Reviewerly/exHarmony huggingface dataset:
A Multimedia Graph Collaborative Filter

The Multimedia recommendation has always been an active research field in the area of personalized recommendation, and the core of multimedia recommenders is learning multimodal representations for the users and items. Traditional multimedia recommenders first extract multimodal (such as visual, acoustic, and textual) features by pre-trained networks, and then incorporate ID embeddings with the features to enrich the representations of both users and items. However, these methods only employ multimodal information of the directly interacting items. Recent graph-based efforts utilize high-order connectivities and the message pass mechanism of the graph. The multimodal information of high-hop items is propagated along with the high-order connectivities in a graph and aggregated to enrich the representations of users and items, such as adopting a parallel graph structure to model user preferences on different modalities. However, the users’ preferences for different modalities are unknown. The bipartite graph structure for the entire modalities should differ from the single modality. In this work, we devise a new multimedia recommendation framework, a Multimedia Graph Collaborative Filter (MGCF). In MGCF, a light graph framework with a fusion component is developed to integrate and distill the useful multimodal information. Moreover, the attention mechanism is adopted to enhance the importance of aggregated information. Extensive experiments are conducted on two public datasets: Tiktok and MovieLens. The results show that our model outperforms several state-of-art multimedia recommenders. Further analysis demonstrates the importance of modeling multimodal information for better user and item representations. The code will be open soon.
Fraud Detection of Credit Card Using Logistic Regression

In modern times, credit card theft has developed a major concern for banks, as identifying fraud in the credit card system has become increasingly difficult. Machine learning plays a key role in detecting credit card fraud in transactions in order to address this challenge. Banks utilize a variety of machine learning techniques to forecast these transactions, collecting historical data and adding new variables to improve prediction capabilities. The suggested method builds the classifier using logistic regression to avoid credit card fraud. A pre-processing phase is employed to handle dirty data and ensure high detection accuracy. To clean the data, the preprocessing step employs two innovative essential strategies: the mean-based technique as well as the clustering-based technique. They are frequently confused with valid approaches that compare both fraud and normal data, but this is never enough to detect fraud adequately. This research shows how machine learning can be used to detect credit card fraud. Credit Card Fraud Detection is a project that shows how to use machine learning to model a knowledge set. Credit card transaction modelling, which has previously been done with fraud transaction data, is part of the Credit Card Fraud Detection problem. Our system will examine a new transaction to see if it is fraudulent. Our aim is to detect all suspicious transactions with as few false positives as possible. A transaction's likelihood of being fraudulent will be determined by our algorithm.
Longest Common Subsequence: Tabular vs. Closed-Form Equation Computation of Subsequence Probability

The Longest Common Subsequence Problem (LCS) deals with finding the longest subsequence among a given set of strings. The LCS problem is an NP-hard problem which makes it a target for lots of effort to find a better solution with heuristics methods. The baseline for most famous heuristics functions is a tabular random, probabilistic approach. This approach approximates the length of the LCS in each iteration. The combination of beam search and tabular probabilistic-based heuristics has led to a large number of proposals and achievements in algorithms for solving the LCS problem. In this work, we introduce a closed-form equation of the probabilistic table calculation for the first time. Moreover, we present other corresponding forms of the closed-form equation and prove all of them. The closed-form equation opens new ways for analysis and further approximations. Using the theorems and beam search, we propose an analytic method for estimating the length of the LCS of the remaining subsequence. Furthermore, we present another heuristic function based on the Coefficient of Variation. The results show that our proposed methods outperform the state-of-the-art methods on the LCS problem.
Regularizing disentangled representations with anatomical temporal consistency

Deep neural networks have shown to be promising approaches for medical image analysis. However, their training is most effective when they learn robust data representations using large-scale annotated datasets, which are tedious to acquire in clinical practice. As medical annotations are often limited, there has been an increasing interest in making data representations robust in case of data lack. In particular, a spate of research focuses on constraining the learned representations to be interpretable and able to separate out, or disentangle, the data explanatory factors. This chapter discusses recent disentanglement frameworks, with a special focus on the image segmentation task. We build on a recent approach for disentanglement of cardiac medical images into disjoint patient anatomy and imaging modality dependent representations. We incorporate into the model a purposely designed architecture (which we term “temporal transformer”) which, from a given image and a time gap, can estimate anatomical representations of an image at a future time-point within the cardiac cycle of cine MRI. The transformer's role is to introduce a self-supervised objective to encourage the emergence of temporally coherent data representations. We show that such a regularization improves the quality of disentangled representations, ultimately increasing semi-supervised segmentation performance when annotations are scarce. Finally, we show that predicting future representations can be potentially used for image synthesis tasks.
YOLO-Based Learning Machine Model for Cancer Diagnostic in Numerical Mammography

