Reviewerly/exHarmony huggingface dataset:
DeepCPR: Deep Path Reasoning Using Sequence of User-Preferred Attributes for Conversational Recommendation

Conversational recommender systems (CRS) have garnered significant attention in academia and industry because of their ability to capture user preferences via system questions and user responses. Typically, in a CRS, reinforcement learning (RL) is utilized to determine the optimal timing for requesting attribute information or suggesting items. However, existing methods consider user-preferred attributes independently and ignore that attributes may be of different importance to the same user, in the attribute and item selection phases, which limits the accuracy and interpretability of CRS. Inspired by this, we propose deep conversational path reasoning (DeepCPR), which involves constructing a reasoning path on a graph with a series of user-favored attributes. It utilizes the attention mechanism to thoroughly examine the connections between these attributes and provide improved explanations for which attributes to inquire about or which items to recommend. In DeepCPR, two deep-learning-based modules are proposed to realize attribute and item selection. In the first module, the sequence of attributes confirmed by the user in conversation is encoded with a gated graph neural network to obtain the user’s long-term preference using a self-attention mechanism for the selection of candidate attributes. In the second module, a self-attention approach with more appropriate strategies is developed to dynamically select candidate items. In addition, to achieve fine-grained user preference modeling, a recurrent neural network is employed to aggregate the sequence of attributes that interact with the users. Numerous experimental evaluations conducted on four real CRS datasets show that the proposed method significantly outperforms existing advanced methods in terms of conversational recommendations.
Securing CRYSTALS-Kyber in FPGA Using Duplication and Clock Randomization

CRYSTALS-Kyber has been selected for standardization by NIST as a post-quantum public-key encryption and key establishment algorithm. Therefore developing CRYSTALS-Kyber implementations resistant to side-channel attacks is necessary. In this paper, we propose using duplication combined with clock randomization to secure CRYSTALS-Kyber FPGA implementations; a countermeasure shown to ensure side-channel resistance of AES FPGA implementations. The advantages of such a countermeasure include universal coverage, glitch immunity, and zero clock cycle overhead. We present a protected version of Kyber768 based on the most efficient FPGA implementation publicly available. Our security evaluation shows that it is resistant to deep learning-based side-channel analysis.
Show Me a Video: A Large-Scale Narrated Video Dataset for Coherent Story Illustration

Illustrating a multi-sentence story with visual content is a significant challenge in multimedia research. While previous works have focused on sequential story-to-visual representations at the image level or representing a single sentence with a video clip, illustrating a long multi-sentence story with coherent videos remains an under-explored area. In this paper, we propose the task of video-based story illustration that focuses on the goal of visually illustrating a story with retrieved video clips. To support this task, we first create a large-scale dataset of coherent video stories in each sample, consisting of 85K narrative stories with 60 pairs of consistent clips and texts. We then propose the Story Context-Enhanced Model, which leverages local and global contextual information within the story, inspired by sequence modeling in language understanding. Through comprehensive quantitative experiments, we demonstrate the effectiveness of our baseline model. In addition, qualitative results and detailed user studies reveal that our method can retrieve coherent video sequences from stories. The dataset and code will be made publicly at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://nfy-dot.github.io/CVSV-dataset/</uri> .
Cutting Learned Index into Pieces: An In-depth Inquiry into Updatable Learned Indexes

Numerous high-performance updatable learned indexes have recently been designed to support the writing requirements in practical systems. Researchers have proposed various strategies to improve the availability of updatable learned indexes. However, it is unclear which strategy is more profitable. Therefore, we deconstruct the design of learned indexes into multiple dimensions and in-depth evaluate their impacts on the overall performance, respectively. Through the in-depth exploration of learned indexes, we reckon that the approximation algorithm is the most crucial design dimension for improving the performance of the learned indexes rather than the popular works that focus on the learned index structure. Moreover, this paper makes a comprehensive end-to-end evaluation based on a high-performance key-value store to answer people’s concerns about which learned index is better and whether learned indexes can outperform traditional ones. Finally, according to end-to-end and in-depth evaluation results, we give some constructive suggestions on designing a better learned index in these dimensions, especially how to design an excellent approximate algorithm to improve the lookup and insertion performance of learned indexes.
Importance of Electronic Accounting Information Systems in Improving Financial Information Security in Jordanian Electronic Payment and Money Transfer Companies

