Reviewerly/exHarmony huggingface dataset:
Automated face recognition in forensic science: Review and perspectives

With recent technological innovations, the multiplication of captured images of criminal events has brought the comparison of faces to the forefront of the judicial scene. Forensic face recognition has become a ubiquitous tool to guide investigations, gather intelligence and provide evidence in court. However, its reliability in court still suffers from the lack of methodological standardization and empirical validation, notably when using automatic systems, which compare images and generate a matching score. Although the use of such systems increases drastically, it still requires more empirical studies based on adequate forensic data (surveillance footage and identity documents) to become a reliable method to present evidence in court. In this paper, we propose a review of the literature leading to the establishment of a methodological workflow to develop a score-based likelihood-ratio computation model using a Bayesian framework. Different approaches are proposed in the literature regarding the within-source and between-sources variability distributions modelling. Depending on the data available, the modelling approach can be specific to the case or generic. Generic approaches allow interpreting the score without any available images of the suspect. Such model is henceforth harder to defend in court because the results are not anchored to the suspect. To make sure the computed score-based LR is robust, we must assess the performance of the model with two main characteristics: the discriminating power and the calibration state of the model. We hence describe the main metrics (Equal Error Rate and Cost of log likelihood-ratio), and graphical representations (Tippett plots, Detection Error Trade-off plot and Empirical Cross-Entropy plot) used to quantify and visualize the performance characteristics.
3D Virtual Reality in K-12 Education: A Thematic Systematic Review

With progressive twenty-first-century 3D virtual technologies, the ability to blur the lines between material reality and digital worlds is proliferating in K-12 classrooms across the globe. A thematic systematic review was used to examine three-dimensional (3D) virtual reality (VR) in K-12 education from 2010 to 2019. Findings revealed specific features and capabilities of contemporary 3D VR technologies, how 3D VR technologies are being used in K-12 education, and how 3D VR supports learning experiences. In 3D virtual worlds3D virtual world, students are able to view peripherally through a virtual presence, experience weather, time, and different environments, and virtually touch and examine objects. The current research showed that 3D VR is commonly applied in middle and high school science classrooms and facilitates the pedagogic approach of inquiry-based learning (IBL)Inquiry-Based Learning (IBL), a branch of constructivismConstructivism. The reviewed studies indicated that integrating 3D VR technologies leads to enhanced learning experiences, leading to increased achievement and motivation.
Through the Looking Glass

Transparency in moderation practices is crucial to the success of an online community. To meet the growing demands of transparency and accountability, several academics came together and proposed the Santa Clara Principles on Transparency and Accountability in Content Moderation (SCP). In 2018, Reddit, home to uniquely moderated communities called subreddits, announced in its transparency report that the company is aligning its content moderation practices with the SCP. But do the moderators of subreddit communities follow these guidelines too? In this paper, we answer this question by employing a mixed-methods approach on public moderation logs collected from 204 subreddits over a period of five months, containing more than 0.5M instances of removals by both human moderators and AutoModerator. Our results reveal a lack of transparency in moderation practices. We find that while subreddits often rely on AutoModerator to sanction newcomer posts based on karma requirements and moderate uncivil content based on automated keyword lists, users are neither notified of these sanctions, nor are these practices formally stated in any of the subreddits' rules. We interviewed 13 Reddit moderators to hear their views on different facets of transparency and to determine why a lack of transparency is a widespread phenomenon. The interviews reveal that moderators' stance on transparency is divided, there is a lack of standardized process to appeal against content removal and Reddit's app and platform design often impede moderators' ability to be transparent in their moderation practices.
Unsupervised feature selection for balanced clustering

In many real-world applications of data mining, such as energy load balance of wireless sensor networks, given data points with balanced distribution, i.e., each class contains approximately the same number of instances, we often need to obtain a clustering result to reflect such balance. In many data, especially the high-dimensional data, such balanced structure is not obvious in the original feature space, due to the noisy and redundant features. Therefore we need to apply feature selection methods to pick several informative features to reveal such balanced structure of data. Feature selection is a fundamental problem in machine learning tasks and has attracted considerable attentions in recent years. However, conventional feature selection methods often focus on how to select the most discriminative features, whereas ignoring the balance property of the data. To tackle this problem, we propose a novel unsupervised feature selection method for balanced clustering which can reveal the intrinsic balanced structure of data. In our method, a balanced regularization term is introduced to select the features which can help to produce balanced clusters. Then, we provide an Alternating Direction Method of Multipliers (ADMM) to optimize the introduced objective function. At last, the experiments are conducted on six benchmark data sets, including Yale and 20NG data sets and so on, by comparing with other state-of-the-art unsupervised feature selection methods published in the literature. The experimental results show that our method not only has better clustering performance but also leads to more balanced clustering structure.
Platform for Tracking Donations of Charitable Foundations Based on Blockchain Technology

