Reviewerly/exHarmony huggingface dataset:
Transitive Topic Modeling with Conversational Structure Context: Discovering Topics that are Most Popular in Online Discussions

With the explosive growth of online discussions published everyday on social media platforms, comprehension and discovery of the most popular topics have become a challenging problem. Conventional topic models have had limited success in online discussions because the corpus is extremely sparse and noisy. To overcome their limitations, we use the discussion thread tree structure and propose a “popularity” metric to quantify the number of replies to a comment to extend the frequency of word occurrences, and the “transitivity” concept to characterize topic dependency among nodes in a nested discussion thread. We build a Conversational Structure Aware Topic Model (CSATM) based on popularity and transitivity to infer topics and their assignments to comments. Experiments on real forum datasets are used to demonstrate improved performance for topic extraction with six different measurements of coherence and impressive accuracy for topic assignments.
Detection and mitigation of JIT-induced side channels

Cyber-attacks stealing confidential information are becoming increasingly frequent and devastating as modern software systems store and manipulate greater amounts of sensitive data. Leaking information about private user data, such as the financial and medical records of individuals, trade secrets of companies and military secrets of states can have drastic consequences. Confidentiality of such private data is critical for users of these systems. Many software development practices, such as the encryption of packages sent over a network, aim to protect the confidentiality of private data by ensuring that an observer is unable to learn anything meaningful about a program's secret input from its public output. Under these protections, the software system's main communication channels, such as the content of the network packets it sends, or the output it writes to a public file, should not leak information about the private data. However, many software systems still contain serious security vulnerabilities. Side channels are an important class of information leaks where secret information can be captured through the observation of non-functional side effects of software systems. Potential side channels include those in execution time, memory usage, size and timings of network packets, and power consumption. Although side-channel vulnerabilities due to hardware (such as vulnerabilities that exploit the cache behavior) have been extensively studied [1, 2, 10, 13, 15-17, 19, 23], software side channels have only recently become an active area of research, including recent results on software side-channel detection [4, 8, 11, 12, 18, 22, 24] and quantification [5, 20, 21], and my own work on a static analysis framework for detection of software side-channels called CoCo-Channel [8]) and a constraint caching framework to accelerate side-channel quantification called Cashew [9].
A Learning Based Approach for Planning with Safe Actions

Given a configuration involving some objects in an environment, the planning problem, considered in this paper, is to find a plan that rearranges these objects so as to place a new object. The challenging aspect here involves deciding when an object can be placed on top of another object. Here only defining standard planning operators would not suffice. For instance, using these operators we can come up with actions that may be performed at a state but it should not be performed. So we introduce the notion of safe actions whose outcomes are consistent with the laws of physics, commonsense, and common practice. A safe action can be performed if a robot performing the action knows the knowledge of the situation. We developed a knowledge engine using a supervised learning technique. However, unlike the common task of learning functions, our approach is to learn predicates–that evaluate to binary values. By learning such a predicate a robot would be able to decide whether or not an object A can be placed on top of another object B. We give a method to handle new objects for which the predicates have not been learned. We suggest a nondeterministic planning algorithm to synthesize plans that contain only safe actions. Experimental results show the efficacy of our approach.
Directly Obtaining Matching Points without Keypoints for Image Stitching

Finding enough accurate matching points is key for image stitching. However, the existing state-of-the-art algorithms fail to find enough accurate matching points when facing the challenge where detectable features are not obvious. In this paper, a novel algorithm called CNN-MP is proposed to directly obtain Matching Points between two images using the feature maps extracted by Convolution Neural Network (CNN) and CNN-MP skips the step of detecting keypoints. There are mainly five contributions in CNN-MP: 1) break the conventional image stitching steps without detecting keypoints; 2) a feature map calculation model is built to obtain matching points between the feature maps of two images; 3) establish a position model to map the obtained matching points to the original images; 4) the process of obtaining matching points is accelerated by dividing it into pre-locate and fine-locate; 5) establish the dataset to evaluate CNN-MP in the case where detectable features are not obvious. The experimental results show that the number of accurate matching points obtained by the proposed CNN-MP is at least 1.7 times that of the state-of-the-art algorithms: ORB, SIFT, LIFT and SuperPoint when facing the challenge where detectable features are not obvious. Moreover, CNN-MP also achieves good performance when the input images own significant detectable features.
Control of Complex Nonlinear Dynamic Systems with Incremental Deep Learning Neural Networks

