Reviewerly/exHarmony huggingface dataset:
variED: an editor for collaborative, real-time feature modeling

Abstract Feature models are a helpful means to document, manage, maintain, and configure the variability of a software system, and thus are a core artifact in software product-line engineering. Due to the various purposes of feature models, they can be a cross-cutting concern in an organization, integrating technical and business aspects. For this reason, various stakeholders (e.g., developers and consultants) may get involved into modeling the features of a software product line. Currently, collaboration in such a scenario can only be done with face-to-face meetings or by combining single-user feature-model editors with additional communication and version-control systems. While face-to-face meetings are often costly and impractical, using version-control systems can cause merge conflicts and inconsistency within a model, due to the different intentions of the involved stakeholders. Advanced tools that solve these problems by enabling collaborative, real-time feature modeling, analogous to Google Docs or Overleaf for text editing, are missing. In this article, we build on a previous paper and describe (1) the extended formal foundations of collaborative, real-time feature modeling, (2) our conflict resolution algorithm in more detail, (3) proofs that our formalization converges and preserves causality as well as user intentions, (4) the implementation of our prototype, and (5) the results of an empirical evaluation to assess the prototype’s usability. Our contributions provide the basis for advancing existing feature-modeling tools and practices to support collaborative feature modeling. The results of our evaluation show that our prototype is considered helpful and valuable by 17 users, also indicating potential for extending our tool and opportunities for new research directions.
Towards Deep Reasoning on Social Rules for Socially Aware Navigation

This work presents ideation and preliminary results of using contextual information and information of the objects present in the scene to query applicable social navigation rules for the sensed context. Prior work in socially-Aware Navigation (SAN) shows its importance in human-robot interaction as it improves the interaction quality, safety and comfort of the interacting partner. In this work, we are interested in automatic detection of social rules in SAN and we present three major components of our method, namely: a Convolutional Neural Network-based context classifier that can autonomously perceive contextual information using camera input; a YOLO-based object detection to localize objects with a scene; and a knowledge base of social rules relationships with the concepts to query them using both contextual and detected objects in the scene. Our preliminary results suggest that our approach can observe an on-going interaction, given an image input, and use that information to query the social navigation rules required in that particular context.
Research of Features and Possibilities of Modern Real Time Video Services

Modern trends in telecommunications are aimed at the applying of science-intensive technologies to the various classes of video information services. Such services gained high popularity during last decade in different spheres of human life, like entertainment segment, and using video-conferences for work or study. At the same time, due to the technical characteristics and limitations of modern communication channels, the requirements needed to ensure high quality of video services that use modern information and communication systems can not be fully provided. Reducing time delays in the delivery of digital video presentations by services that use infocommunication technologies to improve the video quality is an important scientific and applied problam. The method for solving this problem is presented in the article. It is shown and proven that an imbalance between the technical capabilities of modern information and communication systems (ICS) like the data transfer speed, and the rapidly growing requirements for high-quality real time video services is very large, especially for mobile segment and wireless connections. An information model to represent the set of influential elements of video coherence domains in the form of structural-positional numbers with the presence of properties of global and local inequality of adjacent elements is created. A model for evaluating of the informativeness of video areas based on the identification and formation of the set of influential coherence areas is built.
Optimal Approximation Rate of ReLU Networks in terms of Width and Depth

This paper concentrates on the approximation power of deep feed-forward neural networks in terms of width and depth. It is proved by construction that ReLU networks with width $\mathcal{O}\big(\max\{d\lfloor N^{1/d}\rfloor,\, N+2\}\big)$ and depth $\mathcal{O}(L)$ can approximate a Holder continuous function on $[0,1]^d$ with an approximation rate $\mathcal{O}\big(\lambda\sqrt{d} (N^2L^2\ln N)^{-\alpha/d}\big)$, where $\alpha\in (0,1]$ and $\lambda>0$ are Holder order and constant, respectively. Such a rate is optimal up to a constant in terms of width and depth separately, while existing results are only nearly optimal without the logarithmic factor in the approximation rate. More generally, for an arbitrary continuous function $f$ on $[0,1]^d$, the approximation rate becomes $\mathcal{O}\big(\,\sqrt{d}\,\omega_f\big( (N^2L^2\ln N)^{-1/d}\big)\,\big)$, where $\omega_f(\cdot)$ is the modulus of continuity. We also extend our analysis to any continuous function $f$ on a bounded set. Particularly, if ReLU networks with depth $31$ and width $\mathcal{O}(N)$ are used to approximate one-dimensional Lipschitz continuous functions on $[0,1]$ with a Lipschitz constant $\lambda>0$, the approximation rate in terms of the total number of parameters, $W=\mathcal{O}(N^2)$, becomes $\mathcal{O}(\tfrac{\lambda}{W\ln W})$, which has not been discovered in the literature for fixed-depth ReLU networks.
Adversarial Laser Beam: Effective Physical-World Attack to DNNs in a Blink

