Reviewerly/exHarmony huggingface dataset:
Path Planning for Mobile Robots Applied in the Distribution of Materials in an Industrial Environment

This paper presents a new approach to solve the problem of logistics in a factory by automating the distribution of materials. Thus, a general algorithm of path planning is applied for the physical distribution of the production area, in the case of PowerTech Transformer Factory and Man Busses Factory. A modification to the basic RRT algorithm is used to attain the minimum distance to be followed when exploring the entire work area. Accordingly, simulated results demonstrate how the mobile robots distribute the materials from the warehouse to each workstation and return to the warehouse.
Simultaneous Planning of Grasp and Motion using Sample Regions and Gradient-Based Optimization

Motion planning is an essential component of robotic systems. Gradient-based planning has been proposed to produce smooth paths under constraints for manipulation tasks such as picking and placing objects. However, it does not deal well with discontinuities, which occur in many manipulation problems, e.g. when deciding whether to pick an object from the side or from the top. Sampling-based planning is robust against such discontinuities, but often produces paths with unnecessary motions that require heavy post-processing. In this paper, we propose a novel method to solve a complete manipulation task using gradient-based optimization while preserving the advantages of sampling-based planning. By dividing the surface of the object into regions where the grasp pose can be extracted quasi-continuously, we define multiple optimization problems in parallel, which are evaluated independently. As our method generates the motion path and grasp plan for the entire task, constraints that arise from each moment of the task are propagated automatically to the optimization of the entire task, facilitating the setup. We show the effectiveness of the proposed method in simulation and with a real robot.
Deep Hierarchical Attention Flow for Visual Commonsense Reasoning

Visual Commonsense Reasoning (VCR) requires a thoroughly understanding general information connecting language and vision, as well as the background world knowledge. In this paper, we introduce a novel yet powerful deep hierarchical attention flow framework, which takes full advantage of text information in the query and candidate responses to perform reasoning over the image. Moreover, inspired by the success of machine reading comprehension, we also model the correlation among candidate responses to obtain better response representations. Extensive quantitative and qualitative experiments are conducted to evaluate the proposed model. Empirical results on the benchmark VCR1.0 show that the proposed model outperforms existing strong baselines, which demonstrates the effectiveness of our method.
Translation-Invariant Kernels for Multivariable Approximation

Suitability of shallow (one-hidden-layer) networks with translation-invariant kernel units for function approximation and classification tasks is investigated. It is shown that a critical property influencing the capabilities of kernel networks is how the Fourier transforms of kernels converge to zero. The Fourier transforms of kernels suitable for multivariable approximation can have negative values but must be almost everywhere nonzero. In contrast, the Fourier transforms of kernels suitable for maximal margin classification must be everywhere nonnegative but can have large sets where they are equal to zero (e.g., they can be compactly supported). The behavior of the Fourier transforms of multivariable kernels is analyzed using the Hankel transform. The general results are illustrated by examples of both univariable and multivariable kernels (such as Gaussian, Laplace, rectangle, sinc, and cut power kernels).
Optimization of the Orientation of Vertical Surfaces Based on Geographic Parameters for Solar Energy Harvesting

The solar energy potential of vertical façades in Building Integrated Photovoltaics (BIPV) systems represents an alternative for a sustainable development that has been analyzed in different countries but not yet in Colombia. This study analyzes the energy potential of vertical surfaces for four cities in Colombia by optimizing the orientation of the vertical panels using a clear sky radiation model. The results highlight the need for positioning vertical surface designs given the variation of optimal azimuthal angles for each city during both morning and afternoon. In addition, the energy generation potential of vertical façades with respect to horizontal solar panels is analyzed considering the maximum energy received and its alignment with the energy demand curve. Angular deviation tolerances with respect to the optimum angle are also found showing Sincelejo City to be more sensitive to changes than Medellin, Rionegro and Bogota.
Overview of Crowd Counting

Recently, counting the number of people for crowd scenes is a hot topic because of its widespread applications (e.g. video surveillance, public security). The stampede incidents frequently occur in large-scale activities at home and abroad, which have caused a lot of casualties. For example, in the Shanghai Bund stampede incident in 2015, it has reached the level of major casualties prescribed by China. Therefore, the research on the population counting problem is getting hotter and hotter. If the population density of the current scene is accurately estimated and the corresponding security measures are arranged, the occurrence of such events can be effectively reduced or avoided. For this reason, this paper reviews the history of the development of population counting in recent years and some models that are more classic and have better effects. We will also consider pros and cons of these approaches.
Assistance of Student by Web Services based on Annotations

