Reviewerly/exHarmony huggingface dataset:
Contrastive Self-Supervised Pre-Training for Video Quality Assessment

Video quality assessment (VQA) task is an ongoing small sample learning problem due to the costly effort required for manual annotation. Since existing VQA datasets are of limited scale, prior research tries to leverage models pre-trained on ImageNet to mitigate this kind of shortage. Nonetheless, these well-trained models targeting on image classification task can be sub-optimal when applied on VQA data from a significantly different domain. In this paper, we make the first attempt to perform self-supervised pre-training for VQA task built upon contrastive learning method, targeting at exploiting the plentiful unlabeled video data to learn feature representation in a simple-yet-effective way. Specifically, we implement this idea by first generating distorted video samples with diverse distortion characteristics and visual contents based on the proposed distortion augmentation strategy. Afterwards, we conduct contrastive learning to capture quality-aware information by maximizing the agreement on feature representations of future frames and their corresponding predictions in the embedding space. In addition, we further introduce distortion prediction task as an additional learning objective to push the model towards discriminating different distortion categories of the input video. Solving these prediction tasks jointly with the contrastive learning not only provides stronger surrogate supervision signals, but also learns the shared knowledge among the prediction tasks. Extensive experiments demonstrate that our approach sets a new state-of-the-art in self-supervised learning for VQA task. Our results also underscore that the learned pre-trained model can significantly benefit the existing learning based VQA models. Source code is available at https://github.com/cpf0079/CSPT.
A Semantic Approach for Fashion Recommendation Using Logistic Regression and Ontologies

Due to the increased prevalence of web recommendation systems after years of research, it has unarguably become the ultimate solution for efficient functioning of any e-commerce or user supportive digital domain. Though a variety of algorithms have been tested to meet the expectations of users in order to be decision supportive, this paper proposes a potential framework for recommendation of men's clothing. The focus of the system is to improve the efficiency of the recommendation to cope up to the speed of the user's thought process and expectations at the same time generate only those options that have been validated closely to the user's style hunt trajectory. In the presented approach the user's historical click data and searches is preprocessed and converted into query words. The features are extracted from the on ontology of fashion with the help of query words. The ontology used in this paper is highly domain specific. External sources such as fashion reviews, fashion e-magazines, fashion blogs and fashion trends from e-commerce websites are converted into query words and used for feature enrichment. The dataset is provided for classification using logistic regression, and only the top 50% of results from the classification undergoes semantic similarity computation. Normalized google distance and SemantoSim measure are the methods used for emantic similarity computation, this happens mainly for the relevance of the results. The recommendations of fashion items and fashion brands are suggested to the user based on the results gotten from semantic similarity. The accuracy of the Onto infused recommendation system is 97.14% and average precision is 96.31%.
Semantic segmentation of pancreatic medical images by using convolutional neural network

Pancreatic cancer is the most difficult-to-detect cancer with the highest fatality rate. Pancreas analysis through abdominal computed tomography (CT) is challenging because the pancreas possesses a complex background and blurred boundaries with other organs. Traditionally, the notation of pancreatic area requires manual semantic segmentation by professional radiologists, but this is time-consuming. The developments of computer-assisted diagnosis of deep convolutional neural networks on pancreas segmentation are successful but require heavy computational complexity. This research is dedicated to the semantic segmentation of pancreatic CT images using convolutional neural networks to achieve favorable performance on pancreas segmentation with low computational parameters. We propose MobileNet-U-Net (MBU-Net) by combining U-Net model with light-weight MobileNet-V2 network. The proposed MBU-Net is assessed on the NIH pancreas-CT dataset. The averages for dice coefficient, Jaccard similarity coefficient, AUC, precision, specificity, and recall are 82.87%, 70.97%, 90.54%, 89.29%, 99.95%, and 77.37%, respectively. Results have demonstrated that the proposed MBU-Net can effectively reduce the computational parameters and achieve comparable performance when compared to the state-of-the-art methods.
Multi-Task/Single-Task Joint Learning of Ultrasound BI-RADS Features

