Reviewerly/exHarmony huggingface dataset:
Sentiment analysis for product rating using a deep learning approach

Sentiment analysis is a study about opinions, emotions, and attitudes of the people towards an event or issue. Social networking is an invaluable medium for individuals to express their thoughts and views about any subject or topic, contributing to massive quantities of unstructured knowledge. These emotions can be processed and examined to analyze and obtain insights. Therefore, several machine learning and Natural Language Processing (NLP) based methods were used to examine these opinions. Because of the shifts in the sequential order, stream lengths, and complex logic, the exact sentiments in the consumer feedback are still difficult. Recently, deep learning methods have been used to attain improved results. The most common forms of deep learning method used are the Recurrent Neural Network (RNN) and the Convolutional Neural Network (CNN). Long short-term memory (LSTM), particularly with attentive layers, pays greater focus to the sentiment impact. LSTM has an advantage over alternative RNNs and other deep learning approaches because of relative insensitivity to gap length. Deep learning approaches have better accuracy over existing state-of-the-art approaches because of their ability to handle extensive realtime data and the power of feature extraction, but there is still room for improvement. This paper has used Long Short-Term Model (LSTM) model to predict the customer review's opinion, attaining an accuracy of 93.66%. Furthermore, comparative analysis of the deep LSTM model with existing has been presented.
How to overcome online banner blindness? A study on the effects of creativity

Purpose This study aims to explore whether creativity can overcome banner blindness in the viewing of web pages and demonstrate how visual saliency and banner-page congruity constitute the boundary conditions for creativity to improve memory for banner ads. Design/methodology/approach Three studies were conducted to understand the influence of advertising creativity and banner blindness on recognition of banner ads, which were assessed using questionnaires and bias adjustment. The roles of online user tasks (goal-directed vs free-viewing), visual saliency (high vs low) and banner-page congruity (congruent vs incongruent) were considered. Findings The findings suggest that creativity alone is not sufficient to overcome the banner blindness phenomenon. Specifically, in goal-directed tasks, the effect of creativity on recognition of banner ads is dependent on banner ads’ visual saliency and banner-page congruity. Creative banners are high on visual saliency, and banner-page congruity yields higher recognition rates. Practical implications Creativity matters for attracting consumer attention. And in a web page context, where banner blindness prevails, the design of banners becomes even more important in this respect. Given the prominence of banners in online marketing, it is also necessary to tap the potential of creativity of banner ads. Originality/value First, focusing on how creativity influences memory for banner ads across distinct online user tasks not just provides promising theoretical insight on the tackling of banner blindness but also enriches research on advertising creativity. Second, contrary to the popular belief of extant literature, the findings suggest that, in a web page context, improvement in memory for banner ads via creativity is subject to certain boundary conditions. Third, a computational neuroscience software program was used in this study to assess the visual saliency of banner ads, whereas signal detection theory was used for adjustment of recognition scores. This interdisciplinary examination combining the two perspectives sheds new light on online advertising research.
Characteristics and Behaviors of Anonymous Users of Dark Web Platforms Suspected of Child Sexual Offenses

International law enforcement have noted a rise in the use of the Dark Web to facilitate and commit sexual offenses against children, both prior to and since the start of the COVID-19 pandemic. The study presented here therefore aimed to investigate the characteristics and behaviors of anonymous users of Dark Web platforms who were suspected of engaging in the sexual abuse of children. Naturally-occurring data on 53 anonymous suspects, who were active on the Dark Web and had come to police attention in the United Kingdom (UK), were sampled. Analysis of the data yielded 462 features that could be coded reliably. Analysis of these features provided novel insights into suspects’ characteristics, their motivations for using the Dark Web, the nature of the offending behavior they reported engaging in, their technical and security precautions, sexual interests, and the content of their interactions with one another. Findings are discussed in relation to theoretical and practical implications, as well as directions for future research.
Knowledge-based Review Generation by Coherence Enhanced Text Planning

As a natural language generation task, it is challenging to generate informative and coherent review text. In order to enhance the informativeness of the generated text, existing solutions typically learn to copy entities or triples from knowledge graphs (KGs). However, they lack overall consideration to select and arrange the incorporated knowledge, which tends to cause text incoherence. To address the above issue, we focus on improving entity-centric coherence of the generated reviews by leveraging the semantic structure of KGs. In this paper, we propose a novel Coherence Enhanced Text Planning model (CETP) based on knowledge graphs (KGs) to improve both global and local coherence for review generation. The proposed model learns a two-level text plan for generating a document: (1) the document plan is modeled as a sequence of sentence plans in order, and (2) the sentence plan is modeled as an entity-based subgraph from KG. Local coherence can be naturally enforced by KG subgraphs through intra-sentence correlations between entities. For global coherence, we design a hierarchical self-attentive architecture with both subgraph- and node-level attention to enhance the correlations between subgraphs. To our knowledge, we are the first to utilize a KG-based text planning model to enhance text coherence for review generation. Extensive experiments on three datasets confirm the effectiveness of our model on improving the content coherence of generated texts.
Extending two families of maximum rank distance codes