This paper proposes incremental deep learning methods for training neural networks to control the response of complex nonlinear dynamic systems. By analyzing the complexity of the task to be fulfilled by the neural network, learning strategies are formulated and implemented in an incremental scheme starting from simple tasks and continuing with increasingly complex tasks. The Dynamic Back Propagation algorithm is used for training the neuro-controller in each step of the incremental learning process, considering the system nonlinear dynamics. The results obtained in the control of highly unstable nonlinear systems, and the positioning control of mobile robots verify the effectiveness of the proposed incremental deep learning strategies.
Data privacy protection technology of wearable-devices

Wearable-devices have developed rapidly. Meanwhile, the security and privacy protection of user data has also occurred frequently. Aiming at the process of privacy protection of wearable-device data release, based on the conventional V-MDAV algorithm, this paper proposes a WSV-MDAV micro accumulation method based on weight W and susceptible attribute value sensitivity parameter S and introduces differential-privacy after micro accumulation operating. By simulating the Starlog dataset and the Adult dataset, the results show that, compared with the conventional multi-variable variable-length algorithm, the privacy protection method proposed in this paper has improved the privacy protection level of related devices, and the information distortion has been properly resolved. The construction of the release model can prevent susceptible data with identity tags from being tampered with, stolen, and leaked by criminals. It can avoid causing great spiritual and property losses to individuals, and avoid harming public safety caused by information leakage.
Reproducible Academic Writing and Interactive Data Visualization Using R Markdown (R Programming Flex-Dashboard: Flex_Dashboard Packages)

In the computer world, there was a wide range of text editors available. Normally many researchers don’t wish to use the latest editor, however, markdown is the common plain text editor for a wide variety of compatible applications for the common model for large academic writing projects. There is a wide research gap between the academics’ choices for designing the common design framework for PowerPoint, Word document, HTML, PDF, PPT, and much other design that is always required for academician’s daily life and interactive data presentation on web site. Here the researcher designs a web application with interactive HDI functionality, visualization of the data of the literacy index of Nepal, and published on ( http://rpubs.com/yagyarimal/556607 ) with the help of R markdown and flex-dashboard package available in R programming. The first page with interactive HDI, literacy data set with the description of the administrative map of the federal structure of Nepal on the second page. In the first page, the bar chart, the pie chart, the HDI, and the literacy rate and the description of the districts by province of Nepal are shown using dynamic interactive features of different data visualization, and the second page describes the maps of Nepal using shapefile and presents HDI, literacy, and poverty of the country based on the United Nations development datasets of country. The third page describes the page of interactive data visualization of the interactive data table and pivot table, and the final page describes the result of any command for the design report and the author. This type of web hosting with interactive data visualization is another significant feature of programming R and markdown. Therefore, markdown has a better structure for academic writing in various types of presentation from the common plain text for all. Likewise, the markdown has been packaged in the programming R has the advanced functionality of designing interactive data visualization features is available on http://rpubs.com/yagyarimal/556607 and https://github.com/yagyarimal/datavisualization for data source file download.
Fast Binary Field Mutiplication on ARMv7 Embedded Processors

Binary field multiplication (BFM) plays an important role in applications such as elliptic curve cryptography (ECC). For efficient implementation of BFM on hardware platforms, the design of fast BFM algorithms is remaining a hot research challenge. This paper proposes a new multiplication algorithm for performing multiplication of two binary polynomials. Our algorithm combines the Karatsuba algorithm (5 terms), a 64-bit multiplication and a 32-bit multiplication on ARMv7 microprocessors. We apply our proposed algorithm for Elliptic Curve Digital Signing Algorithm (ECDSA) based on NIST-standard at 128-bit security level and present the performance improvement for the point multiplication on binary elliptic curves. Results are performed on two hardware platforms: Xilinx Zynq-7000 SoC ZC702 and IMX6Q-SABRE for computation speed evaluation and comparison.
Quantitative Method of Health State Assessment Inside the Safe Region

Condition Monitoring and fault early warning of equipment is very important for management and decision-making. The current research emphasizes on the abnormal stage. This paper presents a novel method, which consists of center optimization based on genetic algorithm, boundary adjustment based on support vector data description and residual healthy life prediction based on regressive analysis, to realize the health state assessment. Two validations are implemented with the open dataset of XJTU-SY to demonstrate the effectiveness of the proposed method. The experimental results verify the feasibility of our method and we finally discuss the problems to be solved in further study.
Virtual Machine Migration and Rack Consolidation for Energy Management in Cloud Data Centers

