Reviewerly/exHarmony huggingface dataset:
ReFPN-FCOS: One-Stage Object Detection for Feature Learning and Accurate Localization

One-stage object detectors are simple and efficient; however, they cannot extract sufficient object features due to simplistic structures. At the same time, the classification score cannot reflect the actual positioning of the candidate box. Therefore, it is not accurate to use classification score only as the candidate box position score in non-maximum suppression (NMS) stage. These two shortcomings degrade the detection accuracy. In this paper, a novel feature pyramid architecture named refined feature pyramid network (ReFPN) is introduced to obtain better object features. ReFPN designs a refined module which is parallel with feature pyramid network (FPN) to extract the semantic features of objects, and then the extraction of features are used to optimize the features of FPN by summation. In addition, we design the refined center-ness (RCenter-ness) branch that predicts the position score of each point on the feature map to improve the localization accuracy. The predicted position score is multiplied by the classification score to obtain the final position score that has a stronger correlation with localization accuracy. The final position score is inputted to the subsequent NMS, which improves localization accuracy. The proposed method in this paper is named ReFPN-FCOS. The sufficient experiments on COCO2017 datasets demonstrate the effectiveness of ReFPN-FCOS on improving classification accuracy and localization accuracy. The average precisions of this method achieve 1.1% and 1.3 % higher than those of FCOS, when using ResNet50 and ResNet101 as backbone respectively. Code download link: https://github.com/xjl-le/mmdete.
Racism Detection in Twitter Using Deep Learning and Text Mining Techniques for the Arabic Language

In light of the recent cultural withholding of discriminative tendencies, cyber-racism detection is no longer luxurious incorporation to online platforms. As a result, the detection of hate speech has been a vital area of research in the last decade. Nevertheless, the progress of development and incorporation of hate speech detectors in general and racism detectors in particular in the Arab region is considered to be relatively slow in comparison to English speaking countries. Therefore, this work aims to contribute to the detection of cyber-racism in the Arab region by considering the complicated nature of the Arabic language through tailored data acquisition and multi-staged pre-processing models. Furthermore, it takes into account the specificity of the application of racism detection in comparison to more broad sentiment analysis models that validly call for complicated architectures. Our proposed approach utilizes deep learning techniques due to their superiority given the availability of very large datasets. In detail, it employs both convolutional neural network (CNN) and metaheuristic optimization algorithm for the classification of racism-oriented tweets in the Twitter platform.
Blockchain Empowered Differentially Private and Auditable Data Publishing in Industrial IoT

As more and more organizations deploy their sensing devices in the industrial Internet of Things network, it becomes increasingly important for the organizations to share data with others, so that the value of the data can be fully explored. However, individuals' privacy may be compromised because of data sharing. In this article, we study the differentially private data publishing problem, which aims at balancing between privacy and data utility. Specifically, two blockchain-based data publishing protocols are proposed. For histogram publishing, we propose a protocol where the Laplace noise added in the query result is verified by the blockchain. For anonymized data publishing, we propose a protocol, which can prevent the publisher and the recipient from lying about the utility of the published data. With the blockchain acting as a reliable intermediary between the publisher and the recipient, the proposed protocols can help to realize fair and auditable data sharing.
Where is my Deer?-Wildlife Tracking And Counting via Edge Computing And Deep Learning

Reliable, informative and up-to-date information regarding the location, mobility and behavioral patterns of animals enhances our ability to preserve biodiversity, manage invasive species and conduct research. The basis of which is an accurate count of the animals present in a specified region. In literature, previous studies have presented automated animal counting methods, usually relying on using single images. Thus, accuracy is challengeable due to several factors, including wildlife movement, light fluctuations, overlapping, occlusions and re-counting of the same animal reappearing in other images. In this paper, we present a novel approach of identification, introduction to tracking pipeline, and counting wildlife accurately. Having applied the techniques of deep convolutional neural network (CNN), edge computing, and online tracking in a field trial to determine the population density of deer in a given area. Our approach produced accurate and actionable results, thus there is viable commercial potential.
No mark is an island: Precision and category repulsion biases in data reproductions

