Reviewerly/exHarmony huggingface dataset:
An Obstacle Detection and Avoidance System for Mobile Robot with a Laser Radar

In this paper, an obstacle detection and avoidance system based on laser radar for mobile robots is established, and a new integrated improved algorithm is proposed. The system is composed of industrial computer, laser radar and mobile robot. The laser radar is mounted on the mobile robot, and the environmental information in front of the mobile robot is obtained through the laser radar and sent to the industrial computer. Set the scrolling window in front of the laser radar and layer the scrolling window to adopt different control strategies for different levels of obstacles. The experimental results show that the system can effectively detect and avoid obstacles.
YNUWB at SemEval-2019 Task 6: K-max pooling CNN with average meta-embedding for identifying offensive language

This paper describes the system submitted to SemEval 2019 Task 6: OffensEval 2019.The task aims to identify and categorize offensive language in social media, we only participate in Sub-task A, which aims to identify offensive language.In order to address this task, we propose a system based on a K-max pooling convolutional neural network model, and use an argument for averaging as a valid meta-embedding technique to get a metaembedding.Finally, we use a cyclic learning rate policy to improve model performance.Our model achieves a Macro F1-score of 0.802 (ranked 9/103) in the Sub-task A.
Redesigning a Main Menu ATM Interface Using a User-Centered Design Approach Aligned to Design Thinking: A Case Study

Currently, development teams face many challenges when designing user interfaces. There are more, and more, new methodologies, processes and techniques to make user-centered design. However, these teams often lack specific techniques or methods to accomplish these designs themselves in a real-world development process. The following is a proposal of a user-centered design approach through a case study. The approach was applied to successfully redesign the main menu of an ATM interface of a Peruvian Bank. In this iteration, the convenience of aligning the proposal with proven methodologies such as Design Thinking was observed. This proposal continues to be applied successfully in the development process of the ATM team of said bank.
Orientation Robust Object Detection in Aerial Images Based on R-NMS

Object detection in aerial images is a challenging task which plays an important role in many fields, such as intelligent traffic management, fishery management and so on. Different from object detection in natural images, the orientation of objects in aerial images is arbitrary. The axis-aligned bounding box detection, which is always used in traditional object detection methods, will cover a lot of redundant information and deteriorate the detection results when it is used to locate the object in aerial images. Therefore, traditional object detection methods are no longer applicable for aerial images. In order to promote the object detection performance in aerial images, we propose a novel orientation robust object detection model based on rotated non-maximum suppression (R-NMS). In addition, we adjust the anchor setting according to the diversity shapes of the aerial objects to enhance the performance of the model. Our model is tested on the public DOTA dataset, and the mAP is 16.31% higher than the baseline, indicating that our method is very effective and competitive in the object detection of aerial image.
Comparative Study of Various Feature Extraction Techniques for Pedestrian Detection

This paper presents, feature extraction techniques such as center symmetric local binary pattern (CSLBP), extended CSLBP (XCSLBP), speeded-up robust feature (SURF) with 64 and 128 feature descriptors and histograms of oriented gradients (HOG) applied on a set of images from INRIA person database, to detect pedestrians. About fifteen feature sets created using different combinations of the aforementioned methods are compared using two detectors, random forest (RF) and support vector machine (SVM). Performance validation is done based on the accuracy, precision, recall and space required for storing feature vectors. Experimental results have shown that CSLBP and the novel XCSLBP+CSLBP feature sets yield 100% accuracy, when used with RF classifier, whereas, the novel SURF-128+XCSLBP combination and SVM linear classifier gave 99.2% accuracy in detecting pedestrians.
An Evaluation on the Factors Influencing Happiness at Working Environment in UAE Government Entities: A Literature Review

Work environment plays a very vital role in the employee performance in any organization around the world.Conducive work environment motivates employees to work hard to achieve their stated goals and objectives.Providing enabling working environment by UAE government to its employees tremendously affects their performance, it helps in managing employees' work-life balance.Therefore, this paper reviews the related literature based on the factors that influence the happiness at working environment in UAE government entities.This review figures out those factors and explained them in detail on how they influence happiness at working environment in UAE government entities.
Organizational, Psychological and Pedagogical Conditions for the Use of E-Books and e-Textbooks at School

