Reviewerly/exHarmony huggingface dataset:
Visual and buying sequence features-based product image recommendation using optimization based deep residual network

A recommendation system is an imaginative resolution for managing the restrictions in e-commerce services with item details and user details. Also, it is used to determine the user preferences to recommend the items they expected to buy. Several conventional collaborative filtering techniques are devised in the recommender model, but it has some complexities. Hence, an innovative optimization-driven deep residual network is devised in this paper for a product recommendation system. Here, the product of images is used for extracting features where the Convolutional neural network (CNN) features are computed, and then it is given as input to the deep residual network aimed at product recommendation. The deep residual network is trained using developed Elephant Herding Feedback Artificial Optimization (EHFAO), which is obtained by integrating Elephant Herding optimization (EHO) into the Feedback Artificial Tree (FAT). Here, the item grouping is carried out on input data based on K-means clustering. After item grouping, Cosine similarity is used to perform matching of groups, where the best group is acquired among all the available groups. Extraction of list of visitors is done from the best group. Then, the list of items is obtained from the sequence of best visitor. Next, the corresponding binary sequence is obtained for the applicable sequence of visitor. From this sequence of best visitor, the recommended product is acquired. Then, the recommended product is subjected to the sentiment analysis for which the score is determined. Here, the sentiment analysis helps to decide whether the product is recommended or not recommended. If the score is positive, then the same product is recommended; otherwise, the new product is recommended. The proposed EHFAO-based deep residual network attained better performance in comparison to the other techniques with a maximal F-measure at 84.061%, 84.061% precision, 87.845% recall along with minimal Mean Squared Error (MSE) of 0.216.
Platforms as phish farms: Deceptive social engineering at scale

Phishing is a method of social engineering—it attempts to influence behavior and/or beliefs—in which a party either “imitates a trusted source” (Felix &amp; Hauck, 1987) or induces another party to trust or place more or a different kind of trust in it. I argue that by their very nature, social platforms such as Facebook, Twitter, and others are large-scale phishing operations designed to collect information about users surreptitiously. Although providing terms of service and privacy policies, an individual has no way of knowing the extent of the platform’s personal data collection. This article reconsiders platforms as organizational phishing, and just as harmful as that done by hackers or others seeking unjust enrichment. To do this, this article identifies the significant elements of platform phishing by examining the descriptions of platform conduct provided in regulatory actions taken by the US Federal Trade Commission.
Multilabel Text Classification Algorithm Based on Fusion of Two-Stream Transformer

Existing multilabel text classification methods rely on a complex manual design to mine label correlation, which has the risk of overfitting and ignores the relationship between text and labels. To solve the above problems, this paper proposes a multilabel text classification algorithm based on a transformer encoder–decoder, which can adaptively extract the dependency relationship between different labels and text. First, text representation learning is carried out through word embedding and a bidirectional long short-term memory network. Second, the global relationship of the text is modeled by the transformer encoder, and then the multilabel query is adaptively learned by the transformer decoder. Last, a weighted fusion strategy under the supervision of multiple loss functions is proposed to further improve the classification performance. The experimental results on the AAPD and RCV1-V2 datasets show that compared with the existing methods, the proposed algorithm achieves better classification results. The optimal micro-F1 reaches 73.4% and 87.8%, respectively, demonstrating the effectiveness of the proposed algorithm.
Research on occlusion block face recognition based on feature point location

Abstract Aiming at improving the poor face recognition accuracy under occlusion, a multipose block occlusion face recognition method based on feature point location is proposed. Face segmentation is carried out according to the location results of face feature points and occlusion areas. The local features of each face block are extracted by deep convolution neural network. The dynamic adaptive weighting method is used to give different weights to the face block information, and the occluded face recognition is completed according to the results of face segmentation, which effectively reduces the impact of pose change and occlusion on face recognition. The experiment is analyzed from two aspects: frontal occlusion and multipose face occlusion. The results show that fewer blocks in the frontal occlusion experiment are conducive to maintaining good local integrity and relatively good recognition performance; When the proportion of frontal occlusion reaches 50%, the recognition rate of our algorithm can still be up to 92.68%, which is significantly better than other algorithms.
Hand Gesture Authentication Using Optimal Feature Selection and Dynamic Time Warping based K-nearest Neighbor