Data visualization is powerful in large part because it facilitates visual extraction of values. Yet, existing measures of perceptual precision for data channels (e.g., position, length, orientation, etc.) are based largely on verbal reports of ratio judgments between two values (e.g., [7]). Verbal report conflates multiple sources of error beyond actual visual precision, introducing a ratio computation between these values and a requirement to translate that ratio to a verbal number. Here we observe raw measures of precision by eliminating both ratio computations and verbal reports; we simply ask participants to reproduce marks (a single bar or dot) to match a previously seen one. We manipulated whether the mark was initially presented (and later drawn) alone, paired with a reference (e.g. a second `100%' bar also present at test, or a y-axis for the dot), or integrated with the reference (merging that reference bar into a stacked bar graph, or placing the dot directly on the axis). Reproductions of smaller values were overestimated, and larger values were underestimated, suggesting systematic memory biases. Average reproduction error was around 10% of the actual value, regardless of whether the reproduction was done on a common baseline with the original. In the reference and (especially) the integrated conditions, responses were repulsed from an implicit midpoint of the reference mark, such that values above 50% were overestimated, and values below 50% were underestimated. This reproduction paradigm may serve within a new suite of more fundamental measures of the precision of graphical perception.
Hedge algebras-based admittance controller for safe natural human–robot interaction

(2020). Hedge algebras-based admittance controller for safe natural human–robot interaction. Advanced Robotics: Vol. 34, SpeciaI Issue on Robot and Human Interactive Communication 2020 (Part II), pp. 1546-1558.
The open peer review experiment in <i>Educational Philosophy and Theory</i> (EPAT)

Open Peer Review: Educational Philosophy and Theory (EPAT) Michael A. Peters, Beijing Normal University, PR China In 2016 EPAT started experimenting with open peer review for articles that were par...
Activities of daily living with motion: A dataset with accelerometer, magnetometer and gyroscope data from mobile devices

The dataset presented in this paper is related to the performance of five Activities of Daily Living (ADL) with motion, such as walking, running, standing, walking upstairs, and walking downstairs. These activities were performed with a mobile device in a waistband, containing the data acquired from accelerometer, magnetometer, and gyroscope sensors. These data include the motion data, which allow the characterization of the different types of movement. The data acquisition was performed in open environments by 25 individuals (15 man, and 10 woman) in the Covilhã, and Fundão municipalities (Portugal). The data related to the different sensors was acquired with a sampling rate of 100 Hz by the accelerometer, 50 Hz by the magnetometer, and 100 Hz by the gyroscope sensors. It includes the captures related to a minimum of 2000 captures for each ADL, which corresponds to 2.8 h (approximately) for each ADL. In total, this dataset includes 13.9 h (approximately) of captures. These data can be reused for the implementation of data processing techniques, and artificial intelligence methods.
Efficient large scale global optimization through clustering-based population methods

Back in the 80’s clustering methods were considered state of the art for non-structured box constrained global optimization (GO). Their disappearance is mainly due to their increasing difficulties in solving even moderately sized GO problems, yet the basic idea was indeed a brilliant one. More recently population methods and Differential Evolution (DE) in particular has gained much attention in the GO heuristic world due to their easy implementation and good exploration capabilities. In order to improve the exploitation capability of DE, some memetic variants have been proposed with success. In this paper we revisit clustering methods and apply them both to standard low dimensional problems and to large scale ones; in particular we propose a novel approach to apply a clustering-type decision on when to start a local search to variants of memetic DE. The resulting algorithm, C-MDE (Clustering Memetic DE) outperforms the best-known methods both in quality of the returned solution and in the number of calls to the expensive local optimization phase, even in large dimension. For large dimensional problems, random projections are used in order to be able to decide on starting a local search on a limited number of features. The resulting GO method is a revisit of clustering techniques in which all of the defects which made those methods no more feasible are eliminated: in particular, the method is used within an adaptive population method and it efficiently runs also in large dimension. Thus we think the proposed approach will find a place in the top-performing modern GO algorithms.
Variability and Commonality Requirement Specification on Agile Software Development: Scrum, XP, Lean, and Kanban

The user story widely studied insufficiently describes the software requirements. Many studies discuss the challenges of requirements specifications and proposed solutions. However, there are many agile methods and each method embraces different practices. Therefore, the solution has to follow each method's ceremonies. Only a few studies discuss conducted practices in requirements specification development with the agile method and there are no studies that discuss practices comparisons among these methods. In this paper, we performed a systematic literature review to gather the available studies in procuring a current insight into the requirements engineering practices incorporate into the agile method. This study aims to map the variability and commonality practices among agile methods. The various and standard agile RE practices show crucial practices in the requirements specification development process. Moreover, it gives information on practices from other methods that increase the success of requirements specification development. From the systematic literature review, we found a total of 12 studies relevant to the study. There are eight variability and commonality practices between Scrum, XP, and Lean methods.
Scrum: An Effective Software Development Agile Tool