The article defines the organizational, psychological and pedagogical conditions for the use of e-books and e-textbooks at school. The difference between e-books and e-textbooks is distinguished on the basis of systematization and comparison of scientific positions. Content analysis is made to determine the organizational and pedagogical conditions for the introduction of e-textbooks. The experience of using e-textbooks and e-books at school was generalized based on the results of a regional experiment, in which 25 schools participated. The results of the survey and questioning of 352 students, 296 teachers, and 357 parents are described. The results reveal the needs of respondents in e-textbooks and e-books, identify the advantages and disadvantages of their implementation. The main organizational, psychological and pedagogical conditions are defined. These are educational policy concerning the introduction of eLearning (requirements for mandatory e-textbooks, financial support for purchasing and updating, examination, standards for e-textbooks), availability and diversity of e-textbooks and e-books on the market of educational services, logistic, educational and methodological support of educational institutions, ergonomics, readiness of teachers, students, their parents to use e-textbooks and e-books, digital competence of teachers and students.
Deep learning detects impending organ injury in the clinic

Organ damage is often detected late, when treatment options are limited. The use of artificial intelligence to continuously monitor a patient’s medical data can identify people at risk of imminent kidney injury. Artificial intelligence identifies patients at risk of kidney damage.
Privacy-Preserving Spectrum Auction Design: Challenges, Solutions, and Research Directions

Spectrum auction is deemed as an efficient approach for redistributing spectrum and realizing dynamic spectrum access. Though there have been extensive research endeavors in designing spectrum auctions that are truthful with desirable social welfare, security and privacy concerns give rise to new challenges and calls for the design of privacy- preserving spectrum auction mechanisms. In this article, we begin with an overview of conventional spectrum auction mechanisms, then discuss the potential security threats in them. In particular, bidders' private information, for example, bid values and geo-locations, are submitted to the non-trustworthy auctioneer and can be observed by rival bidders who may leverage this information to rig the auction. To address this problem, we outline the framework for privacy-preserving auction mechanism design, which applies cryptographic tools to the original auction and ensures that the private information of bidders is protected, while the basic functionalities of the auction are maintained. Based on the proposed framework, we further construct a privacy-preserving heterogeneous spectrum double auction, which is shown to uphold the closely similar allocation efficiency with the original auction, and realizes privacy preservation with moderate communication and computation overheads.
Ranking and Sampling in Open-Domain Question Answering

Open-domain question answering (OpenQA) aims to answer questions based on a number of unlabeled paragraphs.Existing approaches always follow the distantly supervised setup where some of the paragraphs are wrong-labeled (noisy), and mainly utilize the paragraph-question relevance to denoise.However, the paragraph-paragraph relevance, which may aggregate the evidence among relevant paragraphs, can also be utilized to discover more useful paragraphs.Moreover, current approaches mainly focus on the positive paragraphs which are known to contain the answer during training.This will affect the generalization ability of the model and make it be disturbed by the similar but irrelevant (distracting) paragraphs during testing.In this paper, we first introduce a ranking model leveraging the paragraph-question and the paragraph-paragraph relevance to compute a confidence score for each paragraph.Furthermore, based on the scores, we design a modified weighted sampling strategy for training to mitigate the influence of the noisy and distracting paragraphs.Experiments on three public datasets (Quasar-T, SearchQA and Triv-iaQA) show that our model advances the state of the art.
Implementation of Web AR Applications with Fog Radio Access Networks Based on Openairinterface Platform

Augmented Reality (AR), is a technology that gives you an interactive experience of the real world where the objects reside in are `augmented' by computer-generated perceptual information. Though some AR applications already exist on our smart devices, their development is not as promising as expected due to the requirement of the installation of a dedicated app. Hence the application of AR on large scales has been hampered to a certain extent. The advent of Web AR is a good complementary of the original in-app AR applications. With the convenience and cross-platform characteristics of Web browser, Web AR is being paid increasingly attention from both manufacturers and researchers. Fog Radio Access Networks (F-RAN) architecture takes full advantage of the storage and processing capabilities in edge devices, which significantly alleviates the burden of the fronthaul and BBU pool and reduces the transmission delay. Openairinterface is an LTE-compatible open source platform provided by Openairinterface Software Alliance (OSA). It is a promising demonstration platform, on which many new system architectures and communication technologies can be deployed. In this article, two applications of Web AR, including Web Face AR and Web Space AR, are successfully demonstrated on our F-RAN hardware platform based on Openairinterface. Performance evaluation results validate that they can provide users with good Quality of Experience (QoE) and very low latency.
Advanced Capsule Networks via Context Awareness

