Reviewerly/exHarmony huggingface dataset:
Detection and Classification of Anomalies in Large Datasets on the Basis of Information Granules

Anomaly (outlier) detection is one of the most important problems of modern data analysis. The sources of anomalies are varying. They can be the results of database users’ mistakes, operational errors, or just missing values. The problem is very important because of the fast growth of large datasets. Therefore, in this article, we present detailed results of work on the concept of granular computing-based approach to anomaly detection, classification, and gradation. The aim of the study is to introduce an innovative solution that allows the use of information granules to identify and classify anomalies. The novelty of the proposed solution consists in the use of fuzzy semantics implied by the statistical properties of the data considered. Moreover, instead of the classic approach to detecting anomalies in the data, it is proposed to determine the degree of anomaly for the data transformed to the new resulting state space. Thanks to the use of an innovative approach using the universal descriptor space, it is possible to determine the degree of anomaly, and by using various aggregation methods one can also specify its type.
A Black-Box Attack Method against Machine-Learning-Based Anomaly Network Flow Detection Models

In recent years, machine learning has made tremendous progress in the fields of computer vision, natural language processing, and cybersecurity; however, we cannot ignore that machine learning models are vulnerable to adversarial examples, with some minor malicious input modifications, while appearing unmodified to human observers, the outputs of machine learning-based model can be misled easily. Likewise, attackers can bypass machine-learning-based security defenses model to attack systems in real time by generating adversarial examples. In this paper, we propose a black-box attack method against machine-learning-based anomaly network flow detection algorithms. Our attack strategy consists in training another model to substitute for the target machine learning model. Based on the overall understanding of the substitute model and the migration of the adversarial examples, we use the substitute model to craft adversarial examples. The experiment has shown that our method can attack the target model effectively. We attack several kinds of network flow detection models, which are based on different kinds of machine learning methods, and we find that the adversarial examples crafted by our method can bypass the detection of the target model with high probability.
Boundary Loss-Based 2.5D Fully Convolutional Neural Networks Approach for Segmentation: A Case Study of the Liver and Tumor on Computed Tomography

Image segmentation plays an important role in the field of image processing, helping to understand images and recognize objects. However, most existing methods are often unable to effectively explore the spatial information in 3D image segmentation, and they neglect the information from the contours and boundaries of the observed objects. In addition, shape boundaries can help to locate the positions of the observed objects, but most of the existing loss functions neglect the information from the boundaries. To overcome these shortcomings, this paper presents a new cascaded 2.5D fully convolutional networks (FCNs) learning framework to segment 3D medical images. A new boundary loss that incorporates distance, area, and boundary information is also proposed for the cascaded FCNs to learning more boundary and contour features from the 3D medical images. Moreover, an effective post-processing method is developed to further improve the segmentation accuracy. We verified the proposed method on LITS and 3DIRCADb datasets that include the liver and tumors. The experimental results show that the performance of the proposed method is better than existing methods with a Dice Per Case score of 74.5% for tumor segmentation, indicating the effectiveness of the proposed method.
Extending greedy feature selection algorithms to multiple solutions

Most feature selection methods identify only a single solution. This is acceptable for predictive purposes, but is not sufficient for knowledge discovery if multiple solutions exist. We propose a strategy to extend a class of greedy methods to efficiently identify multiple solutions, and show under which conditions it identifies all solutions. We also introduce a taxonomy of features that takes the existence of multiple solutions into account. Furthermore, we explore different definitions of statistical equivalence of solutions, as well as methods for testing equivalence. A novel algorithm for compactly representing and visualizing multiple solutions is also introduced. In experiments we show that (a) the proposed algorithm is significantly more computationally efficient than the TIE* algorithm, the only alternative approach with similar theoretical guarantees, while identifying similar solutions to it, and (b) that the identified solutions have similar predictive performance.
Early Detection and Guidelines to Improve Unanswered Questions on Stack Overflow

Stack Overflow is one of the largest and most popular question-answering (Q&A) websites. It accumulates millions of programming related questions and answers to support the developers in software development. Unfortunately, a large number of questions are not answered at all, which might hurt the quality or purpose of this community-oriented knowledge base. Up to 29% of Stack Overflow questions do not have any answers. There have been existing attempts in detecting the unanswered questions. Unfortunately, they primarily rely on the question attributes (e.g., score, view count) that are not available during the submission of a question. Detection of the potentially unanswered questions in advance during question submission could help one improve the question and thus receive the answers in time. In this paper, we compare unanswered and answered questions quantitatively and qualitatively by analyzing a total of 4.8 million questions from Stack Overflow. We find that topics discussed in the question, the experience of the question submitter, and readability of question texts could often determine whether a question would be answered or not. Our qualitative study also reveals several other non-trivial factors that not only explain (partially) why the questions remain unanswered but also guide the novice users to improve their questions. We develop four machine learning models to predict the unanswered questions during their submission. According to the experiments, our models predict the unanswered questions with a maximum of about 79% accuracy and significantly outperform the state-of-the-art prediction models.
Realistic simulations of spin squeezing and cooperative coupling effects in large ensembles of interacting two-level systems

