Reviewerly/exHarmony huggingface dataset:
RegLine

The process of verifying linear model assumptions and remedying associated violations is complex, even when dealing with simple linear regression. This process is not well supported by current tools and remains time-consuming, tedious, and error-prone. We present RegLine, a visual analytics tool supporting the iterative process of assumption verification and violation remedy for simple linear regression models. To identify the best possible model, RegLine helps novices perform data transformations, deal with extreme data points, analyze residuals, validate models by their assumptions, and compare and relate models visually. A qualitative user study indicates that these features of RegLine support the exploratory and refinement process of model building, even for those with little statistical expertise. These findings may guide visualization designs on how interactive visualizations can facilitate refining and validating more complex models.
Towards an open repository for teaching software modeling applying active learning strategies

Modeling is a core topic in Software Engineering Education. Nevertheless, students face difficulties while learning software modeling. To teach software modeling effectively in computing courses, instructors who usually employed traditional methods could use active learning strategies. However, instructors are reluctant to change their teaching approaches due to several barriers that hinder the application of active learning strategies. Besides, relatively little research addresses how to mitigate them. The objective of this research is to help instructors implementing active learning strategies when teaching software modeling with UML diagrams. To achieve this objective, we conducted a Design Science Research (DSR). We proposed an artifact called OpenSMALS, an Open Repository for teaching Software Modeling applying Active Learning Strategies. OpenSMALS provides specific guidelines on how instructors can apply these strategies and helping instructors to identify the active learning strategies best suit their teaching context. We performed four DSR Design Cycles---in four different universities---to evaluate and evolve OpenSMALS. Our results show that OpenSMALS satisfactorily reduced the barriers faced by instructors, and it achieved an appropriate maturity level to be adopted by other instructors.
Hiding Data in Images Using Spectral Filtering and Deep Neural Networks

Privacy preserving and data security is one of the major concerns of our world. In this study we deal with one aspect, namely shoulder surfing, where sensitive information may be accessed by looking at the display or keyboard of the user. We propose a method of hiding information in color images so that it can not be perceived by the naked eye and requires a spectral filter to be seen. We term such images Spectral Hiding Images. In this work we developed a system which can automatically generate Spectral Hiding Images. We focus on a basic class of images containing a single numeric digit. We train three deep networks to determine the salient digit in an image, determine the hidden and masked digits in a spectral hiding image and to generate diverse spectral hiding digit images. Mass producing such Spectral Hiding Images using our system will allow for screen content hiding and password hiding. Additionally we show that several Gestalt principles of human perception, are expressed in the trained networks' behavior.
Data Visualization and Data Granularity in Delivering Research Data in African Libraries

Data has been named the new oil that drives development both in academia and in the business world. It has been used to develop new products across all sectors of the global economy and recently in agriculture through precision agriculture and robotic farming. It should, however, be noted that no matter the potential for change and development a data set contains, it is very important to deploy the right interpretation techniques to make the data useful and meaningful. This chapter explores themes such as data visualization tools, data granularity, and data visualization tools. It also explained the advantages of data visualization and the types of data African libraries should be collecting.
Trajectory Planning in 2D Space Using Evolutionary Algorithms

Planning optimal trajectory in a 2D space with large number of nodes or coordinates is a challenging task. Path planning has various applications in the aviation industry, UAV, mobile robots, etc. So path planning algorithms should be efficient enough to manage the associated constraints such as time, travelling cost and energy in an optimal way. Evolutionary algorithm can be very effective in addressing such challenges. This paper reviews various approaches proposed for path planning of UAV using evolutionary algorithms. Formulating the appropriate fitness function according to the requirement of the problem is the major task, which affects the accuracy of the solution.
The Dark Side of Data Science - an Empirical Study of Cyber Risks in German SMEs

Digitization brings new opportunities for companies. One of the most important topics is the possibility of better evaluating old data or generating new data. Data Science and especially Data Analytics can help here. However, it is often forgotten that connecting the company to the Internet also involves risks. In the area of data generation and data analysis, SMEs are not yet up to par with large companies, as they have fewer financial resources and trained employees. These relationships have been well researched. This article deals with the area of conflict between data science and cyber security in German SMEs based on an empirical study from 2019.
Autonomous Multi-Cloud Application Deployment and Optimized Management Using Open Source Frameworks

The dynamic development of Cloud Computing with the introduction of novel Cloud Computing models creates new challenges for the Cloud deployment. This tutorial describes how to implement Multi-Cloud native strategies using an advanced open source framework that allows for Cloud agnostic Cross-Cloud deployment and optimized management of a Cloud application based on flexible monitoring, context aware maximization of the application owner's utility of the deployed application, and autonomic reconfiguration based on the application's current execution context. In this tutorial, we provide a practical introduction to the Multi-Cloud application modelling, configuration, deployment, and adaptation. All stages of the Cloud deployment planning and designing process will be shown. Also, all the key steps in the deployment and autonomic application management will be demonstrated.
An Evaluation of Low-Quality Content Detection Strategies: Which Attributes Are Still Relevant, Which Are Not?

