Reviewerly/exHarmony huggingface dataset:
The Efficiency of Mobile E-Wallet in Palestine – Case Study

The needs of consumers have changed in light of the rapid spread and use of the internet around the world, which led to the development of electronic payment methods in light of the global technological development that facilitated and accelerated payment procedures and the transfer of money between people, including the electronic wallet and mobile phone applications. electronic payment has become the backbone of the electronic commerce market because of its huge potential and positive effects in the commerce market, where the contemporary young generation of this technological development is the most important pioneer in the use of these means, despite the preference of many of the use of traditional payment methods because they believe that they are safer and their fear of losing money and not The ability to use it in all aspects of life. Despite the recent use of the electronic wallet in Palestine, the results of this paper show that we can rely on it as a means of payment in Palestine, but it needs more efforts to encourage people to use and more work to change the culture by focusing on the advantages of using the electronic wallet because of its The advantages and the ability to help manage the budget, reduce the costs of transportation and storage of cash used in the Palestinian market.
Usability Study of an Innovative Application in Public Transport by Using Hardware-Based Security Technology

As part of the OPTIMOS 2.0 funding project of the German Federal Ministry for Economic Affairs and Energy (BMWi), a partner develops the TicketIssuance app for secure hardware-based storage of high-priced tickets. The app has implemented a previously unknown technology using the Secure Element and the NFC interface. It is therefore imperative to investigate the usability of the app for a successful market launch. For this purpose, user tests of a prototype of the app were conducted using the think-aloud method. This study analyses the results of five tasks. Test subjects rate the expected and perceived difficulty level for each task. That forms the basis for identifying improvement strategies. The test subjects’ performance, the frequency of errors and problems encountered, and the need for moderator’s support form the basis for prioritizing usability items within the tasks. The developed structure to determine the test tasks’ prioritization and usability items, layout, navigation, handling, wording, system, and data economy offers improvements to increase usability. Furthermore, the study investigates the determination of a suitable sample size for usability testing.
Editing Actions: A Missing Link Between Translation Process Research and Machine Translation Research

This chapter presents a discussion, on theoretical and methodological grounds, of the subject of editing as a bridge between Translation Process Research (TPR) and Machine Translation (MT). Editing is described in the chapter as a technical dimension of the writing task performed by translators when they apply four actions to previous text: deleting, inserting, replacing, and moving words and groups of words. The chapter shows some of the difficulties in interpreting the results obtained by TPR data collection methods, and it discusses how edit distances can help improve this interpretation. In the next step, the discussion and demonstrations focus on the need to invert the perspective of edit distances, from the errors of MT to the edits made by translators, for metrics to be more accurate in describing editing work. A new form of using Translation Edit Rate (TER) is suggested, which may be called Human Edit Rate (HER). The chapter also analyzes the limitations of perspectives on editing from MT research, suggesting ways to complement these perspectives with process data. The last part of the chapter extends the discussion, to approach questions related to the advantages of common research between TPR and MT, and its implications for the common understanding of the complexity of translation.
Virtual Reality Model Assessment Platform: Transfer Models to a Standalone Virtual Reality Headset

The goal of the paper is to introduce a Virtual Reality Model Assessment Platform. VRMAP (for short) is to be a lightweight, simple to use platform that allows users from various fields to easily check out models in virtual reality, as there are few options to do so natively on the headset. The platform consists of a Model Repository and VR client app. Models can be uploaded to the Model Repository and then downloaded and spawned in the client app for assessment or observation. The VR client app is created in Unity Engine using the new Unity XR framework and supports the GL Transmission Format (glTF), a royalty-free, extensible, common publishing format for 3D models created by Khronos Group. Many of the mainstream formats used in 3D printing and computer graphics can be easily converted to glTF and thus imported into the VRMAP. The Model Repository is built using Express, Node, Typescript and MongoDB. It allows for upload, storage, and download of models. Although this implementation is fully functional, it acts as an example and as long as other, more robust implementation adheres to the defined API standard, it will work as a drop-in replacement.
Optimal Tracking of Stationary Target by Moving Platform with Doppler Information Only in 3D