We describe an efficient numerical method for simulating the dynamics of interacting spin ensembles in the presence of dephasing and decay. The method builds on the discrete truncated Wigner approximation for isolated systems, which combines the mean-field dynamics of a spin ensemble with a Monte Carlo sampling of discrete initial spin values to account for quantum correlations. Here we show how this approach can be generalized for dissipative spin systems by replacing the deterministic mean-field evolution by a stochastic process, which describes the decay of coherences and populations while preserving the length of each spin. We demonstrate the application of this technique for simulating nonclassical spin-squeezing effects or the dynamics and steady states of cavity QED models with hundred thousand interacting two-level systems and without relying on any symmetries. This opens up the possibility to perform accurate real-scale simulations of a diverse range of experiments in quantum optics or with solid-state spin ensembles under realistic laboratory conditions.
Understanding Capacity-Driven Scale-Out Neural Recommendation Inference

Deep learning recommendation models have grown to the terabyte scale. Traditional serving schemes-that load entire models to a single server-are unable to support this scale. One approach to support these models is distributed serving, or distributed inference, which divides the memory requirements of a single large model across multiple servers. This work is a first-step for the systems community to develop novel model-serving solutions, given the huge system design space. Large-scale deep recommender systems are a novel workload and vital to study, as they consume up to 79% of all inference cycles in the data center. To that end, this work is the first to describe and characterize scale-out deep learning recommender inference using data-center serving infrastructure. This work specifically explores latency-bounded inference systems, compared to the throughput-oriented training systems of other recent works. We find that the latency and compute overheads of distributed inference are largely attributed to a model's static embedding table distribution and sparsity of inference request inputs. We evaluate three embedding table mapping strategies on three representative models and specify the challenging design trade-offs in terms of end-to-end latency, compute overhead, and resource efficiency. Overall, we observe a modest latency overhead with distributed inference-P99 latency is increased by only 1% in the best case configuration. The latency overheads are a result of the commodity infrastructure used and the sparsity of embedding tables. Encouragingly, we also show how distributed inference can account for efficiency improvements in data-center scale recommendation serving.
Overview of Image Smoothing Algorithms

Abstract In the process of image generation and transmission, it is easy to be polluted and affected by various kinds of noise, which reduces the quality of the image. In order to avoid the adverse effects of image processing and image visual effect, we can use image smoothing technology to improve the adverse effects, so as to make the image clearer. There are a variety of image smoothing techniques, which had great smoothing effect, but there are also different defects. Based on the analysis of different noise models, this paper uses mean smoothing, median smoothing, Gaussian smoothing, bilateral filtering and other techniques to study and analyse the image processing algorithm, the implementation method and efficiency, which provides efficient theoretical value for image processing and image visual effect in various fields involved in image processing.
ASL Sea Battle: Gamifying Sign Language Data Collection

The development of accurate machine learning models for sign languages like American Sign Language (ASL) has the potential to break down communication barriers for deaf signers. However, to date, no such models have been robust enough for real-world use. The primary barrier to enabling real-world applications is the lack of appropriate training data. Existing training sets suffer from several shortcomings: small size, limited signer diversity, lack of real-world settings, and missing or inaccurate labels. In this work, we present ASL Sea Battle, a sign language game designed to collect datasets that overcome these barriers, while also providing fun and education to users. We conduct a user study to explore the data quality that the game collects, and the user experience of playing the game. Our results suggest that ASL Sea Battle can reliably collect and label real-world sign language videos, and provides fun and education at the expense of data throughput.
Does reviewing experience reduce disagreement in proposals evaluation? Insights from Marie Skłodowska-Curie and COST Actions