Online social networks have gone mainstream: millions of users have come to rely on the wide range of services provided by social networks. However, the ease use of social networks for communicating information also makes them particularly vulnerable to social spammers, i.e., ill-intentioned users whose main purpose is to degrade the information quality of social networks through the proliferation of different types of malicious data (e.g., social spam, malware downloads, and phishing) that are collectively called low-quality content. Since Twitter is also rife with low-quality content, several researchers have devised various low-quality detection strategies that inspect tweets for the existence of this kind of content. We carried out a brief literature survey of these low-quality detection strategies, examining which strategies are still applicable in the current scenario – taken into account that Twitter has undergone a lot of changes in the last few years. To gather some evidence of the usefulness of the attributes used by the low-quality detection strategies, we carried out a preliminary evaluation of these attributes.
A Hot/Cold Task Partition for Energy-Efficient Neural Network Deployment on Heterogeneous Edge Device

In recent years, neural network has made great achievements in the fields of image classification and object detection. At the same time, the rapid development of edge devices has also led many scholars to study the deployment of neural networks on resource-limited edge devices. However, the traditional neural network did not consider the specific application scenarios, which makes the data processing very inefficient. In this paper, we propose a method to deploy neural networks on heterogeneous edge devices. This method fully considers the specific applications scenarios of neural network, and proposes the idea of task partition. The whole dataset is divided into two classes: hot class and cold class, and then corresponding network models are trained respectively to handle the data. In order to speed up the execution efficiency, we propose a simple algorithm, which can balance the front-end and back-end load according to the processing capacity of edge devices. In addition, the time delay and energy consumption are considered in this paper. The experiments show that our method can save 36.4% energy consumption compared with the traditional method.
An ambiguous tag-based query reformulation technique for an effective semantic-based social image research

The Tag-based Document Retrieval technique was adopted for long time as an intuitive way to search for images shared on social networks. Nevertheless, the tag-based queries are often too ambiguous, and consequently they do not constitute an efficient solution for retrieving the most relevant images that meet the users’ needs. As an alternative, the Semantic-based Social Image Retrieval technique has emerged. The policy of this technique consists in retrieving the relevant images covering as much possible the topics that a given ambiguous query may have. In this paper, we propose a novel technique at the ambiguous query pre-processing level, which aims at moving from an ambiguous tag-based query towards a semantic-based one, by relying on a set of predefined ontological semantic rules. Thorough experiments using 8 ambiguous queries over a collection of 25.000 socio-tagged images shared on Flickr service prove the effectiveness of our technique.
FORK: A FORward-looKing Actor for Model-Free Reinforcement Learning

In this paper, we propose a new type of Actor, named forward-looking Actor or FORK for short, for Actor-Critic algorithms. FORK can be easily integrated into a model-free Actor-Critic algorithm. Our experiments on six Box2D and MuJoCo environments with continuous state and action spaces demonstrate significant performance improvement FORK can bring to the state-of-the-art algorithms.
Deep Learning Approach for Identification of Non-linear Dynamic Systems

In this study, we use deep recurrent neural networks to simulate the behaviour of dynamic chaotic attractors and to predict the next state of the system. We compare three dynamic attractors: Ueda, Pickover, and Burke-Shaw. Also, we study the ability to predict such time series simple Recurrent Neural Networks with Long Short Term Memory and Gated Recurrent Unit.
Comprehensive Visualization of Physical and MAC Layer Data for Wireless Network Monitoring

Wireless network monitoring is a crucial tool for network administrators to manage wireless link quality in the indoor environments such as warehouses and factories. In this paper, we propose a comprehensive visualization system for correlative analysis between physical and MAC layer activities. In order to support various types of assessment tasks by the network administrators, the system provides a mode that performs quasi-realtime visualization of the ongoing traffic, and a mode that enables detailed assessment of the link quality for the past traffic by visualizing the data over an arbitrary time scale. We implemented the visualization system using ordinary PCs combined with the sensor node that calculates signal strengths from the observed radio signals and extracts the header information conforming to the IEEE 802.11 standards. We confirmed that the system has enough performance to visualize a large amount of data within a small latency and to achieve smooth operation while the time scale is changing.
Gaze-Based Moving Target Acquisition using Pseudo Stopping for the Time predicted via Fitts' Law

