Reviewerly/exHarmony huggingface dataset:
Test Coverage in Python Programs

We study code coverage in several popular Python projects: flask, matplotlib, pandas, scikit-learn, and scrapy. Coverage data on these projects is gathered and hosted on the Codecov website, from where this data can be mined. Using this data, and a syntactic parse of the code, we examine the effect of control flow structure, statement type (e.g., if, for) and code age on test coverage. We find that coverage depends on control flow structure, with more deeply nested statements being significantly less likely to be covered. This is a clear effect, which holds up in every project, even when controlling for the age of the line (as determined by git blame). We find that the age of a line per se has a small (but statistically significant) positive effect on coverage. Finally, we find that the kind of statement (try, if, except, raise, etc) has varying effects on coverage, with exception-handling statements being covered much less often. These results suggest that developers in Python projects have difficulty writing test sets that cover deeply-nested and error-handling statements, and might need assistance covering such code.
CV3: Visual Exploration, Assessment, and Comparison of CVs

Abstract The Curriculum Vitae (CV, also referred to as “résumé”) is an established representation of a person's academic and professional history. A typical CV is comprised of multiple sections associated with spatio‐temporal, nominal, hierarchical, and ordinal data. The main task of a recruiter is, given a job application with specific requirements, to compare and assess CVs in order to build a short list of promising candidates to interview. Commonly, this is done by viewing CVs in a side‐by‐side fashion. This becomes challenging when comparing more than two CVs, because the reader is required to switch attention between them. Furthermore, there is no guarantee that the CVs are structured similarly, thus making the overview cluttered and significantly slowing down the comparison process. In order to address these challenges, in this paper we propose “CV3”, an interactive exploration environment offering users a new way to explore, assess, and compare multiple CVs, to suggest suitable candidates for specific job requirements. We validate our system by means of domain expert feedback whose results highlight both the efficacy of our approach and its limitations. We learned that CV3 eases the overall burden of recruiters thereby assisting them in the selection process.
Online architecture for predicting live video transcoding resources

End users stream video increasingly from live broadcasters (via YouTube Live, Twitch etc.). Adaptive live video streaming is realised by transcoding different representations of the original video content. Management of transcoding resources creates costs for the service provider, because transcoding is a CPU-intensive task. Additionally, the content must be transcoded within real time with the transcoding resources in order to provide satisfying Quality of Service. The contribution of this paper is validation of an online architecture for enabling live video transcoding with Docker in a Kubernetes-based cloud environment. Particularly, online cloud resource allocation has been focused on by executing experiments in several configurations. The results indicate that Random Forest regressor provided the best overall performance in terms of precision regarding transcoding speed and CPU consumption on resources, and the amount of realised transcoding tasks. Reinforcement Learning provided lower performance, and required more effort in terms of training.
Capitalism, Crisis, and Educational Struggle in the Postdigital

This article explores how crises within capitalism tend to emerge as crises of realization. As the digitalization of capitalism is now presupposed or postdigital, the article explores how this developing context is impacting capitalism’s tendency toward crisis. Arguing that the digitalization of commerce is part of the neoliberalization of capitalism, the article situates the postdigital as not only about accumulation but also about part of a larger capitalist class offensive against the global proletarian class camp of the post–WWII era. Given this postdigital context, the article then explores the pedagogical implications and the pedagogical responses to what Jodi Dean has called communicative capitalism. Finally, the article examines instances of teacher rebellions and strikes as places where the communist educator might demonstrate solidarity and agitate against what is.
Research and Improvement of Isolation Forest in Detection of Local Anomaly Points

Abstract Three algorithms of classification-based, density-based, and isolation-based are researched and compared in this paper. It is concluded that Isolation Forest algorithm has characteristics of low time complexity and quantitative description of anomalies, which is obviously superior to other algorithms. However, it has disadvantage in detecting local anomaly point, which affects the accuracy of algorithm. Therefore, an improved algorithm based on Isolation Forest is proposed, of which the main idea is the K-means algorithm divides samples into different clusters, and the local anomalies before clustering are transformed into global anomalies of adjacent clusters, and finally the anomaly scores of the samples are calculated in each cluster. Experimental results are that the improved algorithm is better than Isolation Forest algorithm in detecting local anomaly points.
A comparative study about mobile learning with gamification and augmented reality in high education instituitions across South Europe, South America, and Asia countries