Cloud computing technology offers resources to the clients in pay as we use manner and changed the way how businesses utilize resources. It is capable of hosting a diverse of applications that executes from few seconds to several hours on shared hardware platform, but the data centers hording applications use enormous volumes of energy and contribute to ecological shortcomings like emission of carbon and result in global warming. High-energy consumption also increases the operational costs since it increases the number of cooling systems deployed to minimize the heat generated in the data center by physical servers. The virtualization technique has the ability to shift a VM among the available servers using live VM migration technique. Here the VMs are migrated to the appropriate servers to reduce the total number of running servers by Modified Best Fit Decreasing (MBFD) algorithm and particle swarm optimization (PSO), and they are finally consolidated into reduced number of servers. Also, this paper aims at minimizing the number of active racks so that energy conservation will be maximum.
Automatic Classification of Road Traffic with Fiber Based Sensors in Smart Cities Applications

Low cost monitoring of road traffic can bring a significant contribution to use the smart cities perspective for safety. The possibility of sensing and classifying vehicles and march conditions by means of simple physical sensors may support both real time applications and studies on traffic dynamics, e.g. support and assistance for car crashes and prevention of accidents, and maintenance planning or support to trials in case of litigation.Optical fibers technology is well known for its wide adoption in data transmissions as a commodity component of computer networks: its popularity led to large availability on the market of high quality fiber at affordable price. As a purely physical application, its optical properties may be exploited to monitor in real time mechanical solicitations the fiber undergoes. In this paper we present a novel approach to using optical fibers as road sensors. As quite popular in literature, fiber is used to sense the vibrations caused by vehicles on the road: in our case, signals are processed by functional classification techniques to obtain a higher quality and a larger flexibility for the reuse of results. Classification aims at enabling profiling of road traffic. Moreover in our approach we would like to optimise the analysis and classification computations by splitting the process among edge nodes and cloud nodes according to the available computation capacity.Our solution has been tested by an experimental campaign to show the suitability of the approach.
A Swarm-fleet Infrastructure as a Scenario for Proactive, Hybrid Adaptation of System Behaviour

This position paper presents a concept for optimising the overall goal achievement in a self-adaptive and self-organising system containing a large number of autonomous and heterogeneous subsystems. Based on introducing the scenario of a `swarm fleet infrastructure', we derive a research roadmap that combines concepts from global planning with adaptation freedom and uncertainty, local autonomous and context-aware learning of subsystems, and proactive control based on self-awareness.
A head-in-hand metaphor for user-centric direct camera control in virtual reality

The explorative examination of constructed 3D models in immersive environments requires suitable user-centric interaction methods. Especially novel concepts for virtual camera control can offer advantages, e.g. for the analysis of model details. We extend the known concept of the camera-in-hand metaphor and implement a multidimensional viewport control technique that can be used with common head-mounted displays and VR-controllers. With our head-in-hand view the user is able to control the virtual camera directly by hand without losing the flexibility of head movements. To ensure convenient operation, the method restricts special rotation parameters and smoothes jerky gestures of the user hand. In addition, we discuss implications and improvement potential of the proposed concept as well as adverse effects on the user, such as motion sickness.
The Problem of Building and Implementing the Didactic Capabilities of a Modern Lecture for Graduate Students (on the Example of the Discipline of Choice “Philosophy of Ethnos”)

The actual applied problems of modern pedagogy of higher education include the processes of educa-tional adaptation of participants in educational rela-tions “lecturer-listener”, as well as pedagogical im-provement of teachers-lecturers of higher education institutions. The authors raise the issue in the con-text of developing both classroom and online lec-tures on the discipline "philosophy of ethnos" for postgraduates. The article examines the role and significance of ethnophilosophical knowledge for the formation of such universal competencies as critical analysis or generating new ideas in science. The peculiarities of pedagogical interaction between the lecturer and students, of course, ensure the suc-cess of the teaching process. Using the example of observations made by one of the authors of the arti-cle at the faculty of philosophy, we describe such key characteristics of lectures by leading Russian philosophers as the identification of the problem field, discussion and interactivity. In the conclu-sions, the applied aspect of lecturer’s didactic and pedagogical skills in shaping the generic competen-cies of graduate students regarding the need to fo-cus the audience's attention through modulation in the intonations and articulations on relevant con-cepts, judgments, conclusions is highlighted. The skills of setting the problem field of learning and understanding the audience of the topic, posing the issues for students to motivate them for analytical and comparative mental operations and acting as a communicative leader, contributing to interactive lectures are also noted.
Empty Region Detection in an Image Using Deep Convolutional Neural Network

