Reviewerly/exHarmony huggingface dataset:
Digital Technology at the Edge of Capitalism

It is sometimes argued that there is hardly a place in the world in the 21st century left untouched by global capitalism [111, 112]. Even so, some places remain at the periphery, participating in this system without being fully absorbed by it. In this article, we take a detailed look at the economic life of such a “pericapitalist” [161] community in the Brazilian Amazon region. We detail how the community increasingly participates in global systems and supply chains, yet also organizes economic life around local and traditional values. We pay special attention to the role of digital technologies in the community, including mobile phones and internet. The contribution of the article is as follows: firstly, it provides a detailed analysis of the material practices of a community at the edge . Secondly, it draws attention to the heterogeneous nature of responses to global capitalism, formed from the relationship between specific material practices, new technology, and elements of cultural identity. Thirdly, it argues for an increased sensibility towards these different relations to capitalism when considering design implications. We argue that close attention to material practice goes some way towards resolving those tensions and, further, provides for an appeal to a more pluralistic views of culture and development [61].
Integration of Automated Essay Scoring Models Using Item Response Theory

Automated essay scoring (AES) is the task of automatically grading essays without human raters. Many AES models offering different benefits have been proposed over the past few decades. This study proposes a new framework for integrating AES models that uses item response theory (IRT). Specifically, the proposed framework uses IRT to average prediction scores from various AES models while considering the characteristics of each model for evaluation of examinee ability. This study demonstrates that the proposed framework provides higher accuracy than individual AES models and simple averaging methods.
Financial Fraud Detection on Micro-credit Loan Scenario via Fuller Location Information Embedding

Micro-credit loan serves as an indispensable supplementary loan for people lacking verifiable credit records or unqualified to get conventional bank loans. And financial fraud detection is one of the mainstream methods to control loan risk. Its goal is to utilize a set of corresponding features (e.g., customer behaviors) to predict whether a customer will fail to make required payments in the future. To the best of our knowledge, few works pay attention to permanent residential locations of customers. However, the real data study shows that customer location information potentially provides additional power in financial fraud detection. Three challenges place barriers to make full use of location information: (1) Data sparsity makes financial fraud detection model hard to learn the relationship between location information and fraud behaviors; (2) Financial fraud detection model considering location information alone without resident personality, might weaken fraud distinguish power of location information; (3) The representation of location information should be effective and easy to apply, for being used in various applications. In this paper, we propose Fuller Location Information Embedding (FLIE) network. FLIE ideally handles above challenges, which performance verified by experiments on the tasks of fraudulent customer prediction and customer segmentation.
Multimodal Sensor Motion Intention Recognition Based on Three-Dimensional Convolutional Neural Network Algorithm

With the development of microelectronic technology and computer systems, the research of motion intention recognition based on multimodal sensors has attracted the attention of the academic community. Deep learning and other nonlinear neural network models have a wide range of applications in big data sets. We propose a motion intention recognition algorithm based on multimodal long-term and short-term spatiotemporal feature fusion. We divide the target data into multiple segments and use a three-dimensional convolutional neural network to extract the short-term spatiotemporal features. The three types of features of the same segment are fused together and input into the LSTM network for time-series modeling to further fuse the features to obtain multimodal long-term spatiotemporal features with higher discrimination. According to the lower limb movement pattern recognition model, the minimum number of muscles and EMG signal characteristics required to accurately recognize the movement state of the lower limbs are determined. This minimizes the redundant calculation cost of the model and ensures the real-time output of the system results.
HackerScope: the dynamics of a massive hacker online ecosystem

One would have thought that hackers would be striving to hide from public view, but we find that this is not the case: they have a public online footprint. Apart from online security forums, this footprint appears also in software development platforms, where authors create publicly accessible malware repositories to share and collaborate. With the exception of a few recent efforts, the existence and the dynamics of this community has received surprisingly limited attention. The goal of our work is to analyze this ecosystem of hackers in order to: (a) understand their collaborative patterns and (b) identify and profile its most influential authors. We develop HackerScope, a systematic approach for analyzing the dynamics of this hacker ecosystem. Leveraging our targeted data collection, we conduct an extensive study of 7389 authors of malware repositories on GitHub, which we combine with their activity on four security forums. From a modelling point of view, we study the ecosystem using three network representations: (a) the author-author network, (b) the author-repository network and (c) cross-platform egonets. Our analysis leads to the following key observations: (a) the ecosystem is growing at an accelerating rate as the number of new malware authors per year triples every 2 years, (b) it is highly collaborative, more so than the rest of GitHub authors, and (c) it includes influential and professional hackers. We find 101 authors maintain an online "brand" across GitHub and our online forums. Our study is a significant step towards using public online information for understanding the malicious hacker community.
Automatic patch linkage detection in code review using textual content and file location features