Network security is becoming a critical issue due to the spread of computer networks to ordinary people. User authentication is one of the most important part for network security. Although passwords are used in many personal computers, it has many well-known problems and it is antiquated. On the other hand, fingerprint or face based authentication has the risk of information leakage by reproducing fingerprint or face from the photograph. In this paper, we have designed a hand gesture-based user authentication using leap motion to overcome of information leakage problem. We have collected data from 25 participants and used the data to analyse the accuracy of our authentication system. The experimental results are also measured by FAR (False Acceptance Rate) and FRR (False Rejection Rate). At first, Feature selection has been used to select important feature out of 246 features. Finally, Dynamic Time Warping (DTW) based K-Nearest Neighbor (KNN) Classifier has been used to authenticate a person. Our system has produced 96.73% accuracy in user authentication. It is also showed 1.25% FAR and 3.62% FRR. Our results indicate that proposed hand gesture with Leap Motion can be viable authentication approach.
Unsupervised feature selection method based on iterative similarity graph factorization and clustering by modularity

Feature selection is an important research area aimed at eliminating unwanted features from high-dimensional datasets. Feature selection methods are categorized according to the availability of labels. Supervised methods usually select features that have high correlations with the data labels, however, this cannot be done by unsupervised methods, since the labels are not available, making the development of them even more challenging. Some unsupervised methods proposed in the literature get around this problem by generating "pseudo-labels", using clustering techniques, and then making feature selection as in the supervised scenario. There are also methods in which the selection is not guided by a clustering criterion, generally performing some kind of reconstruction of the original dataset in low dimensionality. However, in both approaches a local structure is generated, usually starting from a similarity graph built by using the entire set of features. This may compromise the results of the methods when there are many irrelevant and noisy features, which makes it difficult to reveal patterns or an initial representation of the data. Another drawback of some current methods is the large amount of hyper-parameters, or the computational time required for a single execution, which can render such methods unfeasible for some large datasets. In order to address these problems, it is proposed in this work a new unsupervised feature selection method, called KNMFS, which performs the scoring of the features in low computational time using a three-step procedure: (1) a similarity graph learning step based on non-negative iterative matrix factorization together with a Gaussian filter to mitigate the noise effects caused by irrelevant features; (2) a clustering step from the learned graph using a modularity optimization strategy and (3) a random forest algorithm is applied to compute the scores based on the labels generated by the clustering step. To verify the effectiveness of the proposed method, experiments in real and synthetic datasets were conducted. In both cases, the obtained results showed that the KNMFS method, compared to other state-of-the-art methods, obtained good results according to the metrics of Accuracy, ARI and NMI. Friedman's statistical tests were also performed to give stronger evidence to the reported results.
Introducing zoid: A mixture model and R package for modeling proportional data with zeros and ones in ecology

Many ecological data sets are proportional, representing mixtures of constituent elements such as species, populations, or strains. Analyses of proportional data are challenged by categories with zero observations (zeros), all observations (ones), and overdispersion. In lieu of ad hoc data adjustments, we describe and evaluate a zero-and-one inflated Dirichlet regression model, with its corresponding R package (zoid), capable of handling observed data x$$ x $$ consisting of three possible categories: zeros, proportions, or ones. Instead of fitting the model to observations of single biological units (e.g., individual organisms) within a sample, we sum proportional contributions across units and estimate mixture proportions using one aggregated observation per sample. Optional estimation of overdispersion and covariate influences expand model applications. We evaluate model performance, as implemented in Stan, using simulations and two ecological case studies. We show that zoid successfully estimates mixture proportions using simulated data with varying sample sizes and is robust to overdispersion and covariate structure. In empirical case studies, we estimate the composition of a mixed-stock Chinook salmon (Oncorhynchus tshawytscha) fishery and analyze the stomach contents of Atlantic cod (Gadus morhua). Our implementation of the model as an R package facilitates its application to varied ecological data sets composed of proportional observations.
VCSEL pair used as optical pointers in a contact lens for gaze tracking and visual target designation

We present a new eye-tracking and target designation device based on a contact lens incorporating a pair of vertical-cavity surface-emitting lasers (VCSELs). We describe the operating principle, the manufacturing process and characterize the impact of the VCSELs encapsulation on their optical properties. We then describe how such device can be incorporated into an eye-wear or a visual augmented system. We compare two different detection set-ups, the first using a camera and the second a position sensitive device, both illustrating different laser beam detection modes. We analyze their performances in terms of angular accuracy, speed, compactness, manufacturability, compared to current conventional eye-tracking systems. We emphasize how the use of two VCSELs and the control of their orientation during the encapsulation can simplify their integration in host systems and improve the gaze detection performance. Finally, we describe various embodiments and discuss potential improvements that can be expected in future systems.
Speaker verification using attentive multi-scale convolutional recurrent network