In current context, the society is being transforming itself in what genetically is named as digital society. The digital is taking over the organizations that perceive in technology an advancement/added value for their business. In the context of higher education as part of society, it is thus “forced” to change its operant mode in general and in particular in the teaching- learning process making it more digital, and at the same time more motivating for students. The aim of this study is to investigate the perception that higher education professors in the area of technology, across South Europe, South America, and Asia countries have about the use of mobile technologies such as gamification and augmented reality and how they can be used to promote student's engagement inside and outside of the classroom.
A navigation method for mobile robots using interval type-2 fuzzy neural network fitting Q-learning in unknown environments

This paper presents a navigation algorithm based on interval type-2 fuzzy neural network fitting Q-learning (IT2FNN-Q), and succeeds in providing a solution for mobile robot navigation in complex environments. The algorithm utilizes the fuzzy reasoning adaptive ability and extensive functional app roximation features of IT2FNN to solve this problem, mapped from state space to action space, of the Q-learning algorithm in unknown environments. Compared with the BP fitting Q-learning algorithm (BP-Q), IT2FNN-Q endows the robot with better adaptive and real-time decision-making abilities and solves the slow convergence and nonconvergence problems, through its local approximation. By comparison with the fuzzy neural network fitting Q-learning algorithm (FNN-Q), this proposed algorithm has more advantages for dealing with the external uncertainty, enabling the robot to complete a better path with less fuzzy rules. The results of the simulation and comparison of the proposed method with FNN-Q and BP-Q revealed that the mobile robot can navigate itself in complex environments with fewer steps, obtaining more reward values by adopting the algorithm presented in this paper.
Hyperledger Fabric Smart Grid Communication Testbed on Raspberry PI ARM Architecture

Necessary energy policy targets trying to limit climate change effects are very ambitious. New technologies and combinations of available technologies are needed to achieve the goals by 2030/2050. A promising technology being explored in smart grids communications is the Blockchain, allowing peer-to-peer contractual-like or monetary-like interactions without the need for trust for an intermediary. This paper describes a Smart Grid Controller Blockchain Platform, allowing to simulate Virtual Power Plants communication along load profiles or smart meter measurement data. The communication requirements of these recent advances are similar to industrial cyber-physical systems, hence, applicable in other research domains.
GSCPM: CPM-Based Group Spamming Detection in Online Product Reviews

Online product review is becoming one of important reference indicators for people shopping, but the current product review site contains a lot of fraudulent reviews. Group review spamming, which involves a group of fraudulent reviewers writing a lot of fraudulent reviews for one or more target products, becomes the main form of review spamming. However, solutions for group spammer detection are very limited, and due to lack of ground-truth review data, this problem has never been completely solved. In this paper, we propose a novel three-step method to detect group spammers based on Clique Percolation Method (CPM) in a completely unsupervised way, called GSCPM. First, it utilizes clues from behavioral data (timestamp, rating) and relational data (network) to construct a suspicious reviewer graph. Then, it breaks the whole suspicious reviewer graph into k-clique clusters based on CPM, and we consider such k-clique clusters as highly suspicious candidate group spammers. Finally, it ranks candidate groups by group-based and individual-based spam indicators. We use three real-world review datasets from Yelp.com to verify the performance of our proposed method. Experimental results show that our proposed method outperforms four compared methods in terms of prediction precision.
Decentralization is Vulnerable Under the Gap Game

The blockchain is the core mechanism of Bitcoin, which is mainly enabled by a distributed consensus mechanism. Essentially, in Proof-of-Work-based consensus protocol, the miners generate a series of blocks to realize decentralization practical. Miners receive two types of revenue: block rewards and transaction fees, in which the reward of block drops off as time goes on. Carlsten et al. defined a mining gap and then Tsabary et al. analyzed the gap game exploring how mining gaps form. In this paper, we analyze the other implications of the gap game. First, it is well known that the security of Bitcoin decentralized consensus protocol relies on miners behaving correctly. The security of the blockchain system will be threatened in case of the consensus mechanism is breached. Therefore, we also described how the gap game impacts the decentralization of Bitcoin and the stability of blockchain. Second, to confirm the implication, we discuss what aspects of decentralization can be impacted by the gap game; then we defined a new decentralization model and listed its main features. In the end, we analyze the block reward capacity in the blockchain and consider the impact on the existing two common block reward systems when the gap game was formed.
Development of a Heuristic Evaluation Tool for Voice User Interfaces