Contemporary code review tools are a popular choice for software quality assurance. Using these tools, reviewers are able to post a linkage between two patches during a review discussion. Large development teams that use a review-then-commit model risk being unaware of these linkages. Our objective is to first explore how patch linkage impacts the review process. We then propose and evaluate models that detect patch linkage based on realistic time intervals. First, we carry out an exploratory study on three open source projects to conduct linkage impact analysis using 942 manually classified linkages. Second, we propose two techniques using textual and file location similarity to build detection models and evaluate their performance. The study provides evidence of latency in the linkage notification. We show that a patch with the Alternative Solution linkage (i.e., patches that implement similar functionality) undergoes a quicker review and avoids additional revisions after the team has been notified, compared to other linkage types. Our detection model experiments show promising recall rates for the Alternative Solution linkage (from 32% to 95%), but precision has room for improvement. Patch linkage detection is promising, with likely improvements if the practice of posting linkages becomes more prevalent. From our implications, this paper lays the groundwork for future research on how to increase patch linkage awareness to facilitate efficient reviews.
On Workload-Aware DRAM Failure Prediction in Large-Scale Data Centers

DRAM failures are one of the major hardware threats to the reliability of large-scale data centers since the uncorrectable errors in DRAMs may cause servers to shut down. Existing works try to solve this problem by predicting DRAM failures in advance with Machine Learning models. In these works, correctable errors (CEs) are generally deemed as the most important feature. The major reason behind CEs' emergence is the accumulated stress caused by intensive workloads. Moreover, defective DRAMs will not manifest themselves as system errors until the defective cells are accessed by some specific workloads. Therefore, the running workloads on a server are also important for DRAM failure prediction. In this paper, we focus on the impact of workloads on DRAM failures. We design the workload features from both macroscopical and microscopical aspects, i.e. node-level performance metrics and cell-level DRAM access pattern, respectively. Furthermore, we propose Hierarchical DRAM Error Code (HiDEC) to represent the DRAM access pattern. We leverage several Decision Tree-based models for DRAM failure prediction to highlight the generality of our designed features. Experiments are carried out based on the dataset collected from a real-world commercial data center. The results show that both macroscopic and microscopic features can bring significant improvements to the prediction performance.
Online Textual Symptomatic Assessment Chatbot Based on Q&amp;A Weighted Scoring for Female Breast Cancer Prescreening

The increasing number of female breast cancer (FBC) incidences in the East predominated by Chinese language speakers has generated concerns over women’s medicare. To minimize the mortality rate associated with FBC in the region, governments and health experts are jointly encouraging women to undergo mammography screening at the earliest suspicion of FBC symptoms. However, studies show that a huge number of women affected by FBC tend to delay medical consultation at its early stage as a result of factors such as complacency due to unawareness of FBC symptoms, procrastination due to lifestyle, and the feeling of embarrassment in discussing private matters especially with medical personnel of the opposite gender. To address these issues, we propose a symptomatic assessment chatbot (SAC) based on artificial intelligence (AI) designed to prescreen women for FBC symptoms via a textual question-and-answer (Q&amp;A) approach. The purpose of our chatbot is to assist women in engaging in communication regarding FBC symptoms, so as to subsequently initiate formal medical consultations for early FBC diagnosis and treatment. We implemented the SAC systematically with some of the latest natural language processing (NLP) techniques suitable for Chinese word segmentation (CWS) and trained the model with real-world FBC Q&amp;A data obtained from a major hospital in Taiwan. The results from our experiments showed that the SAC achieved very high accuracy in FBC assessment scoring in comparison to FBC patients’ screening benchmark scores obtained from doctors.
Feature Envy Detection based on Bi-LSTM with Self-Attention Mechanism