Though it is well known that the performance of deep neural networks (DNNs) degrades under certain light conditions, there exists no study on the threats of light beams emitted from some physical source as adversarial attacker on DNNs in a real-world scenario. In this work, we show by simply using a laser beam that DNNs are easily fooled. To this end, we propose a novel attack method called Adversarial Laser Beam ($AdvLB$), which enables manipulation of laser beam's physical parameters to perform adversarial attack. Experiments demonstrate the effectiveness of our proposed approach in both digital- and physical-settings. We further empirically analyze the evaluation results and reveal that the proposed laser beam attack may lead to some interesting prediction errors of the state-of-the-art DNNs. We envisage that the proposed $AdvLB$ method enriches the current family of adversarial attacks and builds the foundation for future robustness studies for light.
Learning to Simulate Complex Scenes for Street Scene Segmentation

Data simulation engines like Unity are becoming an increasingly important data source that allows us to acquire ground truth labels conveniently. Moreover, we can flexibly edit the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">content</i> of an image in the engine, such as objects (position, orientation) and environments (illumination, occlusion). When using simulated data as training sets, its editable content can be leveraged to mimick the distribution of real-world data, and thus reduce the content difference between the synthetic and real domains. This paper explores content adaptation in the context of semantic segmentation, where the complex street scenes are fully synthesized using 19 classes of virtual objects from a first person driver perspective and controlled by 23 attributes. To optimize the attribute values and obtain a training set of similar content to real-world data, we propose a scalable discretization-and-relaxation (SDR) approach. Under a reinforcement learning framework, we formulate attribute optimization as a random-to-optimized mapping problem using a neural network. Our method has three characteristics. 1) Instead of editing attributes of individual objects, we focus on global attributes that have large influence on the scene structure, such as object density and illumination. 2) Attributes are quantized to discrete values, so as to reduce search space and training complexity. 3) Correlated attributes are jointly optimized in a group, so as to avoid meaningless scene structures and find better convergence points. Experiment shows our system can generate reasonable and useful scenes, from which we obtain promising real-world segmentation accuracy compared with existing synthetic training sets.
Portrait-Based Academic Performance Evaluation of College Students from the Perspective of Big Data

With the advent of the big data era, significant changes have taken place in every aspect of education. To effectively evaluate the academic performance of college students, this paper firstly establishes a scientific evaluation index system for student portrait. Taking the course Object-Oriented Programming as an example, the authors collected various data on the academic performance of college students. The collected data were normalized, and the weight of each evaluation index was determined through analytic hierarchy process (AHP). Next, a fuzzy evaluation model was constructed based on big data, and used to assess each dimension of college students’ academic performance. The evaluation reveals the problems of college students in learning and practice, and helps to generate the portrait of each student. The research results promote the realization of personalized education.
Implementation of Multi-attribute Rating Technique Simple in Selection of Acceptance Scholarship of PMDK (Case Study: STMIK Widya Cipta Dharma)

This research aimed to create a decision support system for admission selection of PMDK scholarship pathway using the SMART (Simple Multi-Attribute Rating Technique) method with web-based and if this research was successful, it could help STMIK Widya Cipta Dharma to select PMDK scholarship should be given to a student of STMIK Widya Cipta Dharma.PMDK is the tittle of scholarship for interest and talent scouting.This research was conducted at STMIK Widya Cipta Dharma Samarinda, a method of data collection used interviews, that asked questions related to the selection of PMDK scholarship admissions by observation directly to STMIK Widya Cipta Dharma Samarinda.This research development system method used the development of a decision support system method.The software model used PHP programming language and database using MYSQL.The final result of this research is a decision support system for admission selection of the PMDK scholarship pathway using the SMART (Simple Multi-Attribute Rating Technique) method with web-based, can help selection of PMDK scholarship faster by admin, and provide information to students.So contribution of this paper is gives a best recommendation for selection of PMDK scholarship using decision support systems.
Implementation of Swarm Intelligence Algorithms for Path Planning

Abstract The ability to navigate through an unknown terrain is an essential metric in evaluating the performance of multi-robot systems. With the use of swarm intelligence algorithms like Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO), even robots with limited capability can perform this task. In this paper, we implement ACO and PSO to solve the path planning problem, and we also compare their efficiencies in different environments. This paper also implements a new hybrid algorithm that utilizes both ACO and PSO for more efficient path planning.
Vibration-based damage imaging via high-speed cameras with 3D digital image correlation using wavelet transform