Donors have distrust about how donated money is spent. Currently, blockchain technology is being implemented in different sectors. Blockchain technology allows you to make the process of donations and transactions of funds transparent. Single platform for tracking donations that will track all information about donations, transactions and donors need to be developed. This paper considers description of implementation of the platform for tracking donations based on blockchain technology. The System offers transparent accounting of operations donors, charitable foundations and recipients based on blockchain technology, charitable platform should provide transparent donation route, enable public users and donors to track and monitor where, when and to whom went resources of charity funds.
PGCN-TCA: Pseudo Graph Convolutional Network With Temporal and Channel-Wise Attention for Skeleton-Based Action Recognition

Skeleton-based human action recognition has become an active research area in recent years. The key to this task is to fully explore both spatial and temporal features. Recently, GCN-based methods modeling the human body skeletons as spatial-temporal graphs, have achieved remarkable performances. However, most GCN-based methods use a fixed adjacency matrix defined by the dataset, which can only capture the structural information provided by joints directly connected through bones and ignore the dependencies between distant joints that are not connected. In addition, such a fixed adjacency matrix used in all layers leads to the network failing to extract multi-level semantic features. In this paper we propose a pseudo graph convolutional network with temporal and channel-wise attention (PGCN-TCA) to solve this problem. The fixed normalized adjacent matrix is substituted with a learnable matrix. In this way, the matrix can learn the dependencies between connected joints and joints that are not physically connected. At the same time, learnable matrices in different layers can help the network capture multi-level features in spatial domain. Moreover, Since frames and input channels that contain outstanding characteristics play significant roles in distinguishing the action from others, we propose a mixed temporal and channel-wise attention. Our method achieves comparable performances to state-of-the-art methods on NTU-RGB+D and HDM05 datasets.
CAPER: Context-Aware Personalized Emoji Recommendation

With the popularity of social platforms, emoji appears and becomes extremely popular with a large number of users. It expresses more beyond plaintexts and makes the content more vivid. Using appropriate emojis in messages and microblog posts makes you lovely and friendly. Recently, emoji recommendation becomes a significant task since it is hard to choose the appropriate one from thousands of emoji candidates. In this paper, we propose a Context-Aware Personalized Emoji Recommendation (CAPER) model fusing the contextual information and the personal information. It is to learn latent factors of contextual and personal information through a score-ranking matrix factorization framework. The personal factors such as user preference, user gender, and the current time can make the recommended emojis meet users' individual needs. Moreover, we consider the co-occurrence factors of the emojis which could improve the recommendation accuracy. We conduct a series of experiments on the real-world datasets, and experiment results show better performance of our model than existing methods, demonstrating the effectiveness of the considering contextual and personal factors.
Optimal Path Finding With Beetle Antennae Search Algorithm by Using Ant Colony Optimization Initialization and Different Searching Strategies

Intelligent algorithm acts as one of the most important solutions to path planning problem. In order to solve the problems of poor real-time and low accuracy of the heuristic optimization algorithm in 3D path planning, this paper proposes a novel heuristic intelligent algorithm derived from the Beetle Antennae Search (BAS) algorithm. The algorithm proposed in this paper has the advantages of wide search range and high search accuracy, and can still maintain a low time complexity when multiple mechanisms are introduced. This paper combines the BAS algorithm with three non-trivial mechanisms proposed to solve the problems of low search efficiency and poor convergence accuracy in 3D path planning. The algorithm contains three non-trivial mechanisms, including local fast search, aco initial path generation, and searching information orientation. At first, local fast search mechanism presents a specific bounded area and add fast iterative exploration to speed up the convergence of path finding. Then aco initial path generation mechanism is initialized by Ant Colony Optimization (ACO) as a pruning basis. The initialization of the ACO algorithm can quickly obtain an effective path. Using the exploration trend of this path, the algorithm can quickly obtain a locally optimal path. Thirdly, searching information orientation mechanism is employed for BAS algorithm to guarantee the stability of the path finding, thereby avoiding blind exploration and reducing wasted computing resources. Simulation results show that the algorithm proposed in this paper has higher search accuracy and exploration speed than other intelligent algorithms, and improves the adaptability of the path planning algorithms in different environments. The effectiveness of the proposed algorithm is verified in simulation.
Feature selection for multi-label learning with streaming label

