Reviewerly/exHarmony huggingface dataset:
Modeling Cross-session Information with Multi-interest Graph Neural Networks for the Next-item Recommendation

Next-item recommendation involves predicting the next item of interest of a given user from their past behavior. Users tend to browse and purchase various items on e-commerce websites according to their varied interests and needs, as reflected in their purchasing history. Most existing next-item recommendation methods aim at extracting the main point of interest in each browsing session and encapsulate it in a single representation. However, past behavior sequences reflect the multiple interests of a single user, which cannot be captured by methods that focus on single-interest contexts. Indeed, multiple interests cannot be captured in a single representation, and doing so results in missing information. Therefore, we propose a model with a multi-interest structure for capturing the various interests of users from their behavior sequence. Moreover, we adopted a method based on a graph neural network to construct interest graphs based on the historical and current behavior sequences of users. These graphs can capture complex item transition patterns related to different interests. In experiments, the proposed method outperforms state-of-the-art session-based recommendation systems on three real-world datasets, achieving 4% improvement of Recall over the SOTAs on Jdata dataset.
Evaluating Explainable Artificial Intelligence for X-ray Image Analysis

The lack of justification of the results obtained by artificial intelligence (AI) algorithms has limited their usage in the medical context. To increase the explainability of the existing AI methods, explainable artificial intelligence (XAI) is proposed. We performed a systematic literature review, based on the guidelines proposed by Kitchenham and Charters, of studies that applied XAI methods in X-ray-image-related tasks. We identified 141 studies relevant to the objective of this research from five different databases. For each of these studies, we assessed the quality and then analyzed them according to a specific set of research questions. We determined two primary purposes for X-ray images: the detection of bone diseases and lung diseases. We found that most of the AI methods used were based on a CNN. We identified the different techniques to increase the explainability of the models and grouped them depending on the kind of explainability obtained. We found that most of the articles did not evaluate the quality of the explainability obtained, causing problems of confidence in the explanation. Finally, we identified the current challenges and future directions of this subject and provide guidelines to practitioners and researchers to improve the limitations and the weaknesses that we detected.
Knowledge‐enriched joint‐learning model for implicit emotion cause extraction

Abstract Emotion cause extraction (ECE) task that aims at extracting potential trigger events of certain emotions has attracted extensive attention recently. However, current work neglects the implicit emotion expressed without any explicit emotional keywords, which appears more frequently in application scenarios. The lack of explicit emotion information makes it extremely hard to extract emotion causes only with the local context. Moreover, an entire event is usually across multiple clauses, while existing work merely extracts cause events at clause level and cannot effectively capture complete cause event information. To address these issues, the events are first redefined at the tuple level and a span‐based tuple‐level algorithm is proposed to extract events from different clauses. Based on it, a corpus for implicit emotion cause extraction that tries to extract causes of implicit emotions is constructed. The authors propose a knowledge‐enriched joint‐learning model of implicit emotion recognition and implicit emotion cause extraction tasks (KJ‐IECE), which leverages commonsense knowledge from ConceptNet and NRC_VAD to better capture connections between emotion and corresponding cause events. Experiments on both implicit and explicit emotion cause extraction datasets demonstrate the effectiveness of the proposed model.
Extensive Analysis of Deep Learning-based Deepfake Video Detection

Deepfake is the practice of replacing an existing image or video with someone else’s likeness. Currently, the spread of face-swapping deepfake strategies is increasing, producing a considerable range of naturalistic fake videos that cause danger to everyone. Due to the issue made by deepfake, recognizing between real and fake videos becomes a major issue. Deep learning is an efficient and useful approach for detecting deepfake videos and images. Research in recent years has focused on understanding how deepfake works and a number of deep learning-based approaches that are developed to do so. The aim of this study is to give an in-depth look at how several architectures work, along with the challenges encountered when detecting deepfake videos. It examines the existing deepfake detection methods using machine learning and deep learning approaches.
Personalized Music Hybrid Recommendation Algorithms Fusing Gene Features

