Reviewerly/exHarmony huggingface dataset:
Simple contrapositive assumption-based argumentation frameworks

Assumption-based argumentation is one of the most prominent formalisms for logical (or structured) argumentation, with tight links to different forms of defeasible reasoning. In this paper we study the Dung semantics for extended forms of assumption-based argumentation frameworks (ABFs), based on any contrapositive propositional logic, and whose defeasible assumptions are expressed by arbitrary formulas in that logic. We show that unless the falsity propositional constant is part of the defeasible assumptions, the grounded and the well-founded semantics for ABFs lack most of the desirable properties they have in abstract argumentation frameworks (AAFs), and that for simple definitions of the contrariness operator and the attacks relations, preferred and stable semantics are reduced to naive semantics. We also show the redundancy of the closure condition in the standard definition of Dung's semantics for ABFs, and investigate the use of disjunctive attacks in this setting. Finally, we show some close relations of reasoning with ABFs to reasoning with maximally consistent sets of premises, and consider some properties of the induced entailments, such as being cumulative, preferential, or rational relations that satisfy non-interference.
ADT: Object Tracking Algorithm Based on Adaptive Detection

Object tracking is one of the most fundamental and important fields in computer vision with a wide range of applications.Although great progress has been made in object tracking combined with detection, there is still enormous challenges in real-time applications and for the computer cannot effectively capture the temporal correlations of targets and background clutter.In order to improve the performance of tracking algorithms under complex unconstrained conditions, we propose a novel tracking framework based on adaptive detection, called adaptive detection tracking (ADT).First, we exploit the temporal correlation of the recurrent neural network to predict the target's motion direction and efficiently update the region of interest (RoI) in the narrow range of the next frame.Then, the algorithm utilizes the correlation filter to initialize the defined region of interest based on the threshold.If the Interaction of Union (IoU) of the predicted bounding box and the groundtruth bounding box is greater than the set threshold, the predicted bounding box will be directly output as the tracking results, whereas the detection is adaptively carried out in the determined RoI.Finally, the predicted bounding box refines the direction model as the input of the next frame to complete the whole tracking flow.Our proposed adaptive detection tracking mechanism can efficiently realize non-frame-by-frame adaptive detection with excellent tracking accuracy and is more robust in the unconstrained scenes, especially for occlusion.Comprehensive experiments demonstrate that our approach consistently achieves state-of-the-art results and runs in real-time on six large tracking benchmarks, including OTB100, VOT2016, VOT2017, TC128, UAV123 and LaSOT datasets.
Echo State Network (ESN) Based Generator Speed Prediction of Wide Area Signals in a Multimachine Power System

This paper presents an echo state network based prediction of generator speed using a two area four generator multimachine power system in real time. The echo state network (ESN) is trained using the generator speed deviation delayed between 20ms to 50ms as inputs. The output of the ESN is the predicted speed of the generators for steady state scenarios. The trained and test data were compared, the results show a good correlation between these data sets demonstrating that the ESN has effectively learned the nonlinear properties of the multimachine system. This paper also uses ESN to address the need to mitigate power system stability issues time steps ahead e.g. early detection of oscillations in the power systems network that could cascade into system failure, blackouts, and sudden system collapse. Echo State Networks have been used in this research due to its superior performance in terms of the effectiveness of learning nonlinear systems and its ease of training compared to other recurrent neural networks (RNN). The IEEE two area four generator test system was modeled in RSCAD software and tested in real time using the RTDS (real-time digital simulator). The real time implementation was carried out at the Real Time Power Intelligent Systems (RTPIS) laboratory, Clemson University, SC. USA.
Eye Gaze Controlled Immersive Video Navigation System for Disabled People

In recent past, the world has witnessed astounding growth of immersive video technology because of its unique features like self directed control of view and the feeling of presence. Day by day immersive video technology is ushering its dominance in almost everywhere, specially in education, sports, digital marketing and film making. Everyday such applications of immersive video technology are being experienced by numerous people all over the world but there are still many unfortunate people who have no access to such emerging field because of their lack of control over body except eye. An eye gaze controlled immersive video navigating system can act as an assistive device for such disabled people. With the help of such system, these people can participate in the immersive video based learning programs and also get introduced to other immersive video technologies developed for entertainment purpose. The paper presents such two individual systems developed on different eye gaze direction detection algorithms. Both subjective and objective experiments show that systems implemented in this paper can facilitate disable persons with effective and efficient experience of immersive video.
Indonesian Twitter Data Pre-processing for the Emotion Recognition

