Reviewerly/exHarmony huggingface dataset:
What is the adoption level of automated support for testing in open-source ecosystems?

In the last decades, different kinds of automated support for testing have emerged in the open-source community. However, we still have limited evidence about the adoption level of such automated support in practice, considering different programming language ecosystems. In this paper, we investigate the adoption of automated support for testing among 184 popular open-source projects. Besides, we also investigate test coverage and metrics correlations on 571 open-source projects. As results, we found that projects written in Go, PHP, and JavaScript are the ones that most adopt automated support and that JavaScript and Python projects have the largest test coverage, with, on average, 84% and 81%, respectively. Moreover, we also found overall negligible correlations between projects' amount of stars, commits and source lines of code and coverage. Knowing that an open-source project has a high test coverage may enhance users' confidence in using this project. Besides that, we also listed the testing tools, libraries or frameworks that are most adopted for each programming language ecosystem. It may help developers in choosing appropriate automated support. Finally, we established a research agenda on this topic that motivates deeper studies as future work.
The modification of the meta-language of normal forms of knowledge

The set of the relations of normal forms of knowledge (alternative, concatenation, negation and iteration) is expanded by the two new relations: commitment relation – non-zero number of repetitions of some structure, non-commitment relation of some structure and structure brackets. The introduction of new relations is implemented by describing their structures with the basic relations of the meta-language of normal forms of knowledge. The text and graphical descriptions of the meta-language of normal forms of knowledge are presented, extended by the stylistic relations of the information structure descriptions. The examples of graphical variants of representing the structures of new relations between terms, which are used to self-describe the modified meta-language of normal forms of knowledge are presented with the minimally required comments. Problems in programming 2019; 3: 03-10
Single-SPAD Implementation of Quantum Key Distribution

A cost-effective implementation of BB84 protocol for QKD, based on the use of a Faraday rotator variable over four states and a single SPAD, is presented.
Aggregation of Deep Features for Image Retrieval Based on Object Detection

Image retrieval can be tackled using deep features from pre trained Convolutional Neural Networks (CNN). The feature map from the last convolutional layer of a CNN encodes descriptive information from which a discriminative global descriptor can be obtained. However, this global descriptors combine all of the information of the image, giving equal importance to the background and the object of the query. We propose to use an object detection based on saliency models to identify relevant regions in the image and therefore obtain better image descriptors. We extend our proposal to multi-regional image representation and we combine our proposal with other spatial weighting measures. The descriptors derived from the salient regions improve the performance in three well known image retrieval datasets as we show in the experiments.
Towards Interpretable Deep Extreme Multi-Label Learning

Many Machine Learning algorithms, such as deep neural networks, have long been criticized for being "black-boxes"-a kind of models unable to provide how it arrive at a decision without further efforts to interpret. This problem has raised concerns on model applications' trust, safety, nondiscrimination, and other ethical issues. In this paper, we discuss the machine learning interpretability of a real-world application, eXtreme Multi-label Learning (XML), which involves learning models from annotated data with many pre-defined labels. We propose a two-step XML approach that combines deep non-negative autoencoder with other multi-label classifiers to tackle different data applications with a large number of labels. Our experimental result shows that the proposed approach is able to cope with many-label problems as well as to provide interpretable label hierarchies and dependencies that helps us understand how the model recognizes the existences of objects in an image.
Model analytics for industrial MDE ecosystems

Widespread adoption of Model-Driven Engineering (MDE) in industrial contexts, especially in large companies, leads to an abundance of MDE artifacts such as Domain-Specific Languages and models. ASML is an example of such a company where multidisciplinary teams work on various ecosystems with many languages and models. Automated analyses of those artifacts, e.g., for detecting duplication and cloning, can potentially aid the maintenance and evolution of those ecosystems. In this chapter, we explore a variety of model analytics approaches using our framework SAMOS in the industrial context of ASML ecosystems. We have performed case studies involving clone detection on ASML's data and control models within the ASOME ecosystem, cross-language conceptual analysis and language-level clone detection on three ecosystems, and finally architectural analysis and reconstruction on the CARM2G ecosystem. We discuss how model analytics can be used to discover insights in MDE ecosystems (e.g., via model clone detection and architectural analysis) and opportunities such as refactoring to improve them.
Simultaneously Determining Target Object and Transport Velocity for Manipulator and Moving Vehicle in Piece-Picking Operation