Breast cancer is one of the most common cancers among women in the world. The breast imaging reporting and data system (BI-RADS) features effectively improve the accuracy and sensitivity of breast tumors. Based on the description of signs in BI-RADS, a quantitative scoring scheme is proposed based on ultrasound (US) data. This scheme includes feature extraction of high-level semantic, that is, an intermediate step interpreting the subsequent diagnosis. However, the scheme requires doctors to score the features of breast data, which is labor-intensive. To reduce the burden of doctors, we design a multi-task learning (MTL) framework, which can directly output the scores of different BI-RADS features from the raw US images. The MTL framework consists of a shared network that learns global features and K soft attention networks for different BI-RADS features. Thus, it enables the network not only to learn the potential correlation among different BI-RADS features, but also learn the unique specificity of each feature, which can assist each other and jointly improve the scoring accuracy. In addition, we group different BI-RADS features according to the correlation among tasks and build a multi-task/single-task joint framework. Experimental results on the US breast tumor dataset collected from 1859 patients with 4458 US images show that the proposed BI-RADS feature scoring framework achieves an average scoring accuracy of 84.91% for 11 BI-RADS features on the test dataset, which is helpful for the subsequent diagnosis of breast tumors.
The Evaluation of Software Security through Quantum Computing Techniques: A Durability Perspective

The primary goal of this research study, in the field of information technology (IT), is to improve the security and durability of software. A quantum computing-based security algorithm springs quite a lot of symmetrical approaches and procedures to ensure optimum software retreat. The accurate assessment of software’s durability and security is a dynamic aspect in assessing, administrating, and controlling security for strengthening the features of security. This paper essentially emphasises the demarcation and depiction of quantum computing from a software security perspective. At present, different symmetrical-based cryptography approaches or algorithms are being used to protect different government and non-government sectors, such as banks, healthcare sectors, defense, transport, automobiles, navigators, weather forecasting, etc., to ensure software durability and security. However, many crypto schemes are likely to collapse when a large qubit-based quantum computer is developed. In such a scenario, it is necessary to pay attention to the security alternatives based on quantum computing. Presently, the different factors of software durability are usability, dependability, trustworthiness, and human trust. In this study, we have also classified the durability level in the second stage. The intention of the evaluation of the impact on security over quantum duration is to estimate and assess the security durability of software. In this research investigation, we have followed the symmetrical hybrid technique of fuzzy analytic hierarchy process (FAHP) and fuzzy technique for order of preference by similarity to ideal solution (FTOPSIS). The obtained results, and the method used in this estimation, would make a significant contribution to future research for organising software security and durability (SSD) in the presence of a quantum computer.
TransMKR: Translation-based knowledge graph enhanced multi-task point-of-interest recommendation

Point-of-interest (POI) recommendation aims to predict favorite POIs that have never visited before for users given their historical check-ins records. The introduction of knowledge graph can solve the heterogeneity of auxiliary information. However, the existing knowledge graph methods about POI recommendation only make use of the relationship between users and POIs or the context information (such as time series) for recommendation, but they neglect the deep relation consider the attribute connection of POIs themselves. In this paper, we propose a translation-based knowledge graph enhanced multi-task learning framework (TransMKR) for the POI recommendation. We improve the KGE module of MKR with TransR to quantify the relation between POIs and their attributes. As the POIs vector and the entity vector are actually two descriptions of the same item, the cross-sharing of information between them makes them obtain additional information from each other, and this enhances the expressive ability of POI data, thus it can alleviate the problem of data sparsity. An exquisitely designed structure is devised to capture the deep associated attributes of POIs under different relations. Empirically, this approach achieves the state-of-the-art model for POI recommendation based on geographical location on two public real-world datasets.
Investigating the value of immersive virtual reality tools for organizational training: An applied international study in the biotech industry

Abstract Background Immersive virtual reality (VR) is increasingly used in organizational training interventions. However, few studies have systematically investigated VR compared to standard training methods in actual organizational contexts. Objectives The focus of this study was to evaluate the effectiveness of a VR simulation for training professionals in the biotech industry. Aligning training needs to unique media affordances, the study designed an immersive story‐based VR simulation for training customer‐facing employees on a new product and tested it in an international biotech company. Methods The system was evaluated by comparing its effectiveness to a traditional video presentation with the same content in a randomized between subjects experiment. The sample consisted of 95 employees across three locations: Brazil, Denmark, and USA. Results The VR simulation group performed better than the video presentation group on the outcomes of conceptual knowledge ( d = 0.41) spatial knowledge ( d = 0.61), transfer intentions ( d = 0.57), enjoyment ( d = 1.74), self‐efficacy ( d = 0.68), perceived learning ( d = 0.89), personal value ( d = 0.83), and organizational value ( d = 0.82), but no significant difference was found for factual knowledge ( d = −0.10). Implications Results suggest that VR simulations can be effective across cultures in organizational training interventions. VR is specifically effective when the goals of the training are to increase conceptual and spatial understanding as well as enjoyment, and self‐efficacy, but not factual knowledge. Furthermore, employees report higher levels of perceived learning, personal and organizational value and transfer intentions after VR training compared to standard video‐based training.
Influencing Factors on Nursing Students’ Learning Flow during the COVID-19 Pandemic: A Mixed Method Research