In this paper, we properly extend the family of rank-metric codes recently found by Longobardi and Zanella (2021) and by Longobardi, Marino, Trombetti and Zhou (2021). These codes are Fq2t-linear of dimension 2 in the space of linearized polynomials over Fq2t, where t is any integer greater than 2, and we prove that they are maximum rank distance codes. For t≥5, we determine their equivalence classes and these codes turn out to be inequivalent to any other construction known so far, and hence they are really new.
ASVtorch toolkit: Speaker verification with deep neural networks

The human voice differs substantially between individuals. This facilitates automatic speaker verification (ASV) — recognizing a person from his/her voice. ASV accuracy has substantially increased throughout the past decade due to recent advances in machine learning, particularly deep learning methods. An unfortunate downside has been substantially increased complexity of ASV systems. To help non-experts to kick-start reproducible ASV development, a state-of-the-art toolkit implementing various ASV pipelines and functionalities is required. To this end, we introduce a new open-source toolkit, ASVtorch, implemented in Python using the widely used PyTorch machine learning framework.
Effective Blind Image Deblurring Using Matrix-Variable Optimization

Blind image deblurring has been a challenging issue due to the unknown blur and computation problem. Recently, the matrix-variable optimization method successfully demonstrates its potential advantages in computation. This paper proposes an effective matrix-variable optimization method for blind image deblurring. Blur kernel matrix is exactly decomposed by a direct SVD technique. The blur kernel and original image are well estimated by minimizing a matrix-variable optimization problem with blur kernel constraints. A matrix-type alternative iterative algorithm is proposed to solve the matrix-variable optimization problem. Finally, experimental results show that the proposed blind image deblurring method is much superior to the state-of-the-art blind image deblurring algorithms in terms of image quality and computation time.
Survival time prediction by integrating cox proportional hazards network and distribution function network

Abstract Background The Cox proportional hazards model is commonly used to predict hazard ratio, which is the risk or probability of occurrence of an event of interest. However, the Cox proportional hazard model cannot directly generate an individual survival time. To do this, the survival analysis in the Cox model converts the hazard ratio to survival times through distributions such as the exponential, Weibull, Gompertz or log-normal distributions. In other words, to generate the survival time, the Cox model has to select a specific distribution over time. Results This study presents a method to predict the survival time by integrating hazard network and a distribution function network. The Cox proportional hazards network is adapted in DeepSurv for the prediction of the hazard ratio and a distribution function network applied to generate the survival time. To evaluate the performance of the proposed method, a new evaluation metric that calculates the intersection over union between the predicted curve and ground truth was proposed. To further understand significant prognostic factors, we use the 1D gradient-weighted class activation mapping method to highlight the network activations as a heat map visualization over an input data. The performance of the proposed method was experimentally verified and the results compared to other existing methods. Conclusions Our results confirmed that the combination of the two networks, Cox proportional hazards network and distribution function network, can effectively generate accurate survival time.
They Would do Better if They Worked Together: The Case of Interaction Problems Between Password Managers and Websites

Password managers are tools to support users with the secure generation and storage of credentials and logins used in online accounts. Previous work illustrated that building password managers means facing various security and usability challenges. For strong security and good usability, the interaction between password managers and websites needs to be smooth and effortless. However, user reviews for popular password managers suggest interaction problems for some websites. Therefore, to the best of our knowledge, this work is the first to systematically identify these interaction problems and investigate how 15 desktop password managers, including the ten most popular ones, are affected. We use a qualitative analysis approach to identify 39 interaction problems from 2,947 user reviews and 372 GitHub issues for 30 password managers. Next, we implement minimal working examples (MWEs) for all interaction problems we found and evaluate them for all password managers in 585 test cases.Our results illustrate that a) password managers struggle to correctly implement authentication features such as HTTP Basic Authentication and modern standards such as the autocomplete-attribute and b) websites fail to implement clean and well-structured authentication forms. We conclude that some of our findings can be addressed by either PWM providers or web-developers by adhering to already existing standards, recommendations and best practices, while other cases are currently almost impossible to implement securely and require further research.
Wiki2Prop: A Multimodal Approach for Predicting Wikidata Properties from Wikipedia