Aiming at the shortcomings of current music recommendation algorithms, such as low accuracy and poor timeliness, a personalized hybrid recommendation algorithm incorporating genetic features is proposed. The user-based collaborative filtering (UserCF) algorithm analyzes the degree of users’ preference for music genes. The improved neural matrix decomposition collaborative filtering (B-NCF) algorithm calculates the correlation between similar users and constructs the adjacency relationship between users. The results of the two algorithms are fused by using a weighted hybrid approach to generate the recommendation list. Finally, the hybrid recommendation model is built on the Spark platform. The paper’s traditional and hybrid recommendation algorithms are validated using the Yahoo Music dataset. The experimental results show that the advantages of the algorithm in this paper are more significant under the MAE and F1-measure indexes, and the recommendation accuracy and precision have been greatly improved; the hybrid algorithm can ensure the diversity of the recommended contents, the recommendation hit rate is higher, and the timeliness meets the demand of personalized music recommendation.
Sharing Method of Online Teaching Resources of Spoken English Based on Deep Learning

In order to improve the sharing effect of online teaching resources of spoken English, this paper considers the sharing of resources among tasks in a mixed criticality system based on fixed task priority scheduling. Moreover, this paper extends the traditional PCP protocol and proposes a resource sharing protocol suitable for the AMC scheduling model in a verifiable hybrid criticality system. In addition, with the support of deep learning, this paper analyzes the worst blocking time and the worst response time of the task at each stage. Finally, this paper analyzes the sharing methods of online teaching resources of spoken English in combination with deep learning methods and constructs a corresponding intelligent teaching resource sharing system. The experimental research results show that the sharing method of online teaching resources of spoken English based on deep learning has a good resource sharing effect.
Characterizing and Understanding HGNNs on GPUs

Heterogeneous graph neural networks (HGNNs) deliver powerful capacity in heterogeneous graph representation learning. The execution of HGNNs is usually accelerated by GPUs. Therefore, characterizing and understanding the execution pattern of HGNNs on GPUs is important for both software and hardware optimizations. Unfortunately, there is no detailed characterization effort of HGNN workloads on GPUs. In this paper, we characterize HGNN workloads at inference phase and explore the execution of HGNNs on GPU, to disclose the execution semantic and execution pattern of HGNNs. Given the characterization and exploration, we propose several useful guidelines for both software and hardware optimizations for the efficient execution of HGNNs on GPUs.
Fusion-based advanced encryption algorithm for enhancing the security of Big Data in Cloud

Every organization in this digital age is expected to exponentially increase its digital data due to generations from machines. The advanced computations of Big Data are now showing various opportunities for the researchers who work on security enhancements to ensure the efficient accessibility of the data stores. Our research work aims to derive a Fusion-based Advanced Encryption Algorithm (FAEA) for a cost-optimized satisfiable security model toward the usage of Big Data in the cloud. The FAEA method is evaluated for its performance toward efficiency, scalability, and security and proved to be 98% ahead of the existing methods of Security Hadoop Distributed File System Sec (HDFS) and Map Reduce Encryption Scheme (MRE). On the other hand, this work aims to address the problems of usage of Big Data in the cloud toward the sole solution, cost-effective solutioning, and proof of ownership. The outcome analysis of FAEA revolves around addressing these three major problems. This research work would be much helpful for the IT industries to manage Big Data in Cloud with security aspects for the decade.
Teaching of College English Writing from the Perspective of Multimedia Education

The rapid development of science and technology, the rapid updating of computer technology, the constant networking, and computerisation of society are ushering in a new era in which computer technology and various media are used to process and disseminate information. Multimedia technology has also been mentioned and developed in the field of education. Its intervention has had a significant impact on education, leading to a renewed conception of educators, educational methods, and forms of learning. This paper is based on educational theory, educational planning theory, and educational psychology theory, starting from the combination of network multimedia and college English writing instruction through detailed demand analysis and system design, fully using modern information technology to build a multimedia network learning platform of blended college English writing instruction. This paper examines the application of multimedia technology in blended learning college English writing. Based on the reading and collecting of a large number of materials, it combines the long-term experience of teaching practice, the concept of multimedia, characteristics, application status, and development trends of multimedia technology; in-depth analysis of the current situation and problems faced by blended learning English writing in college summarizes the application of multimedia curriculum in teaching practice and feedback on the use of multimedia curriculum in teaching English writing in college. The experiments showed that the <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>P</mi> </math> value of students’ attitude toward English writing and multimedia teaching method is less than 0.05, which showed that the multimedia teaching method could improve students’ self-discovery ability and enhance the learning effect; the repetition of multimedia teaching material conforms to the memory rules and effectively helps students to remember what they learn repeatedly.
DRIN: Deep Recurrent Interaction Network for click-through rate prediction