Advances in computer prerformance, optical devices and digital imaging technology have allowed digital image correlation methods to measure the shape and deformation of the vibrating structures. This paper presents a novel vision-based damage detection framework which can eliminate the sinusoidal shaker step along with a magnified, precise modal shape data especially for higher modes that typical DIC algorithm can hardly detect. For this, an aluminum plate has been selected to make a comparative study on the performance of serval vibration-based damage detection techniques, namely, curvature mode shapes , Gapped smoothing techniques Generalized Fractal Dimension, Modal Strain Energy and Wavelet Transform.
Personal Data Protection in Blockchain with Zero-Knowledge Proof

Blockchain is a trending research topic and provides a high level of security, anonymity, and data integrity without a trust. One of the usage areas of blockchain is personal data protection. There are solutions proposed in literature for this specific need because personal data protection is still a problem in this big data era. It is obvious that the more personal and sensitive data are collected, the harder the problem to keep them secure, stable, and under control. Blockchain comes with significant advantages like verified and trackable transactions which allow anyone to follow the asset trivially. In addition to this, blockchain can be a tool to keep data itself anonymously. The researchers take advantages from blockchain to create more secure personal data protection systems. In this study, we are looking for the best personal data protection system with blockchain solutions. So we summarize proposed models and realize that a model based on off-blockchain with zero-knowledge proof is the best choice. Nowadays, zero-knowledge proof method’s usage on blockchain is popular. We explain in this study why it is the best choice based on personal data protection and how we can benefit from it. The goal of this study is to obtain a general view of the characteristics and usefulness of blockchain for more secure personal data protection reported in scientific literature and used in industry.
Intelligent Augmented Reality System based on Speech Recognition

In general, most of the current augmented reality systems can combine 3D virtual scenes with live reality, and users usually interact with 3D objects of the augmented reality (AR) system through image recognition. Although the image-recognition technology has matured enough to allow users to interact with the system, the interaction process is usually limited by the number of patterns used to identify the image. It is not convenient to handle. To provide a more flexible interactive manipulation mode, this study imports the speech-recognition mechanism that allows users to operate 3D objects in an AR system simply by speech. In terms of implementation, the program uses Unity3D as the main development environment and the AR e-Desk as the main development platform. The AR e-Desk interacts through the identification mechanism of the reacTIVision and its markers. We use Unity3D to build the required 3D virtual scenes and objects in the AR e-Desk and import the Google Cloud Speech suite to the AR e-Desk system to develop the speech-interaction mechanism. Then, the intelligent AR system is developed.
Design and Implementation of Smart Ocean Visualization System Based on Extended Reality Technology

In the context of building a maritime power, building a smart ocean is one of the important means to promote ocean development. However, there is currently a lack of effective smart solutions for ocean development to integrate and manage ocean information. To solve the problem of insufficient development of smart ocean systems, a smart ocean visualization app based on extended reality technology has been developed, using Python crawler technology to collect ocean big data, and produce the system through Unity software. The app is built using C# programming, and the AR animation on the app is realized with the AR Foundation plug-in. Through terminals such as mobile phones or computers, it provides users with real-time ocean data query and expanded realistic ocean tourism services. The visualization function of the system realizes innovation in the way of querying marine data and makes up for the lack of development of smart marine apps.
Net Neutrality: Unexpected Solution to Blockchain Scaling

Provably neutral clouds are undoubtedly a viable solution to blockchain scaling. By optimizing the transport layer, not only can the throughput be fundamentally scaled up, but the latency could be dramatically reduced. Indeed, the latency distribution in today’s data centers is already biased toward microsecond timescales for most of the flows, with millisecond timescales residing only at the tail of the distribution. There is no reason why a BDN point of presence would not be able to achieve a similar performance. Adding dedicated optical infrastructure among such BDN points of presence would further alleviate throughput and reduce latency, creating the backbone of an advanced BDN. The key to this vision, however, lies in establishing trust by the blockchain ecosystem into the underlying networking infrastructure. This, in turn, is achieved by decoupling authority from infrastructure via a provably neutral network design.
Breast Cancer Histopathology Image Classification Using Soft Voting Classifier