This paper describes a technique of gaze-based moving target acquisition achieved by pseudo stopping the target for the time predicted via Fitts' Law, after saccades have been detected. This technique only requires eye-movements for the acquisition of moving targets. The results indicate that participants were able to acquire targets moving at various speeds and with different widths.
Introduction to State-Space Models

SummaryThe sequential analysis of state-space models remains to this day the main application of Sequential Monte Carlo. The intent of this Chapter is to define informally state-space models, and discuss several typical examples of such models from different areas of Science.However, we warn readers beforehand that we will need the mathematical machinery developed in the following chapters to define in sufficient generality state-space models, to develop recursions for filters and smoothers, and design a variety of simulation algorithms, including particle filters.
Investigating the Effect of a Mobile Learning Application for Graph Theory Education on Academic Performance

In this study, the effectiveness of using a mobile learning application developed for graph theory education is investigated by evaluating its impact on the academic performance of students for three academic years. The mobile application supports graph operations such as creating/editing graphs, running basic graph algorithms on graphs, and observing their step-by-step execution. Students can also test their knowledge level by using the quiz section of the mobile learning application. The study is conducted at a public university in Turkey with the participation of voluntary freshman students that take the Discrete Structures course. Graph theory is lectured for three weeks as the final subject of the course. Various features of the mobile application were introduced to the students by the lab assistant at laboratory sessions of 45 min that were conducted for a period of three weeks after the theoretical lectures in which the graph theory topics were covered. After these introduction sessions, students were able to use the mobile application as a supplementary tool any time they want. In order to investigate the effect of using the mobile learning application on the academic performance of students, a quasi-experimental research design with experimental and control groups was utilized. Quantitative data were collected by using the grades students achieved from the questions related to graph theory in the final exams of the course. The data for each year was analyzed by performing independent groups t-tests. The results of the statistical analyzes indicated that the usage of the mobile learning application contributed statistically significantly to the grades of the students in the experimental group for each year it was applied.
Perception Enhanced Frame For Visual Object Tracking

Deep trackers which based on pre-trained network trained on object detection datasets, have shown great potentials in visual object tracking. However, the gap between object detection and object tracking is non-negligible. And the fixed template with the initial target feature during tracking in some previous deep trackers greatly limit the performance of the trackers. Therefore, we propose a perception enhanced frame (PEF) to exploit the target-aware features which can better recognize the target from background and update the template features through response map. Our PEF tracker takes advantage of the fully connected network with mask loss to select target-aware feature channels, and updates the template to enhance the robustness, which enables our trackers to reduce the deep features, enhances the discriminative ability, and ensures the diversity of comparison template. Experimental results on three popular datasets show that our method get superior performance than the state-of-the-art trackers in terms of accuracy and speed.
dTAS: A Decentralized Self-Adaptive Service-Based System Exemplar

Research of self-adaptive systems can benefit from different exemplars to test and compare research methods and results. Although the number of exemplars for this area is rising, the exemplars for decentralized self-adaptive systems are limited. To that end, we introduce dTAS (decentralized Tele-Assistance System), an exemplar in the domain of service-based systems that targets decentralized adaptation of entities equipped with a classic MAPE-K feedback loop. dTAS offers predefined features that enable users to specify systems and plug in MAPE components with protocols to coordinate adaptation using different coordination patterns. We illustrate the exemplar for a scenario with three entities where the planner components coordinate the adaptation to realize their adaptation goals.
Multi-Partition Feature Alignment Network for Unsupervised Domain Adaptation

In this paper, we present a novel unsupervised domain adaptation framework, Multi-Partition Feature Alignment Network, that learns a deep neural model for the target domain without the need for any supervision. Recent leading approaches for unsupervised domain adaptation are based on adversarial alignment. Aligning the global distribution of the domain representations via adversarial training does not guarantee the class-wise distribution alignment. The proposed approach is built on adversarial learning with the focus on carefully aligning class-wise domain representations. Our algorithm utilizes the pseudo-labels (the predicted labels) of the target features to stimulate class-wise alignment. As the pseudo-labels of individual target features can be erroneous, instead of iteratively aligning individual target samples, the proposed framework introduces a generic class-specific multi-partition alignment procedure that enables superior class-discriminative alignment of domain representations. The competitive performance of the proposed framework against state-of-the-art approaches over a wide variety of visual recognition tasks, namely, the digits classification task and the object recognition task, validates its effectiveness for unsupervised domain adaptation.
Accelerating Stereo Matching on Mutlicore ARM Platform