Voice user interfaces (VUIs) are now a common means of interaction with IT systems. To assist in evaluating the usability of such interfaces, a set of evaluation heuristics assessing speech interfaces was developed by following an existing methodology for defining usability heuristics. Two groups of eight participants conducted an evaluation by inspection of three speech-based systems, a mobile phone assistant, a smart speaker and an in-car hands free phone system. One group used Nielsen and Molich’s general heuristics for user interface design while the other group used the VUI heuristics. The second group found, on average, more problems than the first group. However, most heuristics from both sets were rated as useful during the study. This indicates that a mixture of both general and application specific heuristics are needed for a comprehensive evaluation to be performed. Experience from a pilot study, where a smart speaker was set up in a domestic setting, highlighted the need to also consider social and environmental issues to gain a complete picture of user experience when interacting with speech systems.
Towards Facial Expression Analysis in a Driver Assistance System

We present first steps to enable facial expression recognition (FER) in the Smart Eye driver monitoring systems (DMS). To tailor the system to our specific active IR illumination, we record our own multi-camera expression database that is effectively annotated with bounding boxes around the face using the DMS. We compare different deep convolutional neural network architectures suitable for embedded devices on the classification task.
Time Series Imputation via Integration of Revealed Information Based on the Residual Shortcut Connection

Recovering missing values plays a significant role in time series tasks in practical applications.How to replace the missing data and build the dependency relations from the incomplete sample set is still a challenge.The previous research has found that residual network (ResNet) helps to form a deep network and cope with degradation problem by shortcut connection.Gated recurrent unit (GRU) can improve network model and reduce training parameters by update gate which takes the place of forgetting gate and output gate in long short-term memory (LSTM).Inspired by this finding, we observe that shortcut connection and mean of global revealed information can model the relationship among missing items, the previous and overall revealed information.Hence, we design an imputation network with decay factor for shortcut connection and mean of the global revealed information in GRU, called decay residual mean imputation GRU (DRMI-GRU).We introduce a decay residual mean unit (DRMU), which takes full advantage of the previous and global revealed information to model incomplete time series; and the decay factor is applied to balance the previous long-term dependencies and all non-missing values in the sample set.In addition, a mask unit is designed to check the missing data existing or not.An extensive body of empirical comparisons with other existing imputation algorithms over real-world data and public dataset with different ratio of missing data verifies the performance of our model.
Security Requirements Engineering in Safety-Critical Railway Signalling Networks

Securing a safety-critical system is a challenging task, because safety requirements have to be considered alongside security controls. We report on our experience to develop a security architecture for railway signalling systems starting from the bare safety-critical system that requires protection. We use a threat-based approach to determine security risk acceptance criteria and derive security requirements. We discuss the executed process and make suggestions for improvements. Based on the security requirements, we develop a security architecture. The architecture is based on a hardware platform that provides the resources required for safety as well as security applications and is able to run these applications of mixed-criticality (safety-critical applications and other applications run on the same device). To achieve this, we apply the MILS approach, a separation-based high-assurance security architecture to simplify the safety case and security case of our approach. We describe the assurance requirements of the separation kernel subcomponent, which represents the key component of the MILS architecture. We further discuss the security measures of our architecture that are included to protect the safety-critical application from cyberattacks.
Detection of Double Compressed HEVC Videos Using GOP-Based PU Type Statistics

Double compression detection is of great significance to recover the compression history of suspicious videos and help analyzers locate tampering points in forged videos. However, the lack of efficient methods still haunts researchers when it comes to double HEVC compression detection with a shifted group of pictures (GOP) structures. To tackle this problem, we first analyze the properties of re-encoded frames in double compressed HEVC videos. It is found that the abnormal statistics of prediction unit (PU) types in relocated I-frames can be used as the clue to expose double compression. Relocated I-frames are re-encoded P-frames, which were I-frames in the first compression. Different from the existing works, we designed the GOP-based features instead of frame-based features, enabling more efficient utilization of temporal variation patterns of PU-type statistics within each GOP unit. In the proposed method, the ratio of I-PUs and S-Pus is first computed to construct PU sequences for each GOP unit. The three-tap median filter is applied to PU sequences in order to mitigate the influence of various video contents. Several first-order statistics, including average, variation, and maximum/minimum, are selected to generate a 6-D detection feature. Then, multi-layer perceptron (MLP) is used to classify GOP-based features. Finally, the score fusion strategy is applied to obtain the detection result of the input video. Several publicly available YUV sequences are used to construct the double compression dataset with various coding parameters. In experiments, the proposed method outperforms several state-of-the-art methods under different settings of bitrates and GOP structures. The proposed method has more robust detection capability even when tampered videos are generated by combining two video clips with different GOP sizes.
An Improved Optical Flow Algorithm Based on Mask-R-CNN and K-Means for Velocity Calculation