Capsule Networks (CN) offer new architectures for Deep Learning (DL) community. Though its effectiveness has been demonstrated in MNIST and smallNORB datasets, the networks still face challenges in other datasets for images with distinct contexts. In this research, we improve the design of CN (Vector version) namely we expand more Pooling layers to filter image backgrounds and increase Reconstruction layers to make better image restoration. Additionally, we perform experiments to compare accuracy and speed of CN versus DL models. In DL models, we utilize Inception V3 and DenseNet V201 for powerful computers besides NASNet, MobileNet V1 and MobileNet V2 for small and embedded devices. We evaluate our models on a fingerspelling alphabet dataset from American Sign Language (ASL). The results show that CNs perform comparably to DL models while dramatically reducing training time. We also make a demonstration and give a link for the purpose of illustration.
Improving Weight Initialization of ReLU and Output Layers

We introduce a data-dependent weight initialization scheme for ReLU and output layers commonly found in modern neural network architectures. An initial feedforward pass through the network is performed using an initialization set (a subset of the training data set). Using statistics obtained from this pass, we initialize the weights of the network, so the following properties are met: (1) weight matrices are orthogonal; (2) ReLU layers produce a predetermined fraction of non-zero activations; (3) the outputs produced by internal layers have a predetermined variance; (4) weights in the last layer are chosen to minimize the squared error in the initialization set. We evaluate our method on popular architectures (VGG16, VGG19, and InceptionV3) and faster convergence rates are achieved on the ImageNet data set when compared to state-of-the-art initialization techniques (LSUV, He, and Glorot).
Insights from Building an Open-Ended Conversational Agent

Dialogue systems and conversational agents are becoming increasingly popular in modern society. We conceptualized one such conversational agent, Microsoft’s “Ruuh” with the promise to be able to talk to its users on any subject they choose. Building an open-ended conversational agent like Ruuh at onset seems like a daunting task, since the agent needs to think beyond the utilitarian notion of merely generating “relevant” responses and meet a wider range of user social needs, like expressing happiness when user’s favourite sports team wins, sharing a cute comment on showing the pictures of the user’s pet and so on. The agent also needs to detect and respond to abusive language, sensitive topics and trolling behaviour of the users. Many of these problems pose significant research challenges as well as product design limitations as one needs to circumnavigate the technical limitations to create an acceptable user experience. However, as the product reaches the real users the true test begins, and one realizes the challenges and opportunities that lie in the vast domain of conversations. With over 2.5 million real-world users till date who have generated over 300 million user conversations with Ruuh, there is a plethora of learning, insights and opportunities that we will talk about in this paper.
Detection of Free-Form Copy-Move Forgery on Digital Images

Nowadays, production and distribution of digital images has become part of our life. Since digital images, which are important carriers of information, are considered as the concrete proofs of facts in many fields and they can be used as evidence in the courts of law, development of techniques to ensure image authenticity is an active research topic. Copy-move forgery is one of the most common manipulation techniques that are implemented on the digital images, and various techniques have been developed for detection of these kinds of forgeries. JPEG format, which presents the ability of making high rate compression without causing remarkable changes in the meaning of the image, is the most commonly used format on digital images. In this study, the topic of detecting free-form copy-move forgeries on digital images is covered. It has been observed that the developed technique is able to detect the professional forgeries in which the copied region is selected in free-form and which are almost impossible to be detected by human eye, with high success rate, and it is able to give successful results even if the image is exposed to postprocesses such as JPEG compression and Gaussian filtering, which make the detection of forgery harder.
Quantum teleportation of Dirac fields in noninertial frames with amplitude damping

We investigate quantum teleportation between users located in noninertial frames and analyze how the acceleration can improve the fidelity of quantum teleportation between two relatively accelerating partners. We see that the fidelity increases with respect to the acceleration as a consequence of the Unruh effect. In our scheme, Alice and Rob initially share a special $\mathsf{X}$-type state, Alice stays stationary, while Rob is uniformly accelerated with respect to Alice. We additionally study the system-environment dynamics in the noninertial frames, i.e., the $\mathsf{X}$-type state shared between Alice and Rob interacts with the environment via amplitude damping channel. Our results demonstrate a monogamy relation of the fidelity with acceleration and amplitude damping channel, these result suggest that the fidelity of quantum teleportation cannot be enhanced simultaneously by both the acceleration and amplitude damping channel. Such a property may have potential applications in protecting the fidelity of quantum teleportation performed by observers in relative motion.
INSTITUTIONAL MECHANISMS OF UNIVERSITY TEACHER PROFESSIONAL DEVELOPMENT

