Reviewerly/exHarmony huggingface dataset:
Issues and Solutions in Deep Learning-Enabled Recommendation Systems within the E-Commerce Field

In recent years, especially with the (COVID-19) pandemic, shopping has been a challenging task. Increased online shopping has increased information available via the World Wide Web. Finding new products or identifying the most suitable products according to customers’ personalization trends is the main benefit of E-commerce recommendation systems, which use different techniques such as rating, ranking, or reviewing. These recommendations can be formed using different techniques and approaches, particularly using the technology of intelligent agents, and specific interfaces or personal agents can be used to model this type of system. These agents usually use the techniques and algorithms of Artificial Intelligence internally. A recommendation system is a prediction system that has been created to help the user to select the proper product for them, and to reduce the effort spent in the search process using advanced technology such as deep learning techniques. We investigate all studies using a standard review process for collecting and retrieving data from previous studies and illustrate their relevant accuracy and interpretability along with pros and cons helpful to business firms to adopt the most legitimate approach. The study’s findings revealed that recommendation problems are solved better by using deep learning algorithms such as CNN, RNN, and sentiment analysis, especially for popular problems such as cold start and sparsity.
Clustering sequential navigation patterns in<scp>multiple‐source</scp>reading tasks with dynamic time warping method

Abstract Background Data‐driven investigations of how students transit pages in digital reading tasks and how much time they spend on each transition allow mapping sequences of navigation behaviours into students' navigation reading strategies. Objectives The purpose of this study is threefold: (1) to identify students' navigation patterns in multiple‐source reading tasks using a sequence clustering approach; (2) to examine how students' navigation patterns are associated with their reading performance and socio‐demographic characteristics; (3) to showcase how the navigation sequences could be clustered on the similarity measure by dynamic time warping (DTW) methods. Methods This study draws on process data from a sample of 16,957 students from 69 countries participating in the PISA 2018 study to identify how students navigate through a multiple‐source reading item. Students' navigation sequences were characterized by two indicators: the page sequence that tracks the page transition path and the time sequence that records the time duration on each visited page. K‐medoid partitioning clustering analyses were conducted on pairwise distance similarity measures computed by the DTW method. Results and conclusions Students' navigation patterns were found moderately associated with their reading proficiency levels. Students who visited all the pages and spent more time reading without rush transitions obtained the highest reading scores. Girls were more likely to achieve higher scores than boys when longer navigation sequences were used with shorter reading time on transited pages. Students who navigated only limited pages and spent shorter reading time were averagely at the lowest rank of socio‐economic status. Implications This study provides evidence for the exploration of students' navigation patterns and the examination of associations between navigation patterns and reading scores with the use of process data.
Data-driven decision support tool for production planning: a framework combining association rules and simulation

Nowadays, guaranteeing the highest product variety in the shortest delivery time represents one of the main challenges for most of industries. The dynamic contexts where they have to compete push them to quickly readapt their processes, increasing the need for reactive decision-support tools to identify targeted actions to improve performance. Starting from the analysis of existing decision-support tools separately adopting simulation or data mining techniques, a framework that combines Association Rule Mining (ARM) and simulation has been developed to capitalize on the benefits brought by both techniques. On the one hand, ARM supports companies in identifying the main criticalities that slow down production processes, such as different causes of stoppage, giving a priority ranking of interventions. On the other hand, data-driven simulation is used to validate the ARM results and to conduct scenario analyses to compare the KPIs values resulting from different configurations of the production processes. Once the best-impacting mitigating actions have been implemented, the proposed framework can be iteratively used to define an updated set of intervention areas to enhance, promoting continuous improvement. This data-driven approach represents the key value of the framework, guaranteeing its easy-to-readapt and iteratively application. Theoretical contributions refer to the use of simulation with ARM not only to validate relations but to perform scenario analyses in an iterative way, as well as to the novelty application in a low-tech sector. From a practical point of view, a case study in the fashion industry demonstrates the usability and reliability of the proposed framework.
Visual Saliency and Image Reconstruction from EEG Signals via an Effective Geometric Deep Network-Based Generative Adversarial Network

