Reviewerly/exHarmony huggingface dataset:
Enhanced Social Recommendation Method Integrating Rating Bias Offsets

Current social recommendations based on Graph Neural Networks (GNNs) often neglect to extract rating bias from user and item statistics, leading to misinterpreting real user preferences. For example, a high rating from a user with lenient rating standards and a high average rating does not always indicate a real preference for the item. This situation highlights inherent flaws in existing recommendation algorithms that do not adequately account for bias in user and item ratings and rating trends. To address this problem, this paper proposes an enhanced social recommendation method based on GNNs with integrated rating bias offsets (SR-BS). Firstly, we obtain rating bias from users and items by subtracting their average rating value from the historical rating value for each user/item. To enhance the model’s learning capability, we transform the rating biases into vector representations. Secondly, in the model learning, diverse meta-paths are predefined for modeling interaction relations between graph nodes (e.g., user–item–user, user–user). The aggregation of semantic information from these relational paths is achieved by stacking multiple GNN layers, enabling the fusion of higher-order information. Finally, the experimental results on four datasets—Ciao, Epinions, Douban, and FilmTrust—show that our method outperforms other state-of-the-art methods in social recommendation tasks, exhibiting high stability and personalization.
Augmented Reality and its Contribution to Enhance the Operational Capabilities of the Armed Forces

This paper aims to highlight the importance of integrating Augmented Reality technology by the Armed Forces as an institution and by Weapon Systems servicing them since it consists of a significant tool in order to achieve the Objective. Therefore, an analysis of the concept of Augmented Reality and its contribution is attempted for the Armed Forces to achieve their Mission and enhance Situation Awareness on the battlefield. The specific technology provides a multiplier of power for achieving a country's territorial defense, national independence, and supremacy against any external threatening or aggressive action.
CLIP-Driven Prototype Network for Few-Shot Semantic Segmentation

Recent research has shown that visual-text pretrained models perform well in traditional vision tasks. CLIP, as the most influential work, has garnered significant attention from researchers. Thanks to its excellent visual representation capabilities, many recent studies have used CLIP for pixel-level tasks. We explore the potential abilities of CLIP in the field of few-shot segmentation. The current mainstream approach is to utilize support and query features to generate class prototypes and then use the prototype features to match image features. We propose a new method that utilizes CLIP to extract text features for a specific class. These text features are then used as training samples to participate in the model's training process. The addition of text features enables model to extract features that contain richer semantic information, thus making it easier to capture potential class information. To better match the query image features, we also propose a new prototype generation method that incorporates multi-modal fusion features of text and images in the prototype generation process. Adaptive query prototypes were generated by combining foreground and background information from the images with the multi-modal support prototype, thereby allowing for a better matching of image features and improved segmentation accuracy. We provide a new perspective to the task of few-shot segmentation in multi-modal scenarios. Experiments demonstrate that our proposed method achieves excellent results on two common datasets, PASCAL-5i and COCO-20i.
A user study for evaluation of formal verification results and their explanation at Bosch

Abstract Context Ensuring safety for any sophisticated system is getting more complex due to the rising number of features and functionalities. This calls for formal methods to entrust confidence in such systems. Nevertheless, using formal methods in industry is demanding because of their lack of usability and the difficulty of understanding verification results. Objective We evaluate the acceptance of formal methods by Bosch automotive engineers, particularly whether the difficulty of understanding verification results can be reduced. Method We perform two different exploratory studies. First, we conduct a user survey to explore challenges in identifying inconsistent specifications and using formal methods by Bosch automotive engineers. Second, we perform a one-group pretest-posttest experiment to collect impressions from Bosch engineers familiar with formal methods to evaluate whether understanding verification results is simplified by our counterexample explanation approach. Results The results from the user survey indicate that identifying refinement inconsistencies, understanding formal notations, and interpreting verification results are challenging. Nevertheless, engineers are still interested in using formal methods in real-world development processes because it could reduce the manual effort for verification. Additionally, they also believe formal methods could make the system safer. Furthermore, the one-group pretest-posttest experiment results indicate that engineers are more comfortable understanding the counterexample explanation than the raw model checker output. Limitations The main limitation of this study is the generalizability beyond the target group of Bosch automotive engineers.
RBOIRA: Integrating Rules and Reinforcement Learning to Improve Index Recommendation