This study aimed to investigate the factors affecting nursing students' learning flow in COVID-19 pandemic situations through mixed-method research.Of the 245 nursing students participating in the survey, 20 participated in a focus group interview. Quantitative data were analyzed using stepwise multiple regression analysis. Qualitative data were analyzed using content analysis.The factors affecting the learning flow of nursing students during the COVID-19 pandemic were their self-regulated learning ability (β = .24, p = .001); learning motivation (β = .23, p = .001); self-efficacy in clinical practice (β = .14, p = .014); and lecture type, or a mixture of recorded and real-time video lectures (β = .13, p = .022). As a result of the qualitative study, eight categories and 22 subcategories were derived. The eight categories are: a lack of preparation in the starting of virtual classes, adapting and growing in a new learning environment, enhancing nursing knowledge and skills through virtual clinical training, self-regulation difficulties when studying alone due to social distancing, difficulty concentrating when learning online, disadvantages of virtual learning, concerns about academic performance, and missing opportunities to enjoy college life.Students attempted to discover their own learning expertise through virtual learning while concerned that they would be unable to fully establish their competence to work as actual hospital nurses due to a lack of clinical practice. In such a learning environment, systematic support and strategies are needed to increase the learning flow of nursing students.
A novel quantum inspired genetic algorithm to initialize cluster centers in fuzzy C-means

We propose a new quantum-inspired genetic algorithm (QGA) aimed to find the optimal initial cluster centers of the Fuzzy C-Means (FCM). The quantum chromosomes of the quantum population are collapsed to conventional chromosomes formed by binary strings encoding the cluster centers. We set as fitness function to minimize a fuzzy entropy function used to measure the fuzziness of clustering. A quantum rotation gate operator is applied in any generation to evolve the quantum population. The best conventional chromosome is used to set the initial cluster centers running FCM. We execute comparative tests of our QGA with other quantum-inspired method proposed in literature in order to find the best initial cluster centers: the results show that our method provides the best trade-off between the accuracy and precision of results and execution times.
Phishing Website Detection Based on Deep Convolutional Neural Network and Random Forest Ensemble Learning

Phishing has become one of the biggest and most effective cyber threats, causing hundreds of millions of dollars in losses and millions of data breaches every year. Currently, anti-phishing techniques require experts to extract phishing sites features and use third-party services to detect phishing sites. These techniques have some limitations, one of which is that extracting phishing features requires expertise and is time-consuming. Second, the use of third-party services delays the detection of phishing sites. Hence, this paper proposes an integrated phishing website detection method based on convolutional neural networks (CNN) and random forest (RF). The method can predict the legitimacy of URLs without accessing the web content or using third-party services. The proposed technique uses character embedding techniques to convert URLs into fixed-size matrices, extract features at different levels using CNN models, classify multi-level features using multiple RF classifiers, and, finally, output prediction results using a winner-take-all approach. On our dataset, a 99.35% accuracy rate was achieved using the proposed model. An accuracy rate of 99.26% was achieved on the benchmark data, much higher than that of the existing extreme model.
A Survey of Deep Learning Methods on Semantic Similarity and Sentence Modeling

Semantics is a research field that has gained an extensive interest recently. This survey describes recent works in the field of semantics, a part of the broader area of computational linguistics. One of the important aspects of computational linguistics is using proper methods to distribute semantics for obtaining representations of the meaning of words. This survey summarizes the latest state of the art approaches in semantics that use deep learning methods, datasets, and lexical databases, specifying semantics under two categories such as semantic similarity and sentence modeling.
Feature Correlation-Steered Capsule Network for object detection

Despite Convolutional Neural Networks (CNNs) based approaches have been successful in objects detection, they predominantly focus on positioning discriminative regions while overlooking the internal holistic part-whole associations within objects. This would ultimately lead to the neglect of feature relationships between object and its parts as well as among those parts, both of which are significantly helpful for detecting discriminative parts. In this paper, we propose to "look insider the objects" by digging into part-whole feature correlations and take the attempts to leverage those correlations endowed by the Capsule Network (CapsNet) for robust object detection. Actually, highly correlated capsules across adjacent layers share high familiarity, which will be more likely to be routed together. In light of this, we take such correlations between different capsules of the preceding training samples as an awareness to constrain the subsequent candidate voting scope during the routing procedure, and a Feature Correlation-Steered CapsNet (FCS-CapsNet) with Locally-Constrained Expectation-Maximum (EM) Routing Agreement (LCEMRA) is proposed. Different from conventional EM routing, LCEMRA stipulates that only those relevant low-level capsules (parts) meeting the requirement of quantified intra-object cohesiveness can be clustered to make up high-level capsules (objects). In doing so, part-object associations can be dug by transformation weighting matrixes between capsules layers during such "part backtracking" procedure. LCEMRA enables low-level capsules to selectively gather projections from a non-spatially-fixed set of high-level capsules. Experiments on VOC2007, VOC2012, HKU-IS, DUTS, and COCO show that FCS-CapsNet can achieve promising object detection effects across multiple evaluation metrics, which are on-par with state-of-the-arts.
iCaps: Iterative Category-Level Object Pose and Shape Estimation