Breast cancer is the reason of mortality rate among women worldwide. Breast cancer survival rate and mortality rate can be improved if it is identified at an initial stage. However, it is a very tough task to classify histopathology images. Therefore, computer-aided detection using various deep learning models is used to categorize the abnormalities in images. In this research, a soft computing classifier for 7-CNN model has been proposed for breast cancer histopathology image classification. The proposed methodology uses the basic CNN with four convolutional layers, basic CNN with five-layer CNN (with data augmentation), VGG 19 transfer learned model (with and without data augmentation), VGG 16 transfer learned model (without data augmentation) and Xception transfer learner model (with and without data augmentation). It uses seven models, and all the seven models have been used to make predictions. The datasets used in the research are hematoxylin–eosin (H&E) for experimentation. The performance of each model has been compared on the basis of accuracy, precision, recall and F1-score. The accuracy has been taken as the main evaluation criteria. The proposed methodology has achieved maximum accuracy of 96.91% on H&E dataset. The accuracy of proposed methodology has been compared with all the transfer learned models.
Challenges of Teaching Programming in StackOverflow Era

Teaching computer programming has changed a lot during the last 15 years. Instead of visiting lectures, reading books, documentation and manuals, solving individual programming tasks, etc., students prefer to “find” solutions to all problems in Google, StackOverflow and similar forums, YouTube videos and other helpful tools. It could be enjoyed, if there were no negative side effects: programs are created without proper understanding using “copy and paste” from different sources and brute force hacks to comply them to the tests. Often these internet resources are incomplete, contain errors and do not fit to solve the given problem. This article covers some techniques used by the author in the following areas: Daily teaching in a rapidly changing environment necessitates constant updating of teaching methods using peer experience, continuous student feedback, professional publications and common sense. Author has tried “flipped classroom” and “work in pairs” approaches combined with automated assessment and version control for a medium level programming course (“Algorithms and Data Structures”). The results were impressive - dropout decreased from 54% to 17% in two years.
Design and Implementation of College English Teaching Quality Evaluation System Based on Analytic Hierarchy Process

In the design and development of college English system, the analytic hierarchy process is used to analyze the system application, so that we can use the idea of software engineering to solve practical problems. The author has a good grasp of the system development method of ASP.NET structure, an in-depth understanding of the current college English teaching management process, and an in-depth understanding of the needs of teaching quality evaluation. Based on the research and design of the system, we can grasp the current situation, current problems and development direction of the college English teaching evaluation and analysis system, so as to improve it.
Book Review: Smart City Citizenship

Against the backdrop of the current hyperconnected and highly virialised post-COVID-19 societies, we, ‘pandemic citizens’, wherever we are located now, have already become tiny chips inside an algorithmic giant system that nobody really understands. Furthermore, over the last decade, the increasing propagation of sensors and data collections machines and data collections machines in the so-called Smart Cities by both the public and the private sector has created democratic challenges around AI, surveillance capitalism, and protecting citizens’ digital rights to privacy and ownership. Consequently, the demise of democracy is clearly already one of the biggest policy challenges of our time, and the undermining of citizens’ digital rights is part of this issue, particularly when many ‘pandemic citizens’ will likely be unemployed during the COVID-19 crisis. Amidst the AI-driven algorithmic disruption and surveillance capitalism, this book review sheds light on the way citizens take control of the Smart City, and not viceversa, by revolving around the new book entitled Smart City Citizenship recently published by Elsevier. The book review introduces nine key ideas including how to (1) deconstruct, (2) unplug, (3) decipher, (4) democratise, (5) replicate, (6) devolve, (7) commonise, (8) protect, and (9) reset Smart City Citizenship.
CMSRAS: A Novel Chaotic Multi-Specular Reflection Optimization Algorithm Considering Shared Nodes

Specular reflection algorithm (SRA) was a single population meta-heuristic algorithm inspired by the physical function of mirror. However, similar to most of meta-heuristic algorithms, it had the disadvantages of weak population diversity, stagnation in local optimal and low convergence rate. In order to overcome these shortcomings, a chaotic multi-specular reflection optimization algorithm considering shared nodes (CMSRAS) was proposed by the combination of population strategy with shared node, improved Tent chaos strategy and Gaussian mutation strategy. Initially, a single population SRA was extended to the multi-population with shared node and the population was initialized by improved Tent chaos sequence to improve the quality of the initial solution and the population diversity, and to enhance the global search ability. Meanwhile, to strengthen the local search ability and the convergence accuracy, the Gaussian mutation and improved Tent chaotic disturbance strategy were introduced into SRA. And then the influence law and sensitivity analysis of the CMSRAS algorithm between the initial setting parameters were obtained based on the response surface method and the Sobol's method. Finally, compared with both 12 state-of-the-art algorithms and 8 well-known advanced algorithms, the performance of CMSRAS was evaluated on a comprehensive set of 32 benchmark problems. In addition, CMSRAS was applied to solve the complex optimization problems of engineering structure. The results demonstrated that proposed CMSRAS algorithm outperformed most competitive algorithms and efficiently solve the real-life global optimization problems.