In recent years, the effectiveness of the deep learning-based model had been proved in many domains including medical vision processing. This paper proposes an artificial intelligence (AI) computer-aided detection (CAD) model using a state-of-the-art object detection framework namely You Only Look Once (YOLO). In specific, The CNN-based model automatically detected masses in Full-field digital mammograms in the INbreast database and distinguished the malignant mass from the benign one without human intervention. The result indicated that our proposed YOLO-based CAD model achieved 92.4% mAP, with 100% average precision in malignant mass detection, showing the potential to be applied in practice.
Learning Domain-Independent Representations via Shared Weight Auto-Encoder for Transfer Learning in Recommender Systems

Despite many recent advances, state-of-the-art recommender systems still struggle to achieve good performance with sparse datasets. To address the sparsity issue, transfer learning techniques have been investigated for recommender systems, but they tend to impose strict constraints on the content and structure of the data in the source and target domains. For transfer learning methods to work well, there should normally be homogeneity between source and target domains, or a high degree of overlap between the source and target items. In this paper we propose a novel transfer learning framework for mitigating the effects of sparsity and insufficient data. Our method requires neither homogeneity nor overlap between the source and target domains. We describe and evaluate a shared parameter auto-encoder to jointly learn representations of user/item aspects in two domains, applying Maximum Mean Discrepancy (MMD) loss during training to ensure that the source and target representations are similar in the distribution space. The approach is evaluated using a number of benchmark datasets to demonstrate improved recommendation performance when learned representations are used in collaborative filtering. The code used for this work is available on github.com.
Evaluation of the Use of Different Parser Generators in a Compiler Construction Course

Compiler construction is a common course in many computer science degrees. Although there are different lexer and parser generation tools, Lex/Yacc is still the preferred option in multiple universities. ANTLR is a powerful compiler construction tool that, among other features, provides parser generation. Although ANTLR has been used in the implementation of different languages and software tools, it is not as common as Lex/Yacc in compiler construction courses. Therefore, we conduct an experiment to evaluate the impact of using ANTLR instead of Lex/Yacc in courses where students build a compiler. To that aim, we divide the year-3 students of a Programming Language Design and Implementation course into two random groups. The course is delivered using Lex/Yacc for one group and ANTLR for the other one. Different values are measured to statistically compare both approaches, including the work completed by the students, the number of hours it took them to finish different tasks, their anonymous opinion about the parser generator used, and their performance. The use of ANTLR shows significant benefits in most of the evaluations.
Extending OpenMP for Machine Learning-Driven Adaptation

OpenMP 5.0 introduced the directive to support compile-time selection from a set of directive variants based on OpenMP context. OpenMP 5.1 extended context information to include user-defined conditions that enable user-guided runtime adaptation. However, defining conditions that capture the complex interactions between applications and hardware platforms to select an optimized variant is challenging for programmers. This paper explores a novel approach to automate runtime adaptation through machine learning. We design a new directive to describe semantics for model-driven adaptation and also develop a prototype implementation. Using the Smith-Waterman algorithm as a use-case, our experiments demonstrate that the proposed adaptive OpenMP extension automatically chooses the code variants that deliver the best performance in heterogeneous platforms that consist of CPU and GPU processing capabilities. Using decision tree models for tuning has an accuracy of up to 93.1% in selecting the optimal variant, with negligible runtime overhead.
AVR Technologies in Sustainable Tourism: A Bibliometric Review

An extensive, PRISMA-led bibliometric analysis of scientific literature from the last 20 years was conducted to extract future research agenda in the niche field of sustainable tourism and the application of virtual reality+ technologies (extended, mixed, hyper, and augmented). The field being investigated contains significant opportunities through meta-analysis methods like linear–logarithmic transformations, coupling clustering, and network analysis. Astringent keyword inclusion rule ensured that the most pertinent scientific literature entered the analysis. The authors used critical content analysis parameters like Cohen’s kappa to include the best fit papers. This bibliometric analysis convenes the need to focus on emerging areas like cyber-neurotics, telekinesis, cyber-optics, and gamification to provide impetus to sustainable consumption and production in the tourism and travel industry. The paper expresses the paradigm shift in research topics as the world enters the COVID-19-induced pandemic and its impact on future research endeavours.
An Hybrid Deep Learning Approach for Prediction and Binary Classification of Student’s Stress

