Reviewerly/exHarmony huggingface dataset:
Novel American Sign Language Fingerspelling Recognition in the Wild with Weakly Supervised Learning and Feature Embedding

This paper presents a new method in fingerspelling recognition in highly dynamic video sequences. Sign language videos are labeled only in a video sequence level. A deep learning network extracts spatial features of video frames with the AlexNet and uses them to derive a language model with the Long-Short Term Memory (LSTM) network. The results of this deep learning network are the predicted fingerspelling gestures at a frame level. The recognition results of testing video sequences with 100 percent accuracy are used to improve spatial features of video frames. We construct a Siamese network from the recognition results in the first recognition pass. A network deployed in the Siamese network is the ResNet-50. We employ the Siamese network to derive the efficient representation of each fingerspelling gesture. The derived features corresponding to each video frame are fed to the LSTM network to predict fingerspelling gestures. Our proposed method can outperform state of the art fingerspelling recognition algorithms by almost four percent in recognition accuracy from our experimental results.
Computer Aided Mass Detection in Mammograms

Radiologist's expertise in identifying and sleuthing breast cancer can be helped by utilizing some modernized feature extraction and classification techniques. In the proposed Computer Aided Diagnosis (CAD) framework, different techniques have been utilized. Pre-processing of mammograms is performed to stifle the noise present in the mammograms. The images are classified as low dense and high dense. Feature extraction is carried out to recognize the significant and vital parts from the image utilizing the techniques namely Local Phase Quantization (LPQ), Local Binary Pattern (LBP), and Gray- Level Co-Occurrence Matrix (GLCM) for both high dense and low dense images. Experiments were done using the 2749 images from the DDSM dataset. The outcomes exhibited the adequacy of the proposed framework and show the imperativeness for clinical applications.
Comparative analysis of filtering methods in fuzzy C-means: Environment for DICOM image segmentation

The process of breaking the image and finding the location of the object and boundaries is called image segmentation, it is an essential part in the field of image processing. Medical image segmentation, however, is challenging due to the nature of the images that contain noise, unequal in content with low contrast, and inhomogeneity. Thus extra effort is required to remove noise in order to obtain a high-quality image segmentation. This chapter is set to analyze the performance of a fuzzy C-means clustering (FCMC) algorithm along with different image filtering methods based on digital imaging and communications in medicine (DICOM) data set. The main advantage of the FCMC is low false positive rate and intrusion detection is high rate, hence FCMC is used for medical image segmentation. DICOM color images are first converted to gray scale and applied to various filters such as 2D median Filter, Imguided Filter, Imfilter, Wiener 2 filtering, and Gaussian Filter to reduce the noise error. Next, FCMC is applied based on the filtered DICOM data set and the performance is compared. The results show that FCMC is most effective when used in combination with 2D median filters with 97% of accuracy, 12% of precision, and 7% of harmonic mean. The results are hoped to assist in 2D median for segmentation tasks concerning medical images.
Features of the Professional Direction of University Students in the Era of Digitalization

In the digital era, the success of the professional development of future specialists in the process of obtaining higher education largely depends on their professional orientation (PN). Despite the significant interest in the subject of the study, the problem of identifying the factors that affect PN remains poorly understood.&#x0D; The purpose of the study is to determine the factors of the formation of PN of students enrolled in training and retraining programs, and the features of PN in the digital era.&#x0D; The article reveals the content and structure of the PN of future specialists in the era of digitalization. According to the results of the empirical research, the relationship of students' PN with indicators of life-meaning orientations, motives for choosing a profession and training was revealed; the relationship between students' life-meaning orientations and indicators of motives for choosing a profession and training at the stage of digitalization of education. The factors influencing the personal condition, and the factors of the formation of the personal condition of students, who are trained according to the programs of training and retraining of specialists, have been determined.&#x0D; As a result of an empirical study, it was concluded that a negative impact on the formation of PN is created by factors caused by various life circumstances, lack of independence of decisions in choosing a profession, low reflection of life goals, prospects for the future, rigidity of volitional and personal qualities, internal conflict in the structure of personality relationships, low pleasure training and the like. At the same time, the level of PN is significantly higher among students who receive a second higher education and who understand its importance at the stage of digitalization of education.
KB4ConvRec: Knowledge Base Enhanced Memory Network for Conversational Recommendation

Using dialogue as an interactive method can more accurately capture user preferences. However, generating recommendations by conversing with recommender systems is actually a challenging task. Most existing works on conversational recommendation cannot effectively address the following challenges. First, existing end-to-end models do not consider the problem of how to determine which system action, such as interaction and recommendation, to take at each turn. Second, they do not work well to solve the ambiguity problem in user utterances. In this paper, we propose a novel memory network framework for conversational recommendation, which can leverage structural information more effectively in a knowledge base to improve the quality of conversational recommender systems. The extensive experiments are conducted on the personalize bAbI dialog dataset. And the results suggest that our proposed framework achieves state-of-the-art performance in a few tasks.
Two new zero-dimensional qubit codes from bordered metacirculant construction

We construct qubit stabilizer codes with parameters 〚81,0,20〛2 and 〚94,0,22〛2 for the first time. We use symplectic self-dual additive codes over F4 built by modifying the adjacency matrices of suitable metacirculant graphs found by a randomized search procedure.
QAExtractor: A Quality Attributes Extraction Framework in Agile-Based Software Development