Multi-label feature selection has drawn wide attention in recent years. The existing multi-label feature selection algorithms mainly assume that the labels of the training data are obtained before feature selection takes place. However, this assumption does not always founded because the acquisition of labeling data is costly. In real-world applications, the available labels usually arrive one by one over time. To address this problem, we develop a novel multi-label feature selection method under the circumstance of streaming label to select a set of the most relevant and discriminative features. Specifically, we firstly select label-specific features for each newly-arrived label by designing inter-class discrimination and intra-class neighbor recognition. Then, a feature conversion is created to fuse the generated label-specific feature sets. Comprehensive experiments on a series of benchmark data sets clearly demonstrate the superiority of the proposed method against other state-of-the-art multi-label feature selection methods.
Development of new seed with modified validity measures for k-means clustering

Conventional k-means clustering is the widely used partitional method, mainly adapted to machine learning and pattern recognition problems. This algorithm is highly sensitive to initial centroid points, but it cannot guarantee to arrive at a better solution because initial centroids are computed randomly for the given cluster. In this paper, we have developed a new initialization method for k-means clustering. We have also made an effort to improve the Dunn Index and introduced a new validity ratio based on the silhouette index. The sum of squared error, Dunn Index, silhouette index, modified Dunn Index, and silhouette validity ratio were used as criteria to evaluate the performance of the initialization algorithm. Various benchmark datasets have been used to assess the effectiveness of the proposed initialization algorithm, and we compared the results with conventional k-means and k-means++ algorithms. The results have shown that the sum of squared error and number of iterations obtained by our proposed initialization algorithm are minimum. A precision chart is used to test the consistency of the initialization algorithm. The comparative analysis, based on the modified Dunn Index, and silhouette validity ratio have proved that the proposed initialization algorithm has performed better than the other initialization algorithms.
Calculating biotite formula from electron microprobe analysis data using a machine learning method based on principal components regression

We present a new machine learning method for calculating biotite (sensu lato) structural formula from electron microprobe analysis (EMPA) data, which is based on principal components regression (PCR) of a dataset consisting of 155 fully analyzed biotite references that have chemistry and crystal structure refinement. The dataset is randomly grouped into a training set (75% in amount) and a test set (25% in amount). The training set is used to implement the structural formula and the test set is used to evaluate the performance of the model. The resulting linear regression coefficient matrix is then applied to calculate mole proportions of cations and anions of biotite samples using their compositional data from EMPA. Through this method, the distribution of the different cations and anions in the different sites can be calculated, including the tetrahedral Fe3+, octahedral Fe2+, octahedral Fe3+, OH and WO2− at the O(4) site. The O(4) site is assumed to be occupied by anions with a relation of 2 = F + Cl + OH + WO2−. Octahedral and interlayer vacancies could also be estimated in this model. The prediction quality for major elements is perfect with R2 > 0.95. The absolute errors in the estimated octahedral Fe2+, octahedral Al and OH at O(4) site are determined to be ±0.2 apfu (atom per formula unit based on 11O + 2(F, Cl, OH, O)), while those in total Fe3+ and WO2− at O(4) site are approximately ±0.3 apfu. A funnel-shaped relationship between absolute error in Fe3+/ΣFe ratio and FeOT wt% is observed, with the majority falling in the range of ±20%. Compared to previous normalization schemes, our model shows significant improvements in estimating Fe3+/ΣFe and WO2− at O(4) site. Our model is capable for calculating mineral formulae of common igneous and hydrothermal biotites, but not suitable for those that have been modified in a post-formation oxidation or reduction process. A supplementary Excel spreadsheet is provided that can be easily used for performing calculation from EMPA data.
Explainable Anatomical Shape Analysis Through Deep Hierarchical Generative Models

Quantification of anatomical shape changes currently relies on scalar global indexes which are largely insensitive to regional or asymmetric modifications. Accurate assessment of pathology-driven anatomical remodeling is a crucial step for the diagnosis and treatment of many conditions. Deep learning approaches have recently achieved wide success in the analysis of medical images, but they lack interpretability in the feature extraction and decision processes. In this work, we propose a new interpretable deep learning model for shape analysis. In particular, we exploit deep generative networks to model a population of anatomical segmentations through a hierarchy of conditional latent variables. At the highest level of this hierarchy, a two-dimensional latent space is simultaneously optimised to discriminate distinct clinical conditions, enabling the direct visualisation of the classification space. Moreover, the anatomical variability encoded by this discriminative latent space can be visualised in the segmentation space thanks to the generative properties of the model, making the classification task transparent. This approach yielded high accuracy in the categorisation of healthy and remodelled left ventricles when tested on unseen segmentations from our own multi-centre dataset as well as in an external validation set, and on hippocampi from healthy controls and patients with Alzheimer's disease when tested on ADNI data. More importantly, it enabled the visualisation in three-dimensions of both global and regional anatomical features which better discriminate between the conditions under exam. The proposed approach scales effectively to large populations, facilitating high-throughput analysis of normal anatomy and pathology in large-scale studies of volumetric imaging.
Super-resolution of cardiac magnetic resonance images using Laplacian Pyramid based on Generative Adversarial Networks

