Reviewerly/exHarmony huggingface dataset:
Identity markers in the Internet usernames adopted by female users of a Persian public discussion forum: A sociolinguistic analysis

Abstract Drawing on the feminist poststructuralist perspective, the current study explored the usernames adopted by female users of Ninisite, that is, a Persian discussion forum, and aimed at identifying their identity markers. To this end, a corpus of 947 usernames in Ninisite was compiled. Using thematic analysis, the recurrent themes in the usernames were pinpointed, which led to the identification of six themes as identity markers, namely, gender, religion/ideology, ethnicity, occupation/profession, being humorous, and sense of uniqueness. With regard to the socioculturally unique context of Iran, a continuum of specificity versus generality can be observed in the usernames on Ninisite, with specificity emphasizing differences, sense of uniqueness, and individualization of the users, and generality highlighting neutrality, commonalities, and conventionality.
Topological Feature Based Object Matching for Traffic Light Detection Using Multi-view Camera System

A telephoto image with a high object resolution can help detect long-distance traffic light objects earlier, which is of great importance to improve traffic efficiency and safety of automated vehicles. However, it usually cannot tackle close-range detection because of its narrow field of view, and vice versa. For this reason, a multi-view camera system composed of a telephoto camera and a wide-angle camera is proposed in this work. Furthermore, we propose a strategy for two cameras to work alternately. And the proposed topological feature based object matching algorithm and a post-fusion algorithm are introduced during the switching process. The matching and fusion algorithms ensure the information interaction and consistency of the results of the two cameras. The proposed camera switching strategy and the object matching algorithm are evaluated on a real-world dataset.
A Corpus of Neutral Voice Speech in Brazilian Portuguese

This work presents a new database containing high sampling rate recordings of a single male speaker reading sentences in Brazilian Portuguese with neutral voice, along with the corresponding text corpus. Intended for synthesis and other speech-oriented applications, the dataset contains text scripts extracted from a popular Brazilian news TV program, read out loud by a trained individual in a controlled environment, resulting in roughly 20 h of audio data. The text was normalized in the recording process and special textual occurrences (e.g. acronyms, numbers, foreign names etc.) were replaced by their phonetic translation to a readable text in Portuguese. There are no noticeable accidental sounds and background noise has been kept to a minimum in all audio samples. To illustrate the potential benefits of having this data available, text-to-speech experiments were conducted using state-of-the-art models for speech synthesis (Tacotron 2 and Waveglow). As a result, we obtained intelligible and natural sounding voices from as few as 8 min of audio samples coming from an unseen target speaker, after having trained over our data; moreover, by increasing the target recording time to 75 min, we have noticeably improved accuracy in pronunciation.
Evaluate learner level assessment in intelligent e-learning systems using probabilistic network model

E-learning is a broader term than “m-learning” and “online learning.” The originality is that it provides the learner with the chance to learn anywhere and anytime. As technology is improving, teaching-learning methodologies are also changing by making electronic learning one of the leading trends in the education system. Due to the complexity of the education system, the prospective dynamics of e-learning require the development of a knowledge delivery and evaluation system. In the history of the education system, a pandemic situation created major disruption affecting 1.6 billion learners in 190 countries all over the globe. In this chapter, an evaluation method for assessing the learner assessment in an intelligent e-learning system is presented. Our method is called the probabilistic network (PN) model and is a well-known evaluation method for the learners in a framework. However, evaluation of the learner’s learning and outcomes still remains a challenge. The main objective of this chapter is to add its dimension toward the evaluation of learning outcomes especially for the learners of intelligent e-learning systems. In this case, intelligent tutoring systems (ITSs) were the bone for the smooth running of teaching and have been playing an important role in the e-learning system. An ITS is a type of knowledge-based system whose main objective is to effectively complement a human tutor with a computer. Unlike conventional classroom teaching, ITSs have the potential to fit according to an individual learner’s needs. A probability-based ITS structure is suggested in this chapter, consisting of four models, specifically the learner’s action model, the pedagogical model, the knowledge base model, and the learner assessment model. The importance is being given to test the learner level model where an element of complexity has been introduced and handled by the probabilistic network or Bayesian network model. The purpose of this assessment learner model is to correctly determine the level of knowledge of each learner based on their response to the level of questions, where the level of questions is random to the evaluation process of the learner. With the aid of Bayes’ law, the method has integrated into it the complexity and has found positive results that take the possibility of learners into account.
Data Preparation for Artificial Intelligence