As Convolutional Neural Networks (CNN) have addressed many computer vision problems like image classification, object detection, object recognition etc. Many object detection algorithms detect an object present in an image by drawing bounding box around that object to localize that particular object using different CNN architectures and have achieved good results. When images are used as background to display text, the arrangement of text in relation to the image can make image not to be so visually appealing. So far object detection and recognition algorithms focused on finding out specific object either in an image or video, but our paper mainly focuses on finding out an empty area in a background image using CNN so that user can enter text inside this empty area without affecting the saliency part of image and also making image to look visually appealing. Finally, we demonstrate the results for empty region detection with accuracy of 85% using IoU as metric.
Digital Signature in the XAdES Standard as a REST Service

The Brazilian government has been discussing the topic of information security and the availability of information through digital media. Information made available in digital format must have the same protection as information in physical format. One of the solutions proposed in the educational area to make documents available in a secure digital format was introduced by the Ministry of Education (MEC), which was the creation of a model for signing papers and University Degree in digital format. The government defined the main guidelines for the creation of this digital signature solution through ordinances. According to the MEC, the deadline for implementing a system for signing University Degree and documents in digital format must be established by 2022. This work presents a digital signature solution for the University of Brasïlia (UnB). Besides, we demonstrate the current architecture used by UnB and how the creation of new solutions can be incorporated into this architecture. Thus, we present the internal architecture of the digital signature module and demonstrate how the developed solution will be integrated into the current architecture of UnB. As a main result, the proposed solution presents a reduction in the costs of signing digital documents and allows higher speed in signing University Degree and documents in digital format.
A New Library of Bio-Inspired Algorithms

Real engineering, science, and economics problems cannot be ever solved exactly due to the high computation time to find the optimal solution. One way to solve such problems is to apply bio-inspired algorithms, to minimize the time to search for potential solutions. Bio-inspired algorithms are based on the collective behavior of social organisms and are used to solve optimization problems. This article presents a new library of bio-inspired algorithms. The library offers the implementation of some algorithms, being easily extensible through interfaces. An evaluation was made using 7 test functions applied to each of the implemented algorithms. The tests have shown that the ABC algorithm obtained the best convergence results in 5 tests and the ACO algorithm in 2 tests.
언어하기 활동이 한국 고등학생의 영어학습 동기 및 초인지책략 사용에 미치는 영향

This paper examined the effect of Motivational Languaging Activity (MLA) on Korean high school students’ English learning motivation, their metacognitive strategy use, and English proficiency. MLA is a classroom-friendly activity in which learners express the reason for studying English in words and writing. MLA helps learners to plan, evaluate, and monitor their English learning. This study conducted experiments on two types of languaging activities. The control group did not participate in the languaging activity. The experimental group was divided into two subgroups: one underwent individual writing (IW), and the other first participated in individual writing and then in group discussion (IWGD). The results of the research revealed that there was a significant difference between the two experimental groups. However, the control group did not show any changes. It showed that motivations for learning English have significant changes in the ideal L2 self, instrumentality (promotion), and motivated behavior. Especially the IWGD was more effective than IW regarding their motivational increase. In the case of metacognitive strategies, global learning strategies, learning plans and self-assessment, and self-monitoring showed significant changes. The IW was more effective than IWGD in metacognitive strategies. However, no statistically significant difference was identified in the participants’ English proficiency measured by pre- and post-tests. The results suggest that an intensive languaging activity in a short period can make a significant increase in their motivation and metacognitive strategies.
Measuring Disagreement with Interpolants

We consider the problem of quantitatively assessing the conflict between knowledge bases in knowledge merging scenarios. Using the notion of Craig interpolation we define a series of disagreement measures and analyse their compliance with properties proposed in previous work by Potyka. We study basic complexity theoretic questions in that scenario and discuss the suitability of our approaches .
Exploiting Answer Set Programming for Building explainable Recommendations

The capability of a recommendation system to justify its proposals becomes an ever more important aspect in light of recent legislation and skeptic users. Answer Set Programming (ASP) is a logic programming paradigm aiming at expressing complex problems in a succinct and declarative manner. Due to its rich set of high level language constructs it turns out that ASP is also perfectly suitable for realizing knowledge and/or utility-based recommendation applications, since every aspect of such a utility-based recommendation capable of producing explanations can be specified within ASP. In this paper we give an introduction to the concepts of ASP and how they can be applied in the domain of recommender systems. Based on a small excerpt of a real life recommender database we exemplify how utility based recommendation engines can be implemented with just some few lines of code and show how meaningful explanations can be derived out of the box.