Code Smell refers to suboptimal or harmful structures in the source code that may impede the maintainability of software. It serves as an effective way to detect refactoring opportunities. As the most prevailing smell, Feature Envy and its detection has been deeply explored for many years, which produces massive automated detection methods. Nevertheless, the heuristic-based approach cannot reach a satisfying level, and the machine learning approach still needs further optimization. Recent advances in deep learning inspire the birth of deep learning based approach. In this paper, we define a simpler distance metric as numerical feature and we collect class name and method name as text feature. Then we leverage Bidirectional Long-Short Term Memory (Bi-LSTM) Network with self-attention mechanism to extract semantic distance information in the text part, and we adopt embedding technology to enhance the structure distance information in the numerical part. Combined with the two sophisticatedly designed modules and the final classification module, a more reliable and accurate model is presented. Experimental results on seven open-source Java projects show that our model significantly outperforms existing methods.
ViTrack: Efficient Tracking on the Edge for Commodity Video Surveillance Systems

Nowadays, video surveillance systems are widely deployed in various places, e.g., schools, parks, airports, roads, etc. However, existing video surveillance systems are far from full utilization due to high computation overhead in video processing. In this work, we present ViTrack, a framework for efficient multi-video tracking using computation resource on the edge for commodity video surveillance systems. In the heart of ViTrack lies a two layer spatial/temporal compressed target detection method to significantly reduce the computation overhead by combining videos from multiple cameras. Further, ViTrack derives the video relationship and camera information even in absence of camera location, direction, etc. To alleviate the impact of variant video quality and missing targets, ViTrack leverages a Markov Model based approach to efficiently recover missing information and finally derive the complete trajectory. We implement ViTrack on a real deployed video surveillance system with 110 cameras. The experiment results demonstrate that ViTrack can provide efficient trajectory tracking with processing time 45x less than the existing approach. For 110 video cameras, ViTrack can run on a Dell OptiPlex 390 computer to track given targets in almost real time. We believe ViTrack can enable practical video analysis for widely deployed commodity video surveillance systems.
XBNet: An extremely boosted neural network

Neural networks have proved to be very robust at processing unstructured data like images, text, videos, and audio. However, it has been observed that their performance is not up to the mark in tabular data; hence tree-based models are preferred in such scenarios. A popular model for tabular data is boosted trees, a highly efficacious and extensively used machine learning method, and it also provides good interpretability compared to neural networks. In this paper, we describe a novel architecture XBNet (Extremely Boosted Neural Network), which tries to combine tree-based models with neural networks to create a robust architecture trained by using a novel optimization technique, Boosted Gradient Descent for Tabular Data which increases its interpretability and performance.
Optimal task replication considering reliability, performance, and energy consumption for parallel computing in cloud systems

In a cloud-based cyber–physical system, many jobs consist of multiple parallel tasks. The cloud system usually adopts active task replication to improve performance and guarantee the reliability of a job. This technology creates redundant replicas for each task and then executes the replicas concurrently. In the cloud system, each replica is a virtual machine (VM) image that can be easily assigned to different physical machines (PMs) to overcome resource heterogeneity. However, how to design a rational task replication strategy (including replica creation and VM assignment) is indeed a complex issue. It should comprehensively consider correlations and tradeoffs among reliability, performance, and energy consumption. This paper first proposes a reliability–performance correlation model for a job executed by using active task replication. We design a general method to avoid analyzing complex failure correlations and give a Bayesian approach to calculate the performability metric of the job. The paper also proposes a reliability–energy correlation model to evaluate random energy consumption of a PM hosting multiple VMs by using mixed random variables. Finally, an expected net profit optimization model and a genetic algorithm are developed to search for an optimal task replication strategy balancing tradeoffs among reliability, performance, and energy consumption. Illustrative examples are provided. • Correlation modeling for a parallel job with active task replication. • A general method avoids analyzing complex failure correlations in job execution. • Evaluating random energy consumption by using mixed random variables. • Deriving a rational task replication strategy balancing tradeoffs.
Towards annotation-efficient segmentation via image-to-image translation