One of the major hurdles for the development and clinical deployment of machine learning (ML) and deep learning (DL) models is the availability of structured, well-curated, large medical imaging data sets with high-quality labels. For most researchers and companies, access to medical imaging data is limited to small datasets from a narrow geographic area. We describe the fundamental steps that need to be taken in the process of preparing cardiovascular imaging data for the development of ML/DL models.
The SMART Framework: Selection of Machine Learning Algorithms With ReplicaTions—A Case Study on the Microvascular Complications of Diabetes

Over 34 million people in the US have diabetes, a major cause of blindness, renal failure, and amputations. Machine learning (ML) models can predict high-risk patients to help prevent adverse outcomes. Selecting the 'best' prediction model for a given disease, population, and clinical application is challenging due to the hundreds of health-related ML models in the literature and the increasing availability of ML methodologies. To support this decision process, we developed the Selection of Machine-learning Algorithms with ReplicaTions (SMART) Framework that integrates building and selecting ML models with decision theory. We build ML models and estimate performance for multiple plausible future populations with a replicated nested cross-validation technique. We rank ML models by simulating decision-maker priorities, using a range of accuracy measures (e.g., AUC) and robustness metrics from decision theory (e.g., minimax Regret). We present the SMART Framework through a case study on the microvascular complications of diabetes using data from the ACCORD clinical trial. We compare selections made by risk-averse, -neutral, and -seeking decision-makers, finding agreement in 80% of the risk-averse and risk-neutral selections, with the risk-averse selections showing consistency for a given complication. We also found that the models that best predicted outcomes in the validation set were those with low performance variance on the testing set, indicating a risk-averse approach in model selection is ideal when there is a potential for high population feature variability. The SMART Framework is a powerful, interactive tool that incorporates various ML algorithms and stakeholder preferences, generalizable to new data and technological advancements.
Patch Re-Segmentation and Packing for Dynamic Point Cloud Compression via Back-and-Forth Structure

The dynamic point cloud is widely needed in 3D vision related applications such as virtual reality and telepresence.Due to the huge amount of data, a key technology before the effective application is the dynamic point cloud compression.The state-of-the-art dynamic point cloud compression scheme, video-based point cloud compression (V-PCC), generates 2D videos with some uncorrelation due to the patch segmentation and packing process, which will affect the compression efficiency.In this paper, we propose a Packing with Patch Correlation Improvement (PPCI) algorithm to adaptively remove the uncorrelated parts between matched patches in packing for the sake of inter-prediction performance.We first propose a basic unidirectional patch re-segmentation operator to remove the uncorrelated parts of the patches in the current point cloud relative to the patches in its reference point cloud.The removed parts will be formed as new patches and added to the patch collection of the current point cloud.Then we propose a back-and-forth structure, which is a combination of several basic patch re-segmentation operators, to bilaterally remove the uncorrelated parts of matched patches in a back-and-forth (BF) unit.Furthermore, we propose a framework to adaptively decide the best length of each BF unit in a point cloud sequence.Experimental results show that our method achieves noticeable bitrate savings compared with the existing V-PCC packing methods, particularly for sequences with small motion.
Big Data Platform for Daily Management of Higher Vocational Students in the Information Age

With the development of the school, the number of students and teachers is increasing, campus activities become more complex, and the student information management system has gradually become an indispensable part of a unit. It can provide users with enough information and fast query methods, so that students can easily view their grades and academic information. Therefore, a powerful student management information system will effectively help school teachers and students and provide all-round guarantee for their study and school life. Therefore, a powerful student management information system will effectively help all teachers and students to provide all-round protection for their study and life in school. Based on big data mining technology, this paper studies the daily management of higher vocational students. Firstly, this paper studies the characteristics of higher vocational students and the application of information technology. Then it designs the information platform module about the daily management of higher vocational students, and finally tests the system. The test results show that the performance of the system meets the needs of daily management.
A Stacked Ensemble-Based Classifier for Breast Invasive Ductal Carcinoma Detection on Histopathology Images

