Reviewerly/exHarmony huggingface dataset:
A garbage collection method based on integrity and mobile communication technology

Combined with the current situation of the country, by analysing the national policies on recycling and investigating the existing waste classification methods, a waste recycling method based on integrity and mobile communication technology is proposed. At the same time, it is put into practical application, which realizes the scientific classification of waste, improves the delivery efficiency, and ensures that waste can be classified scientifically and priced accurately, so as to realize human the benign and scientific sustainable development model of resources and environment.
Russian Speech Conversion Algorithm Based on a Parallel Corpus and Machine Translation

The phonetic conversion technology is crucial in the resource construction of Russian phonetic information processing. This paper explains how to build a corpus and the key algorithms that are used, as well as how to design auxiliary translation software and implement the key algorithms. This paper focuses on the “parallel corpus” method of problem solving and the indispensable role of a parallel corpus in Russian learning. This paper examines the foundations, motivations, and methods for using parallel corpora in translation instruction. The main way of using a parallel corpus in the classroom environment is to present data, so that learners can be exposed to a large amount of easily screened bilingual data, and translation skills and specific language item translation can be taught in a concentrated and focused manner. Among them, the creation of a large-scale Russian-Chinese parallel corpus will play an important role not only in improving the translation quality of Russian-Chinese machine translation systems but also in Chinese and Russian teaching as well as other branches of linguistics and translation studies, all of which should be given sufficient attention. This paper proposes the use of automatic speech analysis technology to assist Russian pronunciation learning and designs a Russian word pronunciation learning assistant system with demonstration, scoring, and feedback functions, in response to the shortcomings of pronunciation teaching in Russian teaching in China. It can provide corpus support for gathering a large number of parallel corpora and, in the future, enabling online translation. This system is used for corpus automatic construction, and future corpus automatic construction systems could be built on top of it. The proper application of parallel corpus data will aid in the development of a high-quality autonomous learning and translation teaching environment.
Key pre-distribution scheme over the continuous periodic functions space

Abstract Implementation for the key preliminary distribution scheme in the continuous functions space is proposed. Key materials are formed based on linear differential operator. User characteristics are presented in the form of a harmonic series. We show that this approach allows you to develop a common encryption key. A linear differential operator is formed only from even order derivatives. Odd order derivatives do not affect the encryption key value.
Determination of the porous structure parameters from experimental images for the porous phantoms generation algorithm

A technique for determining the parameter values from experimental images for the porous structures generator for synthesizing porous phantoms is presented. The algorithms for fast determination of porosity and the standard deviation of the Gaussian filter used as input parameters of the phantom generator are considered. The phantoms generated according to the found parameters have geometric characteristics similar to the original images, which makes it possible to use such phantoms both for studying and modeling processes in porous media and as basic structures for creating training samples for segmentation algorithms of experimental images using machine learning methods..
Intelligent book robot and its method of moving, handling, and loading

Aiming at the problem of "difficult to put books on shelves" in the current library management, an intelligent book robot and its method of handling, sorting and loading are developed. It can autonomously locate, move, and avoid obstacles, and plan the path after obtaining the position of the book, and then pick up and transport the books, and place them on the bookshelf position. A new shelf device is created to grab stably most of the books and push them into the bookshelf. Its two-stage flexible expansion plate is developed to effectively expand the gap between books and have a high degree of tolerance. Mecanum wheels are used in the motion module, and the gray-scale sensor array are installed under the chassis in order to control effectively the posture of the car body. Book position locating is implemented by the visual recognition module with simultaneously recognizing multiple QR codes on the book spine. The results show that the rates of recognition and the completion of successful handling and loading are quite high.
Construction and Launch of a Virtual Reality Laboratory for EFL Learners: A Crossroad Between Cognitive and Emotional Learning