INTRODUCTION: The index is one of the most effective ways to improve the database query performance. The expert-based index recommendation approach cannot adjust the index configuration in real time. At the same time, reinforcement learning can automatically update the index and improve the recommended configuration by leveraging expert experience.OBJECTIVES: This paper proposes the RBOIRA, which combines rules and reinforcement learning to recommend the optimal index configuration for a set of workloads in a dynamic database.METHODS: Firstly, RBOIRA designed three heuristic rules for pruning index candidates. Secondly, it uses reinforcement learning to recommend the optimal index configuration for a set of workloads in the database. Finally, we conducted extensive experiments to evaluate RBOIRA using the TPC-H database benchmark.RESULTS: RBOIRA recommends index configurations with superior performance compared to the baselines we define and other reinforcement learning methods used in related work and also has robustness in different database sizes.
Equity-Oriented Learning Design: An Entangled Future

Abstract There is growing interest in the literature towards a more distributed, collaborative view of learning design that focuses on relationships and connection. In this paper, we propose a vision of learning design that is entangled and crosses boundaries, framed by an equity-oriented mindset that blurs and resists boundaries, and merges learning design with facilitation, external with internal communities, and academia with activism. Based on interviews with learning design practitioners across the world, and framed by theories of posthumanism and postdigital education, we share eight orientations that form a future and equity-oriented learning design pattern: awareness of context, matter matters, co-creating with humans and non-humans, relationality and connection, modelling vulnerability, the entanglement of the personal and political, and creating transformative spaces. We then use one of our responses to the advent of ChatGPT to show how these orientations have helped us in times of uncertainty and disruption, an agential cut that highlights the value of acknowledging the material-discursive relationships emerging in and through our work. As such, rather than focusing on conventional, static definitions and models, we are interested in knowledge-making processes that come into existence when we practise learning design and engage with each other and the world around us, and who we become in our relationships with others, both human and non-human, and the patterns that we form in this entanglement.
Dynamic access control method for SDP-based network environments

Abstract With online work environments and other distributed computing systems—such as cloud technologies or Internet of Things systems—becoming increasingly popular today due to the COVID-19 pandemic and general technological advances, the question of how to keep them secure has also become a pertinent concern. With this increased dependence on online systems for companies, cyberattacks have also been on the rise. To protect terminal devices, many companies have resorted to implementing a single boundary-defense model. This method has yielded positive results in securing the network from external threats, but it does not effectively protect network from internal security threats. With the vulnerabilities in the internal network security in mind, a dynamic access control method used with a zero-trust software-defined perimeter security model could be a viable solution. This study proposes a dynamic access control method using an engine with a new reward and penalty point-based system (RP Engine) and a dynamic task engine (DT Engine) for a zero-trust SDP security model.
Assessment of subject and methodological competencies of graduates of a pedagogical university — future teachers of informatics: Deficits and prospects

The article is devoted to the analysis of the assessment results of pedagogical university graduates’ subject and methodological competencies to determine the professional deficits of future informatics teachers, their readiness for professional pedagogical activity, building continuity between the training of pedagogical personnel and the subsequent support of graduates of a pedagogical university in professional activities in the creation and implementation of a continuous system of pedagogical education. 35 students of the Faculty of Physics and Mathematics of the Voronezh State Pedagogical University took part in the study in 2022: 10 graduates in the field of training 44.03.01 “Pedagogical Education” (profile “Informatics and ICT”) and 25 graduates in the field of training 44.03.05 “Pedagogical Education (with two training profiles)” (profiles “Mathematics”, “Informatics”). The maximum number of primary points for diagnostic work is 38 points. In the course of the study, the graduates performed a diagnostic work consisting of 16 tasks: the first part of the work evaluated subject competencies, and the second part assessed methodological competencies of future teachers of informatics. The analysis of the obtained results was carried out using the single-sample Kolmogorov—Smirnov test, the Student’s t-test for independent samples. The results were systematized and visualized in Microsoft Excel spreadsheets; statistical analysis was carried out in the SPSS Statistics 26.0 software. The results of the study indicate that the majority of graduates of the pedagogical university, future teachers of informatics, do not reach the basic level of formation of subject and methodological competencies and that graduates have deficits in subject and methodological training. Graduates of the 4th year of “Pedagogical Education” (profile “Informatics and ICT”) demonstrated higher results compared to graduates of the 5th year of “Pedagogical Education (with two training profiles)” (profile “Mathematics”, “Informatics”). The results of the study show the need for changes in the content and organization of the educational process of training future informatics teachers at the pedagogical university. The obtained results may be of interest to university teachers who train future teachers of informatics, institutes of education development, and centers of continuous improvement of pedagogical skills, providing advanced training and professional retraining of teachers of informatics.
Research on the Architecture of Transactional Smart Contracts Based on Blockchains