The problem of estimating the three-dimensional position of a stationary target given Doppler frequency measurements from a moving platform only is addressed. The existing approach for estimation a stationary target location from Doppler only information is by using a multi-sensor network. The motion of the sensor makes the problem observable without the need for multi-sensors. This is as the motion of the sensor makes a full continuum of sensors. The measurement of the closing velocity, proportional to the Doppler frequency, is a nonlinear function of the Cartesian location of the target. The recently introduced Optimal Nonlinear Recursive Least Square (RNLS) filter, the State-Dependent Differential/Difference Riccati Equation (SDDRE) based filter, and the Extended Kalman Filter are compared on a common basis, for the Doppler measurement only position estimation. This work shows that despite the possibility of multiple solutions and the dependence of the estimation quality on the sensor's trajectory, optimal tracking of a stationary target from Doppler measurements only by a single sensor is feasible. The superior performance of the optimal Recursive Nonlinear Least Square filter is demonstrated by simulations.
ENGLISH TEACHERS’ STRATEGIES IN ASSESSING EIGHTH GRADERS' SPEAKING SKILL

The objective of the research are to find the strategies used by teachers in assessing speaking at SMPN 9 Kota Bengkulu. This research was quantitative approach. The subjects were twelve English lesson plans who use at eighth grade. The data were collected from documentation. The results showed that lesson plans were used five strategies from fourteen strategies in assessing speaking namely Oral Presentation, News Event, Roleplay, Discussion &amp; Conversation, and Giving Instruction/Direction. In addition, online lesson plans used two strategies from fourteen strategies and offline lesson plans used four strategies from fourteen strategies. Further, the other nine strategies are not appeared in the class such as Repetition, Directed Response, Read Aloud, Sentence/Dialogue Completion, Question and Answer, Paraphrasing, Interview, Games and Story Telling. It can be happened because the lesson plans applied the strategies based on the students’ material, need, and condition in the meeting.
Hit Detection in Sports Pistol Shooting

Score calculation and performance analysis of shooting targets is an important aspect in the development of sports shooting ability. An image-based automatic scoring algorithm would provide automation of this procedure and digital visualization of the result. Existing solutions are able to detect hits with high precision. However, these methods are either too expensive or adapted to unrealistic use cases where high quality paper targets are photographed in very favorable environments. Usually, precision pistol shooting is performed outdoors and bullet holes are covered with stickers between shooting rounds. The targets are reused until they are destroyed. This paper introduces the first generation of an image-based method for automatic hit detection adapted to realistic shooting conditions. It relies solely on available image processing techniques. The proposed algorithm detects hits with 40 percent detection rate in low-quality targets, reaching 88 percent detection rate in targets of higher quality.
The Canadian Cultural Diversity Dashboard: Data Storytelling and Visualization for the Cultural Sector

The Canadian Cultural Diversity Dashboard (CCDD) is an interactive online data visualization dashboard designed to elicit insights regarding diversity in the consumption and creation of cultural expressions in Canada. While designed for a particular context it is broadly applicable. Urgency for this type of analytics has amplified given the negative impacts of COVID-19 on the cultural sector and mounting concerns around racial justice and inclusion in society, reflected in considerations regarding cultural funding and creation. The complex, scalable, heterogenous, and multivariate data sets that underlie the CCDD are sourced from demographic data and include factors such as age, geographic location, education level, literacy levels, immigration status, linguistic background and languages spoken, race, Indigenous identity, disability, and gender. Each demographic segment is analyzed according to cultural consumption and creative expression across a breadth of disciplines from literary, visual, to performing arts. We discuss the challenges of collecting, analyzing, and representing diverse demographic data, including biases in data collection. We implemented a dashboard interface to enhance user experience (UX), because similarly to visualizations acting as a repository for data, the dashboard interface is a repository for the visualizations. We considered the level of interaction and user engagement needed to explore this data which led us to the use of an interactive dashboard. A narrative visualization and infographics strategies are combined in order to support editorial layering and interaction design in a co-design project, allowing us to increase research in the application of these two fields.
3D Traffic Sign Detection Using Camera-LiDAR Projection