Reaching out the function of the brain in perceiving input data from the outside world is one of the great targets of neuroscience. Neural decoding helps us to model the connection between brain activities and the visual stimulation. The reconstruction of images from brain activity can be achieved through this modelling. Recent studies have shown that brain activity is impressed by visual saliency, the important parts of an image stimuli. In this paper, a deep model is proposed to reconstruct the image stimuli from electroencephalogram (EEG) recordings via visual saliency. To this end, the proposed geometric deep network-based generative adversarial network (GDN-GAN) is trained to map the EEG signals to the visual saliency maps corresponding to each image. The first part of the proposed GDN-GAN consists of Chebyshev graph convolutional layers. The input of the GDN part of the proposed network is the functional connectivity-based graph representation of the EEG channels. The output of the GDN is imposed to the GAN part of the proposed network to reconstruct the image saliency. The proposed GDN-GAN is trained using the Google Colaboratory Pro platform. The saliency metrics validate the viability and efficiency of the proposed saliency reconstruction network. The weights of the trained network are used as initial weights to reconstruct the grayscale image stimuli. The proposed network realizes the image reconstruction from EEG signals.
RFIENet: RGB-thermal feature interactive enhancement network for semantic segmentation of insulator in backlight scenes

Segmentation of insulators from complex images is a critical step for automatic fault inspection. However, it remains challenging in backlight scene parsing due to the visible imaging limitations. Thermal images are robust to harsh lighting conditions. In this paper, an RGB-thermal feature interactive enhancement network (RFIENet) is proposed to achieve backlight scene parsing of the insulator. First, two parallel single-modal feature encoders are constructed to extract the features of RGB or thermal images individually. Subsequently, the RGB and thermal modal features are integrated by two fusion modules separately, i.e. the multi-modal feature fusion module (MFFM) and the global feature fusion module (GFFM), which are used for shallow and deep features, respectively. Finally, the interactive enhancement decoder is developed to restore the resolution of the feature maps. The experimental results demonstrate the superiority of the proposed RFIENet.
Human and artificial cognition

Predictions of the timelines for when machines will be able to perform general cognitive activities that rival humans, or even the arrival of "super intelligence", range from years to decades to never. For researchers in the education sector, the potential future state of AI, while provocative, is secondary to important shorter-term questions that influence how AI is integrated into learning and knowledge practices such as sensemaking and decision making. AI is not a future technology. It is already present in our daily lives, often shaping, behind the scenes, the types of information we encounter. It is, therefore, important to consider immediate questions surrounding the dynamics of human-machine interactions. In this paper, we focus on the relationship between human and artificial cognition and treat these as separate systems, each with distinct strengths and capabilities. We adopt a functional view (i.e., discrete tasks) of the activities that artificial cognition completes and those that are best handled by humans. This creates a foundation to then evaluate models for how these two cognitive systems interact and the mechanisms for coordination that are required. In doing so, we create a basis for future researchers to develop testable hypotheses regarding the impact of artificial cognition on knowledge processes such as learning, sensemaking, and decision making. Our evaluation provides insight for researchers regarding the optimal relationship between which cognitive activities should be handed off to the machine, which should remain the domain of human performance, and how these two should then be integrated when outputs are passed from one cognitive system (human or artificial) to the other.
Excavating multimodal correlation for representation learning

A majority of previous methods for multimodal representation learning ignore the rich correlation information inherently stored in each sample, leading to a lack of robustness when trained on small datasets. Although a few contrastive learning frameworks leverage that information in a self-supervised manner, they generally encourage the intra-sample unimodal representations to be identical, neglecting the modality-specific information carried by individual modalities. In contrast, we propose a novel algorithm that learns the correlations between modalities to facilitate downstream multimodal tasks by leveraging the prior information across samples, and we explore the feasibility of the proposed method on elaborately designed unsupervised and supervised auxiliary learning tasks. Specifically, we construct the positive and negative sets for correlation learning as unimodal embeddings from the same sample and from different samples, respectively. A weak predictor is employed on the concatenated unimodal embeddings to learn the correspondence relationship for each set. In this way, the model can correlate unimodal features and discover the shared information across modalities. In contrast to contrastive learning methods, the proposed framework is compatible with any number of modalities and can retain modality-specific information, enabling multimodal representation to capture richer information. Moreover, in the supervised version, one of the main novelties is that the sample labels are further utilized to learn more discriminative features, where the assigned correlation scores of negative sets vary according to the label variations between the associated samples. Extensive experiments suggest that the proposed method reaches state-of-the-art performance on the tasks of multimodal sentiment analysis, emotion recognition, and humor detection, and can improve the performance of various fusion approaches.
A Redhead Walks into a Bar: Experiences of Writing Fiction with Artificial Intelligence