Abstract Present days the world is surviving with respect to the software products. The development of the software product is a challenging issue and to run with the competitive world rapid development of software products are necessary. It is not enough to go with the traditional software development products like waterfall model, spiral model and all. Here we proposed the SCRUM TOOL, one of the effective techniques of Agile Methodology. Agile is an incremental and timeframe iterative approach. It supplies the software developers with a working framework for traditional software development practices like waterfall model. Although the traditional models are best suited for small products where there is no changing requirements but not suitable for the products which have rapidly changing requirements and thus here we recommend SCRUM methodology. In this approach we can develop the software product by taking the regular feedback from the customer through review meetings. So whenever the customer requests the changes we can upgrade the product with respect to those changes and can also develop the product more efficiently and rapidly.
Temporal Action Localization Using Long Short-Term Dependency

Temporal action localization in untrimmed videos is an important but difficult task. Difficulties are encountered in the application of existing methods when modeling the temporal structures of videos. In the present study, we develop a novel method, referred to as the Gemini Network, for effective modeling of temporal structures and achieving high-performance temporal action localization. The significant improvements afforded by the proposed method are due to three major factors. First, temporal dependencies are explicitly distinguished as long-term temporal dependencies and short-term temporal dependencies and are separately captured by two dedicated subnets. Second, a long-range temporal dependency capture module combined with a self-adaptive pooling module is proposed to capture long-term temporal dependency. Third, the proposed method uses auxiliary supervision, with the auxiliary classifier losses affording additional constraints for improving the modeling capability of the network. As a demonstration of its effectiveness, the Gemini Network is used to achieve a state-of-the-art temporal action localization performance on two challenging datasets, namely, THUMOS14 and ActivityNet.
Multi-Camera-Based Person Recognition System for Autonomous Tractors

Recently, the development of autonomous tractors is being carried out as an alternative to solving the labor shortage problem of agricultural workers due to an aging population and low birth rate. As the level of autonomous driving technology advances, tractor manufacturers should develop technology with the safety of their customers as a top priority. In this paper, we suggest a person recognition system for the entire environment of the tractor using a four-channel camera mounted on the tractor and the NVIDIA Jetson Xavier platform. The four-channel frame synchronization and preprocessing were performed, and the methods of recognizing people in the agricultural environment were combined using the YOLO-v3 algorithm. Among the many objects provided by COCO dataset for learning the YOLO-v3 algorithm, only person objects were extracted and the network was learned. A total of 8602 image frames were collected at the LSMtron driving test field to measure the recognition performance of actual autonomous tractors. In the collected images, various postures of agricultural workers (ex. Parts of the body are obscured by crops, squatting, etc.) that may appear in the agricultural environment were required to be expressed. The person object labeling was performed manually for the collected test datasets. For this test dataset, a comparison of the person recognition performance of the standard YOLO-v3 (80 classes detect) and Our YOLO-v3 (only person detect) was performed. As a result, our system showed 88.43% precision and 86.19% recall. This was 0.71% higher precision and 2.3 fps faster than the standard YOLO-v3. This recognition performance was judged to be sufficient considering the working conditions of autonomous tractors.
Culture and vision in virtual reality narratives

Abstract Hansen states that “FL studies must learn to conceive of culture as an open, multi‐voiced and dialogical interaction full of contradictions.” One advocated approach to teach transcultural understanding is through the analysis of cultural narratives. Kearney defines cultural narratives as “the multiple (sometimes competing), conventional storylines that cultural groups produce and use to make sense of and attribute meaning to their shared experiences.” This article will showcase a project in a beginning French course in which four different Parisians from the same quarter were asked to document and share the stories of their lives with a virtual reality (VR) camera. Findings reveal that the VR narratives allowed students to envision, experience, and understand diverse facets of Parisian culture and more vividly imagine their future role as participants in Parisian communities.
Antimony in quartz as a vector to mineralization: A statistical approach from five Variscan Sb occurrences (France)