We propose a deep learning-based method that simultaneously determines a target object to be picked up by an autonomous manipulator and the velocity of an automated guided vehicle (AGV) that passes in front of the manipulator while the AGV carries a carton case containing the target and other objects. Our method can efficiently perform automated piece-picking operations in warehouses without the AGV needing to pause in front of the manipulator. In our method, for preparing supervised data sets with color images of objects that are randomly piled up in the carton case, a simulator checks whether each object is “pickable” or not by trying to plan the manipulator's motion to have its hand reach the object while avoiding surrounding obstacles by using the depth images in consideration of the carton case's movement and velocity. Then, we make each of multiple deep convolutional neural networks (DCNNs) corresponding to multiple levels of velocity learn to detect grasp points for only pickable objects from an RGB image. In our experimental test, using our method, a prototype of the system successfully picked ordered objects up without the AGV pausing while the AGV changed its velocity depending on the layout of the objects in the carton case.
Optimization of the Novelty Detection Model Based on LSTM Autoencoder for ICS Environment

The recent evolution in cybersecurity shows how vulnerable our technology is. In addition, contemporary society becoming more reliant on “vulnerable technology”. This is especially relevant in case of critical information infrastructure, which is vital to retain the functionality of modern society. Furthermore, the cyber-physical systems as Industrial control systems are an essential part of critical information infrastructure; and therefore, need to be protected. This article presents a comprehensive optimization methodology in the field of industrial network anomaly detection. We introduce a recurrent neural network preparation for a one-class classification task. In order to optimize the recurrent neural network, we adopted a genetic algorithm. The main goal is to create a robust predictive model in an unsupervised manner. Therefore, we use hyperparameter optimization according to the validation loss function, which defines how well the machine learning algorithm models the given data. To achieve this goal, we adopted multiple techniques as data preprocessing, feature reduction, genetic algorithm, etc.
Towards Efficient Compact Network Training on Edge-Devices

Currently, there is a trend to deploy training on edge devices, which is crucial to future AI applications in various scenarios with transfer and online learning demands. Specifically, there may be a severe degradation of accuracy when directly deploying the trained models on edge devices, because the local environment forms an edge local dataset that is often different from the generic dataset. However, training on edge devices with limited computing and memory capability is a challenge problem. In this paper, we propose a novel quantization training framework for efficient compact network training on edge devices. Firstly, training-aware symmetric quantization is introduced to quantize all of the data types in the training process. Then, channel-wise quantization method is adopted for comapact network quantization, which has significantly high tolerance to quantization errors and can make the training process more stable. For further efficient training, we build a hardware evaluation platform to evaluate different settings of the network, so as to achieve a better trade-off among accuracy, energy and latency. Finally, we evaluate two widely used compact networks on a domain adaptation dataset for image classification, and the results demonstrate that the proposed methods can allow us achieve an improvement of 8.4 × -17.2× in energy reduction and 11.9 × -16.3× in latency reduction compared with 32-bit implementations, while maintaining the classification accuracy.
The Loom Machines of <i>Boott Mill (Lowell)</i>: A Composition from the New England Soundscape Project

The author reports on the development of an original piece, Boott Mill (Lowell), in which he takes field recordings of loom machines from the Boott Mill Museum in Lowell, Massachusetts, and uses the recordings as the foundation for a fully realized composition featuring percussion, strings, keyboards and assorted musical textures.
Semantic scene understanding on mobile device with illumination invariance for the visually impaired

For Visually Impaired People (VIP), it’s very difficult to perceive their surroundings. To address this problem, we propose a scene understanding system to aid VIP in indoor and outdoor environments. Semantic segmentation performance is generally sensitive to the environment and illumination changes, including the change between indoor and outdoor environments and the change across different weather conditions. Meanwhile, most existing methods have paid more attention on either the accuracy or the efficiency, instead of the balance between both of them. In the proposed system, the training dataset is preprocessed by using an illumination-invariant transformation to weaken the impact of illumination changes and improve the robustness of the semantic segmentation network. Regarding the structure of semantic segmentation network, the lightweight networks such as MobileNetV2 and ShuffleNet V2 are employed as the backbone of DeepLabv3+ to improve the accuracy with little increasing of computation, which is suitable for mobile assistance device. We evaluate the robustness of the segmentation model across different environments on the Gardens Point Walking dataset, and demonstrate the extremely positive effect of the illumination-invariant pre-transformation in challenging real-world domain. The network trained on computer achieves a relatively high accuracy on ADE20K relabeled into 20 classes. The frame rate of the proposed system is up to 83 FPS on a 1080Ti GPU.
SoTaRePo: Society-Tag Relationship Protocol based architecture for UIP construction