The 2019 Presidential Election in Indonesia causes sharp political polarization. The battle of discourse between two massive camps took place on social media. Various aspects of public opinion showing how people think and act can be found easily. Twitter as the most popular microblogging platform, offers a place to express a variety of thoughts and opinions. This makes Twitter as a source of opinion mining that can be used to detect people's emotional feelings about an event. This paper explores the pre-processing stages of text classification for the emotion recognition based on Twitter conversations that correlate with the debate of Indonesian presidential candidates. Data pre-processing is an important step in sentiment analysis because the results of the analysis are strongly affected by the quality of the data provided. A combination of data processing has been carried out using Indonesian Twitter datasets. The accuracy of the analysis was tested using a deep learning model MLP and LSTM. The results show that the use of appropriate pre-processing techniques can improve accuracy.
Multilevel segmentation for automatic detection of malignant masses in digital mammograms based on threshold comparison

Digital image processing implies a fundamental process in the field of medicine, since it supports the improvement of images facilitating their interpretation for the specialists. A technique that has had great relevance is image segmentation, applied to mammography images, in order to improve the diagnosis of breast cancer disease. In this work it is proposed the implementation of a new multilevel segmentation approach based on the minimum cross-entropy threshold - Harris Hawks Optimization (MCET-HHO) metaheuristic algorithm, where five different levels are used in order to compare the behavior of the thresholds applied in mammograms with presence of cancer by allowing the identification of malignant tumors. According to the results, by including four levels in the segmentation of the image, it is possible to identify with a significantly high precision the region of interest (ROI) where the tumor is located, obtaining a similarity of 0.902 with the ROI identified by the specialist. Therefore, it is concluded that the implementation of the MCET-HHO algorithm for multilevel segmentation of mammograms allows to determine the ROI that contains malignant masses, presenting a preliminary support tool for the diagnosis of breast cancer.
XAIT

Explainable AI (XAI) for text is an emerging field focused on developing novel techniques to render black-box models more interpretable for text-related tasks. To understand the recent advances in XAI for text, we have done an extensive literature review and user studies. Allowing users to easily explore the assets we created is a major challenge. In this demo we present an interactive website named XAIT. The core of XAIT is a tree-like taxonomy, with which the users can interactively explore and understand the field of XAI for text through different dimensions: (1) the type of text tasks in consideration; (2) the explanation techniques used for a particular task; (3) who are the target and appropriate users for a particular explanation technique. XAIT can be used as a recommender system for users to find out what are the appropriate and suitable explanation techniques for their text-related tasks, or for researchers who want to find out publications and tools relating to XAI for text.
An optimization approach to load balancing, scheduling and right sizing of cloud computing systems with data locality

We model a cloud computing infrastructure over a set of locations, with multiple server instances per location. The service rate offered by each server is differentiated by the type of task, depending on whether its data is locally available. Resource allocation issues include: load balancing between locations, scheduling of tasks within each location, and sizing of the active server population at each location.Using a fluid queue model, we first characterize the capacity region of a system with a fixed number of servers at each location, recovering known results on throughput optimality of certain policies. Next we allow the server populations to vary, and pose the problem of minimizing a convex cost function subject to load stabilization. Such right sizing of service capacity must be done dynamically, without knowledge of the load. Invoking Lagrange duality, we propose a primal-dual dynamic control with queues and server populations as state variables, that also embeds the optimal load balancing and scheduling. We prove its stability for fixed, unknown load, and explore by simulation its behavior under time-varying loads.
Report Now. Report Effectively. Conceptualizing the Industry Practice for Cybercrime Reporting

Reporting plays a vital role in combatting cyber-crimes. The data collected from cybercrime reporting can not only bolster the efforts of those on the responding end of such attacks (i.e., government, law enforcement agencies, and the technology industry), but also provide relevant prevention tips to computer users to help mitigate their cybercrime risk. Despite the importance of cybercrime reporting, we observe that many cybercrimes go unreported, which arguably can be due to an overall lack of effectiveness of currently existing cybercrime reporting mechanisms. This study aims to streamline cybercrime reporting processes at PayPal by proposing a design of an interactive customer-facing cybercrime reporting interface. The overall goals of the proposed design are to (1) appropriately triage reports both within the company and to relevant external entities and (2) to educate the customer base about cybercrimes and cybercrime reporting through helpful links. The proposed design was tested with 523 Amazon Mechanical Turk workers and was considered user-friendly and informative by our participants. Moreover, a statistical model demonstrated that aside from our proposed interface's usability, users' victimization, self-efficacy, and perception of cybercrimes' severity all had a positive effect on the likelihood to report a cybercrime. Our proposed design and evaluation have the potential to improve the efficiency and effectiveness of cybercrime reporting to corporations and government/law enforcement agencies.
3D Simulator for Navigation of a Mobile Robot Using Simscape-SIMULINK