In this paper, we propose a speaker verification method by an Attentive Multi-scale Convolutional Recurrent Network (AMCRN). The proposed AMCRN can acquire both local spatial information and global sequential information from the input speech recordings. In the proposed method, logarithm Mel spectrum is extracted from each speech recording and then fed to the proposed AMCRN for learning speaker embedding. Afterwards, the learned speaker embedding is fed to the back-end classifier (such as cosine similarity metric) for scoring in the testing stage. The proposed method is compared with state-of-the-art methods for speaker verification. Experimental data are three public datasets that are selected from two large-scale speech corpora (VoxCeleb1 and VoxCeleb2). Experimental results show that our method exceeds baseline methods in terms of equal error rate and minimal detection cost function, and has advantages over most of baseline methods in terms of computational complexity and memory requirement. In addition, our method generalizes well across truncated speech segments with different durations, and the speaker embedding learned by the proposed AMCRN has stronger generalization ability across two back-end classifiers.
Continuous mapping of large surfaces with a quality inspection robot

This paper addresses the problem of mapping large surfaces with a moving sensor. In particular, it proposes image registration and mapping algorithms that enable to use in continuous motion sensors that need multiple shots to perform a measurement. These methods exploit the knowledge of the shape of the part to inspect in a both efficient and accurate fashion, thus allowing to obtain a measurement quality comparable to that of static measurements, while guaranteeing fast sensor motion and thus short scanning times. This work describes the application of these methods for the mapping of carbon fibre parts with an inspection robot equipped with a sensor estimating 3D carbon fibre orientation from multiple 2D images captured with different illumination. Experiments on carbon fibre preforms of complex 3D shape demonstrates that this system accurately reconstructs in real-time the 3D fibre orientations of the outer layer of carbon fibre parts. Accuracy assessments report small errors within the tolerances allowed by the automotive industry on flat and generic 3D surfaces. The inspection robot system presented in this paper has been demonstrated both as an in-line quality inspection robot for production of carbon fibre preforms and as a measurement device for improving the draping process in the prototyping of carbon fibre parts.
Unsupervised Domain Adaptive Person Re-Identification via Intermediate Domains

Recent years have witnessed outstanding success in supervised domain adaptive person re-identification (ReID). However, the model often suffers serious performance drops when transferring to another domain in real-world applications. To address the domain gap situations, many unsupervised domain adaptive (UDA) methods have been proposed to adapt the model trained on the source domain to a target domain. Such methods are typically based on clustering algorithms to generate pseudo labels. Noisy labels, which often exist due to the instability of clustering algorithms, will substantially affect the performance of UDA methods. In this study, we focused on intermediate domains that can be regarded as a bridge that connects source and target domains. We added a domainness factor in the loss function of SPGAN that can decide the style of the image generated by the GAN model. We obtained a series of intermediate domains by changing the value of the domainness factor. Pseudo labels are more reliable because intermediate domains are closer to the source domain compared with the target domain. We then fine-tuned the model pre-trained with source data on these intermediate domains. The fine-tuning process was conducted repeatedly because intermediate domains are composed of more than one dataset. Finally, the model fine-tuned on intermediate domains was adapted to the target domain. The model can easily adapt to changes in image style as we gradually transfer the model to the target domain along the bridge consisting of several intermediate domains. To the best of our knowledge, we are the first to apply intermediate domains to UDA problems. We evaluated our method on Market1501, DukeMTMC-reID and MSMT17 datasets. Experimental results proved that our method brings a significant improvement and achieves a state-of-the-art performance.
Analysis of Different Image Enhancement and Feature Extraction Methods

This paper describes an image enhancement method for reliable image feature matching. Image features such as SIFT and SURF have been widely used in various computer vision tasks such as image registration and object recognition. However, the reliable extraction of such features is difficult in poorly illuminated scenes. One promising approach is to apply an image enhancement method before feature extraction, which preserves the original characteristics of the scene. We thus propose to use the Multi-Scale Retinex algorithm, which is aimed to emulate the human visual system and it provides more information of a poorly illuminated scene. We experimentally assessed various combinations of image enhancement (MSR, Gamma correction, Histogram Equalization and Sharpening) and feature extraction methods (SIFT, SURF, ORB, AKAZE) using images of a large variety of scenes, demonstrating that the combination of the Multi-Scale Retinex and SIFT provides the best results in terms of the number of reliable feature matches.
Blockchain Token-Based Wild-Simulated Ginseng Quality Management Method

