Reviewerly/exHarmony huggingface dataset:
Learning attention-based representations from multiple patterns for relation prediction in knowledge graphs

Knowledge bases, and their representations in the form of knowledge graphs (KGs), are naturally incomplete. Since scientific and industrial applications have extensively adopted them, there is a high demand for solutions that complete their information. Several recent works tackle this challenge by learning embeddings for entities and relations, then employing them to predict new relations among the entities. Despite their aggrandizement, most of those methods focus only on the local neighbors of a relation to learn the embeddings. As a result, they may fail to capture the KGs’ context information by neglecting long-term dependencies and the propagation of entities’ semantics. In this manuscript, we propose ÆMP ( A ttention-based E mbeddings from M ultiple P atterns), a novel model for learning contextualized representations by: (i) acquiring entities context information through an attention-enhanced message-passing scheme, which captures the entities local semantics while focusing on different aspects of their neighborhood; and (ii) capturing the semantic context, by leveraging the paths and their relationships between entities. Our empirical findings draw insights into how attention mechanisms can improve entities’ context representation and how combining entities and semantic path contexts improves the general representation of entities and the relation predictions. Experimental results on several large and small knowledge graph benchmarks show that ÆMP either outperforms or competes with state-of-the-art relation prediction methods. • Attention-based Embeddings from Multiple Patterns of a Knowledge Graph to learn contextualized representations. • Attention-enhanced message-passing to capture local semantics but focusing on different aspects of the entity neighborhood. • Experimental results with large and small knowledge graphs.
Text-based Emotion Recognition using Sentiment Analysis

The reason for sentiment analysis is to distinguish the fundamental mentalities of individuals locally. The Internet is where clients of the world put their perspectives to various things. They talk about regular issues, whine about what they use, and offer positive or negative viewpoints about the item. This makes the Internet a significant source of data for opinion mining and sentiment analysis. In Software engineering, sentiment analysis has many purposes such as, recognizing thoughts that will further develop customers message conveyance and distinguishing their thoughts on a composed email. Furthermore, phenomenal composed data must be utilized for few articulations, accordingly lessening the possibilities of accurate analysis. The authors have gathered data on the utilization of instant messages that can be utilized to determine such issues. The fundamental reason for the task is to offer a concise composed expression, which is just enthusiastic. The proposed model aims to utilize two strategies and to realize what works best. The hardest part in sentiment analysis is figuring out the text(s) as per the thoughts. So, this model analysis data from corpora text and recognizes emotion out of it. In addition to customer satisfaction assessment and brand belief analysis, sentiment analysis through text is widely used currently.
A Cross-Company Ethnographic Study on Software Teams for DevOps and Microservices: Organization, Benefits, and Issues

Context: DevOps and microservices are acknowledged to be important new paradigms to tackle contemporary software demands and provide capabilities for rapid and reliable software development. Industrial reports show that they are quickly adopted together in massive software companies. However, because of the technical and organizational requirements, many difficulties against efficient implementation of the both emerge in real software teams. Objectives: This study aims to discover the organization, benefits and issues of software teams using DevOps & microservices from an immersive perspective. Method: An ethnographic study was carried out in three companies with different business, size, products, customers, and degree of globalization. All the three companies claimed their adoption of DevOps and microservices. Seven months (cumulative) of participant observations and nine interviews with practitioners were conducted to collect the data of software teams related to DevOps and microservices. A cross-company empirical investigation using grounded theory was done by analyzing the archive data. Results: The virtual software teams were organized for adopting DevOps and microservices under the stubborn organizational structure. The adoption of DevOps and microservices brings benefits to rapid delivery, ability improvements and burden reduction, whilst the high cost and lack of practical guidance were emerged. Two major issues of adopting DevOps and microservices in software teams (i.e. fragmentary DevOps and abuse of microservices) were found common in the companies. Moreover, our observations and interviews reflect that in software teams, the relationship between DevOps and microservices is not significant, which differs from the relationship described in the previous studies. Four lessons for practitioners and four implications for researchers were discussed based on our findings. Conclusion: Our findings contribute to the understanding of the organization, benefits and issues of adopting DevOps and microservices from an immersive perspective of software teams.
FPGA-driven High Density Photonic Reservoir Computing

We present a demo system of FPGA-driven time-delay photonic reservoir computer. The architecture of the reservoir is based on a high bandwidth density optical interconnect technologies using VCSEL and photodiode chips with multi-mode optical fiber splitter / combiner. Using the demo system, we also present preliminary results on voice utterance dataset.
On the Effectiveness of Machine Learning Experiment Management Tools