This letter proposes a category-level 6D object pose and shape estimation approach iCaps, which allows tracking 6D poses of unseen objects in a category and estimating their 3D shapes. We develop a category-level auto-encoder network using depth images as input, where feature embeddings from the auto-encoder encode poses of objects in a category. The auto-encoder can be used in a particle filter framework to estimate and track 6D poses of objects in a category. By exploiting an implicit shape representation based on signed distance functions, we build a LatentNet to estimate a latent representation of the 3D shape given the estimated pose of an object. Then the estimated pose and shape can be used to update each other in an iterative way. Our category-level 6D object pose and shape estimation pipeline only requires 2D detection and segmentation for initialization. We evaluate our approach on a publicly available dataset and demonstrate its effectiveness. In particular, our method achieves comparably high accuracy on shape estimation.
Electronic Voting System Using an Enterprise Blockchain

Conventional electronic voting systems use a centralized scheme. A central administration of these systems manages the entire voting process and has partial or total control over the database and the system itself. This creates some problems, accidental or intentional, such as possible manipulation of the database and double voting. Many of these problems have been solved thanks to permissionless blockchain technologies in new voting systems; however, the classic consensus method of such blockchains requires specific computing power during each voting operation. This has a significant impact on power consumption, compromises the efficiency and increases the system latency. However, using a permissioned blockchain improves efficiency and reduces system energy consumption, mainly due to the elimination of the typical consensus protocols used by public blockchains. The use of smart contracts provides a secure mechanism to guarantee the accuracy of the voting result and make the counting procedure public and protected against fraudulent actions, and contributes to preserving the anonymity of the votes. Its adoption in electronic voting systems can help mitigate part of these problems. Therefore, this paper proposes a system that ensures high reliability by applying enterprise blockchain technology to electronic voting, securing the secret ballot. In addition, a flexible network configuration is presented, discussing how the solution addresses some of the security and reliability issues commonly faced by electronic voting system solutions.
Noise-Suppressing Neural Dynamics for Time-Dependent Constrained Nonlinear Optimization With Applications

Up to date, the existing methods for nonlinear optimization with time-dependent parameters can be classified into two types: 1) static methods are capable of handling inequality constraints but may generate large lagging errors in the solution of the intrinsically time-dependent constrained nonlinear optimization (TDCNO) problem due to the hypothesis of short-time invariance and 2) time-variant methods, e.g., zeroing neural networks, are able to remedy the lagging error but fail to solve the TDCNO problem under inequality constraints. To resolve this contradiction, a noise-suppressing neural dynamics (NSND) model is proposed to solve the TDCNO problem subject to both equality and inequality constraints via the nonlinear complementary problem (NCP) function. The proposed method allows inequality constraints for unknown variables, removes the short-time invariance hypothesis, and further eliminates lagging errors during the solving process in the presence of noises. Besides, the rapid convergence, global stability, and noise processing of the NSND model are verified by the theoretical analyses. Simulation results of illustrative examples, including dimensionality reduction on principal component analyses (PCA) and a robot motion control, show that the NSND model outperforms the existing models for the TDCNO problem.
A fast parallel batch image encryption algorithm using intrinsic properties of chaos

In real-world applications, the ability to encrypt a large quantity of images is highly desirable. A branch of works resort chaotic encryption to encrypt images but mostly aim at one image for one-time encryption. Besides, encryption algorithms that can support processing batch images are strongly needed for acceleration in the scenario of massive data. To this end, we propose a fast and more secure algorithm for protecting batch images by employing chaotic intrinsic properties, reversible steganography, and parallel computing. First, our algorithm assigns batch images evenly to each thread, in which Cipher Block Chaining (CBC) mode is applied among neighboring images for encryption. Later, the identifier number of each thread and its CBC indexes are encrypted and embedded into the corresponding cipher-images using reversible steganography, which enhances the security of existing chaotic encryption. Besides, keystreams are associated with the plaintext for the goal of resisting chosen-plaintext attack. The experimental results and security analyses show that our algorithm can achieve superior results with high efficiency.