Purpose of the study: Conditions of qualifications’ dynamic development, rapid obsolescence of existing competencies actualize the need to create a system of continuous professional growth of teachers on staff training. The purpose of the article is to develop organizational mechanisms that contribute to the professional growth of the teacher inflexible response conditions of education to socio-economic changes.&#x0D; Methodology: The research methodology relies on the lifelong education concept, defining the design principles of a teacher professional growth system: consistency, continuity, self-development, participative nature, pedagogical reflexing&#x0D; Results: The system of the teacher’s professional growth in the educational institution possessing properties of hierarchy, flexibility, and dynamism is developed. The principles and organizational mechanisms for this system’s implementation are revealed: corporatism as a way to create targeted educational platform based on digital technology; tutoring as the insurance of the teacher’s and the educational organization’s interests through the accompany of career advancement; branding as a result of the teacher’s and the educational organization’s competitiveness in the market of educational services; road maps for planning, monitoring and support of the teacher’s career development.&#x0D; Applications of this study: The article is intended for teachers, managers, employees of personnel services of educational organizations.&#x0D; Novelty/Originality of this study: For the first time the self - learning system of the teacher professional growth is offered which is capable to exist and productively function in modern dynamic conditions at the expense of adaptive organizational mechanisms of reaction to social and economic changes.
Wheelchair Able to Assist the Elderly to Move on Stairs and Stand up

With the increasing in the number of elderly people and the deterioration of their leg muscles, wheelchairs have become an important mean of transportation. In the market, most wheelchairs that can climb stairs use a crawler-type or double planetary gears' wheel. But the crawler has the disadvantages of being bulky and has big friction on the ground and double planetary gears' wheel will cause big shake while climbing stairs. Rocker-bogie is also a mechanism can step over high obstacles but cannot climb stairs. To climb stairs smoothly and safely, we designed a wheelchair that based on the three mechanisms with advantages of all of them. With linkage mechanisms, the wheelchair can also adjust the location of the center of gravity (COG) of the user for safer climbing. Additionally, the linkage mechanism can also assist the user to stand up, which is more helpful for the user of the wheelchair. It is composed of four-linkage and parallelogram mechanisms. To assist the user to stand up with the normal posture of a healthy elderly, three parameters were taken into account in the design of chair part: trajectory of COG, the change of the upper body angle and the trajectory of the hip joint of the user while standing up. From the simulation results, the torque of the knee and ankle joint of the user being assisted to stand up decreased much. From theoretical calculations and experiments of the scaled models, different new mechanisms were compared and the one that was able to climb stairs most smoothly and stably was chosen. The effectiveness of the new mechanism was confirmed.
A novel recommendation method based on general matrix factorization and artificial neural networks

Collaborative filtering is a successful approach in relevant item or service recommendation provision to users in rich, online domains. This approach has been widely applied in commercial environments with success, especially in online marketing, similar product suggestion and selection and tailor-made consumer suggestions. However, regardless of its market penetration, there are still considerable limitations in terms of accuracy in the proposed recommendations stemming from the high-frequency low-relevance user-item bias, data specificities and individual user patterns and needs that may be hidden in data. We propose a novel recommendation approach that improves accuracy and requires significantly less maintenance compared to traditional collaborative filtering. For the experimental evaluation, we use two real data sets and well-known metrics with the results validating our method. Our proposed method outperforms all the alternative recommendation methods for each of the two data sets and metrics and seems holistically effective against alternatives since it requires fewer settings to be considered without affecting the output.
Learning isolated polysemous words: identifying the intended meaning of language learners in informal ubiquitous language learning environments

Abstract A growing number of language learners use ubiquitous language learning applications to learn anytime and anywhere. Learners translate and learn isolated words inspired by their activities and surroundings. However, isolated words may have several meanings that change depending on the context. Since learners don’t have the opportunity to indicate the meaning they are looking for in an online learning environment, they risk learning translations that do not correspond to their intended meaning. Identifying the intended meaning of the learner is needed to provide them with an appropriate translation. However, isolated words are difficult to disambiguate due to a lack of text around them. To this end, informal ubiquitous learning environments can offer another type of context, one that is formed by the users’ past learning logs. In this work, we propose using the learners’ past vocabulary to disambiguate their intended meaning when they look up isolated words. Accordingly, we propose and evaluate three methods. The first method considers that the intended meaning of the learner is the one that is the most semantically similar to the learner’s past vocabulary. The second method builds on the first method but gives more weight to the vocabulary that the learner logged shortly before the target word. The third method addresses situations where the semantic similarities between the different meanings of the word and the past vocabulary have similar values. In those cases, the method considers that the intended meaning of the learner is the most common meaning in the target language. The three methods were evaluated using 148 logs of SCROLL, a ubiquitous informal language learning environment. The success rates of the three methods were 72.180%, 75.630%, and 83.050% respectively. This work shows that the past activity of language learners in informal ubiquitous language learning environments could be used to identify their intended meaning when learning a new word.