We focus in our research on the annotative activity of learner because annotation practice is very common and omnipresent. While reading, learner usually uses comments, highlights, circles sections and posts it to annotate the consulted resources. So, we propose an approach of recommendation of web services from the learner's annotative activity to assist him in his learning activates. This process of recommendation is based on two preparatory phases: the phase of modelling learner's personality profile through analysis of annotation digital traces in learning environment realized through a profile constructor module and the phase of discovery of web services which can meet the goals of annotations made by learner via the web service discovery module. The evaluation of these two main modules (web service discovery module and profile constructor module) through empirical studies realized on groups of learners based on the Student's t-test showed significant results
The Dependency of Healthcare on Security: Issues and Challenges

Information security and privacy in the sector of healthcare is an important issue that has to be given importance. With the growing adoption of electronic health records of patients, the need of accessing and sharing information between different healthcare professionals is also increasing. This gives rise to the attention that has to be provided for securing the information. Also the adoption of the Internet of Things in wireless body sensor networks, leads to the usage of Cloud and Fog in healthcare systems. Thus pointing towards secure methods of accessing, storing, processing of sensitive data. In this paper, an overview of different issues and challenges pertaining to the security of healthcare systems is presented. Also, the solutions to address the security concerns in the healthcare systems are also discussed.
Applications of Distributed Learning for Optical Communications Networks

In this talk, we will present a privacy-preserving distributed learning framework, which enables shared governance and ownership of ML models. The framework can contribute to the realization of autonomous optical networking and transmission paradigm.
Expert System for Diagnosing Pregnancy Complaints by Forward Chaining

Limited time in consulting becomes an obstacle for midwives in diagnosing complaints in pregnant women, especially those who are already in the III trimester and approaching the labor process. Misdiagnosis results in inaccuracies in the provision of solutions and actions. Initial treatment that corresponds to the complaints of pregnant women especially the third trimester is expected to reduce mortality rates in the mother and fetus. Expert System can be a timely solution with not too long so as to improve the quality of examination on midwives. The methods used are identification, primary and secondary data collection, forward chaining data analysis combined with bayesian, and evaluation with the calculation of the percentage of system success. Samples taken by 20 patients and 4 patients were declared unsyed because they had only one complaint. Meanwhile, 16 patients had some complaints that complied with the Rules. A total of 11 out of 16 patients or about 70% had valid results between the diagnosis of experts/midwives with the system. It can be concluded that the system works well to diagnose complaints in patients with a third trimester gestational age so that midwives can provide appropriate initial solutions and treatment in reducing maternal and infant mortality.
Learning to Answer Word-Meaning-Explanation Questions for Chinese Gaokao Reading Comprehension

Word sense understanding (WSU) is fundamental for human beings’ reading and the word-meaning-explanation question is an important kind of questions in Chinese reading comprehension (RC) in the college entrance exams of China (called as ‘Gaokao’ for short), which requires students to explain the meaning for a target word. This paper proposes a method to answer the word-meaning-explanation questions, which combines the attractive VAE framework with the BERT and Transformer to learn rich, nonlinear representations for producing the high-quality explanation for a target word within a certain context. In order to generate multi-style explanations, we construct not only the Chinese dictionary-style datasets, but also the essay-style dataset as a supplement to Chinese Gaokao application. We also build the Gaokao-style test set to evaluate our model. The experimental results show that our model can perform better than the baseline models. The code and the relevant dataset will be released on Github.
Exploiting Active Learning in Novel Refractive Error Detection with Smartphones

Refractive errors, such as myopia and astigmatism, can lead to severe visual impairment if not detected and corrected in time. Traditional methods of refractive error diagnosis rely on well-trained optometrists operating expensive and importable devices, constraining the vision screening process. Advance in smartphone camera has enabled novel low-cost ubiquitous vision screening to detect refractive error or ametropia through eye image processing, based on the principle of photorefraction. However, contemporary smartphone-based methods rely heavily on hand-crafted features and sufficiency of well-labeled data. To address these challenges, this paper exploits active learning methods with a set of Convolutional Neural Network features encoding information of human eyes from pre-trained gaze estimation model. This enables more effective training on refractive error detection models with less labeled data. Our experimental results demonstrate the encouraging effectiveness of our active learning approach. The new set of features is able to attain screening accuracy of more than 80% with mean absolute error less than 0.66, meeting the expectation of optometrists for 0.5 to 1. The proposed active learning also requires significantly fewer training samples of 18% in achieving satisfactory performance.
Media Freedom in South Africa Today: Unravelling Multifarious Threats toward a Research and Advocacy Response