Countries require measures to prevent food fraud, such as forgery of certificates or content change during production, which can occur throughout the supply chain, even if they have a certification system for quality food management. Therefore, there are recent cases of the introduction of blockchain tokens for quality and supply chain management; however, there are difficulties in introducing tokens in food fields, such as forest and agricultural products. To introduce tokens in the food sector, we selected wild-simulated ginseng, subject to quality management in Korea, analyzed the quality management process of wild-simulated ginseng, and selected the target for blockchain token introduction. We then identified potential token-related issues from consumers and suggested possible solutions.
Lost in OCR-Translation: Pixel-based Text Reflow to the Rescue

Low-vision users have been accustomed to viewing accessible electronic documents with reflow and responsive layouts that adapt to the user's text size requirements. However, mainstream technologies do not work optimally with image-based documents scanned from paper as well as documents that do not adhere to accessibility guidelines. Users are often dependent on viewing magnifications of such documents by scrolling in two-dimensions which most users find confusing. This paper discusses the capabilities of key state-of-the-art technologies and describes a browser-based document magnification implementation that reflow document contents at the pixel-level to prevent horizontal scrolling. The implementation provides low-vision users a practical alternative for simplified access to the content of archival documents with a different view than the state-of-the-art technologies. The paper also discusses practical and operational issues as well as unresolved challenges.
Optical differentiation based on weak measurements

Optical differentiation shows much potential to be applied in computation due to its strong parallelizability. Currently, each optical differential method can only obtain partial differential information. Here, we propose a general approach to obtain complete differentiation. Compared to previous methods, we can separately obtain the differentiation of amplitude and phase, reserve the negative value of the differentiation, and acquire the differentiation in arbitrary directions of the two-dimensional field. We measure the differentiation of the Gaussian beam to demonstrate this method. A practical experiment of identifying the move direction of the motion-blurred image is also presented to verify the practicability of our method. Our method can further be applied to intelligence algorithms, image identification, and optical analog computing.
Attention mechanism feedback network for image super-resolution

The number of photosensitive elements in the image sensor determines the resolution of the original image. One promising method for increasing the resolution of an image without increasing hardware cost is image super-resolution. Many existing image super-resolution methods based on deep learning lack feedback mechanisms and deal with feature channels equally, which hinders the expressive ability of deep learning networks and makes it difficult to resolve the dependence between high- and low-resolution (LR) images. An image super-resolution method based on an attention mechanism feedback network was presented in this study. First, the proposed multiscale separable convolution layer is used to extract the characteristics of the input image. Second, a feedback module is designed to process the feedback connections between subnetworks and generate richer high-level representations through densely connected upper and lower projection units, enabling the recovery of high-resolution (HR) images from LR images. Subsequently, an attention mechanism is introduced in the feedback module to redistribute channel attention resources, enhance interdependence between channels, and focus the network on learning high-frequency information. Finally, the reconstructed image was gradually generated using the reconstruction layer through an iterative network. The experimental results for ×2 to ×8 image super-resolution on five standard datasets showed that the quality of the reconstructed image of the proposed method outperforms that of the comparative image super-resolution methods in terms of subjective perception and objective evaluation indices. It can retain more detail for super-resolution and obtain better HR images.
Systematic literature review of arabic aspect-based sentiment analysis

Aspect-based sentiment analysis (ABSA) is a natural language processing task that provides a detailed analysis of clients' opinions about various aspects of a product or service. Although several review papers have examined Arabic ABSA studies, the number of studies covered is small, or the included studies are inadequately analyzed. Moreover, only one systematic literature review devoted to Arabic ABSA has been published to our knowledge, which covered only 21 primary studies. Therefore, this systemic literature review was conducted to analyze the existing techniques and resources used for Arabic ABSA. This review covered 47 primary studies published between 2012 and 2021 that were retrieved from eight bibliographic databases and search engines. The included studies were analyzed according to the dataset utilized, domain covered, Arabic language type, preprocessing procedures, selected features, word representation, employed techniques, and evaluation metrics used to assess the proposed techniques. As a result of this analysis, different limitations and issues were identified, and multiple future research directions were suggested. A new taxonomy was also constructed for the techniques employed, which were classified according to aspect-based sentiment analysis tasks and approaches.