Machine learning experiment management tools support developers and data scientists on planning, tracking, and retrieving machine-learning experiments and assets when building intelligent software systems. Among others, they allow tracing back system behavior to experiment runs, for instance, when model performance drifts. Unfortunately, despite a surge of these tools, they are not well integrated with traditional software engineering tooling, and no hard empirical data exists on their effectiveness and value for users. We present a short research agenda and early results towards unified and effective software engineering and experiment management software.
Threshold selection of wavelet denoising based on optimization algorithms

The wavelet threshold denoising is widely used to suppress the interference of noise and improve the precision of signal processing. The selection of appropriate thresholding values applied to the decomposition coefficients is very critical for the effect of noise filtering. The issue of threshold selection can be converted to optimization tasks by using different algorithms. In this study, the Aquila optimizer (AO), the gradient-based optimizer (GBO) and the modified grey wolf optimizer (GNHGWO) were utilized to optimize the threshold values. The well-known benchmark signals such as Blocks, Bumps, Doppler and Heavy sine were used for verifying the effect of different methods. The denoised signals were evaluated by two indices of signal-to-noise ratio (SNR) and root mean square error (RMSE). The simulation results on four benchmark signals have shown that the AO, G-NHGWO, GBO optimization algorithms used in this study have exhibited an encouraging effectiveness and practicability in threshold selection of wavelet denoising.
Using a Semantic Knowledge Base to Improve the Management of Security Reports in Industrial DevOps Projects

Integrating security activities into the software development lifecycle to detect security flaws is essential for any project. These activities produce reports that must be managed and looped back to project stakeholders like developers to enable security improvements. This so-called Feedback Loop is a crucial part of any project and is required by various industrial security standards and models. However, the operation of this loop presents a variety of challenges. These challenges range from ensuring that feedback data is of sufficient quality over providing different stakeholders with the information they need to the enormous effort to manage the reports. In this paper, we propose a novel approach for treating findings from security activity reports as belief in a Knowledge Base (KB). By utilizing continuous logical inferences, we derive information necessary for practitioners and address existing challenges in the industry. This approach is currently evaluated in industrial DevOps projects, using data from continuous security testing.
H2RUN: An efficient vendor lock‐in solution for multi‐cloud environment using horse herd Runge Kutta based data placement optimization

Abstract Vendor lock‐in is a scenario where the cost of switching to another vendor is mainly high and for this reason, the consumer is mainly struck with a single cloud vendor. The vendor lock‐in mainly implies the situation of an organization where it is locked into a service or product of a single Cloud Service Providers (CSP) to prevent the disruption of the business operation or due to an insufficient workforce. Due to its high availability, portability, and other benefits, various researchers frequently adopt the multi‐cloud strategy to prevent vendor lock‐in. However, the multi‐cloud architecture also comes with major drawbacks such as cost, multiple CSP management, and the tradeoff between cost and availability. The organizations are mainly considerate about achieving the tradeoff between the cost and availability. To overcome this problem, we have proposed an H 2 RUN (Horse Herd Runge Kutta) optimization algorithm to achieve a tradeoff between the cost and availability using erasure coding. The proposed H 2 RUN optimization algorithm selects the optimal CSP and erasure coding parameters that enhance the availability of the CSP with minimal cost. The efficiency of the proposed methodology can be evaluated using the experiments conducted on different real‐time CSPs using various metrics. Based on the results achieved, the H 2 RUN optimizer minimizes the cost up to 48% and availability up to 58%.
Chaos and order in maker coaching

Ten years have passed since makerspaces first appeared in libraries, and their contribution to developing digital literacy and 21st-century skills is widely recognized. However, mounting and running a makerspace still means embarking into new territory for each next library, as this Dutch study with novice maker coaches showed.
An Empirical Study on Quality Issues of eBay's Big Data SQL Analytics Platform