The Virtual Reality (VR) budget at an Omani technical college was utilized for the design, construction, and launch of a VR lab. The decision remained, however, as to what sorts of immersive activities/games would most benefit the English language students of the General Foundation Program (GFP). Over the course of 2 years, as approvals were given and equipment ordered, received, and tested, GFP staff members identified dozens of freely-available 360-degree VR videos and matched them to the general topics of each unit of every textbook in the program. They next created mini-quizzes in the form of “pop-up” multiple choice questions within the VR videos themselves, with score results being auto-emailed to teachers. During the roll out of the lab in academic year 2018/2019, inter-departmental meetings were held, and notes taken, on the perceived successes and failures of the new laboratory. Several clear areas for improvement were identified in the lab and video set up that could stunt the VR lab’s growth if not properly dealt with. This chapter reports on the VR team’s efforts to employ Extended Reality tools (e.g. VR, AR, MR) to “create new learning experiences, test new hypotheses, and inspire new models” (Bengfort J, Virtual reality advances bring new possibilities to higher education. EdTech. Retrieved from https://edtechmagazine.com/higher/article/2020/02/virtual-reality-advances-bring-new-possibilities-higher-education , 2020, para. 4) in the English foundation program. The impact of VR on learners’ cognitive and emotional learning is also presented, along with a discussion of pathways to enhance English learning through VR.
Seismic Change Detection Technology of Multi-source Data Remote Sensing Image

The seismic multi-scale edge detection technique proposed in this paper is based on Canny’s best edge detection criterion and multi-resolution analysis of wavelet transform. It can easily and reliably identify the lateral variation of underground medium in seismic records. These lateral variations include concealed features which can not be found directly on the seismic retrofacies axis with naked eyes but actually exist. It can be used to identify the strata pinch out, mirror body, lithologic change, pore and fracture development zone. The results of model calculation and actual seismic data processing show that the method has practical application value.
Edge Based Video Clip Searching and Retrieval in Broadcast News Videos

The study of image-based retrieval when the database consists of videos becomes a very challenging task. This variation of visual search is important for a broad range of applications that require indexing video databases based on their visual contents. Our paper presents the search of a database based on broadcasting news. The main agenda of this paper is to retrieve the data of a particular shot of a video and compare it with a part of broadcast news and check its occurrence in the broadcast news video. Outnumbering different algorithms we have come up with an efficient way of retrieving the data. In this paper, we have first extracted the frames for a particular segment of video and converted each frame to binary form and detected the edges of each frame using the edge detection method, and stored the no of edges detected into arrays for the video shot as well as the broadcast news. For the rectification, we have checked the edge similarity between the detected edge of the particular video shot and broadcast news. To refine our algorithm of edge similarity, we have also performed SSIM to achieve the most desired results. Moreover, as the commercial frames may occur more than once in broadcast news we can have an analysis of the no of times of occurrence of commercials shot in broadcast news.
A Study on Occupational Therapy for Adults and Older Adults in Korea Using Big Data: A Systematic Review

Objectives: This study systematically reviewed the literatures related to domestic occupational therapy and big data and suggested the need for changes in occupational therapy and big data research for adults and the older adults.Methods: From January 2010 to December 2020, Google Scholar, Korean Studies Information Service System, and Research Information Sharing Service were utilized to search for literatures related to occupational therapy and big data in South Korea.Results: Of the 954 searched literatures, 17 literatures were selected. Most of the literatures were the evidence level II (n = 7, 41.2%), 7 (41.2%) literatures conducted with older adults, and 3 literatures (17.6%) utilized the Korean National Health and Nutrition Examination Survey as a big data source. Finally, in the literature, the research was conducted by selecting variables suitable for the subject from various panel surveys.Conclusions: In south Korea, panel surveys for each topic are being created and various variables are being collected. As a result, research in occupational therapy for adults and the elderly using big data became possible, making it possible to conduct various studies.
A Fixed probability Cuckoo Search algorithm to optimize SPARQL queries

The Semantic Web is a network of linked data. The idea of the Semantic Web is to distribute and process the data according to the user’s needs successfully. In order to retrieve the content required by the user in minimum amount of time, the web analyst must depend on an efficient retrieval mechanism. Many mechanisms and tools are existing for retrieving the RDF data as per the user needs, but there is a less attention on the query execution time. With an increase in the volume of data, the search space also increases and also there is a need to handle large search spaces. When the number of web pages increases, the number of joins in the queries also increases. The traditional query optimizer is not able to handle huge joins in queries. Based on the research performed, it was found that the use of nature inspired algorithms is best suited for the Semantic Web environment. In this research, an Adaptive Cuckoo Search algorithm called the Adaptive Cuckoo search with Fixed Probability (ACSFP) algorithm is applied to generate the optimal query plan. The importance of the proposed algorithm lies in setting the parameter that abandons the worst query plans. The experimental results obtained for the benchmark dataset shows the efficacy of the proposed algorithm when compared with the existing algorithms.
Exploiting acquisition path to detect shadows in ultrasound images