Aiming at enhancing the accuracy and reliability of velocity calculation in vision navigation, an improved method is proposed in this paper. The method integrates Mask-R-CNN (Mask Region-based Convolutional Neural Network) and K-Means with the pyramid Lucas Kanade algorithm in order to reduce the harmful effect of moving objects on velocity calculation. Firstly, Mask-R-CNN is used to recognize the objects which have motions relative to the ground and covers them with masks to enhance the similarity between pixels and to reduce the impacts of the noisy moving pixels. Then, the pyramid Lucas Kanade algorithm is used to calculate the optical flow value. Finally, the value is clustered by the K-Means algorithm to abandon the outliers, and vehicle velocity is calculated by the processed optical flow. The prominent advantages of the proposed algorithm are (i) decreasing the bad impacts to velocity calculation, due to the objects which have relative motions; (ii) obtaining the correct optical flow sets and velocity calculation outputs with less fluctuation; and (iii) the applicability enhancement of the optical flow algorithm in complex navigation environment. The proposed algorithm is tested by actual experiments. Results with superior precision and reliability show the feasibility and effectiveness of the proposed method for vehicle velocity calculation in vision navigation system.
Recognizing Human Activities From Video Using Weakly Supervised Contextual Features

Human activity recognition from the video is an important problem due to its potential applications in remote surveillance, content-based video retrieval, and in humanoid robots. Most of the visual activity recognition research in the last decade focus on recognizing basic human actions from well-constrained laboratory videos and depend upon fully annotated video dataset for model training. Whereas activity recognition from unconstrained videos is still very challenging due to large variations in object appearance and pose, occlusion, and inter and intraclass variations. It is an extremely laborious task to prepare large scale realistic video activity dataset with detailed annotations of the human, object, and their mutual interactions in each frame. Although it is intuitive to model contextual relationships form fully annotated dataset, however, it is unknown as to how reliably multilevel contextual features can be extracted in the absence of annotated dataset. To mitigate these challenges, we propose a weakly supervised approach for complex human activity recognition from realistic videos. The proposed approach requires only activity labels for each video to train the model. A novel multilevel contextual features and context estimation procedure from the un-annotated dataset is also introduced. Restricted Boltzman machine is used to systematically integrate multilevel contextual features. We evaluate the proposed approach on benchmark realistic surveillance video datasets for human-human and human-object interaction activity recognition. The experimental results show improved accuracies on benchmark datasets without using fully annotated datasets.
Post-processing for intra coding through perceptual adversarial learning and progressive refinement

Video compression is an indispensable technology in image/video communications, due to its highly desirable ability to reduce the huge data volume. However, video compression introduces complex compression artifacts, such as blocking, ringing, and blurring artifacts. To reduce this issue, post-processing techniques have been extensively studied. In this paper, we propose a novel post-processing technique using multi-level progressive refinement network, which we term MPRNet. A joint training loss function is defined to optimize MPRNet via an adversarial training approach, which can boost both subjective and objective visual quality. Furthermore, our network employs progressive refine strategy, which predicts multi-level residues in one feed-forward pass. This way of coarse-to-fine refinement manner allows our network to make trade-off between refined quality and computational complexity, which facilitates the resource-aware applications. Extensive evaluations on benchmark datasets verify the superiority of our proposed MPRNet model over the latest state-of-the-art methods, achieves 8.2%, 8.3% and 9.5% of BD-rate savings for the Y/Cb/Cr channel over the HEVC baseline respectively.
GCN-TC: Combining Trace Graph with Statistical Features for Network Traffic Classification

For machine-learning-based network traffic classification, we usually need large number of correctly labeled samples (ground truth) for model-training to get high accuracy. However in practical environments obtaining ground truth may be time-consuming and needs massive manual work, so achieving higher accuracy at low labeling rate is still a tricky problem. In this paper we propose a novel Graph Convolutional Network (GCN) based network traffic classification method named GCN-TC. We combine the traffic trace graph with statistical features in GCN model-training, so as to take advantage of both of them to achieve higher classification accuracy with very few labeled data. Experiment results show that the proposed model achieves the best performance with 13% higher in Fl-score and 7.7% higher in accuracy than the best method of the rest.