Big data SQL analytics platform has evolved as the key infrastructure for business data analysis. Compared with traditional costly commercial RDBMS, scalable solutions with open-source projects, such as SQL-on-Hadoop, are more popular and attractive to enter-prises. In eBay, we build Carmel, a company-wide interactive SQL analytics platform based on Apache Spark. Carmel has been serving thousands of customers from hundreds of teams globally for more than 3 years. Meanwhile, despite the popularity of open-source based big data SQL analytics platforms, few empirical studies on service quality issues (e.g., job failure) were carried out for them. However, a deep understanding of service quality issues and taking right mitigation are significant to the ease of manual maintenance efforts. To fill this gap, we conduct a comprehensive empirical study on 1,884 real-word service quality issues from Carmel. We summa-rize the common symptoms and identify the root causes with typical cases. Stakeholders including system developers, researchers, and platform maintainers can benefit from our findings and implications. Furthermore, we also present lessons learned from critical cases in our daily practice, as well as insights to motivate automatic tool support and future research directions.
An Asynchronous Call Graph for JavaScript

Asynchronous JavaScript has become omnipresent, yet is inherently difficult to reason about. While many recent debugging tools are trying to address this issue with (semi-)automatic methods, interactive analysis tools are few and far between. To this date, developers are required to build mental models of complex concurrent control flows with little to no tool support. Thus, asynchrony is making life hard for novices and catches even seasoned developers off-guard, especially when dealing with unfamiliar code. That is why we propose the Asynchronous Call Graph. It is the first approach to capture and visualize concurrent control flow between call graph roots. It is also the first concurrency analysis tool for JavaScript that is fully interactive and integrated with an omniscient debugger in a popular IDE. First tests show that the ACG works successfully on real-world codebases. This approach has the potential to set a new standard for how developers can analyze asynchrony.
Autonomous Mobile Robot Navigation using Adaptive Neuro Fuzzy Inference System

Navigation of autonomous robots in unknown and cluttered environments lies among the marked trends in robotics. Unlike animals and humans, the collision-free movement of a robot is challenging and requires processing complex information. An autonomous robot needs to cope with a large amount of uncertainty while navigating. The previous methods have limitations, such as lacking obstacle avoidance behaviour, having a large number of governing rules, designing a separate controller for each navigation and obstacle avoidance, not considering the robot's dynamics, computationally expensive training, and poor performance in a cluttered environment. This paper proposes a method that comprises a single adaptive neuro fuzzy inference system (ANFIS) based controller with 16 rules compared to hundred of rules used by previous methods to address such problems. Our method takes heading angle along with distance sensors data as input. AU the inputs are fuzzified into linguistic variables such as near-far and left-right. Additionally, a fuzzy inference system (FIS) is designed and trained using the generated dataset for optimum performance of ANFIS. The proposed method efficiently provides collision-free navigation of the mobile robot in densely cluttered environments. Comprehensive experiments are performed to prove the robustness and potency of the proposed ANFIS controller. Moreover, the performance of the proposed method is compared with various previous methods. The results of these comparisons indicate our proposed method's superiority in finding a near-optimal path.
An Intelligent Penetration Test Simulation Environment Construction Method Incorporating Social Engineering Factors

The penetration test has many repetitive operations and requires advanced expert knowledge, therefore, the manual penetration test is inefficient. With the development of reinforcement learning, the intelligent penetration test has been a research hotspot. However, the existing intelligent penetration test simulation environments only focus on the exploits of target hosts by the penetration tester agent’s actions while ignoring the important role of social engineering in the penetration test in reality. In addition, the construction of the existing penetration test simulation environment is based on the traditional network graph model without integrating security factors and attributes, and it is difficult to express the interaction between the penetration tester and the target network. This paper constructs an improved network graph model for penetration test (NMPT), which integrates the relevant security attributes of the penetration test. The NMPT model lays the foundation for extending the penetration tester’s social engineering actions. Then, we propose an intelligent penetration test method that incorporates social engineering factors (SE-AIPT) based on the Markov Decision Process. We adopt several mainstream reinforcement learning algorithms to train attack agents. The experiments show that the SE-AIPT method could vividly model the penetration tester agent’s social engineering actions, which effectively improves the reality of the simulation environment. Moreover, the penetration tester agent shows superior effects in the attack path discovery in the intelligent penetration test simulation environment constructed by the SE-AIPT method.
Dozer: Migrating Shell Commands to Ansible Modules via Execution Profiling and Synthesis

Software developers frequently use the system shell to perform configuration management tasks. Unfortunately, the shell does not scale well to large systems, and configuration management tools like Ansible are more difficult to learn. We address this problem with Dozer, a technique to help developers push their shell commands into Ansible task definitions. It operates by tracing and comparing system calls to find Ansible modules with similar behaviors to shell commands, then generating and validating migrations to find the task which produces the most similar changes to the system. Dozer is syntax agnostic, which should allow it to generalize to other configuration management platforms. We evaluate Dozer using datasets from open source configuration scripts.
Scientific Landscape of Embodied Experience in the Virtual Environment: A Bibliometric Analysis