Localization using High definition (HD) maps is one of the key solutions for high precision autonomous driving on the urban environment. For accurate HD map-based localization, more features included in HD maps are required to be detected while driving in real-time. Although there were a few studies about detecting road features to match with HD map, mostly used only a simple intensity filter or dimensionality filter, which is not robust in varying environments. This paper proposes a novel traffic sign detection algorithm in the 3D point cloud using a 2D image for robust 3D object detection. Deep learning 2D object detection is the first step, and then by using LiDAR-camera calibration data and outlier removal algorithm, 3D point cloud data of the traffic signs are collected. The performance evaluation was done using Waymo Open Dataset and we compared the proposed method with other 3D object detection results in the challenges board. The proposed method is the robust solution for any feature recognition and 3D detection included in the HD map for precise localization.
How Blockchain Innovations Emerge: From the Perspective of Knowledge Search

Emerging technologies like Blockchain are firms' important resources, rendering it significant to clarify their generation mechanisms. One potential way is knowledge search, which is an essential source of firms' lacking knowledge. There are two types of search: local search and boundary-spanning search. They have a different impact on innovative outcomes. Besides, the innovations of emerging technologies have two aspects: R&D aspect and application aspect, depending on the domain to which the target of an innovation belongs. Because of the distinct features of these two aspects, knowledge search may influence R&D innovations and application innovations differently. We empirically test our hypotheses on Blockchain patent data retrieved from PATSTAT. The results show that there exist different effects that knowledge search has on R&D innovations and application innovations. While searching across the boundary may benefit patent novelty in application innovations, it will improve patent quality in R&D innovations. Our study has many academic contributions and practical implications, and its limitations leave room for further research on the topic.
Geometric Calculi and Automatic Learning An Outline

Signal representation and processing are the backbone of mathematically-aided engineering. Among the myriad of ideas and results in that realm, many sorts of algorithms and techniques capable of learning from experience have taken the stage in the last decades, with a crescendo of great successes in a variety of fronts in recent years. This paper provides a sketchy outline of those developments that seem more relevant or promising in view of their bearing on the geometric calculus (multivector) representations of signals and the concomitant automatic learning algorithms. The corresponding artificial neurons, and their organization in networks, may be seen as a way to transcend the biologically inspired neuron networks much as the wheel or aviation transcended legs or bird flight. Recent developments suggest that there are exciting research opportunities ahead.
Strategies for Improving Classroom Teaching Quality in Independent Colleges

As a school running mode under the background of higher education popularization, although the number, overall scale and teaching staff of independent colleges in China are constantly improving in recent years, compared with public colleges, there are still some unreasonable resource structure, inadequate teaching equipment, weak student base and other phenomena. At the same time, in order to encourage social forces to set up education, realize the connotation development of higher education, and promote the goal of education equity, the Ministry of Education issued the implementation plan on accelerating the transformation of independent colleges in 2020, which shows that the country attaches great importance to the future development of independent colleges. From the perspective of students, teachers or schools, we should work together to analyze the teaching problems of independent colleges, solve the problems and improve the teaching structure, so as to improve the teaching quality of independent colleges.
GenPath - A Genetic Multi-Round Path Planning Algorithm for Aerial Vehicles

The past few years have witnessed unprecedented proliferation of Unmanned Aerial Vehicles (UAVs). They are employed in a growing number of scenarios, from parcel delivery to search and rescue operations, requiring coordinated missions of a fleet of drones. Recently, there has been growing interest in optimized techniques to assign tasks and related trajectories to drones. While these techniques promise high coverage of inspected area, their applicability in real scenarios is precluded by unconsidered constraints. Among these, the limited amount of power of UAVs, and the consequent need of performing multiple trips to provide complete monitoring coverage, with battery replacement/charging and data offloading in between. To address this problem we develop Gen-Path, a genetic algorithm for efficient scheduling of multi-round UAV missions, under several objective functions. By means of simulations we show that Gen-Path fits various scenarios, improving existing solutions in terms of covered points, and energetic cost.
Data Handling Approach for Machine Learning in Wireless Communication: A Survey

Recently, wireless communication network has evolved with different types of communication architectures and protocols. Operation and management of such heterogeneous networks with huge demand are manually difficult for network engineers. In the recent past, Machine Learning (ML) has proven its capability by significantly improving the performance in various fields such as natural language processing and medical diagnostic. Using ML in wireless communication is also not a simple task, as we have to track the user’s Quality of Experience (QoE) on the one hand and network resource management on the other, with continuously changing wireless scenarios. Identifying channel variability with proper decision-making is the crucial task of ML in Wireless Communication Network. In this paper, based on a systematic review of the current use of machine learning techniques in WCN, a set of crucial design limitations are identified, and a novel computationally efficient data exchange approach is proposed.
Collaborative Maintenance of EDOAL Alignments in VocBench