Wikidata is rapidly emerging as a key resource for a multitude of online tasks such as Speech Recognition, Entity Linking, Question Answering, or Semantic Search. The value of Wikidata is directly linked to the rich information associated with each entity – that is, the properties describing each entity as well as the relationships to other entities. Despite the tremendous manual and automatic efforts the community invested in the Wikidata project, the growing number of entities (now more than 100 million) presents multiple challenges in terms of knowledge gaps in the graph that are hard to track. To help guide the community in filling the gaps in Wikidata, we propose to identify and rank the properties that an entity might be missing. In this work, we focus on entities with a dedicated Wikipedia page in any language to make predictions directly based on textual content. We show that this problem can be formulated as a multi-label classification problem where every property defined in Wikidata is a potential label. Our main contribution, Wiki2Prop, solves this problem using a multimodal Deep Learning method to predict which properties should be attached to a given entity, using its Wikipedia page embeddings. Moreover, Wiki2Prop is able to incorporate additional features in the form of multilingual embeddings and multimodal data such as images whenever available. We empirically evaluate our approach against the state of the art and show how Wiki2Prop significantly outperforms its competitors for the task of property prediction in Wikidata, and how the use of multilingual and multimodal data improves the results further. Finally, we make Wiki2Prop available as a property recommender system that can be activated and used directly in the context of a Wikidata entity page.
Extended XOR-based visual cryptography schemes by integer linear program

XOR-based visual cryptography scheme (XVCS) is a promising branch of VCS since superior visual quality of recovered secret image is provided. To assist the shadow management, meaningful shadow is preferred. However, existing extended XVCSs (EXVCSs) that generate meaningful shadows are confined to the (n,n) threshold. They are not practical for real-world applications. In this paper, we introduce EXVCS for (k,n) threshold and general access structure (GAS) by using integer linear program (ILP) technique. First of all, an approach for implementing the (k,n)-XVCS is given. We associate the (k,n)-XVCS with a minimizing problem, where the constraints in this problem are derived from the contrast and security conditions in (k,n)-XVCS. The ILP technique is adopted to solve this problem for obtaining the best solutions for constructing the base matrices for (k,n)-XVCS. Further, the construction is extended to (k,n)-EXVCS, in which the shadows with meaningful contents are constructed. To realize complicated sharing strategies, a construction of EXVCS for GAS is described as well. The base matrices for XVCS and EXVCS are demonstrated, as well as the pixel expansion and contrast. Comparisons among related methods are illustrated to show the advantages of the proposed schemes.
Supporting Metacognition during Exploratory Search with the OrgBox

Current search systems provide effective support to users engaged in fact-finding and look-up oriented tasks. However, they provide relatively little support for users engaged in exploratory search tasks that involve cognitive and metacognitive activities such as learning, synthesis, planning, and reflection. We conducted a within-subject user study (N=24) that investigated the effects of a novel knowledge organization tool called the OrgBox, designed to assist users with organizing and synthesizing information, and metacognitive activities. The OrgBox included features to allow users to drag-drop information they found through search into "boxes" that could be created, labelled, and re-arranged. Study participants completed two exploratory search tasks, one with the OrgBox, and one with the OrgDoc, a baseline tool that included features of a rich-text editor (e.g., formatting, bullets) for taking notes.
Improving adversarial attacks on deep neural networks via constricted gradient-based perturbations

Despite the remarkable success achieved by the deep learning techniques, adversarial attacks on deep neural networks unveiled the security issues posted in specific domains. Such carefully crafted adversarial instances generated by the adversarial strategies on Lp norm bounds freely mislead the deep neural models on many professional tasks. Existing gradient-based adversarial attack methods fool the state-of-the-art classification systems into lapses and gain good adversarial effectiveness on vast professional missions. Nevertheless, we find that adversarial examples generated on gradient-based present massive pixel modifications on the generated adversarial examples. Moreover, the adversarial attack strategies based on stable gradient take the accumulation of the gradient into account. It introduces redundant perturbations with frequently occurring features in the generation of adversarial examples, yet the changes induced on the generated examples are easily detected and perceptible visually. Based on such situations, we propose types of adversarial attack approaches with constricted gradient-based strategy termed Constricted Iterative Fast Gradient Sign Method (CI-FGSM). It focuses on lessening the impact of accumulated information on the crafted perturbations via deducting the mount of preceding cumulative gradient-based entities. CI-FGSM requires few freely gradient-based operations on the generated inputs to reduce the accumulation of historical gradient-based objects, thus crafts natural and imperceptible adversarial perturbations added to the generated examples. We conduct the experiments on MNIST, CIFAR10, and IMAGENET ILSVRC2012(Val) to evaluate the performance of the proposed adversarial approaches in misleading the commonly-used deep neural classification networks. Compared to other gradient-based adversarial attack methods, experimental results reveal that CI-FGSM efficaciously reduces the extra modifications on pristine inputs and maintains perfect effect of adversarial attacks in fooling the classifiers with different norm constraint strategies.
A Secure Asymmetric Optical Image Encryption Based on Phase Truncation and Singular Value Decomposition in Linear Canonical Transform Domain