Shadow artefacts in Ultrasound make clinical interpretation of the image difficult and even impossible in certain scenarios. Shadow detection and avoidance is therefore a very important feature for automatic interpretation Ultrasound images. Deep Learning (DL) based methods for automatic shadow detection have approached it as a segmentation problem achieving limited accuracy. Since that acoustic shadows appear along the acquisition path, we propose a novel approach of extracting slivers of images called fanlets along the acquisition path and employ a simpler classification approach to detect presence of shadows. Limiting the spatial context for shadow detection helps us to achieve a very high accuracy of 97%. On a database of abdominal ultrasound videos from 128 subjects, we show that our approach is superior to UNet based shadow segmentation. Since any Ultrasound image can be broken into a series of fanlets, our approach can be readily applied to a wide variety of acquisitions.
Descent Methods

In this section, we discuss fundamental methods, mostly based on gradient information, that yield descent, that is, the function value decreases at each iteration. We start with the most basic method, the steepest-descent method, analyzing its convergence under different convexity/nonconvexity assumptions on the objective function. We then discuss more general descent methods, based on descent directions other than the negative gradient, showing conditions on the search direction and the steplength that allow convergence results to be proved. We also discuss a method that also makes use of Hessian information, showing that it can find a point satisfying approximate second-order optimality conditions and finding an upper bound on the number of iterations required to do so. We then discuss mirror descent, a class of gradient methods based on more general distance metrics that are particularly useful in optimizing over the unit simplex – a problem that arises often in data science. We conclude by discussing the PL condition, a generalization of the strong convexity condition that allows linear convergence rates to be proved.
Adopting attention-mechanisms for Neural Logic Rule Layers

Abstract In previous works we discovered that rule-based systems severely suffer in performance when increasing the number of rules. In order to increase the amount of possible boolean relations while keeping the number of rules fixed, we employ ideas from well known Spatial Transformer Systems and Self-Attention Networks: here, our learned rules are not static but are dynamically adjusted to fit the input data by training a separate rule-prediction system, which is predicting parameter matrices used in Neural Logic Rule Layers. We show, that these networks, termed Adaptive Neural Logic Rule Layers, outperform their static counterpart both in terms of final performance, as well as training stability and excitability during early stages of training.
The Analysis and Predictive Modeling of Performance Data Based on Information Technology

Aiming at the problem of fuzzy cluster center in academic analysis and prediction model, this paper uses K-means clustering algorithm to analyze students’ performance data, and proposes a prediction model of students’ academic performance. On the basis of defining the cluster center, cluster analysis is carried out on the data related to learners’ learning to obtain the analysis and prediction model of students’ academic performance. The experimental results show that the model can accurately calculate the score of learners’ loyalty to the course, improve the enthusiasm of users’ online learning, and control the accuracy and recall rate above 90%, which can provide decision-making help for educational institutions.
Optimizing Blockchain using Embedded Checkpoints

Blockchain, the distributed network based digital record is a hot topic of research today. It has immense applications and finds its use in almost all fields. However, the technology is still under development and faces many challenges. In this paper, we explored basic challenges that restrict the scalability of ordinary blockchain systems and proposed a novel technique to tackle the issue of growing storage costs by reducing the chain length using checkpointing. We also proved that the resulting storage cost is better than the original blockchain. To the best of our knowledge, ours is the first solution for reducing the chain length of blockchain using checkpoints. We also made sure that the proposed method can easily be incorporated into already existing blockchain systems.
Designing Stemmer for Afaraf Text Using Rule Based Approach

Stemming algorithm is crucial part of huge number of natural language processing (NLP), information retrieval system (IRS), dictionary search, text summarize indexing and so on. Stemming algorithm for any language fundamentally reduces input words in to root or stem form. Afaraf language is an Afro-asiatic language which belonging to Cushitic family’s and spoke by Afar people live in East Africa specially Ethiopia, Djibouti and Eritrea. Afaraf words are very rich in morphology and needs an effective stemming algorithm, which can control different morphological arrangements that are connected with words. In this paper, rule based stemmer for Afaraf language is developed which reduces the term by Affix removal. Affix is an aggregation of suffix and prefix. And analyzed the accuracy and correctness of stemmer by O-stemming (over stemming) and U-stemming (under stemming) techniques. The stemmer is completely rule-based, for that we achieved an accuracy of 95% with the assistance of rules such as prefix stripping rule and suffix stripping rules.
Autonomous Prostate Segmentation in 2D B-Mode Ultrasound Images

