Reviewerly/exHarmony huggingface dataset:
Visible-Xray Cross-Modality Package Re-Identification

During the package inspection, once prohibited articles are checked under the X-ray, the inspector needs to find the corresponding package in time for further confirmation. When the number of passengers increases, this process is time-consuming. Recently, prohibited articles detection as a detection task has attracted much attention, but few studies have focused on Visible-Xray package re-identification (VX-ReID) task. In this paper, we mainly explore the VX-ReID task. Firstly, we establish the first VX-ReID dataset RX01, which includes 55883 Visible and 29174 X-ray package images. Furthermore, we introduce a baseline model that includes a cross-modality channel attention module (CMCA) and momentum contrast mAP (MoCoAP). CMCA is used to enhance channels that contain modality-invariant information. MoCoAP is a differentiable mAP approximation strategy that directly optimizes the retrieve performance of the model. By combining these two strategies, we achieve competitive performance on the RX01 and SYSU-MM01 datasets. Code will be released at https://github.com/cjjjao/VX-ReID.
Just Noticeable Difference Estimation for Screen Content Images: A Content Uncertainty-guided Approach

Just-noticeable-difference (JND) effectively describes the threshold of the human visual system (HVS) perceiving visual signal changes, which reflects the visual redundancy. Researches indicate that the information distribution characteristics of images play an important role in the information inference of HVS. Inspired by this, we present a content uncertainty-guided JND estimation approach for screen content images (SCIs). Specifically, we divide SCIs into certain content and uncertain content according to content uncertainty modeling. Based on the analysis of content characteristics, we apply oblique masking (OM) and contrast masking (CM) in uncertain content, and consider luminance adaptation (LA) and blur adaptation (BA) in certain content. Besides, we adjust parameters with subjective experiments to better fit HVS characteristics. Compared with several state-of-the-art JND models, our method tolerates more distortion and owns better perceptual performance under the same injected-noise energy.
Physically Verifying the First Nonzero Term in a Sequence: Physical ZKPs for ABC End View and Goishi Hiroi

In this paper, we develop a physical protocol to verify the first nonzero term of a sequence using a deck of cards. This protocol enables a prover to show a verifier the value of the first nonzero term in a given sequence without revealing which term it is. Our protocol uses $$\varTheta (1)$$ shuffles, making it simpler and more practical than a similar protocol recently developed by Fukusawa and Manabe in 2022, which uses $$\varTheta (n)$$ shuffles, where n is the length of the sequence. We also apply our protocol to construct zero-knowledge proof protocols for two famous logic puzzles: ABC End View and Goishi Hiroi. These zero-knowledge proof protocols allow a prover to physically show that he/she know solutions of the puzzles without revealing them.
Advancements in OCR: A Deep Learning Algorithm for Enhanced Text Recognition

Optical Character Recognition (OCR) has significantly evolved with the rise of deep learning techniques. In this research paper, we present a novel and advanced OCR algorithm that leverages the power of deep learning for improved text recognition accuracy. Traditional OCR methods have faced limitations in handling complex layouts, noisy images, and diverse fonts, affecting overall performance. Our proposed algorithm addresses these challenges through the integration of deep neural networks, specifically convolutional and recurrent layers. The algorithm undergoes comprehensive training on large-scale datasets, enabling it to learn intricate patterns and features, resulting in robust recognition capabilities. Furthermore, we introduce an attention mechanism that enhances the model's ability to focus on critical text regions, enhancing accuracy and efficiency. Through extensive experiments and evaluations on benchmark datasets, we demonstrate the superiority of our deep learning-based OCR algorithm over conventional approaches. Our algorithm achieves state-of-the-art performance on various OCR tasks, including multilingual text recognition and document digitization. Additionally, we conduct an in-depth analysis of the algorithm's behaviour under various scenarios, such as low-resolution inputs and challenging environmental conditions. The findings from this research not only contribute to the field of OCR but also open avenues for applications in document analysis, text extraction, and content digitization in real-world scenarios. The integration of deep learning in OCR showcases its potential in revolutionising text recognition tasks, pushing the boundaries of accuracy and efficiency in this domain.
CoAxNN: Optimizing on-device deep learning with conditional approximate neural networks