At present, smart contracts are designed based on the application field, and their structure and functions are closely related to specific businesses. Even smart contracts within the same field and the same business have different structures and functions due to different developers, resulting in structural confusion, repeated development, and low levels of sharing. In response to this problem, this study conducts a full investigation of smart contracts in various fields, using big data technology to compare and analyze the structures of each contract and extracting the common content of their main bodies to study each feature, as well as to conduct induction and fusion. This study also generally designs a hierarchical structure and formulates structural modules such as transaction rules and the analysis of rights and responsibilities, as well as a reward and punishment mechanism. Data traceability is established, and the overall architectural specification of smart contracts is constructed. Additions, deletions, and improvements are made based on specific application environments to realize the dynamic updates of the architecture of contracts. Experiments show that the architecture of contracts can realize the various functions required in a transaction, solve the problem of the repeated development of current transactional smart contracts, and improve the sharing level.
Memory-Efficient Discrete Cosine Transform Domain Weight Modulation Transformer for Arbitrary-Scale Super-Resolution

Recently, several arbitrary-scale models have been proposed for single-image super-resolution. Furthermore, the importance of arbitrary-scale single image super-resolution is emphasized for applications such as satellite image processing, HR display, and video-based surveillance. However, the baseline integer-scale model must be retrained to fit the existing network, and the learning speed is slow. This paper proposes a network to solve these problems, processing super-resolution by restoring the high-frequency information lost in the remaining arbitrary-scale while maintaining the baseline integer scale. The proposed network extends an integer-scaled image to an arbitrary-scale target in the discrete cosine transform spectral domain. We also modulate the high-frequency restoration weights of the depthwise multi-head attention to use memory efficiently. Finally, we demonstrate the performance through experiments with existing state-of-the-art models and their flexibility through integration with existing integer-scale models in terms of peak signal-to-noise ratio (PSNR) and similarity index measure (SSIM) scores. This means that the proposed network restores high-resolution (HR) images appropriately by improving the image sharpness of low-resolution (LR) images.
Tutorial: calibration refinement in quantum annealing

Quantum annealing has emerged as a powerful platform for simulating and optimizing classical and quantum Ising models. Quantum annealers, like other quantum and/or analog computing devices, are susceptible to non-idealities including crosstalk, device variation, and environmental noise. Compensating for these effects through calibration refinement or “shimming” can significantly improve performance but often relies on ad-hoc methods that exploit symmetries in both the problem being solved and the quantum annealer itself. In this tutorial, we attempt to demystify these methods. We introduce methods for finding exploitable symmetries in Ising models and discuss how to use these symmetries to suppress unwanted bias. We work through several examples of increasing complexity and provide complete Python code. We include automated methods for two important tasks: finding copies of small subgraphs in the qubit connectivity graph and automatically finding symmetries of an Ising model via generalized graph automorphism. We conclude the tutorial by surveying additional methods, providing practical implementation tips, and discussing limitations and remedies of the calibration procedure. Code is available at: https://github.com/dwavesystems/shimming-tutorial .
An Operational Capacity Assessment Method for an Urban Low-Altitude Unmanned Aerial Vehicle Logistics Route Network

The Federal Aviation Administration introduced the concept of urban air mobility (UAM), a new three-dimensional transport system that operates with a fusion of manned/unmanned aerial vehicles on an urban or intercity scale. The rapid development of UAM has brought innovation and dynamism to many industries, especially in the field of logistics. Various types of unmanned aerial vehicles (UAVs) for use in transport logistics are being designed and produced. UAV logistics refers to the use of UAVs, usually carrying goods and parcels, to achieve route planning, identify risk perception, facilitate parcel delivery, and carry out other functions. This research provides a method for assessing the operational capacity of a UAV logistics route network. The concept of “logistics UAV route network operation capacity” is defined, and a bi-objective optimization model for assessing the route network’s operating capacity is developed. The first objective is to maximize the number of UAV logistics delivery plans that can be executed in a fixed operation time. The second objective is to minimize the total operational impedance value in a fixed operation time. To solve the bi-objective optimization model, the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is utilized. A UAV logistics route network with 62 nodes is developed to assess the rationale and validity of the proposed concept. The experiments show that with an increase in operation time, the route network’s optimal operational capacity gradually increases, the convergence speed of the algorithm slows down, and the optimization magnitude gradually reduces. Two key parameters—operational safety interval and flight speed—are further analyzed in the experiments. According to the experiments, as the safety interval increases, the route network’s average operational capacity steadily diminishes, as does its sensitivity to the safety interval. The average operational capacity steadily rose with the rise in flight speed, especially when the UAV logistics flight speed was between 10 m/s and 10.5 m/s. In that range, the operational capacity of the route network was substantially impacted by the flight speed.
Simcan2Cloud: a discrete-event-based simulator for modelling and simulating cloud computing infrastructures