An important challenge and limiting factor in deep learning methods for medical imaging segmentation is the lack of available of annotated data to properly train models. For the specific task of tumor segmentation, the process entails clinicians labeling every slice of volumetric scans for every patient, which becomes prohibitive at the scale of datasets required to train neural networks to optimal performance. To address this, we propose a novel semi-supervised framework that allows training any segmentation (encoder-decoder) model using only information readily available in radiological data, namely the presence of a tumor in the image, in addition to a few annotated images. Specifically, we conjecture that a generative model performing domain translation on this weak label - healthy vs diseased scans - helps achieve tumor segmentation. The proposed GenSeg method first disentangles tumoral tissue from healthy "background" tissue. The latent representation is separated into (1) the common background information across both domains, and (2) the unique tumoral information. GenSeg then achieves diseased-to-healthy image translation by decoding a healthy version of the image from just the common representation, as well as a residual image that allows adding back the tumors. The same decoder that produces this residual tumor image, also outputs a tumor segmentation. Implicit data augmentation is achieved by re-using the same framework for healthy-to-diseased image translation, where a residual tumor image is produced from a prior distribution. By performing both image translation and segmentation simultaneously, GenSeg allows training on only partially annotated datasets. To test the framework, we trained U-Net-like architectures using GenSeg and evaluated their performance on 3 variants of a synthetic task, as well as on 2 benchmark datasets: brain tumor segmentation in MRI (derived from BraTS) and liver metastasis segmentation in CT (derived from LiTS). Our method outperforms the baseline semi-supervised (autoencoder and mean teacher) and supervised segmentation methods, with improvements ranging between 8-14% Dice score on the brain task and 5-8% on the liver task, when only 1% of the training images were annotated. These results show the proposed framework is ideal at addressing the problem of training deep segmentation models when a large portion of the available data is unlabeled and unpaired, a common issue in tumor segmentation.
Enhancing Knowledge Graph Extraction and Validation From Scholarly Publications Using Bibliographic Metadata

OPINION article Front. Res. Metr. Anal., 28 May 2021 | https://doi.org/10.3389/frma.2021.694307
Implementation of Real-Time Edge Detection Using Canny and Sobel Algorithms

Abstract The process of performing tasks related to image processing requires the use of potential techniques such as edge detection. The technique can be used to accomplish tasks related to video and image manipulation. The process of edge detection is very virtual as it acts as the first phase of image analysis and understanding. In this paper, the edges of real-time images were detected for easy extraction of meaningful information from images. A prototype was implemented on OpenCV which is an in-built function cv2. () using python. Canny and Sobel edge detection algorithms are chosen to be used in this paper for comparison purposes to find out which method is better at edge detection. After using both algorithms to detect edges of real-time images, the result showed that the Canny algorithm produced thick edges compared to the Sobel algorithm. The canny algorithm follows the criteria of; Good detection, Good localization, and Minimal response, to output good edge detection results More so, canny uses a double threshold for edge revelation and applies the Gaussian filter which removes of any noise from an image, unlike Sobel which is not resistant to noise. The developed prototype for this study can detect edges of real-time objects without the intervention of any sensor input.
Cyberphysical Security Through Resiliency: A Systems-Centric Approach

Cyber-physical systems (CPS) are often defended in the same manner as information technology (IT) systems -- by using perimeter security. Multiple factors make such defenses insufficient for CPS. Resiliency shows potential in overcoming these shortfalls. Techniques for achieving resilience exist; however, methods and theory for evaluating resilience in CPS are lacking. We argue that such methods and theory should assist stakeholders in deciding where and how to apply design patterns for resilience. Such a problem potentially involves tradeoffs between different objectives and criteria, and such decisions need to be driven by traceable, defensible, repeatable engineering evidence. Multi-criteria resiliency problems require a system-oriented approach that evaluates systems in the presence of threats as well as potential design solutions once vulnerabilities have been identified. We present a systems-oriented view of cyber-physical security, termed Mission Aware, that is based on a holistic understanding of mission goals, system dynamics, and risk.
TüEyeQ, a rich IQ test performance data set with eye movement, educational and socio-demographic information

We present the TüEyeQ data set - to the best of our knowledge - the most comprehensive data set generated on a culture fair intelligence test (CFT 20-R), i.e., an IQ Test, consisting of 56 single tasks, taken by 315 individuals aged between 18 and 30 years. In addition to socio-demographic and educational information, the data set also includes the eye movements of the individuals while taking the IQ test. Along with distributional information we also highlight the potential for predictive analysis on the TüEyeQ data set and report the most important covariates for predicting the performance of a participant on a given task along with their influence on the prediction.
A theory of universal learning

How quickly can a given class of concepts be learned from examples? It is common to measure the performance of a supervised machine learning algorithm by plotting its "learning curve", that is, the decay of the error rate as a function of the number of training examples. However, the classical theoretical framework for understanding learnability, the PAC model of Vapnik-Chervonenkis and Valiant, does not explain the behavior of learning curves: the distribution-free PAC model of learning can only bound the upper envelope of the learning curves over all possible data distributions. This does not match the practice of machine learning, where the data source is typically fixed in any given scenario, while the learner may choose the number of training examples on the basis of factors such as computational resources and desired accuracy.