Using in-situ LA-ICP-MS, the trace-element composition of hydrothermal quartz from five Sb mineralized Variscan districts, from different parts of France, were compared. Each of these districts is characterized by the presence of stibnite, with quartz being a ubiquitous gangue mineral. The resulting dataset was processed using a set of multivariate statistical analyses (i.e. principal component analysis, hierarchical cluster analysis, and permutational multivariate analysis of variance). This approach highlights differences in ore-forming processes (e.g., fluid chemistry), recorded in the trace-element pattern of gangue minerals. Using this methodology, we could distinguish different mineralizing events with intricate paragenesis, such as those in the Sioule nappe, which appear to have recorded two different events of Sb precipitation. Results indicate that Sb incorporation in quartz is weakly correlated to other trace elements, but that its contents increase sharply in quartz from stibnite-bearing veins. In this case, Sb shows values >10 ppm and up to 200 ppm, considerably higher than the values measured in quartz unrelated to Sb mineralization (mainly ≤1 ppm). Intermediate Sb contents in quartz would point to the potential for a fluid to precipitate Sb ore. It is concluded that incorporation of Sb in quartz can be confidentially correlated to its content in the mineralizing fluid. Based on our data from five Sb districts in the French Variscan belt, supplemented by published Sb trace-element data in quartz from a number of other Sb deposits around the world, this study highlights the strong potential of the Sb content of quartz as a proxy for tracing Sb mineralization.
Digital Technologies Development in the Educational Process of Ecology and Industrial Safety Department, Bauman Moscow State Technical University

The report gives current status overview of how the digital technologies integration evolves in the educational process as well as gives brief overview of the whole educational activities of Ecology and Industrial Safety department in Bauman Moscow State Technical University. The most attention is payed to the development and introduction of the online course Life safety in the educational process and done using Open BMSTU platform and the educational portal of the department е9.bmstu.ru. The contents and the structure of course are presented. The peculiarities of the work at online course are pointed out. The forms of online presentations, access for authorized teachers and students and control tests are considered. The procedure of the creation of video lectures and introductions to laboratory works is briefly depicted. Other components of the educational portal including online video library of the department, laboratory works online timetable, life safety course online introduction videos to the laboratory works are also presented.
Non‐interactive integrated membership authentication and group arithmetic computation output for 5G sensor networks

Group-oriented applications show its potential ability in the next generation of wireless sensor networks (5G WSNs), which have the particularity of being heterogeneous and so have different capabilities in terms of storage, computing, communicating and energy. One of the main challenges for secure group-oriented applications (SGA) in 5G WSNs is how to secure communication between these heterogeneous devices. Conventional protocols are not suitable for SGA in 5G sensor networks since multiparty output establishment in this environment requires lightweight communication and computation overhead, further the primary task of SGA in 5G WSNs is to securely transmit various types of jointly computing data. Hence, membership authentication and multiparty output for arithmetic computations become two fundamental and necessary security services in SGA for 5G WSNs. In this paper we propose a novel design of non-interactive integrated membership authenticated multiparty output for arithmetic computations in 5G sensor networks, which embeds the function of membership authentication and multiparty output for arithmetic computations. Since any arithmetic computation function is composed of multiple additions and multiplications, our result serves as a general method for multiparty computation output in SGA. This design is more suitable for lightweight membership authenticated multiparty arithmetic computations output in 5G sensor networks.
Website interactivity and processing: Menu customization and sense of agency are keys to better interaction design

Particulate matter (PM) pollution is a serious issue in Korea, but people are not actively engaged in preventive actions. Therefore, this study aimed to develop a PM website that includes interactive features where users can easily learn PM information; further, we aimed to explore the effects of website interactivity on behavioral intentions. We conducted an online experiment with four versions of a website with differs regarding the presence/absence of menu customization and highlighter options. Data from 100 participants in South Korea showed that menu customization was effective in increasing the users’ sense of agency and their intentions to recommend the website, seek more PM information, and engage in the activities of the website owner. However, a highlighter tool was not a significant prompt for enhancing perceptual bandwidth and behavioral intentions. The findings of this study can contribute to the development of interactivity research and models and better website design.
Automatic interior layout with user-specified furniture

A new automatic layout scheme for interior furniture is presented. According to user-specified furniture, an empty room region is divided into several functional areas by use of conditional generative adversarial networks. Each type of functional area can contain some categories of particular furniture objects, while each category of furniture belongs to one or several types of functional areas. We design a data-driven approach to place the furniture in all the functional areas. We expound the learning process of the division of the functional areas for the case with the user-specified furniture constraint, including to improve the furniture vector, objective function, and training process. Experiments show the proposed layout scheme has advantages in performance and effect compared with existing methods.