Human creativity has been often aided and supported by artificial tools, spanning traditional tools such as ideation cards, pens, and paper, to computed and software. Tools for creativity are increasingly using artificial intelligence to not only support the creative process, but also to act upon the creation with a higher level of agency. This paper focuses on writing fiction as a creative activity and explores human-AI co-writing through a research product, which employs a natural language processing model, the Generative Pre-trained Transformer 3 (GPT-3), to assist the co-authoring of narrative fiction. We report on two progressive – not comparative – autoethnographic studies to attain our own creative practices in light of our engagement with the research product: (1) a co-writing activity initiated by basic textual prompts using basic elements of narrative and (2) a co-writing activity initiated by more advanced textual prompts using elements of narrative, including dialects and metaphors undertaken by one of the authors of this paper who has doctoral training in literature. In both studies, we quickly came up against the limitations of the system; then, we repositioned our goals and practices to maximize our chances of success. As a result, we discovered not only limitations but also hidden capabilities, which not only altered our creative practices and outcomes, but which began to change the ways we were relating to the AI as collaborator.
Making Python code idiomatic by automatic refactoring non-idiomatic Python code with pythonic idioms

Compared to other programming languages (e.g., Java), Python has more idioms to make Python code concise and efficient. Although pythonic idioms are well accepted in the Python community, Python programmers are often faced with many challenges in using them, for example, being unaware of certain pythonic idioms or do not know how to use them properly. Based on an analysis of 7,638 Python repositories on GitHub, we find that non-idiomatic Python code that can be implemented with pythonic idioms occurs frequently and widely. Unfortunately, there is no tool for automatically refactoring such non-idiomatic code into idiomatic code. In this paper, we design and implement an automatic refactoring tool to make Python code idiomatic. We identify nine pythonic idioms by systematically contrasting the abstract syntax grammar of Python and Java. Then we define the syntactic patterns for detecting non-idiomatic code for each pythonic idiom. Finally, we devise atomic AST-rewriting operations and refactoring steps to refactor non-idiomatic code into idiomatic code. We test and review over 4,115 refactorings applied to 1,065 Python projects from GitHub, and submit 90 pull requests for the 90 randomly sampled refactorings to 84 projects. These evaluations confirm the high accuracy, practicality and usefulness of our refactoring tool on real-world Python code.
G<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.svg"><mml:msup><mml:mrow /><mml:mn>2</mml:mn></mml:msup></mml:math>DA: Geometry-guided dual-alignment learning for RGB-infrared person re-identification

RGB-Infrared (IR) person re-identification aims to retrieve person-of-interest from heterogeneous cameras, easily suffering from large image modality discrepancy caused by different sensing wavelength ranges. Existing works usually minimize such discrepancy by aligning modality distribution of global features, while neglecting deep semantics and high-order structural relations within each class. This might render the misalignment between heterogeneous samples. In this paper, we propose Geometry-Guided Dual-Alignment (G2DA) learning, which yields better sample-level modality alignment for RGB-IR ReID by solving a graph-enabled distribution matching task that maximizes agreement between multi-modality node representations considering edge topology. Specifically, we covert RGB/IR images into semantic-aligned graphs, in which whole-part features and their similarities are represented by nodes and associated edges, respectively. To simultaneously implement node- and edge-wise alignment (Dual Alignment), we introduce Optimal Transport (OT) as a metric to calculate cross-modality human body matching scores. By minimizing the displacement cost across RGB-IR graphs, G2DA could learn not just modality-invariant but structurally consistent cross-modality representations. Furthermore, we advance a Message Fusion Attention (MFA) mechanism to adaptively smooth the node representations within each RGB/IR graph, effectively alleviating occlusions caused by other individuals and/or objects. Extensive experiments on two standard benchmark datasets validate the superiority of G2DA, yielding competitive performance against previous state-of-the-arts.
A new Bayesian network model for risk assessment based on cloud model, interval type-2 fuzzy sets and improved D-S evidence theory