Breast cancer is one of the main causes of death in women.When body tissues start behaves abnormally and the ratio of tissues growth becomes asymmetrical then this stage is called cancer.Invasive ductal carcinoma (IDC) is the early stage of breast cancer.The early detection and diagnosis of invasive ductal carcinoma is a significant step for the cure of IDC breast cancer.This paper presents a convolutional neural network (CNN) approach to detect and visualize the IDC tissues in breast on histological images dataset.The dataset consists of 90 thousand histopathological images containing two categories: Invasive Ductal Carcinoma positive (IDC+) and Invasive Ductal Carcinoma negative (IDC-).For the detection of this disease, we have proposed an ensemble learning-based deep learning model that consists of the multi-model structure having different internal configuration settings of CNN.As a result, the proposed method has significant progress as per present techniques in terms of accuracy as 92.7%.Therefore, the ensemble approach of multi-model CNN can provide the evolution of IDC at different stages, and this would help the pathologist in the cure of cancer.The experimented results show that the proposed ensemble-based approach of multi-model CNN generated the preeminent results with an accuracy of 92.7% as compared to the already existing techniques.
Automatic Permission Check Analysis for Linux Kernel

Permission checks play an essential role in operating system security by providing access control to privileged functionalities. However, it is challenging for kernel developers to scalably verify the soundness of existing checks due to the large codebase and complexity of the kernel. In fact, Linux kernel contains millions of lines of code with hundreds of permission checks, and even worse its complexity is fast-growing. This paper presents PeX, a static permission check error detector for Linux, which takes as input a kernel source code and reports any missing, inconsistent, and redundant permission checks. PeX uses KIRIN (Kernel InteRface based Indirect call aNalysis), a novel, precise, and scalable indirect call analysis technique. Over the interprocedural control flow graph built by KIRIN, PeX automatically identifies permission checks and infers the mappings between permission checks and privileged functions. For each privileged function, PeX examines all possible paths to the function to check if necessary permission checks are correctly enforced. We evaluated PeX on the latest stable Linux kernel v4.18.5 for three types of permission checks: Discretionary Access Controls (DAC), Capabilities, and Linux Security Modules (LSM). PeX reported 45 new permission check errors, 17 of which have been confirmed by the kernel developers.
MGMSN: Multi-Granularity Matching Model Based on Siamese Neural Network

Aiming to overcome the shortcomings of the existing text matching algorithms, in this research, we have studied the related technologies of sentence matching and dialogue retrieval and proposed a multi-granularity matching model based on Siamese neural networks. This method considers both deep semantic similarity and shallow semantic similarity of input sentences to completely mine similar information between sentences. Moreover, to alleviate the problem of out of vocabulary in sentences, we have combined both word and character granularity in deep semantic similarity to further learn information. Finally, comparative experiments were carried out on the Chinese data set LCQMC. The experimental results confirm the effectiveness and generalization ability of this method, and several ablation experiments also show the importance of each part of the model.
Downsampled depth encoding for enhanced 3D range geometry compression

The use of three-dimensional (3D) range geometry is expanding across a variety of disciplines ranging from medicine to the visual arts. A large amount of information is available in 3D range geometry, causing some applications to be limited in their ability to effectively store or transmit captured data. To help alleviate this constraint, a variety of 3D range data compression techniques have been proposed. One method, multiwavelength depth (MWD) encoding, smoothly encodes 3D range geometry into the three color channels of a 2D RGB image. To the best of our knowledge, we present a novel compression enhancement to further reduce file sizes that employs image downsampling, MWD encoding, and lossless (e.g., PNG) or lossy (e.g., JPEG) compression. Image upsampling is used to return downsampled encodings to their original resolution from which the 3D information is then decoded. The proposed method is robust to various scales of downsampling and levels of lossy compression. For example, when this method was applied with 50% downsampling and JPEG 85 to an encoding of a 3D face scan, a compression ratio of 68.85:1 versus the raw data was achieved with a global RMS reconstruction accuracy of 98.77%. Experimental results demonstrate that the proposed method can provide substantial file size savings at minimal reduction in overall reconstruction accuracy.
Quantifying the Body: Body Image, Body Awareness and Self-Tracking Technologies