As with the advancement of web services, there has been a rapid proliferation in web size and number of web users, where, each user holds a different viewpoint towards the same information. This, in turn, has become a big challenge for the web search platforms to interpret the preferences of the users and provide the desired information to them. The most suitable solution to the problem of search platforms is personalization of web search. A personalization system is a kind of expert and intelligent system which can automatically learn about the preferences of a user so that the system can provide the search results as per their relevance to a user. The process of acquiring knowledge about user’s preferences by a personalization system is known as User Interest Profile (UIP). In the field of search personalization, it can also not be denied that only an efficient and complete UIP can lead to an effective and high performing web search personalization methodology design. But most of the studies conducted for web search personalization have only focused on UIP modeling without any thought about the quality of UIP. Rather limited attention has been paid to sparsity issue of UIP modeling. In this paper, we propose a novel protocol based architecture model to create an efficient UIP by exploiting direct and indirect interest of a user. Direct interest aims at mining user’s preferences from his own activities on a social information platform. The explicitly defined society and real-world activity relationships of a user on a social platform are used to predict his indirect interest as UIP constructed solely on the basis of direct interest is sparse and ineffective. In order to unearth user’s activity relationships the concept of semantic relatedness, computed using Word2vec model, has been used. Moreover, different trust levels in society relationships have also been incorporated into the proposed model to facilitate the prediction of user’s indirect interest. A series of experiments have been conducted on a del.icio.us dataset to evaluate the effectiveness of the proposed model. The results show that the model has outperformed each and every baseline in relation to complete and efficient UIP construction.
C.S. Lewis’s Post‐Edenic Wanderings: Uncovering the Object of Longing

The Heythrop JournalVolume 61, Issue 4 p. 642-647 Article C.S. Lewis’s Post-Edenic Wanderings: Uncovering the Object of Longing George Harold Trudeau, The College at Southeastern, Wake Forest, North CarolinaSearch for more papers by this author George Harold Trudeau, The College at Southeastern, Wake Forest, North CarolinaSearch for more papers by this author First published: 16 September 2019 https://doi.org/10.1111/heyj.13342Citations: 1Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinked InRedditWechat Citing Literature Volume61, Issue4Special Issue: ApologeticsJuly 2020Pages 642-647 RelatedInformation
Reasoning in Highly Reactive Environments

The aim of my Ph.D. thesis concerns Reasoning in Highly Reactive Environments. As reasoning in highly reactive environments, we identify the setting in which a knowledge-based agent, with given goals, is deployed in an environment subject to repeated, sudden and possibly unknown changes. This is for instance the typical setting in which, e.g., artificial agents for video-games (the so called "bots"), cleaning robots, bomb clearing robots, and so on are deployed. In all these settings one can follow the classical approach in which the operations of the agent are distinguished in "sensing" the environment with proper interface devices, "thinking", and then behaving accordingly using proper actuators. In order to operate in an highly reactive environment, an artificial agent needs to be: 1. Responsive -> The agent must be able to react repeatedly and in a reasonable amount of time; 2. Elastic -> The agent must stay reactive also under varying workload; 3. Resilient -> The agent must stay responsive also in case of internal failure or failure of one of the programmed actions in the environment. Nowadays, thanks to new technologies in the field of Artificial Intelligence, it is already technically possible to create AI agents that are able to operate in reactive environments. Nevertheless, several issues stay unsolved, and are subject of ongoing research.
Application of Patient Similarity in Smart Health: A Case Study in Medical Education

Patient similarity relies on computations that synthesize EHRs (Electronic Health Records) to give personalized predictions, which inform diagnoses and treatments. Given the complexities in pre-processing EHRs, representing patient data and utilizing the most suitable similarity metrics and evaluation methods, patient similarity computations are far from the era of regular use in hospitals. This paper aims to both support further patient similarity research and to inform the importance of its application in medical education. It accomplishes this by examining relevant literature that offer techniques to tackle the computational challenges and by presenting their various applications in the healthcare industry.
Cyber security detection and monitoring at IHEP private cloud for web services

