Abstract: We spell out the paradigm of exact conditioning as an intuitive and powerful way of conditioning on observations in probabilistic programs. This is contrasted with likelihood-based scoring known from languages such as Stan . We study exact conditioning in the cases of discrete and Gaussian probability, presenting prototypical languages for each case and giving semantics to them. We make use of categorical probability (namely Markov and CD categories) to give a general account of exact conditioning, which avoids limits and measure theory, instead focusing on restructuring dataflow and program equations. The correspondence between such categories and a class of programming languages is made precise by defining the internal language of a CD category.