Cardiac magnetic resonance imaging (MRI) can assist in both functional and structural analysis of the heart, but due to hardware and physical limitations, high-resolution MRI scans is time consuming and peak signal-to-noise ratio (PSNR) is low. The existing super-resolution methods attempt to resolve this issue, but there are still shortcomings, such as hallucinate details after super-resolution, low precision after reconstruction, etc. To dispose these problems, we propose the Laplacian Pyramid Generation Adversarial Network (LSRGAN) in order to generate visually better cardiovascular ultrasound images so as to aid physician diagnosis and treatment. In order to address the problem of low image resolution, we used the Laplacian Pyramid to analyze the high-frequency detail features of super-resolution (SR) reconstruction of images with different pixel sizes. To eliminate gradient disappearance, we implemented the least squares loss function as the discriminator, we introduce the residual-dense block (RDB) as the basic network building unit is used to generate higher quality images. The experimental results show that the LSRGAN can effectively avoid the illusion details after super-resolution and has the best reconstruction quality. Compared with the state-of-the-art methods, our proposed algorithm generates higher quality super-resolution images that comes with higher peak signal-to-noise ratio and structural similarity (SSIM) scores. We implemented a novel LSRGAN network model, which solves reduces insufficient resolution and hallucinate details of MRI after super-resolution. Our research presents a superior super-resolution method for medical experts to diagnose and treat myocardial ischemia and myocardial infarction.
Label propagation-based approach for detecting review spammer groups on e-commerce websites

Online product reviews are very important information resources on e-commerce websites and significantly influence consumers' purchase decisions. Driven by interests, however, some merchants might hire a group of reviewers working together to promote or demote a set of target products by writing fake reviews. Such a collusive fraudulent reviewer group is generally termed a review spammer group and is more harmful to e-commerce websites than individual review spammers. To address this issue, in this paper we propose a label propagation-based approach to detect review spammer groups on e-commerce websites. First, based on the evaluation data of reviewers, we extract the associations between reviewers with respect to review time and product ratings to construct a relationship graph of reviewers. Second, we propose an improved label propagation algorithm with a propagation intensity and an automatic filtering mechanism to find candidate spammer groups based on the constructed reviewer relationship graph. Finally, we propose a ranking algorithm that combines the entropy method and the analytic hierarchy process to rank the candidate spammer groups and thus identify the top-k review spammer groups. The experimental results of the real-world Amazon and Yelp datasets show that the proposed approach performs better than the baseline methods.
A game theoretic analysis of resource mining in blockchain

Abstract Blockchain and cryptocurrency are a hot topic in today’s digital world. In this paper, we create a game theoretic model in continuous time. We consider a dynamic game model of the bitcoin market, where miners or players use mining systems to mine bitcoin by investing electricity into the mining system. Although this work is motivated by BTC, the work presented can be applicable to other mining systems similar to BTC. We propose three concepts of dynamic game theoretic solutions to the model: Social optimum , Nash equilibrium and myopic Nash equilibrium . Using the model that a player represents a single “miner” or a “mining pool”, we develop novel and interesting results for the cryptocurrency world.
Holistic cyber hygiene education: Accounting for the human factors

Cybersecurity is paramount in modern cyberdefense. One important factor linked to reducing human-instigated breaches of cybersecurity includes cyber hygiene. Cyber hygiene is the adaptive knowledge and behavior to mitigate risky online activities that put an individual's social, financial, and personal information at risk – a danger that is significantly compounded when discussing the risk to entire countries as opposed to a single individual. Interestingly, even though the human is the greatest risk to cybersecurity, very little research has examined the latent individual differences associated with developing cyber hygiene-related knowledge, attitudes, and behaviors. Thus, the goal of the present study was to address this gap in cyber hygiene research. The results from 173 university participants demonstrated that several factors, such as information handling, incident reporting, and password management were associated with better cyber hygiene. Individual differences such as trust in technology and intrinsic motivation were predictive of improved cyber hygiene – but were subject to significant sex differences. Differences across academic majors, such as science and technology majors, also emerged. Finally, we discuss the importance of understanding the role of human factors in modern cybersecurity and the potential practical implications associated with refining current course curricula in computer and information sciences.