Purpose: The study aimed to assess the level of effectiveness of electronic accounting information systems in enhancing the security and control of financial information in Jordanian payment and electronic transfer companies and its ability to provide reliability. And analyzing the ability of the system used in the payment and financial transfer companies to meet the operational requirements imposed by the regulators of the sector. Theoretical framework: The theoretical framework dealt with the topics of the study from the security threats of electronic accounting information systems (Jacob, 2012: 67) and the risks of accounting information systems that are related to electronic payment systems, especially information security (Al-Maghribi, 2017). Accounting in reducing the risks of transactions, and the obstacles facing accounting systems in the face of the risks and threats of electronic systems Design/Methodology/Approach: The researcher used the inductive approach by referring to the literature related to the subjects of the study and the deductive approach in the field study by distributing a questionnaire to the electronic payment companies in Jordan. 96 questionnaires were retrieved. Using the spss program to analyze the data and reach the results Findings: The study Fined that there are factors that help to penetrate the accounting information systems in the Jordanian electronic payment and financial transfer companies. Network communication. Electronic accounting information in enhancing the security of financial information and control over it, and there are (obstacles) in providing security requirements and information control, in electronic payment and financial transfer companies related to the rapid development of electronic financial systems and electronic payment methods and their development. Research, practical &amp; social implications: This study is considered one of the important and modern topics because electronic payment processes are considered one of the modern concepts with which many risks are associated. This study sheds light on these risks and threats and the ability of the strong accounting system to confront these threats and risks.. Originality/Value: The results indicate the importance of the topic and the importance of conducting a study on the risks of electronic payment systems, the threats facing the use of electronic payment methods and the obstacles facing accounting information systems in one of the newly established economic sectors in Jordan, which is the electronic payment companies.
Red Blood Cell Antigen Typing Based on Image Processing and Machine Learning

Red blood cell (RBC) antigen typing is mainly used in blood group testing, which is critical to providing compatible blood and minimizing the risk of hemolytic transfusion reactions in blood transfusion. This study developed a procedure based on image processing and machine learning, to automatically determinate red blood cell antigen type from the RBC agglutination images. Red blood cell agglutination samples were prepared by mixing blood samples with antibodies in a multi-channel microfluidic device and then captured with a high-speed document scanner. Each image contains 24 blood agglutination sub-areas, which would be automatically cut into 24 sub-images and analyzed in the procedure. HOG (Histogram of oriented gradients), Grayscale Histogram and LBP (Local Binary Pattern) features have been extracted from the sub-images and combined with machine learning algorithms for classification. The machine learning algorithms used in this study included k-Nearest Neighbors (K-NN), Support Vector Machine (SVM), Logistic Regression and Multilayer Perceptron (MLP). Among them, the average accuracy rate of the combination of SVM and histogram reached 93.41% in determining the blood agglutination patterns (agglutination or no agglutination, binary classification), and the accuracy has reached more than 98% on some data sets. Besides, the study adopted a hierarchical classification strategy using different features and achieved an accuracy of 86.57% in detecting blood agglutination degrees (5 classification).
The Role of Software Trust in Selection of Open-Source and Closed Software

Software selection is a critical part of any software engineering project, as projects depend on a rich ecosystem of components and frameworks. Software engineers always find it challenging to select a trustworthy software product despite the availability of a large number of software selection tools. This study explores the trust factors in open-source and closed software selection from a practitioner’s perspective, to gain insight into perceptions of software trust. To this aim, we conducted semi-structured interviews with 24 software practitioners from different businesses, including 12 experts in open-source software selection and 12 experts in closed software selection. By summarizing we found: (1) software selection is affected by three categories of factors, being technical, organizational, and structural assurance factors; (2) the most important factors are technical factors and organizational factors; and (3) software selection process and trust factors differ depending on the project risk tolerance. In addition, we compared the trust factors derived from the interviews with those found in the literature. Our findings revealed that there is a difference between the two.
A pipeline to further enhance quality, integrity and reusability of the NCCID clinical data

The National COVID-19 Chest Imaging Database (NCCID) is a centralized UK database of thoracic imaging and corresponding clinical data. It is made available by the National Health Service Artificial Intelligence (NHS AI) Lab to support the development of machine learning tools focused on Coronavirus Disease 2019 (COVID-19). A bespoke cleaning pipeline for NCCID, developed by the NHSx, was introduced in 2021. We present an extension to the original cleaning pipeline for the clinical data of the database. It has been adjusted to correct additional systematic inconsistencies in the raw data such as patient sex, oxygen levels and date values. The most important changes will be discussed in this paper, whilst the code and further explanations are made publicly available on GitLab. The suggested cleaning will allow global users to work with more consistent data for the development of machine learning tools without being an expert. In addition, it highlights some of the challenges when working with clinical multi-center data and includes recommendations for similar future initiatives.
Fulfilling entanglement’s optimal advantage via converting correlation to coherence