Abstract We have limited understanding of why reviewers tend to strongly disagree when scoring the same research proposal. Thus far, research that explored disagreement has focused on the characteristics of the proposal or the applicants, while ignoring the characteristics of the reviewers themselves. This article aims to address this gap by exploring which reviewer characteristics most affect disagreement among reviewers. We present hypotheses regarding the effect of a reviewer’s level of experience in evaluating research proposals for a specific granting scheme, that is, scheme reviewing experience. We test our hypotheses by studying two of the most important research funding programmes in the European Union from 2014 to 2018, namely, 52,488 proposals evaluated under three funding schemes of the Horizon 2020 Marie Sklodowska-Curie Actions (MSCA), and 1,939 proposals evaluated under the European Cooperation in Science and Technology Actions. We find that reviewing experience on previous calls of a specific scheme significantly reduces disagreement, while experience of evaluating proposals in other schemes—namely, general reviewing experience, does not have any effect. Moreover, in MSCA—Individual Fellowships, we observe an inverted U relationship between the number of proposals a reviewer evaluates in a given call and disagreement, with a remarkable decrease in disagreement above 13 evaluated proposals. Our results indicate that reviewing experience in a specific scheme improves reliability, curbing unwarranted disagreement by fine-tuning reviewers’ evaluation.
Federated Multi-view Learning for Private Medical Data Integration and Analysis

Along with the rapid expansion of information technology and digitalization of health data, there is an increasing concern on maintaining data privacy while garnering the benefits in the medical field. Two critical challenges are identified: First, medical data is naturally distributed across multiple local sites, making it difficult to collectively train machine learning models without data leakage. Second, in medical applications, data are often collected from different sources and views, resulting in heterogeneity and complexity that requires reconciliation. In this article, we present a generic Federated Multi-view Learning (FedMV) framework for multi-view data leakage prevention. Specifically, we apply this framework to two types of problems based on local data availability: Vertical Federated Multi-view Learning (V-FedMV) and Horizontal Federated Multi-view Learning (H-FedMV). We experimented with real-world keyboard data collected from BiAffect study. Our results demonstrated that the proposed approach can make full use of multi-view data in a privacy-preserving way, and both V-FedMV and H-FedMV perform better than their single-view and pairwise counterparts. Besides, the framework can be easily adapted to deal with multi-view sequential data. We have developed a sequential model (S-FedMV) that takes sequence of multi-view data as input and demonstrated it experimentally. To the best of our knowledge, this framework is the first to consider both vertical and horizontal diversification in the multi-view setting, as well as their sequential federated learning.
Hiding an Image in an Audio File using LSB Audio Technique

In the previous few decades the information transmission isn't secure a result of assaults by gatecrasher or an assailant. In open correspondence framework Data transmission isn't secure a direct result of capture and inappropriate control by busybody. Along these lines, Steganography is the alluring answer for this issue, which is a strategy for composing covered up, messages separated from the sender and collector so that nobody acquaintances the existence in the midst of the significance, a category of sanctuary through be short of of characterization. Audio Steganography is the preparation of concealing the information in type of anonymous information by covering it into a different format (audio record). Data stowing away is the innovation to insert the mystery data into a cover information such that stays discreet data imperceptible. This paper presents another steganographic strategy for implanting a picture into an audio document. Accentuation will be on the proposed plan of picture stowing away in sound and its examination with straightforward Least Significant Bit addition strategy for information covering up in sound.
User Responses Towards Augmented Reality Face Filters: Implications for Social Media and Brands

Augmented reality (AR) is increasingly shaping brand-customer touchpoints. On social media, brands are developing AR face filters to engage consumers. This study analyses the use of AR filters in users’ experiences. Data from social media users of AR filters was gathered using an online questionnaire. Results showed that branded AR filters generated higher perceived originality, aesthetic quality and interactivity than non-branded filters. For branded AR filters, usefulness (determined mainly by interactivity) fostered behavioural intentions towards brands; while enjoyment (determined mainly by aesthetic quality) enhanced behavioural intentions towards social networks. The results offer interesting implications for brand managers and AR filters developers.
Digital Evidence and Cloud Forensics: Contemporary Legal Challenges and the Power of Disposal

Fighting crime in cyberspace requires law enforcement authorities to immerse in a digital ocean of vast amount of information and also to acquire and objectify the evidence of criminal activity. Handling digital evidence is a complex and multifaceted process as they can provide critical evidentiary information in an unquestionable and irrefutable way. When digital evidence resides in a cloud storage environment the criminal investigation is faced with unprecedented contemporary legal challenges. In this paper, the authors identify three main legal challenges that arise from the current cloud-based technological landscape, i.e., territoriality (the loss of location), possession (the cloud content ownership) and confiscation procedure (user authentication/data preservation issues). On the onset of the identified challenges, the existing American, European and International legal frameworks are thoroughly evaluated. Finally, the authors discuss and endorse the Power of Disposal, a newly formed legal notion and a multidisciplinary solution with a global effect as a result of collaboration between technical, organizational and legal perspectives as an effective first step to mitigate the identified legal challenges.
An Empirical Comparison of BERT, RoBERTa, and Electra for Fact Verification