This paper presents the implementation of a mobile robot in 3D environment. A non-holonomic mobile robot (TurtleBot Burger 3) is designed to navigate following different trajectories by controlling its motors, and it is expected to be useful to test autonomous navigation algorithm. The study of a non-holonomic mobile robot is so difficult due to the complexity of its mathematical description. Therefore, this paper introduces a simulator based on the geometrical structure of the robot. After that, a 3D mobile robot is built and simulated in Matlab-Simscape environment. Different schemes of tracking trajectories have been introduced in this work to study the behaviour of the mobile robot. The purpose of this 3D environment is to create a hostile environment with random obstacles and it optimize the path tracking of the robot. The proposed simulation modelling is valuable to give a clear understanding of complex systems and it will help to have a correct digital representation of the robot.
Pattern Classification of Handwritten Kannada Digits Using Customized CNN

Upadhye, Gopal D. Kulkarni, U. V.In the area of pattern classification, handwritten digit classification is a challenging problem. Handwritten digits seem different due to writing styles and sizes. There is a wide scope of research on regional languages like Kannada. As Kannada digits symmetric as well as curvy, they are difficult to recognize accurately. Customized convolutional neural network architecture is proposed in this paper for Kannada digits’ classification which are handwritten. This provides automatic learning feature facility of handwritten digit and predicts its class. Layered model of customized CNN architecture is also given which shows flow of working of the proposed method. Large-sized dataset is unavailable for handwritten Kannada digits, so prepared dataset of 20,200 samples. Out of 20,200 total samples, 16,000 samples used for training and the remaining 4200 samples used for testing. The proposed method gives average 93.13% testing accuracy and 91.58% validation accuracy.
Time- and Learner-Dependent Hidden Markov Model for Writing Process Analysis Using Keystroke Log Data

Teaching writing strategies based on writing processes has attracted wide attention as a method for developing writing skills. The writing process can be generally defined as a sequence of subtasks, such as planning, formulation, and revision. Therefore, instructor feedback is often given based on sequence patterns of those subtasks. For such feedback, instructors need to analyze sequence patterns for all learners, which becomes problematic as the number of learners increases. To resolve this problem, this study proposes a new machine-learning method that estimates sequence patterns from keystroke log data. Specifically, we propose an extension of the Gaussian hidden Markov model that incorporates parameters representing temporal change in a subtask appearance distribution for each learner. Furthermore, we propose a collapsed Gibbs sampling algorithm as the parameter estimation method for the proposed model. We demonstrate effectiveness of the proposed model by applying it to actual keystroke log datasets.
MPC-DPOS

The blockchain is a decentralized distributed accounting system. With the development of the blockchain technology and its wide application, the core of the decentralization-consensus algorithm has attracted the attention of researchers. However, the previous POW mining consensus algorithm is a low-efficiency and high-cost consensus mechanism. In this paper, we propose an MPC-DPOS election consensus algorithm, which is improved by integrating the logic ring-based election algorithm and the Yao's millionaire algorithm into the basic DPOS algorithm to address secure multi-party computation problem. Specifically, the MPC-DPOS algorithm first selects the 101 candidate nodes from the many nodes of the blockchain system into a logical ring in the first stage, which will participate in the next election process of the proxy nodes to generate new blocks. In addition, since the weights are compared while the shares are not known, Yao's millionaire algorithm is added in the second election process, and the agent nodes are selected to generate new blocks finally. Extensive experiments show that the proposed MPC-DPOS election consensus algorithm achieves state-of-the-art performance with the respect to safety and adaptability to meet the high efficiency and low-cost requirements of commercial applications.
Bengali Handwritten Alphabet Recognition using Deep Convolutional Neural Network

The technology of object recognition has advanced in recent times. There are so many models that performed brilliantly when used to detect English handwritten alphanumeric characters. But when it comes to Bengali handwritten character recognition, these predefined model slightly underperformed. The complexity of Bengali characters and unavailability of a good dataset are the main reasons for the underperformance of these models. So the problem still quite unsolved. But in Bengali digit recognition some of the models performed very well. We propose a Deep Convolutional Neural Network model to recognize Bengali handwritten alphabets. The dataset used in this experiment is pretty new and have not tested with DCNN models. Till now, our model achieves 95% accuracy in recognizing the alphabets.
Data Augmentation for GrossMotor-ActivityRecognition Using DCGAN