This article assesses the multiple factors which are barriers to media freedom in South Africa. These include crises of financial sustainability of the media sector which have recently been compounded by the onset of the COVID-19 pandemic; widespread job losses in journalism; and harassment and physical violence against journalists from political representatives, security services, the judiciary, citizens and from within the media sector itself. Notwithstanding the contested liberal normative role of the press, the assessment of “media freedom inhibitors” offered here is theoretically premised on the role of the media in a democracy, and the importance of media freedom to the political, economic and social welfare of a democratic society. Methodologically, the data was collated from the State of the Newsroom (SoN) publications, and via the South African National Editors’’ Forum (SANEF) archive, which revealed that the intensity of attacks on journalists in South Africa is escalating, and from different quarters. Thus, the article argues for broadening the scope of research and advocacy efforts, beyond government-proposed legislation, to the multiple other obstacles to media freedom in the country.
A Jump-Markov Regularized Particle Filter for the estimation of ambiguous sensor faults

Sensor or actuator faults occurring on a Unmanned Aerial Vehicle (UAV) can compromise the system integrity. Fault diagnosis methods is then becoming a required feature for those systems. In this paper, the focus is on fault estimation for a fixed-wing UAVs in the presence of simultaneous sensor faults. The altitude measurements of a UAV are commonly obtained from the combination of two different types of sensors: a Global Navigation Satellite System (GNSS) receiver and a barometer. Both sensors are subject to additive abrupt faults. To deal with the multimodal nature of the faulty modes, a Jump-Markov Regularized Particle Filter (JMRPF) is proposed in this paper to estimate the barometric altitude and GNSS altitude measurement faults, including the case when both faults occur simultaneously. This method is based on a regularization step that improves the robustness thanks to the approximation of the conditional density by a kernel mixture. In addition, the new jump strategy estimates the correct failure mode in 100 % of the 100 simulations performed in this paper. This approach is compared with an Interacting Multiple Model Kalman Filter (IMM-KF) and the results show that the JMRPF outperforms the IMM-KF approach, particularly in the ambiguous case when both sensors are simultaneously subject to additive abrupt faults.
Conversion of Stream Cipher into Block Cipher

In the time of internet when we are sending and receiving the information over the network in the presence of the third party then it is very necessary to protect our data from the attacker’s, in the series to protect the data we are using the cryptography algorithms. Day to day the new cryptography algorithms are found and there is modification in the previous algorithm also. The proposed paper is an example of the converting the stream cipher into the block cipher. In this paper I taken the Playfair example is as the stream cipher, and tried to convert into the block cipher. This is also an example of the symmetric key algorithm. This is a two steps algorithm. In first step, I taken the 5x5 matrix that is traditional Playfair matrix of 26 characters and I take a second matrix of 5x5 of words. For converting the plain text into cipher text first of all se the plain text pair in the first matrix according the position of these character’s we replace the words from the second matrix. I used the all rules of traditional Playfair algorithm.
StarGAN-EgVA: Emotion Guided Continuous Affect Synthesis

Recent advancement of Generative Adversarial Network (GAN) based architectures has achieved impressive performance on static facial expression synthesis. Continuous affect synthesis, which has applications in generating videos and movies, is underexplored. Synthesizing continuous photo-realistic facial emotion expressions from a static image is challenging because a) there is a lack of consensus on what parameters produce a smooth shift of facial expressions and b) it is difficult to balance between the consistency over personalized features and granularity among continuous emotional states. We adapt one of the most successful networks, StarGAN, and propose StarGAN-EgVA to generate continuous facial emotions based on 2D emotional representations, i.e., valence and arousal (VA). We propose to utilize categorical emotions ( e.g., happy, sad) to guide the regression training on VA intensities so that the model learns both the domain-specific features and subtle changes introduced by different VA intensities. A special trick at testing is also exploited to automatically infer emotion labels from the VA point on a 2D emotional plane to ensure smooth transition among emotional states. Qualitative and quantitative experiments demonstrate our proposed model's ability to generate more photo-realistic and consistent affect sequences than the state-of-the-art.
Video 360 Content Navigation for Mobile HMD Devices

We demonstrate a video 360 navigation and streaming system for Mobile HMD devices. The Navigation Graph (NG) concept is used to predict future views that use a graph model that captures both temporal and spatial viewing behavior of prior viewers. Visualization of video 360 content navigation and view prediction algorithms is used for assessment of Quality of Experience (QoE) and evaluation of the accuracy of the NG-based view prediction algorithm.
A New Method for Pupil Detection in Gaze-Point Estimation Systems Based on Active Contours