While deep neural networks have achieved superior performance in a variety of intelligent applications, the increasing computational complexity makes them difficult to be deployed on resource-constrained devices. To improve the performance of on-device inference, prior studies have explored various approximate strategies, such as neural network pruning, to optimize models based on different principles. However, when combining these approximate strategies, a large parameter space needs to be explored. Meanwhile, different configuration parameters may interfere with each other, damaging the performance optimization effect. In this paper, we propose a novel model optimization framework, CoAxNN, which effectively combines different approximate strategies, to facilitate on-device deep learning via model approximation. Based on the principles of different approximate optimizations, our approach constructs the design space and automatically finds reasonable configurations through genetic algorithm-based design space exploration. By combining the strengths of different approximation methods, CoAxNN enables efficient conditional inference for models at runtime. We evaluate our approach by leveraging state-of-the-art neural networks on a representative intelligent edge platform, Jetson AGX Orin. The experimental results demonstrate the effectiveness of CoAxNN, which achieves up to 1.53× speedup while reducing energy by up to 34.61%, with trivial accuracy loss on CIFAR-10/100 and CINIC-10 datasets.
Views on Edits to Variational Software

Software systems are subject to frequent changes, for example to fix bugs or meet new customer requirements. In variational software systems, developers are confronted with the complexity of evolution and configurability on a daily basis; essentially handling changes to many distinct software variants simultaneously. To reduce the complexity of configurability for developers, filtered or projectional editing was introduced: By providing a partial or complete configuration, developers can interact with a simpler view of the variational system that shows only artifacts belonging to that configuration. Yet, such views are available for individual revisions only but not for edits performed across revisions. To reduce the complexity of evolution in variational software for developers, we extend the concept of views to edits. We formulate a correctness criterion for views on edits and introduce two correct operators for view generation, one operator suitable for formal reasoning, and a runtime optimized operator. In an empirical study, we demonstrate the feasibility of our operators by applying them to the change histories of 44 open-source software systems.
Benefits and Challenges: Data Management Plans in Two Collaborative Projects

The data-driven shift in the science research leads to a wider range of research data. To manage this data in a sustainable and adequate way, data management plans (DMPs) were established as a method. However, some researchers still do not create DMPs due to lack of time, resources and understanding of the needs. Furthermore, most of the existing templates and tools are largely unknown. In this article, we investigated the benefits and challenges of DMPs in two joint research projects of several academic institutions. For this, we described the process during the DMP creation, potential challenges and benefits experienced. We showed that a DMP with completely uniform content among the partner institutions was not possible due to individual and subject differences (e.g., in storage and policies). Instead, individual texts had to be formulated in some cases to overcome the diversity. This complexity could not be handled with the existing tools. Therefore, both projects created an own adapted template with some generic contents. Existing guidelines and internal project policies helped during the generation. We experienced that fewer people work more efficiently on a DMP than many and that all researchers within the project can profit from every individual DMP. Although we were not required to produce one, we recognised the associated benefits as a guide during the research process in joint projects.
Approaching Pattern Catalogues for Variability Realization in Model-Based Systems and Software Engineering

Managing variants, versions, and variability in system-level engineering is becoming increasingly important in industry and represents a challenge for model-based systems engineering.
A Comparison of Visualization Concepts and Tools for Variant-Rich System Engineering

Software product-line engineering is concerned with developing a set of similar, yet customized, software systems that share a common codebase. To develop such a variant-rich system, various development processes, techniques, and tools have been studied in research and are used in practice. Specifically, to help developers manage the complexity of developing large-scale variant-rich systems, researchers have proposed visualizations to visually present different properties of such systems and their engineering---such as feature models, configurations, the similarity of variants, or process traces. Two recent mapping studies have systematically elicited the state-of-the-art on such visualizations, but neither of them provides a comparative analysis of the underlying visualization concepts and tools. In this paper, we report a qualitative meta-analysis of the 64 papers that we primarily selected from these two mapping studies. Advancing on the previous studies, we compare the use cases, pros, cons, and relations between visualization concepts and tools used with respect to engineering variant-rich systems. Our results provide insights---orthogonal to those from the mapping studies---regarding the purposes for which visualization concepts are used and the tools that are available to implement these concepts. The overview we provide can help researchers as well as practitioners decide to use specific established visualization concepts or design new ones, and identify tools that can help them to implement these.
The Applications of Machine Learning in Medicine

The area where Machine Learning can be helpful in medicine is the prediction of outbreaks. We have seen the importance of outbreak predictions in the past two years. This paper presents an in-depth study of different applications of Machine Learning in medicine. We will discuss Artificial Intelligence based technologies that can prevent future outbreaks and digital diagnoses that are based on Machine Learning, and the difference between supervised and unsupervised learning.
Digital forensics and strong AI: A structured literature review

Forensics is an established field of research. Digital forensics started 44 years ago with the Florida Computer Crimes Act (1978) including legislation against the unauthorized modification of data on a computer system. Since then, the field has flourished in different subdomains. The overall definitions and concepts have been specified by a small group of experts. Furthermore, the need for development is created by the amount of digital evidence which is collected concerning most crimes. This paper gives an overview of the state-of-the-art by presenting a structured literature review of digital forensic about methods and concepts using Artificial Intelligence (AI) technologies. The review focuses on science done on topics in strong AI and forensics.
Several classes of optimal p-ary cyclic codes with minimum distance four