Prostate brachytherapy is a treatment for prostate cancer; during the planning of the procedure, ultrasound images of the prostate are taken. The prostate must be segmented out in each of the ultrasound images, and to assist with the procedure, an autonomous prostate segmentation algorithm is proposed. The prostate contouring system presented here is based on a novel superpixel algorithm, whereby pixels in the ultrasound image are grouped into superpixel regions that are optimized based on statistical similarity measures, so that the various structures within the ultrasound image can be differentiated. An active shape prostate contour model is developed and then used to delineate the prostate within the image based on the superpixel regions. Before segmentation, this contour model was fit to a series of point-based clinician-segmented prostate contours exported from conventional prostate brachytherapy planning software to develop a statistical model of the shape of the prostate. The algorithm was evaluated on nine sets of in vivo prostate ultrasound images and compared with manually segmented contours from a clinician, where the algorithm had an average volume difference of 4.49 mL or 10.89%.
An Analysis on Face Recognition using Principal Component Analysis Approach

Face recognition is a type of biometric recognition based on human facial feature information. Human face images or videos can be automatically collected using a high definition camera. Advanced technologies then can be used for face recognition by tracking on collected images to detect human faces. The facial recognition algorithm can cut out the main facial area after detecting the face and find the key facial feature points, and input it into the recognition algorithm after processing. To extract and compare the facial features, the recognition algorithm is used to the complete the final classification. This research is to study the face recognition using PCA (Principal Component Analysis. The PCA-based is used to eigenface recognition, Hotelling transform in PCA is used to obtain the main components of the face distribution, i.e. the feature vectors (eigenfaces). The face images from the training library and the face images to be recognized are projected onto this space separately to match the recognized image output based on the principle of minimum geometric distance. The face angle, face mask, and face expression factors are selected for testing against face recognition. Hence questions and hypotheses are formulated to verify whether the recognition rate of face recognition is influenced by these factors for this recognition method. Based on the results of the analysis it was that face angle, face masking have a positive effect on face recognition. Furthermore, according to the analysis, it can be concluded that face masking has the highest significance for face recognition.
Holographic Wide-Angle System for Deformation Measurement of Extended Structures

We introduce a holographic wide angle system that combines the accuracy of a long focal length with the extended field of view of a wide angle lens. To accomplish this, we use a computer-generated hologram (CGH) in front of the lens to diffract light from (a discrete number of) specific angular locations. This method is tested in laboratory conditions, as well as under real-world conditions. This measurement system was developed as a possible tool for real-time movement tracking and control of extended dynamic structures, such as bridges and high-rise buildings. Within that application, the obtained measurement uncertainty is 10 μm in object space at 10 m distance spanning 10 m width.
On the Presburger fragment of logics with multiteam semantics

In team semantics, which is the basis of modern logics of dependence and independence, formulae are evaluated on sets of assignments, called teams. Multiteam semantics instead takes mulitplicities of data into account and is based on multisets of assignments, called multiteams. Logics with multiteam semantics can be embedded into a two-sorted variant of existential second-order logics, with arithmetic operations on multiplicities. Here we study the Presburger fragment of such logics, permitting only addition, but not multiplication on multiplicities. It can be shown that this fragment corresponds to inclusion-exclusion logic in multiteam semantics, but, in contrast to the situation in team semantics, that it is strictly contained in independence logic. We give different characterisations of this fragment by various atomic dependency notions.
Engaging with Immersive Technologies in Medical Library: An Investigation of Students’ Perceived Affordances and Constraints

The library's information practices on immersive technologies help to enhance the scenario-based user experience. However, there has been limited research on user engagement with immersive technologies in the medical library setting. In this exploratory study, we conducted interviews and on-site observations with 17 medical students who had experience with immersive technologies at the medical library. The affordance lens was used to explore how the relationship between immersive technologies and medical students enables and constrains their information practices. The preliminary results suggest that users’ perceived affordances of immersive technologies are not a solid outcome but a dynamic process. In our case, users’ perceptions of technology affordances can be categorized into perceived entertainment affordances, perceived learning affordances, perceived instrumental affordances, and perceived socializing affordances. We also identified three perceived constraints in medical students’ experiences with immersive technologies in the library setting. The study contributes to the literature on user engagement from an affordance perspective, and yield practical implications for medical training and education by leveraging the immersive technologies in the library context.