We reviewed some features of a number of fact verification techniques by comparing 3 (three) algorithms, namely BERT, RoBERTa, and Electra. These 3 (three) algorithms have different advantages, i.e., BERT and RoBERTa predict hidden words using a huge dataset, and Electra verifies facts by detecting tokens that are replaced in a text or sentence. It is necessary to find the model with a good performance evaluation value to produce the best fact verification results. The evaluation of the performance model in this study uses the F1-Score. Our experimental results show that RoBERTa achieves the best accuracy and F1-Score with a value of 95.4% and 95.3% with the parameter value of epoch of 5 (five) and a batch size of 32. The second position is occupied by BERT, with the best result of accuracy and F1-Score at the same value of 94.3% with the epoch of10 (ten) and a batch size of32. Although it provides a shorter elapsed time, unfortunately, Electra does not outperform other models in fact verification.
Predictive Maintenance of Relative Humidity Using Random Forest Method

The massive development of Industry 4.0 inseparable with improvement of Machine Learning. In order to protect manufacturing sector from unwanted events such as electrical failures due to high level of humidity, the predictive maintenance based on Machine Learning should be developed accurately. This paper describes the implementation work of predicting Relative Humidity (RH) in the smart factory's environment by using Random Forest method as a part of Machine Learning. In order to support data reliability and interoperability in smart factory environment, IIoT devices based oneM2M standard platform was used to collect the data. The result of this Random Forest method for predict relative humidity shows 82.49% which considered as an excellent accuracy. This research goal may contribute to the manufacturing fields to be able to lower the cost and increase efficiency in maintenance.
Reasoning Graph Networks for Kinship Verification: From Star-Shaped to Hierarchical

In this paper, we investigate the problem of facial kinship verification by learning hierarchical reasoning graph networks. Conventional methods usually focus on learning discriminative features for each facial image of a paired sample and neglect how to fuse the obtained two facial image features and reason about the relations between them. To address this, we propose a Star-shaped Reasoning Graph Network (S-RGN). Our S-RGN first constructs a star-shaped graph where each surrounding node encodes the information of comparisons in a feature dimension and the central node is employed as the bridge for the interaction of surrounding nodes. Then we perform relational reasoning on this star graph with iterative message passing. The proposed S-RGN uses only one central node to analyze and process information from all surrounding nodes, which limits its reasoning capacity. We further develop a Hierarchical Reasoning Graph Network (H-RGN) to exploit more powerful and flexible capacity. More specifically, our H-RGN introduces a set of latent reasoning nodes and constructs a hierarchical graph with them. Then bottom-up comparative information abstraction and top-down comprehensive signal propagation are iteratively performed on the hierarchical graph to update the node features. Extensive experimental results on four widely used kinship databases show that the proposed methods achieve very competitive results.
Hyperspectral Image Superresolution Using Global Gradient Sparse and Nonlocal Low-Rank Tensor Decomposition With Hyper-Laplacian Prior

This article presents a novel global gradient sparse and nonlocal low-rank tensor decomposition model with a hyper-Laplacian prior for hyperspectral image (HSI) superresolution to produce a high-resolution HSI (HR-HSI) by fusing a low-resolution HSI (LR-HSI) with an HR multispectral image (HR-MSI). Inspired by the investigated hyper-Laplacian distribution of the gradients of the difference images between the upsampled LR-HSI and latent HR-HSI, we formulate the relationship between these two datasets as a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\ell _{p}$</tex-math></inline-formula> <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$(0 &lt; p &lt; 1)$</tex-math></inline-formula> -norm term to enforce spectral preservation. Then, the relationship between the HR-MSI and latent HR-HSI is built using a tensor-based fidelity term to recover the spatial details. To effectively capture the high spatio-spectral-nonlocal similarities of the latent HR-HSI, we design a novel nonlocal low-rank Tucker decomposition to model the 3-D regular tensors constructed from the grouped nonlocal similar HR-HSI cubes. The global spatial-spectral total variation regularization is then adopted to ensure the global spatial piecewise smoothness and spectral consistency of the reconstructed HR-HSI from nonlocal low-rank cubes. Finally, an alternating direction method of multipliers-based algorithm is designed to efficiently solve the optimization problem. Experiments on both the synthetic and real datasets collected by different sensors show the effectiveness of the proposed method, from visual and quantitative assessments.