• A new BN model for risk assessment was developed. • The BN model is based on CM, IT2FSs, and the improved D-S evidence theory. • A new BPA function and a new evidence fusion rule algorithm were defined. • D-S evidence theory was improved under the IT2FSs environment. • The BN model could be used to predict the risk and analyze the sensitivity. Traditional Bayesian network (BN) model is established by crisp sets and probabilities, and its effectiveness and applicability are restricted. In order to solve this problem, a new BN model for risk assessment based on cloud model (CM), interval type-2 fuzzy sets (IT2FSs) and the improved Dempster-Shafer (D-S) evidence theory was developed in the following route. Firstly, CM and IT2FSs were used to define a new solution for the digital characteristics, and the standard and index cloud membership functions for the IT2FSs-CM were established. Then, the index cloud membership function for the IT2FSs-CM was defined as the basic probability assignment (BPA) function, a new evidence fusion rule algorithm was defined, and the improved D-S evidence theory was proposed. Furthermore, the improved D-S evidence theory was used to transform the BPA function into the IT2FSs based prior probability, and a new BN model for risk assessment was developed. Finally, the BN model was applied to a nuclear power plant (NPP) construction project, its risk was assessed at level Ⅰ (very low risk), and the results by IT2FSs-CM and Bayesian reasoning were compared with those by common methods. The findings show that the new BN model is effective and applicable in risk assessment.
Robust and secured watermarking using Ja-Fi optimization for digital image transmission in social media

Widespread transmission of digital image in social media has come up with security, confidentiality and authentication issues. Ensuring copyright protection of digital images shared through social media has become inevitable. To address these issues, a robust and secure digital image watermarking scheme using Redundant discrete wavelet transform (RDWT) - Singular value decomposition (SVD) hybrid transform is proposed in this paper. In the proposed scheme, digital image is divided into 4 × 4 non-overlapping blocks, and low information blocks are selected for embedding to ensure higher imperceptibility. For watermark embedding 1-level RDWT is applied on the selected blocks followed by SVD decomposition to make the proposed scheme highly robust against common attacks. One watermark bit is embedded in each left and right singular SVD matrices by adjusting the coefficients. This makes the proposed scheme free from false positive error and achieve high embedding capacity. Before embedding, watermark encryption is done by using a pseudo random key. The pseudo random key is generated adaptively from the cover image by using discrete wavelet transform saliency map, block mean approach and cosine functions. High imperceptibility and robustness is indispensable for the digital images shared through social media. But, these watermarking characteristics are in trade-off. In the proposed scheme, the trade-off is balanced by using optimized scaling factor (embedding strength). Scaling factor is optimized by using the proposed JAYA-Firefly (Ja-Fi) optimization. Experimental results demonstrate that the proposed scheme provides high imperceptibility, robustness, embedding capacity and security. Furthermore, performance comparison with the recent state-of-the-art schemes affirms that the proposed scheme has superior performance.
Single underwater image enhancement based on differential attenuation compensation

High quality underwater images and videos are important for exploitation tasks in the underwater environment, but the complexity of the underwater imaging environment makes the quality of the acquired underwater images generally low. To correct the chromatic aberration and enhance the sharpness of underwater images in order to improve the quality of underwater images, we based on the differential compensation proposed a Differential Attenuation Compensation (DAC) method. The underwater image is contrast stretched to improve the contrast of the image, as well as the underwater image is denoised, for the red channel with serious loss of detail information we choose the blue and green channels with more detail information to compensate for this, and finally the image is restored through the grayscale world to obtain more realistic colors. Our method is qualitatively and quantitatively compared with multiple state-of-the-art methods in the public underwater image dataset, underwater image enhancement benchmark (UIEB) and enhancing underwater visual perception (EUVP), demonstrating that the underwater images processed by our method better resolve the problems of chromatic aberration and blur, with more realistic color, detail and better underwater image quality evaluation indicators
Thermal quantum memory, Bell-non-locality, and entanglement behaviors in a two-spin Heisenberg chain model

This paper explores thermal quantum memory-assisted entropic uncertainty, Bell-non-locality, and entanglement in a two-qubit Heisenberg XXX chain with x-directional Dzyaloshinskii-Moriya (DM) and Kaplan-Shekhtman-Entin-Wohlman-Aharony (KSEA) interactions in thermal equilibrium. We find this crucial that the initial degree of nonlocality and entanglement in the two-qubit state entirely depends upon the temperature, orientation, and strength of the DM and KSEA interaction of the magnetic field. Among many other situations, the magnetic field characterized by DM interaction with minimal temperature, exchange coupling strength, and KSEA interaction is the most reliable for generating and maintaining maximal nonlocality and entanglement while completely suppressing entropic uncertainty. On the other hand, repeated revivals and non-Markovian dynamics have been observed when the magnetic field is characterized by exchange coupling constant, DM and KSEA interaction with a higher temperature limit. Except for temperature-based magnetic fields, we show that maximal entanglement and nonlocality are generated and preserved in the two qubits with zero-order entropic uncertainty, which we believe is a better result than previous studies.