Abstract The availability of wearable devices recently boosted the popularity of self-tracking technologies. Self-trackers are involved in a complex process of knowledge development, but this cannot be achieved without knowing the body. However, self-tracking devices seem to embrace an abstract and scattered conception of the body, based on unrelated numbers, graphs, and depictions, which may not be integrated into a coherent body image. This may turn into biases and distortions of how we look at our bodies, worsening, rather than improving, our self-knowledge. In this chapter we explore the ways through which the progressive “quantification” introduced by self-tracking technologies is affecting the body. We first explain a series of theoretical constructs concerning the body, which are essential to understand the impact of self-tracking on our bodies, like body schema, body image, and body awareness . Then, we illustrate how individuals’ body image and awareness are affected by the usage of self-tracking technologies in the sports domain. Finally, we point out some lines of future research aimed at providing people with more meaningful representations of their own body, improving their body awareness and even their body image.
<scp>User‐guided</scp> global explanations for deep image recognition: A user study

Abstract We study a user‐guided approach for producing global explanations of deep networks for image recognition. The global explanations are produced with respect to a test data set and give the overall frequency of different “recognition reasons” across the data. Each reason corresponds to a small number of the most significant human‐recognizable visual concepts used by the network. The key challenge is that the visual concepts cannot be predetermined and those concepts will often not correspond to existing vocabulary or have labeled data sets. We address this issue via an interactive‐naming interface, which allows users to freely cluster significant image regions in the data into visually similar concepts. Our main contribution is a user study on two visual recognition tasks. The results show that the participants were able to produce a small number of visual concepts sufficient for explanation and that there was significant agreement among the concepts, and hence global explanations, produced by different participants.
Accessible from the open web: a qualitative analysis of the available open-source information involving cyber security and critical infrastructure

Abstract In order to efficiently manage and operate industrial-level production, an increasing number of industrial devices and critical infrastructure (CI) are now connected to the internet, exposed to malicious hackers and cyberterrorists who aim to cause significant damage to institutions and countries. Throughout the various stages of a cyber-attack, Open-source Intelligence (OSINT) tools could gather data from various publicly available platforms, and thus help hackers identify vulnerabilities and develop malware and attack strategies against targeted CI sectors. The purpose of the current study is to explore and identify the types of OSINT data that are useful for malicious individuals intending to conduct cyber-attacks against the CI industry. Applying and searching keyword queries in four open-source surface web platforms (Google, YouTube, Reddit, and Shodan), search results published between 2015 and 2020 were reviewed and qualitatively analyzed to categorize CI information that could be useful to hackers. Over 4000 results were analyzed from the open-source websites, 250 of which were found to provide information related to hacking and/or cybersecurity of CI facilities to malicious actors. Using thematic content analysis, we identified three major types of data malicious attackers could retrieve using OSINT tools: indirect reconnaissance data, proof-of-concept codes, and educational materials. The thematic results from this study reveal an increasing amount of open-source information useful for malicious attackers against industrial devices, as well as the need for programs, training, and policies required to protect and secure industrial systems and CI.
Ensemble Learning for Assessing Degree of Humor

Sentiment analysis has always been a major research area for natural language processing, which aims at computing people’s opinions, attitudes, and emotions toward an entity. Benefiting from the development of deep convolution neural networks, the performance of sentiment analysis has made a breakthrough. However, existing sentiment analysis methods often fluctuate greatly in different sentiment analysis tasks. In this paper, to solve this problem, we introduce ensemble learning on a variant of deep learning models to gain a better score on sentiment analysis tasks. Ensemble learning is one of the most efficient methods for improving representation ability on many tasks. Comprehensive experiments on benchmark dataset demonstrate that the ensemble method greatly improves the performance of the model through combining the advantages of the different previous and current state-of-the-art. Also, we carried out experiment to compare different type of ensemble methods to further enhance model performance.
Effect of Anthropomorphic Glyph Design on the Accuracy of Categorization Tasks

Data glyphs continue to gain popularity for information communication. However, the cognition and perception theory of glyphs is largely unknown for many tasks including "categorization". Categorization tasks are common in everyday life from sorting objects to a doctor diagnosing a patient's disease. However it is unknown how glyph designs, specifically anthropomorphic human-like representations which in prior visualization research have demonstrated improved information recall, affect accuracy in a categorization task. To better understand how people comprehend and perceive glyphs for categorization, including anthropomorphic representations, we conducted a crowdsourced experiment to evaluate whether more human-like glyphs would lead to higher categorization accuracy. Contrary to our hypothesis, we found evidence that subjects are more accurate with a less anthropomorphic glyph. A posthoc analysis also reveals that anthropomorphic glyphs introduce biases due to their anatomically salient features. Based on these results we propose design guidelines for glyphs used in categorization tasks. The supplemental material of this paper available is on https://osf.io/3bgcv/.