Abstract Cloud computing is an evolving paradigm whose adoption has been increasing over the last few years. This fact has led to the growth of the cloud computing market, together with fierce competition for the leading market share, with an increase in the number of cloud service providers. Novel techniques are continuously being proposed to increase the cloud service provider’s profitability. However, only those techniques that are proven not to hinder the service agreements are considered for production clouds. Analysing the expected behaviour and performance of the cloud infrastructure is challenging, as the repeatability and reproducibility of experiments on these systems are made difficult by the large number of users concurrently accessing the infrastructure. To this, must be added the complications of using different provisioning policies, managing several workloads, and applying different resource configurations. Therefore, in order to alleviate these issues, we present Simcan2Cloud, a discrete-event-based simulator for modelling and simulating cloud computing environments. Simcan2Cloud focuses on modelling and simulating the behaviour of the cloud provider with a high level of detail, where both the cloud infrastructure and the interactions of the users with the cloud are integrated in the simulated scenarios. For this purpose, Simcan2Cloud supports different resource allocation policies, service level agreements (SLAs), and an intuitive and complete API for including new management policies. Finally, a thorough experimental study to measure the suitability and applicability of Simcan2Cloud, using both real-world traces and synthetic workloads, is presented.
ClueCatcher: Catching Domain-Wise Independent Clues for Deepfake Detection

Deepfake detection is a focus of extensive research to combat the proliferation of manipulated media. Existing approaches suffer from limited generalizability and struggle to detect deepfakes created using unseen techniques. This paper proposes a novel deepfake detection method to improve generalizability. We observe domain-wise independent clues in deepfake images, including inconsistencies in facial colors, detectable artifacts at synthesis boundaries, and disparities in quality between facial and nonfacial regions. This approach uses an interpatch dissimilarity estimator and a multistream convolutional neural network to capture deepfake clues unique to each feature. By exploiting these clues, we enhance the effectiveness and generalizability of deepfake detection. The experimental results demonstrate the improved performance and robustness of this method.
FPGA Implementation of a Novel Multifunction Modulo (2n ± 1) Multiplier Using Radix-4 Booth Encoding Scheme

The residue number system is widely used in applications such as communication systems, cryptography, digital filters, digital signal processors, fault-tolerant detection, and so on. This paper proposes a multifunction modulo (2n ± 1) multiplier based on the radix-4 Booth encoding scheme that can operate both modulo (2n − 1) and modulo (2n + 1) multipliers using the same hardware structure with only one control signal. A novel modulo (2n ± 1) multiplier based on radix-4 Booth encoding is proposed that can achieve superior performance, with low power, fast operation, high area efficiency, and low area-delay product (ADP) and power-delay product (PDP) compared with similar modified Booth-encoding methods. In addition, by integrating the separate modulo functions of the modulo (2n − 1) multiplier and modulo (2n + 1) multiplier into a single multifunction modulo (2n ± 1) multiplier, the proposed method can save up to 52.59% (n = 16) of hardware area, up to 5.45% (n = 32) of delay time, up to 49.05% (n = 16) of dynamic power, up to 50.92% (n = 32) of ADP, and up to 50.02% (n = 32) of PDP compared with the original separate circuits merged together. Furthermore, the operation ranges of the multiplicand and multiplier of the proposed modulo (2n + 1) multiplier and modulo (2n − 1) multiplier are {0, 2n + 1} and {0, 2n}, respectively, which are wider than for other reported hardware structures. The hardware area, power consumption, and delay time are simulated and verified using Verilog HDL and Xilinx FPGA (Field Programmable Gate Array) Vivado tools. The Xilinx Artix-7 XC7A35T-CSG324-1 FPGA chipset is adopted in the proposed work.