Gross Motor Activity Recognition(GM-AR) AI has been studied to develop assessment AI that automatically evaluates child's GM skill. However, collecting and creating datasets takes a lot of effort and time. Therefore, the goal is to automatically generate a GM-AR data set. Since the data set used in this paper is a special time series image, DCGAN was used for data generation. Therefore, this paper aims to generate time-series images by GM-GAN using CNN for GMAR as Discriminator. Newly designed GM-AR could achieve 68.8 % accuracy. The generated image of GM-GAN could be converted into a skeleton with both hands and feet, and the skeleton was confirmed to work with 40 frames. Therefore, GM-GAN was able to generate PK time-series images.
Accelerating Legacy String Kernels via Bounded Automata Learning

The adoption of hardware accelerators, such as FPGAs, into general-purpose computation pipelines continues to rise, but programming models for these devices lag far behind their CPU counterparts. Legacy programs must often be rewritten at very low levels of abstraction, requiring intimate knowledge of the target accelerator architecture. While techniques such as high-level synthesis can help port some legacy software, many programs perform poorly without manual, architecture-specific optimization.
Runtime Detection of Userspace Implants

Userspace integrity is a necessary and often-overlooked component of overall system integrity. We present the concept of userspace integrity measurement to validate the state of the system against a set of carefully chosen invariants based on the expected behavior of userspace and key behaviors of advanced malware. Userspace integrity measurement may be combined with existing filesystem and kernel integrity measurement approaches to both provide stronger guarantees that a platform is executing the expected software and that the software is in an expected state. We also introduce the Userspace Integrity Measurement (USIM) Toolkit, a preliminary set of integrity measurement tools to detect advanced malware threats, such as memory-only implants, that evade traditional defenses.
SPSA Method Using Diagonalized Hessian Estimate

Simultaneous perturbation stochastic approximation (SPSA) and its adaptive version (ASPSA) are two commonly used methods in stochastic optimization problems, analagous to the gradient descent and Newton-Raphson methods in deterministic optimization. However, both methods have potential shortcomings. SPSA, as a first-order-type method, has typically rapid improvement in the early stages, but slow convergence at the later stages of the search process. ASPSA, as a second-order method, has typically faster convergence in the later stages, but a more numerically challenging implementation. We propose a method (diagSPSA or diagSG) using only diagonal elements of Hessian estimates to re-scale gradients when updating parameters in each iteration. This method uses part of the information of Hessian matrices and has low computational cost. We prove the convergence performance and asymptotic behaviors of diagSPSA. In addition, this paper presents a theoretical efficiency analysis, comparing the new method diagSG against stochastic gradient method (SG). We also make numerical tests for the efficiency of both diagSPSA and diagSG.
Development of Mobile Learning Media Based Android to Suport Students Understanding

Abstract Technological developments have an impact on the education sector. This study aims to develop and produce android-based learning media on the subject of quadratic equations and knowing the ability to understand material concepts. This type of research is Research and Development. The research model used is the ADDIE model which consists of 5 steps namely (1) analysis (analyze), (2) design (design), (3) development (develpoment), (4) implementation (5), (5) and evaluation (evaluation). This research was conducted at Jiwan State Vocational High School 2. The subjects in this study were the tenth grade students of Machining Technique. The instruments used were learning implementation plans, media validation sheets, student response questionnaire sheets, and post test questions. The results showed that: (1) M-Learning fulfills the element of validity with the average results obtained from the media validation sheet from media experts amounting to 97.33% and from material experts at 88.33%. (2) M-Learning is able to give a positive influence seen from the results of the post test with a percentage of 92.37% and (3) The response of students after using M-Learning in the main field test shows a percentage of 83.68%, so as to attract students in the process learning.
Multi-sensor Information Fusion Obstacle Avoidance based on Fuzzy Control

This paper designs and manufactures an intelligent wheeled robot with autonomous walking and obstacle avoidance functions. The obstacle avoidance of robot is realized by fuzzy control technology. In order to avoid obstacles quickly and accurately, the difference between left and right distance is calculated as a new input, which can reduce the input parameters and the complexity of fuzzy control design. Simulation and experimental results show that the obstacle avoidance path based on fuzzy control is smoother than that based on intuitive control.
Mass Higher Education in Russia: Features of Dynamics

The article considers the massification of higher education in post-Soviet Russia and shows its special aspects against the background of the studentship history in our country from 1917 to 2017. The main cause of the boom in the sphere of higher education at the turn of the century is the social and economic transformation of the society. The emergence of private universities and fee-based education in the public sector of higher education provided opportunities for broad segments of society. The unmet demand for higher education has resulted in an upsurge in the interest to higher education among the society. The similar boom and wave-like dynamics may be traced regarding postgraduate training due to long lasting deficit. The paper presents the results of comparing student body dynamics in universities, postgraduate and doctoral schools and shows the boom synchronicity at the levels of universities and postgraduate schools.