Eye tracking and gaze-point estimation has increasing applications in the field of human-machine interface. Although so far a number of gaze-point estimation algorithms were investigated by researchers, video-based methods can be counted as the most important and efficient category in which eye features are obtained by processing of eye images. One of the most important factors affecting on the accuracy of gaze-point estimation is high-accurate extraction of pupil boundary. In this paper, a new method based on active contours is proposed for pupil boundary extraction. Active contours are among the conventional and useful methods for image segmentation. Generally, deformable models are curves that can evolve in order to minimize the internal and external energies in image domain. The internal energy keeps the curve smooth and differentiable, while the external energy directs the curve to the desired properties. Experimental results demonstrated suitable performance of the proposed method for a number of benchmark eye-images. Also, we used our method in an eye-tracker system for pupil segmentation. Significantly good performance of that system compared to a number of other eye-trackers can be counted as another concrete evidence for high solution quality of our method.
Joint Sentiment Part Topic Regression Model for Multimodal Analysis

The development of multimodal media compensates for the lack of information expression in a single modality and thus gradually becomes the main carrier of sentiment. In this situation, automatic assessment for sentiment information in multimodal contents is of increasing importance for many applications. To achieve this, we propose a joint sentiment part topic regression model (JSP) based on latent Dirichlet allocation (LDA), with a sentiment part, which effectively utilizes the complementary information between the modalities and strengthens the relationship between the sentiment layer and multimodal content. Specifically, a linear regression module is developed to share implicit variables between image–text pairs, so that one modality can predict the other. Moreover, a sentiment label layer is added to model the relationship between sentiment distribution parameters and multimodal contents. Experimental results on several datasets verify the feasibility of our proposed approach for multimodal sentiment analysis.
Fast and Space Efficient Convex Hull Vector Network For Breast Cancer Classification

This work is all about an efficient new algorithm to find out Convex Hull for a large dataset which may or may not have duplicate values. This algorithm is basically an improvement over Graham Scan Convex Hull algorithm with the help of SSGM Sort. This algorithm is further referred as Efficient Convex Hull (ECH) algorithm throughout this article. The complexity of time taken by Efficient Convex Hull algorithm presented over here is O(n) at its best and O(n log n) in the worst scenario for a problem of size n elements. The amount of memory space taken by ECH is O(1) which breaches the lower bound rendered on it by sorting algorithms on Graham Scan convex hull. Here it would be better to understand that the type of problems convex hull deals with generally has large number of duplicate values. ECH utilizes this fact and give time complexity of small “o” that is o(n log n) which is much lesser than O(n log n). Further an application of ECH is Efficient Convex Hull Vector Network (ECHVN) is proposed by combining benefits of ECH and SVMs.
Attack Vectors for Blockchain and Mapping OWASP Vulnerabilities to Smart Contracts

Smart contracts powered by Blockchain render transaction processes more effective, secure and efficient when viewed alongside conventional contacts. Smart Contracts facilitate a trustworthy process and are characterized by time efficiency, cost-effectiveness and transparency, without any necessity for intervention by third-party intermediaries like lawyers. While Blockchains can counter, in a good way, traditional cyber-security attacks on Smart Contract Applications, cyber-criminals keep evolving new mechanisms of threats and attack vectors, capable of hacking Blockchain technologies. The research done in this chapter presents a unique framework to perform Application Security Testing on Blockchain-based Smart Contracts and also compares Manual Penetration Testing with two automated Smart Contract tools to identify critical vulnerabilities on the commercial scale in the Blockchain environment.
Agile Software Projects Implementation in Technical Education for Learners – A Case Study

Software Development process is quite mature now a days and various software development models are existing in the software industry since last many decades. Software development has very high failure rate due to various reasons. In the recent years Agile has gain popularity in the software industry and there is need to teach this process is to students studying in technical institutions. Students studies the traditional models like waterfall model, Iterative process, RAD model etc. in the courses and Implements their project using above software development models. This research paper is the study of the learning of students while Implementations agile in the project. this paper focus on how much students have learned while applying agile in their university curriculum projects and their short comings and identification of weakness.
Fighting Filterbubbles with Adversarial Training

Recommender engines play a role in the emergence and reinforcement of filter bubbles. When these systems learn that a user prefers content from a particular site, the user will be less likely to be exposed to different sources or opinions and, ultimately, is more likely to develop extremist tendencies. We trace roots of this phenomenon to the way the recommender engine represents news articles. The vectorial features modern systems extract from the plain text of news articles are already highly predictive of the associated news outlet. We propose a new training scheme based on adversarial machine learning to tackle this issue . Our preliminary experiments show that the features we can extract this way are significantly less predictive of the news outlet and thus offer the possibility to reduce the risk of manifestation of new filter bubbles.