To improve hardware utilization and save manpower in system maintenance, most of the web services in IHEP have been migrated to a private cloud build upon OpenStack. However, cyber security attacks becomes a serious threats to the cloud progressively. Therefore, a cyber security detection and monitoring system is deployed for this cloud platform. This system collects various security related logs as data sources, and processes them in a framework composed of open source data store, analysis and visualization tools. With this system, security incidents and events can be handled in time and rapid response can be taken to protect cloud platform against cyber security threats.
The role of fixation disengagement in the parallel programming of sequences of saccades

One of the core mechanisms involved in the control of saccade responses to selected target stimuli is the disengagement from the current fixation location, so that the next saccade can be executed. To carry out everyday visual tasks, we make multiple eye movements that can be programmed in parallel. However, the role of disengagement in the parallel programming of saccades has not been examined. It is well established that the need for disengagement slows down saccadic response time. This may be important in allowing the system to program accurate eye movements and have a role to play in the control of multiple eye movements but as yet this remains untested. Here, we report two experiments that seek to examine whether fixation disengagement reduces saccade latencies when the task completion demands multiple saccade responses. A saccade contingent paradigm was employed and participants were asked to execute saccadic eye movements to a series of seven targets while manipulating when these targets were shown. This both promotes fixation disengagement and controls the extent that parallel programming can occur. We found that trial duration decreased as more targets were made available prior to fixation: this was a result both of a reduction in the number of saccades being executed and in their saccade latencies. This supports the view that even when fixation disengagement is not required, parallel programming of multiple sequential saccadic eye movements is still present. By comparison with previous published data, we demonstrate a substantial speeded of response times in these condition ("a gap effect") and that parallel programming is attenuated in these conditions.
An Improved Posteriori Variance-Covariance Components Estimation Applied to Unconventional GPS and Multiple Low-Cost Imus Integration Strategy

Meliorating a priori stochastic model of Kalman filer (KF) is always challenging. To address this challenge, this paper simultaneously estimates and corrects the variance components for all of the process noise and measurement matrix (Q & R) by a posteriori variance-covariance components estimation (VCE) algorithm, which makes the most of the process noise residuals and measurement residuals and measurement redundancy contribution. Unsurprisingly, in the conventional error states-based integration mechanization, the stochastic model tuning is not easy for IMU because of the error measurements between the observables from inertial sensors and other aiding sensors. This research utilizes an unconventional multi-sensor integration strategy, in which a 3D kinematic trajectory model is deployed as the main part of system equation and the systematic errors of each IMU and the measurements of all sensors are individually modelled. Furthermore, the weights of measurements from each inertial sensor are defined on the basis of the posterior variances, so that we could properly distribute the function of each measurement in the fusion algorithm. A real dataset involving GPS and multiple IMUs is processed to validate the proposed posteriori VCE algorithm by applying the unconventional integration strategy.
Billion-Scale Matrix Compression and Multiplication with Implications in Data Mining

Billion-scale Boolean matrices in the era of big data occupy storage that is measured in 100's of petabytes to zetabytes. The fundamental operation on these matrices for data mining involves multiplication which suffers a significant slow-down as the required data cannot fit in most main memories. In this paper, we propose new algorithms to perform Matrix-Vector and Matrix-Matrix operations directly on compressed Boolean matrices using innovative techniques extended from our previous work on compression. Our extension involves the development of a row-by-row differential compression technique which reduces the overall space requirement and the number of matrix operations. We have provided extensive empirical results on billion-scale Boolean matrices that are Boolean adjacency matrices of web graphs. Our work has significant implications on key problems such as page-ranking and itemset mining that use matrix multiplication.
Architecture for Context-Aware Systems

The ubiquitous computing devices, smartphones and applications are being used beyond traditional desktops. Diverse environment, distributed architecture and enhancement in the behaviour are the main challenges towards the emerging ubiquitous computing era. This primarily focuses on the design and development of context-aware applications. This chapter presents architectural requirements and various approaches to develop applications using context. This chapter also presents the standard reference model and application platform, design and development of context-aware algorithms along with issues and challenges. Last part of this chapter presents the importance of machine learning to develop context-aware applications and finally concludes with the case studies.