Cyclic codes are a subclass of linear codes and have wide applications in data storage systems, communication systems and consumer electronics due to their efficient encoding and decoding algorithms. Let p≥5 be an odd prime and m be a positive integer. Let C(1,e,s) denote the p-ary cyclic code with three nonzeros α, αe, and αs, where α is a generator of Fpm⁎, s=pm−12, and 2≤e≤pm−2. In this paper, by analyzing the solutions of certain equations over finite fields, we present four classes of optimal p-ary cyclic codes C(1,e,s) with parameters [pm−1,pm−2m−2,4]. Some known results on optimal quinary cyclic codes with parameters [5m−1,5m−2m−2,4] are special cases of our constructions. In addition, by analyzing the irreducible factors of certain polynomials over F5m, we present two classes of optimal quinary cyclic codes C(1,e,s).
Enhancing Occlusion Handling in Real-Time Tracking Systems through Geometric Mapping and 3D Reconstruction Validation

Object detection is a classic research problem in the area of Computer Vision. Many smart world applications, like, video surveillance or autonomous navigation systems require a high accuracy in pose detection of objects. One of the main challenges in Object detection is the problem of detecting occluded objects and its respective 3D reconstruction. The focus of this paper is inter-object occlusion where two or more objects being tracked occlude each other. A novel algorithm has been proposed for handling object occlusion by using the technique of geometric matching and its 3D projection obtained. The developed algorithm has been tested using sample data and the results are presented.
Monocular Depth Estimation for Glass Walls With Context: A New Dataset and Method

Traditional monocular depth estimation assumes that all objects are reliably visible in the RGB color domain. However, this is not always the case as more and more buildings are decorated with transparent glass walls. This problem has not been explored due to the difficulties in annotating the depth levels of glass walls, as commercial depth sensors cannot provide correct feedbacks on transparent objects. Furthermore, estimating depths from transparent glass walls requires the aids of surrounding context, which has not been considered in prior works. To cope with this problem, we introduce the first Glass Walls Depth Dataset (GW-Depth dataset). We annotate the depth levels of transparent glass walls by propagating the context depth values within neighboring flat areas, and the glass segmentation mask and instance level line segments of glass edges are also provided. On the other hand, a tailored monocular depth estimation method is proposed to fully activate the glass wall contextual understanding. First, we propose to exploit the glass structure context by incorporating the structural prior knowledge embedded in glass boundary line segment detections. Furthermore, to make our method adaptive to scenes without structure context where the glass boundary is either absent in the image or too narrow to be recognized, we propose to derive a reflection context by utilizing the depth reliable points sampled according to the variance between two depth estimations from different resolutions. High-resolution depth is thus estimated by the weighted summation of depths by those reliable points. Extensive experiments are conducted to evaluate the effectiveness of the proposed dual context design. Superior performances of our method is also demonstrated by comparing with state-of-the-art methods. We present the first feasible solution for monocular depth estimation in the presence of glass walls, which can be widely adopted in autonomous navigation.
Artificial Intelligence Techniques in Software Design for Mathematics Education

This chapter presents a state of the art in the design of digital environments for mathematics education, with a particular focus on artificial intelligence techniques. A review of the work done in this area over the last few decades highlights current challenges and distinguishes between symbolic approaches and machine learning. About symbolic approaches, we review automatic reasoning tools in geometry and their potential. We also consider the design and research work around the Casyopée environment and the use of logic programming in the QED-Tutrix intelligent tutoring system. With respect to machine learning, four classes of techniques constitute contemporary AI in computer science. Two examples are discussed: a deep learning system of monument analysis for learning situations in mathematics, technology and art, and a computer classroom simulator that provides a new approach to training teachers.
Automatic Screening System to Distinguish Benign/Malignant Breast-Cancer Histology Images Using Optimized Deep and Handcrafted Features

Abstract Breast Cancer (BC) has been increasing in incidence among women for a variety of reasons, and prompt detection and management are essential to reducing mortality rates. In the context of clinical-level breast cancer screening, the needle biopsy sample is used to generate Breast Histology Images (BHIs), which will then be used to confirm the results. Using a novel Deep-Learning Plan (DLP), the proposed work identifies BHI accurately and confirms the severity of BC by confirming its severity. As part of the proposed DLP implementation, four phases are involved: (i) the collection and enhancement of images, (ii) the extraction of features, (iii) the reduction of features and their integration, and (iv) binary classification and validation. The purpose of this study is to optimize deep features and machine features using particle swarm algorithms. To evaluate the performance of the proposed scheme, we compare the results obtained using individual deep features, dual deep features, and hybrid features. Using the hybrid image features in the classifier, this study has determined that ResNet18 with k-nearest neighbor provides superior classification accuracy (&gt; 94%).
SHGCN: Socially Enhanced Heterogeneous Graph Convolutional Network for Multi-behavior Prediction