Entanglement boosts performance limits in sensing and communication even in noisy environments. We propose a conversion module to transform quantum correlation to coherent quadrature displacement, fulfilling the optimal receiver for quantum illumination and entanglement-assisted communication. [1]
Algorithmic Theory of Qubit Routing

The qubit routing problem, also known as the swap minimization problem, is a (classical) combinatorial optimization problem that arises in the design of compilers of quantum programs. We study the qubit routing problem from the viewpoint of theoretical computer science, while most of the existing studies investigated the practical aspects. We concentrate on the linear nearest neighbor (LNN) architectures of quantum computers, in which the graph topology is a path. Our results are three-fold. (1) We prove that the qubit routing problem is NP-hard. (2) We give a fixed-parameter algorithm when the number of two-qubit gates is a parameter. (3) We give a polynomial-time algorithm when each qubit is involved in at most one two-qubit gate.
Surformer: An interpretable pattern-perceptive survival transformer for cancer survival prediction from histopathology whole slide images

High-resolution histopathology whole slide images (WSIs) contain abundant valuable information for cancer prognosis. However, most computational pathology methods for survival prediction have weak interpretability and cannot explain the decision-making processes reasonably. To address this issue, we propose a highly interpretable neural network termed pattern-perceptive survival transformer (Surformer) for cancer survival prediction from WSIs. Notably, Surformer can quantify specific histological patterns through bag-level labels without any patch/cell-level auxiliary information. Specifically, the proposed ratio-reserved cross-attention module (RRCA) generates global and local features with the learnable prototypes (pglobal, plocals) as detectors and quantify the patches correlative to each plocal in the form of ratio factors (rfs). Afterward, multi-head self&cross-attention modules proceed with the computation for feature enhancement against noise. Eventually, the designed disentangling loss function guides multiple local features to focus on distinct patterns, thereby assisting rfs from RRCA in achieving more explicit histological feature quantification. Extensive experiments on five TCGA datasets illustrate that Surformer outperforms existing state-of-the-art methods. In addition, we highlight its interpretation by visualizing rfs distribution across high-risk and low-risk cohorts and retrieving and analyzing critical histological patterns contributing to the survival prediction. Surformer is expected to be exploited as a useful tool for performing histopathology image data-driven analysis and gaining new insights for interpreting the associations between such images and patient survival states.
Mobile Edge Adversarial Detection for Digital Twinning to the Metaverse: A Deep Reinforcement Learning Approach

Digital Twinning of physical world scenes onto the Metaverse is necessary for augmented reality (AR)-assisted driving. In AR-assisted driving, physical environment scenes are first captured by AR vehicles and are uploaded to the Metaverse for the construction of the Metaverse Map. However, the development of AR-assisted driving applications invites adversaries. These attackers may place adversarial patches on physical objects, seeking to contort the Metaverse Map. As real-time, accurate detection of adversarial patches is compute-intensive, these physical world scenes have to be offloaded to the Metaverse Map Base Station (MMBS) for computation. Therefore, we considered a scenario where AR vehicles capture physical world scenes and upload these scenes in real-time to the MMBSs. We formulated an optimization problem where the MMSP’s objective is to maximize adversarial patch detection mean Average Precision (mAP), while minimizing the computed AR scene uplink transmission latency and minimizing the worst-case (largest) AR vehicle’s uplink transmission battery charge consumption, through optimizing the AR vehicle-MMBS allocation, AR vehicle uplink scene resolution selection, and AR vehicle uplink power output selection. We proposed a Heterogeneous Action (HA) algorithm to tackle the proposed problem. Extensive experiments show our HA models outperforms baseline models when compared against key metrics.
A Utility-aware Anonymization Model for Multiple Sensitive Attributes Based on Association Concealment