Nowadays, Stress has repercussions on the mental, physical and psychological health for many persons in life especially students in universities. Stressful life has many negative consequences such as anxiety disorders and depression. The negative influences of stress can be seen in several areas such as mental health. The prediction of stress can compensate for the negative effects and does not lead to an advanced state. This prediction can be made through smartphones. In this paper, we aim to classify psychological students state on two classes "stressed" and "not stressed" using smartphones by analyzing extracted features from heterogenous smartphone sensors input information. Indeed, we suggest hybrid deep learning method using both attentional model and Long Short-Term Memory (LSTM) recurrent network. The obtained result achieves 93% accuracy on the test set and comparing to other studies.
Supervisory framework for threat detection withmultilayer processing in CNN

Face recognition has received huge acknowledgement due to its various uses in Internet communication, security, access control, surveillance, PC entertainment and law enforcement. Conventional methods of recognition based on the ownerships of identity-cards or full knowledge such as a security number or password are not totally solid. Physical ID cards can be lost or forged, passwords can be hacked or forgotten but a face is undoubtedly connected to its owner. It cannot be stolen, borrowed or easily forged. Our current system has a lot of weaknesses wherever it is simply taken and merged. The focus of this paper is to help users for development of the security by utilizing face identification and recognition. The proposed framework principally comprises of subsystems specifically picture capture, face identification and detection, email alerts and metal detection. Furthermore, the improvement in Computer Vision through Deep Learning algorithms has been an impressive achievement, especially with the Convolutional Neural Network algorithm. A convolutional neural network is a feed-forward neural network that is by and large used to break down visual pictures by using grid-like topology. It is also called as ConvNet. The objects in a picture are distinguished and arranged using convolutional neural network. CNN detects various simple complex patterns in images and data in its different layers of Convolution Layer, Max Polling Layer and Fully Connected Layer .This field intends to allow and configure machines to see the world as people do, and utilize the information for doing tasks (such as Image Analysis, Image Recognition and Classification, etc).
Performance Analysis of an Ontology Model Enabling Interoperability of Artificial Intelligence Agents

In the context of current information systems, the necessity of processing vast amounts of information enforces a significant paradigm shift. The monolithic data formats are gradually replaced with semantic models enclosing light, layered, and easily replicable structures. Advances represented by blockchain, decentralized data storage and replication along with semantic models open new possibilities to combine Artificial Intelligence (AI) algorithms in multi-agent, cross-organizational systems. By taking a semantic model approach, the communication and coordination between agents can be abstracted by the ontology concepts used to define the data models. This paper explores the feasibility of utilizing ontology-based semantic data models as communication interfaces among multiple AI agents. Specifically, it focuses on the storage, retrieval, and replication part of this process in a decentralized medium.
An E2E-ASR-Based Iteratively-Trained Timestamp Estimator

Text-to-speech alignment, also known as time alignment, is essential for automatic speech recognition (ASR) systems used for speech retrieval tasks, such as keyword search and speech segment extraction. Previous works have used the Gaussian mixture model-hidden Markov model (GMM-HMM) forced alignment to improve the alignment performance. However, when used with end-to-end (E2E) ASR, GMM-HMM forced alignment causes extra reliance on expertise such as pronunciation lexica. It also increases the system complexity because GMM-HMMs are very dissimilar to E2E models. To tackle these two problems, we propose an E2E-ASR-based iteratively-trained timestamp estimator (ITSE), which performs alignment between token-level transcription and speech. We train ITSE first with coarse initial alignment targets generated using connectionist temporal classification (CTC) posteriors. During training, we iteratively perform realignment to update the targets. We attribute the effectiveness of the iterative training to ITSE&#x2019;s two vital features. First, ITSE performs alignment using similarities between token and speech embeddings instead of frame-wise token classification posteriors. Second, ITSE uses speech embeddings that are aware of left context rather than global context. ITSE significantly outperforms CTC-based baselines in word alignment accuracy and is comparable to a GMM-HMM forced aligner. In short, ITSE is an accurate, lightweight text-to-speech alignment module implemented without expertise such as pronunciation lexica.
Approximate Search for Keywords in Handwritten Text Images

Thanks to the ability to deal with the intrinsic uncertainty of handwritten text in historical documents, Probabilistic Indexing (PrIx) has emerged as an alternative to traditional automatic transcription to retrieve information from such documents. Using PrIx, adequate search techniques have been developed that not only allow for typical single-word queries, but also support complex multi-word boolean and word-sequence queries, which are commonly used in many free-text document search applications today. Here we focus on another type of text-image PrIx-based queries; namely approximate (or "fuzzy") word spelling, also commonly provided by many conventional plain-text search tools. When handwritten historical documents are considered, approximate spelling has proved to be a remarkably useful search asset in practice. However, its performance had not been formally assessed so far. We explain how approximate-spelling has been developed for large-scale PrIx's and provide an empirical analysis of precision-recall performance and computational efficiency. Experiments with the well-known "Bentham Papers" large manuscript collection show that the proposed approximate-spelling search techniques generally improve the already good search accuracy of exact-spelling queries, while computing performance gracefully scales up to deal with very large collections of handwritten text images.
FPGA Implementations of BLAKE3 Compression Function with Intra-Round Pipelining