The realm of architecture has been influenced by the expansion in virtual environments (VEs), along with Industry 4.0 technologies transforming human–VE interactions. Despite increasing scholarly interest in embodied experience-integrated VE, there have been few comprehensive literature reviews undertaken on VEs from a holistic experience perspective. Therefore, this article reviews the literature on the embodied experiential dimension of VEs that has become necessary to adapt theories and methodologies in a way that enhances the user experience in a VE. This study employs a bibliometric analysis to review research performance and undertake a science mapping of the literature. The 969 pieces of data retrieved from Web of Science were subjected to a performance analysis, and VOSviewer was used to visualize the intellectual structure and research themes. The results of this study emphasize the strength and growing interest in VEs from the embodied experience perspective. Another significant finding is that VE experience studies are mostly based on embodied technologies. In this paper, the results of analyses are discussed in terms of productivity, collaboration, and research themes for future. This study contributes to the literature by providing a significant theoretical reference for the potential of the embodied experience in VE research, which will mostly attract the interest of architectural design researchers.
Deep residual-SVD network for brain image registration

Objective.Medical image registration aims to find the deformation field that can align two images in a spatial position. A medical image registration method based on U-Net architecture has been proposed currently. However, U-Net architecture has few training parameters, which leads to weak learning ability, and it ignores the adverse effects of image noise on the registration accuracy. The article aims at addressing the problem of weak network learning ability and the adverse effects of noisy images on registration.Approach.Here we propose a novel unsupervised 3D brain image registration framework, which introduces the residual unit and singular value decomposition (SVD) denoising layer on the U-Net architecture. Residual unit solves the problem of network degradation, that is, registration accuracy becomes saturated and then degrades rapidly with the increase in network depth. SVD denoising layer uses the estimated model order for SVD-based low-rank image reconstruction. we use Akaike information criterion to estimate the appropriate model order, which is used to remove noise components. We use the exponential linear unit (ELU) as the activation function, which is more robust to noise than other peers.Main results.The proposed method is evaluated on the publicly available brain MRI datasets: Mindboggle101 and LPBA40. Experimental results demonstrate our method outperforms several state-of-the-art methods for the metric of Dice Score. The mean number of folding voxels and registration time are comparable to state-of-the-art methods.Significance.This study shows that Deep Residual-SVD Network can improve registration accuracy. This study also demonstrate that the residual unit can enhance the learning ability of the network, the SVD denoising layer can denoise effectively, and the ELU is more robust to noise.
Feature compensation network based on non-uniform quantization of channels for digital image global manipulation forensics

With the popularity of image editing software and technology, it has become very easy to manipulate an image, which has challenged the authenticity and security of digital images. Digital Image Manipulation Forensics (DIMF) has become an important research direction to confirm the authenticity of images, detect the manipulations that images have undergone, and avoid the misuse of image editing. Currently, most DIMF targets the detection of multiple image manipulations with fixed parameters. However, we consider detecting image manipulation in a more complex scenario where the parameters are chosen to be arbitrary. In this paper, we propose a Feature Compensation Network (FCNet) based on non-uniform quantization of channels. Briefly, it contains three important parts: (1) feature enhancement block, which extracts and enhances valid information from low-level features and eliminates semantic gaps between them and the high-level features. (2) sensitivity estimation block, which obtains the importance coefficients of each channel and guides the non-uniform quantization of low-level features. (3) adaptive average pooling, which keeps the resolution of low-level features and high-level features consistent and ensures that subsequent feature fusion is appropriate. Through extensive experiments, we have demonstrated the effectiveness of the proposed method in detecting multiple image manipulations. • For global manipulation forensics, the spatial information of the low-level features can assist in extracting more manipulated traces, and the semantic information of the high-level features can help understand which manipulation the input image has undergone, and one cannot be without the other. • We propose a feature compensation network based on the non-uniform quantization of channels, which reuses low-level features to enhance the ability of the network to extract manipulated traces. • We design a feature enhancement module to extract manipulated traces in the low-level features and eliminate semantic gaps between the low-level features and the high-level features. At the same time, the importance of the features of different channels is estimated by using the sensitive property of the CNN.