Ontology Alignment, intended in its broadest meaning of alignment between datasets of different nature – thesauri, ontologies, and even mere instance data – is a well-known practice aiming at realizing semantic links between datasets on the (Semantic) Web. Considerable investigation has been carried on the automatic computation of alignments and on how to assess the quality of such process. This is indeed a critical aspect, considering the non-trivial size of many datasets. However, since human intervention is in any case essential, no less care should be paid on scalability both in terms of distribution of work and of maintenance of achieved results within the lifecycle of the aligned resources. In this paper we guide the reader through the diverse solutions that have been implemented in VocBench, a collaborative editing platform for RDF datasets, under a holistic approach to collaborative alignment development and maintenance.
PixE: Home Fitness Method Using Machine Learning with Smartphone

The COVID-19 outbreak has caused worldwide confusion. However, in the Republic of Korea, it has overcome the situation by using a variety of communication infrastructures. Especially in Korea, a new word Homt appeared. Home fitness is referred to as ‘home training' in Korea and is called Homt for short. It is a word for a new method of home fitness which has emerged based on Internet infrastructure. This is a service made possible on a fast network in Korea, where a great deal of data is transmitted at high speed. Our research goal is to create fitness game, PixE, using a smartphone camera that everybody has. Basically, it is a game of strength exercises such as squats and lunges. It uses machine learning technology to exchange information over a fast network in Korea while minimizing the burden of poor smartphone resources. It is based on the detection of human movements by getting images from a smartphone in real time on the server. Motion recognition and a variety of effects are created using extracted images. The final image is pixelated and sent back to the smartphone. With the edited image by using ML, the user himself becomes a main character in the fitness game. It is a fitness game for Homt that can be easily used anytime, anywhere. If the user only has a smartphone, that's all! In particular, it has a user focus and be immersed in it by becoming the main character that appears directly into the game. Before the official release, we demonstrated with two groups, exercise experts and laymen. We found that the effects of the exercise were no different than the existing effects. We hope to create an environment where you can easily home fitness anytime, anywhere, not in a special situation such as COVID-19.
Rapid Prospective Risk Assessment of Mobile Products in Healthcare

There is much hope and hype surrounding the development of innovative technologies, but are we as motivated and effective in assessing the potential risks of these technologies? It is prudent to assess and mitigate risks prior to deploying a new product or process, but how often is a risk assessment performed when new functionality or changes are proposed? This paper describes a rapid, Agile approach for intermittent risk assessment and surveillance when there are substantial changes to systems and their context of use. Fault Tree Analysis and Failure Mode and Effect Analysis were used as models in a customized approach to assess for risk and potential hazards of added functionality and changes to mobile products after initial deployment. We also describe benefits and challenges. A rapid risk assessment could also be used as an initial assessment in situations where the software development cycle is not amenable to a more time-consuming approach to risk assessment. A lesson learned in preparing to rapidly assess for risks is to consider several or some combination of approaches.
Identification with Face Recognition Methods in Real Life Applications

The development of technologies such as employment tracking systems, personal security, and the use of robots has led a lot of studies on face recognition systems.In the most of studies considering face recognition, recognition accuracies are very high, since training and testing images are selected randomly from the same databases.However, in real life applications, these images are not randomly selected from the same database.Because, these systems are trained during installation of the recognition system or when a new person needs to be introduced to the system.On the other hand, images used for predictions are uploaded to the system at other times.In this study, it is aimed to show that the accuracy rates of real-life face recognition systems differ from the systems trained and tested with randomly selected images as usually done in literature.To observe this difference in the first step, training and test images are selected randomly.In the second step, training and test images are divided according to the recording dates as in real life.Accuracy rates are evaluated by using linear discriminant analysis, local binary patterns and principal component analysis methods.Although the accuracies are very high for the first step, it is seen that the accuracies fell dramatically in the second step for all methods.Afterwards a new method is searched also in this study to increase these low accuracy rates.It is shown that usage of eye area images instead of face images has higher accuracy rates in all above methods for real life applications.