The click-through rate (CTR) prediction focuses on modelling feature interactions effectively in the domains of computational advertising and recommender systems. However, to obtain explicit feature interactions, vector-multiplication operations were usually utilized as the inner product or the outer product, which severely limited their abilities. For improving interaction performance, the Recurrent Interaction Network (RIN) was proposed, which employed two products types in the matrix-multiplication operations to fulfill the explicit feature interactions and borrowed the 1 × 1 Convolutional Neural Network (CNN) to fuse the different orders interactions. Therefore, RIN can model arbitrary order explicit interactions between two different layers, leading to increased efficiency, compared with the existing methods. Furthermore, RIN was combined with a standard Deep Neural Network (DNN) creating a new composite model, called the Deep Recurrent Interaction Network (DRIN). DRIN can learn both the implicit and explicit interactions of features, which specifically has more ability to learn explicit interactions than existing models. Comprehensive experiments show that DRIN outperforms state-of-the-art models in terms of AUC and Logloss performance.
SOLO taxonomy-based knowledge structure with subjective items

As an important mathematical theory in intelligent learning and assessment system, knowledge space theory merely cares about items are mastered or non-mastered. Thus it needs to be further explored to achieve more precise and interpretable analysis. To this end, this paper mainly focuses on knowledge structures in corporate with Solo taxonomy. Then, fuzzy knowledge structure and fuzzy learning space are gradually developed. The corresponding knowledge base and surmise relation are explored respectively as well. In such case, the induced maximal knowledge space and its properties are further studied sufficiently. And three kinds of skill models are put forward based on skill proficiency. Finally, a case study is presented to illustrate the advantage in learning description.
Improving and Diagnosing Knowledge-Based Visual Question Answering via Entity Enhanced Knowledge Injection

Knowledge-Based Visual Question Answering (KBVQA) is a bi-modal task requiring external world knowledge in order to correctly answer a text question and associated image. Recent single modality text work has shown knowledge injection into pre-trained language models, specifically entity enhanced knowledge graph embeddings, can improve performance on downstream entity-centric tasks. In this work, we empirically study how and whether such methods, applied in a bi-modal setting, can improve an existing VQA system's performance on the KBVQA task. We experiment with two large publicly available VQA datasets, (1) KVQA which contains mostly rare Wikipedia entities and (2) OKVQA which is less entity-centric and more aligned with common sense reasoning. Both lack explicit entity spans, and we study the effect of different weakly supervised and manual methods for obtaining them. Additionally, we analyze how recently proposed bi-modal and single modal attention explanations are affected by the incorporation of such entity enhanced representations. Our results show substantially improved performance on the KBVQA task without the need for additional costly pre-training, and we provide insights for when entity knowledge injection helps improve a model's understanding. We provide code and enhanced datasets for reproducibility1.
Tutorial on the universal variability language

The multitude of formats for specifying variability models induces additional effort for researchers and practitioners and hinders exchange. The Universal Variability Language (UVL) is a community effort towards a unified format. Over the last two years, several tools integrated UVL allowing a wide usage. This tutorial consists of three major parts, where we showcase (1) the basics of UVL and how to use it for modeling within FeatureIDE, (2) how to transform other formats to UVL and back with TraVarT, and (3) how to perform feature-model analyses on UVL models with FLAMA.
Can internal audit function impact artificial intelligence? Case of public listed companies of Oman

Artificial intelligence is well integrated into the fabric of all organizational operations and internal audit function can impact artificial intelligence in providing better services to organizations and their shareholders. In the current business environment, artificial intelligence can be utilized as a governance management system that can assist organizational management in making well informed and strategic decisions. The purpose of this study is to identify whether internal audit function can impact artificial intelligence or not within Omani public listed companies. For this study, internal audit function is an independent variable and artificial intelligence is the dependent variable. Descriptive cross-sectional survey is utilized with Omani public listed companies as the unit of analysis. Data is collected by internet-based tool and analysed via PLS-SEM and SPSS. Result suggests that the internal audit function has a significant and direct impact on artificial intelligence as evaluations and assessments conducted by artificial intelligence are dependent upon the feed data provided by control activities available within the organization such as the internal audit function. To the best of the knowledge of researchers, this study is distinctive from others because artificial intelligence's support in the context of organizational governance is never being studied before. Contrary to the previous studies, this study emphasizes the fact that artificial intelligence can operate as a support function and as a governance management system working for the organization. This study can assist regulators, professional bodies and organizations in amending their codes of corporate governance and organizational policies by introducing artificial intelligence as a governance management system working for organizational development which can also enhance shareholders' satisfaction.
A robust structure and texture aware model for image Retinex