BLAKE3 is the latest evolution of the cryptographic hash function BLAKE – a cipher which, although was not selected as the new SHA-3 standard, after the NIST contest has become an accepted method of choice in contemporary software cryptographic systems. This work explores selected FPGA organizations of the BLAKE3 core– its compression function – realized in Xilinx Spartan-7 devices and evaluates benefits of the new modification from the point of view of efficient hardware implementation which has never been a strong point of this algorithm. In total 6 architectures are proposed and evaluated: the basic iterative one with one round instance, modifications with the round split into two and four pipeline stages and three possible versions with six stages. The results allow for evaluation of various speed vs. size trade-offs of the proposed organizations. Additionally, the paper includes a comparison with equivalent implementations of the predecessor BLAKE2 as well as SHA3-256 (Keccak) standard.
A Fuzzy Model for Selection of Information and Location-Based Security Attributes in Cloud Environment

Security is a prime concern in cloud computing-based environment in which user accesses resources remotely and pays on used bases. Nowadays, the information and location-based security is one of the most important issues to access these cloud resources. This paper proposed a fuzzy model for assigning weights to information and location-based security attributes (FMAW-ILSA). This model is used to assign an experienced-based fuzzy weight to security attacks attributes, information and location-based security attributes. The assigned weight helps in selection of information and location-based security attributes. The criteria for assigning the weights depend on the attribute frequencies. The motive behind this paper is to select the information and location-based security attributes which are less utilized in the existing work such as information security (IS), data location and relocation (DLRL), phishing attack (PA), location tracking (LT) and location and control of data (LCD). The developed fuzzy model helps the researchers for better decision making in order to select information and location-based security attributes for future research to more enhance the cloud security.
Hotel Technology Innovations as Drivers of Safety and Hygiene in Hotel Customers

Technology innovations are making their mark within the hospitality industry, and in the present COVID-19 sanitary crisis, they will be a major driver of hotel strategy. The COVID-19 disease is highly contagious, and its fear is likely to lead to distrust of facilities and spaces shared by different people, particularly in hotels where hotel customers encounter various people and use a variety of spaces. Such contact and use may expose individuals to a perceived health risk what raised issues in the hospitality industry and lead to the redesigning of safety measures and hygiene standards and to the introduction of technology innovations to help manual processes or replace personal interactions to make them secure and safe. As the hotel customer's perception of sanitary and hygienic procedures and health safety is crucial for the recovery of the hotel industry, in this article, the main objective is to understand how hotel technology innovations influence hotel customers' perception of hygiene and safety which influences their perceived health risk and hotel selection behaviors. Therefore, this study focused on different scenarios associated with hotel technologies, such as kiosk check-in system, mobile check-in system, robot cleaning system, and ultraviolet (UV) light cleaning system. The findings indicate that robotics, artificial intelligence, and human–robot interactions have gained an increased presence to help manage the spread of COVID-19, and that these new preferred technologies for making a safe and secure hotel environment for the guests and employees are paving their way to the new normal in post-COVID-19 pandemic times.
Autothrottle: Satisfying Network Performance Requirements for Containers

This article investigates how to satisfy network performance requirements that are crucial in achieving the service level objectives (SLOs) in clouds. Traditional techniques for network performance management have a limited ability to satisfy the network SLOs. Our in-depth analysis reveals that the fundamental reason comes from decoupling of the CPU scheduler and the network traffic controller as the current CPU scheduler is not aware of such network requirements but only provides a fair-share amount of CPU to all containers. Thus, the container cannot perform the amount of network processing as needed to satisfy its SLO when the CPU allocation is insufficient. In this article, we propose Autothrottle that dynamically adjusts the CPU allocation for the containers to satisfy their network SLOs. The key element of Autothrottle is a throttle algorithm that autonomously determines the amount of CPU for each container needed to satisfy the requirement. We implement Autothrottle in the Linux kernel and evaluate it with massive real-world workloads such as Apache Kafka. Our evaluation results show that Autothrottle successfully satisfies the given network SLO only with a 2% gap while the existing scheme achieves 20% less than the SLO. We further observe that Autothrottle also reduces the CPU overhead in network processing by 19%, improving the network throughput by 27% compared to the existing scheme.