Relational data usually contain multiple Sensitive Attributes ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SAs</i> ) and Quasi-Identifiers ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">QIs</i> ). Privacy leakage may occur if they are published directly. Therefore, many privacy models have been proposed. However, one of the most challenging issues is the association between attributes, which can cause both identity disclosure and attribute disclosure. Furthermore, these models always prioritize privacy over utility, so a rigorous (but often unnecessary) setting of privacy parameters could cause poor utility or even useless data. In this paper we propose a scheme called MSAAC that addresses both issues. To balance data privacy and utility, MSAAC adopts a utility-aware ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\alpha ,\beta$</tex-math></inline-formula> ) privacy model. To guide data publishers to set <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\alpha$</tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\beta$</tex-math></inline-formula> reasonably, MSAAC has built-in measures on privacy gain and utility loss, and quantitatively trades privacy for utility and vice versa. Our second contribution is quantifying the association of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SA-SA</i> using lift degree and the association of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">QI-SA</i> using a chi-square value. Based on them, MSAAC applies suppression and permutation techniques to properly anonymize them. Through both theoretical and experimental results, we show MSAAC can achieve better privacy while retaining higher utility than state-of-the-art solutions.
Balancing Algorithms for Digitally Controlled DC Amplifier

The article considers algorithms for automatic initial balancing of the DC amplifying stage using a built-in microcontroller. The problem of initial balancing is relevant in measuring multi-stage DC systems, where it is necessary to eliminate the initial bias voltage at the output of the measuring path. The paper considers an amplifying stage with a built-in microcontroller and the ability to control parameters, including a bias circuit, using electronic potentiometers. The paper proposes and describes two algorithms that differ in the complexity of software implementation and the principle of controlling the bias voltage step. The proposed algorithms are implemented as a software model of a control microcontroller in the SimInTech environment. The paper presents a study of algorithms for various application conditions; a comparative assessment of their effectiveness is carried out according to the criteria for the accuracy of establishing a zero value, speed and stability. It is shown that both algorithms are stable when working with noisy signals, while the balancing accuracy and performance for each algorithm is determined by a set of initial conditions.
The Effect of Model Compression on Fairness in Facial Expression Recognition

Deep neural networks are computationally expensive which has motivated the development of model compression techniques to reduce the resource consumption. Nevertheless, recent studies have suggested that model compression can have an adverse effect on algorithmic fairness, amplifying existing biases in machine learning models. With this work we aim to extend those studies to the context of facial expression recognition. To do that, we set up a neural network classifier to perform facial expression recognition and implement several model compression techniques on top of it. We then run experiments on two facial expression datasets, namely the Extended Cohn-Kanade Dataset (CK+DB) and the Real-World Affective Faces Database (RAF-DB), to examine the individual and combined effect that compression techniques have on the model size, accuracy and fairness. Our experimental results show that: (i) Compression and quantisation achieve significant reduction in model size with minimal impact on overall accuracy for both CK+ DB and RAF-DB; (ii) in terms of model accuracy, the classifier trained and tested on RAF-DB is more robust to compression compared to the CK+ DB; and (iii) for RAF-DB, the different compression strategies do not increase the gap in predictive performance across the sensitive attributes of gender, race and age which is in contrast with the results on the CK+ DB where compression seems to amplify existing biases for gender.
Continuous-variable quantum key distribution with true local oscillator

In continuous variable quantum key distribution (CV-QKD), using a truly local (not transmitted over the network) oscillator improves security. Here we report the first implementation of this CV-QKD scheme over a deployed optical fiber network.
The Application Research of Artificial Intelligence Neural Network in the Posture Analysis of Six Joints of Piano Player and Arm

With the development of society, people's living standards are getting higher and higher, and more and more people are learning to play piano.However, there are not many ways to put students on the right track.Most students give up learning halfway, mostly because the cooperation between hands and arm joints is not good enough.Many researchers have provided new ideas for the study of the use of the six joint postures of the piano player and arm.This paper is based on this as the research direction and research basis.This paper analyses the significance of piano performance and carries out academic research and summary on the use of the six joint posture of piano player and arm; Then the algorithm model is established, and relevant algorithms are proposed to provide theoretical basis for the application of artificial intelligence neural network in the analysis of the six joint posture of piano player and arm; Then it puts forward the application method of the six joint posture of piano player and arm; At the end of the article, the simulation experiment is carried out, and the experiment is summarized and discussed; According to the evaluation of the students' grades in the two classes, the number of students in Class A with grades of 70-90 is the largest, while the number of students in Class B with grades of more than 90 is the largest, and the excellent rate of Class A's grades is 20%, while the excellent rate of Class B's grades is 55%.At the same time, with the continuous popularity of piano performance, the use of the six joint postures of hand and arm is also facing new opportunities and challenges.