Retinex theory is developed mainly to decompose an image into the product of illumination and reflectance based on local derivatives, in which smaller derivatives are given to the illumination and larger derivatives are attributed to the changes in the reflectance. In this paper, we propose a Retinex decomposition model that classifies image derivatives into illumination and reflectance components via structure/texture aware maps. First, we introduce a novel measure called maximum neighbor difference (MND), and utilize it to generate robust structure aware map (RSAM) and robust texture aware map (RTAM) by Huber loss weight from robust statistics. Then, we use RSAM and RTAM as weighting matrices to construct regularization terms of illumination and reflectance, respectively, and obtain a robust structure and texture aware Retinex (RSTAR) model for Retinex decomposition. Finally, we design an alternatively updated algorithm to solve RSTAR, in which the original nonlinear minimization problem is transformed into two linear sub-problems. The performance of RSTAR is evaluated subjectively and objectively on four image datasets in terms of image decomposition/low-light image enhancement, in comparison to eight state-of-the-art Retinex decomposition models. Evaluation results have demonstrated the superior performance of RSTAR in low-light image enhancement.
Tamil Handwritten Character Recognition System using Statistical Algorithmic Approaches

This framework gives a detailed research on recognizing Tamil handwritten characters using locational and directional approaches embedded with different combinations of zone and quad methodologies. Tamil language has 247 character classes and is widely spoken by the people in India (Tamil Nadu), Malaysia, Singapore, Sri Lanka and so on. For considering the large character sets with their general and handwritten complexities, the two-stage feature extraction process has been experimented with to represent the character's structure. In the initial stage, the character's image is divided into nine equal zones and the structural features were extracted from each zone by the directional algorithmic approach, which denotes unique shape possibilities represented in zone divisions. A classification test has been performed to identify characters in this stage, but a structural portion of handwritten characters like unwanted loops and curves leads to negative results. Hence, locational features have been introduced to identify the position of structures. Each zone is subdivided into four quads further and the pixel availability has been taken as features from the quads to provide the solution for unnecessary portions and loops. With directional features taken from upper (3 columns × 1 row) and lower zones (3 columns × 1 row), corresponding location features have been added up for labeling a unique shape. Finally, to classify the characters, the directional features taken from middle zones (3 columns × 1 row) and their respective locational features have been added with labeled shapes of upper and lower zones. A suitable machine learning algorithm has been chosen for classifying the character classes. HP-Lab-India dataset and two different handwritten documents collected from the people of Tamil Nadu, India, have been tested by these approaches. This experimental research shows significant improvement in recognizing accurate characters. The final results of this approach have created a benchmark for the recognition of handwritten Tamil characters.
A novel exponential one-dimensional chaotic map enhancer and its application in an image encryption scheme using modified ZigZag transform

The rapid development of Internet communication technology requires more secure and effective chaotic map to encrypt and protect digital images. The common approach is to combine one-dimensional chaotic map with sines or cosines. However many existing sine or cosine type 1D chaotic map present the safety risk of lacking chaotic performance when the control parameter approaches zero. To address this issue, in this paper, we propose a one-dimensional exponential chaotic map enhancer (called 1D-ECE) by combining the exponential factor eμ. The experimental results suggest that proposed 1D-ECE solves the problem of insufficient chaotic performance when the control parameter is close to zero in cosine type chaotic amplifier, and better improve the randomness of input chaotic maps. Next, we have designed a modified ZigZag transform (called BNIZ-transform), which performs ZigZag transform on the adaptive indexes of the matrix elements after blocking and flipping at the bit-level, and then the columns of the matrix are permutated at the pixel level according to the chaotic sequence. Proposed BNIZ-transform not only transforms non-square matrices, but also enables simultaneous confusion and diffusion. Finally, to demonstrate that the proposed 1D-ECE and BNIZ-transform can be applied for cryptographic purposes, we propose an image encryption scheme including 1D-ECE and BNIZ-transform. The simulation results indicate that our proposed scheme is highly secure and can resist chosen plaintext attacks.