Stereo vision is a well-known technique in computer vision used to acquire the 3D depth information of a scene from two or more 2D images. One of the main issues with any stereo vision system is how to make a good trade off between the processing speed and the quality of the disparity map. This issue can be resolved through the use of dedicated hardware platforms, like Field Programmable Gate Arrays and Graphical Processing Units, which are considered as expensive solutions. In this work, the challenge of accelerating stereo matching on low cost multicore platforms is tackled. We present a novel software implementation of a sparse Rank algorithm, that uses a modified Sum of Absolute Differences 1D box filtering algorithm in the correlation stage. Consequently, we reduce the number of computations and memory space needed for computing the disparity map. The system is implemented on a multicore Advanced Risc Machine platform (ODROID XU4). Experimental results show that the system is capable of achieveing a processing speed of 59 Frames Per Second for images of size 320×240 pixels with a disparity range of 20 pixels. Furthermore, the sparse Rank structure does not affect significantly the overall quality of the disparity map.
Fast Multiple Montgomery Multiplications Using Intel AVX-512IFMA Instructions

In this paper, we propose a fast implementation of multiple Montgomery multiplications using Intel AVX-512IFMA (Integer Fused Multiply-Add) instructions. The proposed implementation is based on a modified Montgomery multiplication. For Montgomery multiplication operands with 52 bits or fewer, the proposed implementation using Intel AVX-512IFMA instructions is up to approximately 12.22 and 4.30 times faster than the implementations using Intel 64 and Intel AVX-512F (Foundation) instructions on an Intel Core i3-8121U processor, respectively.
Fast and Efficient Data Masking Method for Securing Image Over Cloud Computing Environment

Objective: Preserving the confidentiality of sensitive information is becoming more and more difficult and challenging, considering current scenarios, as a huge amount of multimedia data are stored and communicated over the internet among users and cloud computing environment. The existing cryptography security model for storing images on a cloud platform cannot resist various kinds of modern attacks, such as statistical, differential, brute force, cropping attack, etc., therefore, an improved bit scrambling technique using chaotic maps that can resist various kinds of security attacks is needed. The FEDM cipher image provides less correlation among neighboring pixels and images can be decrypted even under the presence of noise. This study proposed a FEDM model to achieve better UACI, NPCR, histogram, runtime, and processing time performance than the existing image security methods. Methods: Preserving the confidentiality of sensitive information is becoming more and more difficult and challenging considering current scenarios as a huge amount of multimedia data are stored and communicated over the internet among users and cloud computing environment. The existing cryptography security model for storing images on a cloud platform cannot resist various kinds of modern attacks such as statistical, differential, brute force, cropping attack, etc. Results: The overall results show that the proposed FEDM model attains much superior performance considering histogram, UACI, NPCR, and runtime. The FEDM model can resist against SA. The FEDM model attains better performance because IBS is used in each step of CS. Thus, a correlation between adjacent pixels is less and aids superior security performance. Further, the FEDM model attains better UACI and NPCR performance when compared with the exiting image encryption model. Conclusion: The FEDM security method can resist DA, noise, cropping attack, and linear attacks more efficiently due to a larger keyspace. Further, the FEDM takes less time for provisioning security. Along with this, FEDM works smoothly under a cloud computing environment. No prior work has considered runtime performance evaluation under the cloud computing environment. FEDM model will significantly aid in reducing the overall operational cost of a cloud computing environment with a reduction in processing time as cloud charge is based on hours of usage.
Quantum Access

Quantum computers are available via the Internet for students and small-scale research. What if similar access could be extended to quantum hardware?
Data-Oriented State Space Discretization for Crowdsourced Robot Learning of Physical Skills

Abstract This work discusses a crowdsourced learning scheme for robot physical intelligence. Using a large amount of data from crowdsourced mentors, the scheme allows robots to synthesize new physical skills that are never demonstrated or only partially demonstrated without heavy re-training. The learning scheme features a data management method to sustainably manage continuously collected data and a growing knowledge library. The method is validated using a simulated challenge of solving a bottle puzzle. The learning scheme aims at realizing ubiquitous robot learning of physical skills and has the potential of automating many demanding tasks that are currently hard to robotize.
Ethics in Self- Sociotechnical Systems: (Tutorial Abstract)

The surprising capabilities demonstrated by AI technologies overlaid on detailed data and fine-grained control give cause for concern that agents can wield enormous power over human welfare, drawing increasing attention to ethics in AI.