In recent years, multi-behavior information has been utilized to address data sparsity and cold-start issues. The general multi-behavior models capture multiple behaviors of users to make the representation of relevant features more fine-grained and informative. However, most current multi-behavior recommendation methods neglect the exploration of social relations between users. Actually, users’ potential social connections are critical to assist them in filtering multifarious messages, which may be one key for models to tap deeper into users’ interests. Additionally, existing models usually focus on the positive behaviors (e.g., click , follow , and purchase ) of users and tend to ignore the value of negative behaviors (e.g., unfollow and badpost ). In this work, we present a Multi-Behavior Graph (MBG) construction method based on user behaviors and social relationships and then introduce a novel socially enhanced and behavior-aware graph neural network for behavior prediction. Specifically, we propose a Socially Enhanced Heterogeneous Graph Convolutional Network (SHGCN) model, which utilizes behavior heterogeneous graph convolution module and social graph convolution module to effectively incorporate behavior features and social information to achieve precise multi-behavior prediction. In addition, the aggregation pooling mechanism is suggested to integrate the outputs of different graph convolution layers, and a dynamic adaptive loss (DAL) method is presented to explore the weight of each behavior. The experimental results on the datasets of the e-commerce platforms (i.e., Epinions and Ciao) indicate the promising performance of SHGCN. Compared with the most powerful baseline, SHGCN achieves 3.3% and 1.4% uplift in terms of AUC on the Epinions and Ciao datasets. Further experiments, including model efficiency analysis, DAL mechanism, and ablation experiments, confirm the validity of the multi-behavior information and social enhancement.
An empirical study of learning‐to‐rank for spare parts consumption in the repair process

Abstract The repair process of devices is an important part of the business of many original equipment manufacturers. The consumption of spare parts, during the repair process, is driven by the defects found during inspection of the devices, and these parts are a big part of the costs in the repair process. In previous work, we proposed a data‐driven method for Supply Chain Control Tower solutions to provide support for the automatic check of spare parts consumption in the repair process. In this article, we continue our investigation of a multi‐label classification problem and explore alternatives in the learning‐to‐rank approach, where we simulate the passage of time using more data while training and comparing hundreds of Machine Learning models to provide an automatic check in the consumption of spare parts. We investigate the effects of different train set sizes, retraining intervals, models and hyper‐parameter search using Bayesian Optimization. We define a custom metric, the Ratio of Marked Parts, measuring how many spare parts are marked for revision at the end of the repair process. The results show that we were able to improve the trained models and achieve a higher mean NDCG@20 score of 86% when ranking the expected parts. While focusing on the most recent data, we achieved a NDCG@20 score of 90% and obtained a Ratio of Marked Parts of just 4% of the consumed parts for use in alert generation.
ClayRS: An end-to-end framework for reproducible knowledge-aware recommender systems

Knowledge-aware recommender systems represent one of the most innovative research directions in the area of recommender systems, aiming at giving meaning to information expressed in natural language and obtaining a deeper comprehension of the information conveyed by textual content. Though rich and constantly evolving, the literature on knowledge-aware recommender systems is particularly scattered when considering software libraries. This makes it difficult to easily exploit advanced content representation and implement replicable experimental protocols. Accordingly, this work aims to fill in these gaps by introducing ClayRS, an end-to-end framework for replicable knowledge-aware recommender systems. ClayRS provides researchers and practitioners with the most recent state-of-the-art methodologies to build knowledge-aware content representations and also includes methods to exploit these representations in content-based recommendation algorithms. Finally, the structure of the framework also allows for building replicable pipelines to push forward the current research in the area and to develop accountable recommender systems.
Decentralized Finance: Concept and Characteristics

The purpose of this chapter is to guide you through the mechanics of decentralized finance (DeFi) and its potential applications. As a first step, we will discuss how to implement DeFi by examining smart contracts in order to provide perspectives on the technical foundation required for this type of financing. For a basic understanding of DeFi, we will examine blockchain technology as well as the Ethereum ecosystem on which it will run. We will also illustrate the different categories of DeFi as well as the potential applications of the technology in the future. In conclusion, we will discuss specific aspects of regulation that are necessary for safety and security.