A new asymmetric optical double image encryption algorithm is proposed, which combines phase truncation and singular value decomposition. The plain text is encrypted with two-stage phase keys to obtain a uniformly distributed cipher text and two new decryption keys. These keys are generated during the encryption process and are different from encryption keys. It realizes asymmetric encryption and improves the security of the system. The unscrambling keys in the encryption operation are mainly related to plain text. At the same time, the system is more resistant to selective plain text attacks; it also improves the sensitivity of decryption keys. With the application of phase truncation, the key space expanded and the security of the cryptographic system is enhanced. The efficacy of the system is calculated by evaluating the estimated error between the input and retrieved images. The proposed technique provides innumerable security keys and is robust against various potential attacks. Numerical simulations verify the effectiveness and security of the proposed technique.
The Heterogeneous Effects of Government Size and Press Freedom on Corruption in Sub-Saharan Africa: Method of Moment Quantile Regression Approach

Although an active body of literature exists on the relationship between government size and corruption, different findings proliferate the literature due to the nuances of geographical context, methods, and variables employed. The paper employs the novel method of moment quantile regression with fixed effects over the period 1984–2018 to investigate the dynamic impact of government size and press freedom on corruption in Sub-Saharan African (SSA) countries. The main contribution is to examine the impact of government size and press freedom across the conditional distribution of corruption in SSA. The findings show that the impacts of government size and press freedom are heterogeneous across the conditional quantile distribution of corruption. Among the most corrupt countries in SSA, larger government size and press freedom significantly increase corruption. Some policy recommendations are offered based on these findings.
Fuzzy cognitive networks with functional weights for time series and pattern recognition applications

Over the last decades, a number of remarkable pattern recognition algorithms have been proposed, as a result of the continuous raising of pattern classification in one of the major application areas of artificial intelligence. Many real world problems require the use of efficient classification models pointing out the need for continuous research and study of new techniques. Such a proposal is to use Fuzzy Cognitive Maps (FCMs) and their extensions to solve distinct classification tasks. The extension named Fuzzy Cognitive Network (FCN) has the clear advantage of guaranteed convergence to equilibrium points, which in turn makes it more suitable for pattern recognition applications. However, in order to store the broad range of associations using FCN, large fuzzy rule databases have to be built. In this work, the FCNs with functional weights are introduced and their use in pattern recognition and time series prediction is proposed. The new scheme keeps the nice convergence properties of FCN but is alleviated from the memory and computational requirements of using large fuzzy rule databases, as well as from the inevitable human intervention. The training of the classifier is performed by using a combination of a gradient descent like procedure which uses either a linear or a bilinear parametric model of the network and a least squares method for the estimation of the functional weights. The efficiency and reliability of the proposed classifier is supported by its high overall performance on a set of publicly available time series and pattern recognition datasets outperforming other well-known machine learning models, as well as the most efficient FCM based classifiers.
Multi-human Parsing with a Graph-based Generative Adversarial Model

Human parsing is an important task in human-centric image understanding in computer vision and multimedia systems. However, most existing works on human parsing mainly tackle the single-person scenario, which deviates from real-world applications where multiple persons are present simultaneously with interaction and occlusion. To address such a challenging multi-human parsing problem, we introduce a novel multi-human parsing model named MH-Parser, which uses a graph-based generative adversarial model to address the challenges of close-person interaction and occlusion in multi-human parsing. To validate the effectiveness of the new model, we collect a new dataset named Multi-Human Parsing (MHP), which contains multiple persons with intensive person interaction and entanglement. Experiments on the new MHP dataset and existing datasets demonstrate that the proposed method is effective in addressing the multi-human parsing problem compared with existing solutions in the literature.