Many software projects fail due to an inadequate understanding of their quality at the initial stages of the development.Ahmed, Mohsin The quality of the software heavily depends on non-functional requirements.Khan, Saif Ur Rehman But requirements elicitation team pay less attention towards non-functional requirements, during the requirements elicitation stage of software development life cycle.Alam, Khubaib Amjad In agile-based software development, the requirements elicitation team only focuses on the user stories (i.e. functional requirements in an agile-based software development context). The non-functional requirements are neglected, and team members are left only with the user stories. This makes it very difficult for them to make decisions about quality while holding user stories in their hands. Furthermore, it is essential to affirm the quality of the software as early as possible. This is mainly because the quality of software can negatively affect the different artefacts of the software at later development stages, i.e. designing, developing, etc. The early stage conformance of software quality is more important in the Agile-based Software Development context, where the requirements are more volatile than any other development environments. Hence, there is a need to develop such an automated framework which can extract quality attributes from user stories automatically. In this work, we propose a Quality Attributes Extraction Framework, named as QAExtractor, for the extraction of key quality attributes from functional requirements (i.e. user stories in Agile-based Software Development context). The core of this framework is based on Natural Language Processing. The proposed technique grounds on the regular expressions, which generalise the user stories for a specific quality factor and attribute. The quality factor defines the context of the user story, i.e. security, while quality attribute states the quality aspect, i.e. integrity. The effectiveness of the proposed technique is validated using a case study. The experimentation results revealed that the proposed framework outperforms the existing ones in terms of accuracy, precision, recall and F measure.
Mathematical Problems in Engineering Augmented Reality Method Based on Improved KCF

Aiming at the problem of registration failure caused by partial occlusion and illumination intensity change of the area to be registered. This paper proposes an augmented reality method based on improved kernel correlation filtering (KCF) algorithm. In target tracking process, feature fusion KCF algorithm is used to track and obtain the target area to be registered. For the target area that has been tracked, gradient information and scale-space theory are added to the detection of feature points to make matching results rotation-invariant and scale-invariant. Then, random sampling consistency (RANSAC) algorithm is used to improve registration rate. Finally, registration matrix is calculated by the matching relationship between feature points, and augmented reality system is implemented by superimposing virtual information to registration matrix. The experimental results show that the proposed method effectively solves the problem of tracking instable, missing targets easily and getting low accuracy under the conditions of partial occlusion and illumination intensity changes compared with traditional methods. Augmented reality systems based on improved KCF are more real-time and robust.
Analytical and reflexive competence of future doctor: essence, structure, conditions of formation

Due to the release of new educational standards regulating the activities of educational structures of the Russian Federation the requirements for the results of professional training of a competent specialist with a developed set of competencies have increased. According to the requirements of the Federal State Educational Standards of Higher Education in 31.05.01 “Medical Care” and 31.05.02 “Pediatrics”, the future doctor must have a whole range of skills and abilities that determines his professionalism. These include the qualitative analysis of information, the identification of cause-and-effect relationships, the structuring of source data, and the reflection of one's own activities, which ultimately lead to the formation of analytical and reflexive activities. The formation of analytical and reflexive competence of a future doctor in the course of professional training is defined as a continuous and holistic psychological and pedagogical process aimed at strengthening motivation, ability and readiness to effectively carry out analytical and reflexive activities within the framework of professional activity. This has determined the need for the development of new didactic tools that contribute to its improvement in the process of professional training: models, pedagogical conditions, technology of formation, criteria apparatus and diagnostic tools.
Detection of Irregularities and Abnormal Behaviour in Extreme-Scale Data Streams

In the last few years, terrorism, organized crime and cybercrime have gained more ground as society is becoming increasingly digitized. In this landscape, Law Enforcement Agencies (LEAs) should adapt by applying cross-domain expertise and follow an integrated approach that supersedes the traditional barriers in policing practices. In this light, the authors propose a novel approach that provides near real-time advanced behaviour analytics by using abnormal detection based on extracted historical patterns. In particular, appropriate tools are used to correlate different pieces of data leading to the discovery and recording of forensic evidence. Then, the collected data are combined to handle inconsistencies, and machine learning techniques are applied, in order to detect trends. It is expected that the current system will assist the LEA officers in their daily workload by minimizing the time to detect and solve a crime.
Improving Writing Ability Through Media Story Images Of The Eight Grade Students Private SD HKBP Pematangsiantar In Academic Year 2018/2019: Improving Writing Ability Through Media Story Images Of The Eight Grade Students Private SD HKBP Pematangsiantar In Academic Year 2018/2019

The aims of this study is to improve students' writing abilities in elementary schools. Learning media picture story is one of the Efforts made in the learning process to improve the writing ability of elementary students. This picture is more media story Strategically applied Because it can inspire students 'reasoning power, so that students' creative ideas emerge, and Ultimately they are Able to write. This is in line with the purpose of the study, the which is to find out the level of writing ability through the medium of the picture stories of VI grade students of Pematangsiantar Private Elementary School in 2018/2019. The research Carried out is classified as a class action research (research actions) conducted by the teacher for the purpose of optimizing student learning outcomes Also while increasing the professional competence of teachers According to the topic under study. The topic studied was related to the ability to write based on the use of instructional media items, namely picture stories. The research respondents were grade VI elementary school students, with the application of the pre-test and post-test. Before learning is done, students' ability to write is still low, after learning with the application of media images the ability of students has Increased. In the first cycle, the pre-test results of students' writing abilities were 16:12%, and the post-test results Reached 25.80%. In the second cycle, the results Obtained by students in writing Reached 43. 93%, and in cycle III the results Achieved by students Became 70.96%. Barriers experienced by teachers and students are learning unpreparedness in changing situations and minimal practice time.
A dual-scale convolutional neural network for super-resolution

The booming development of deep convolutional neural networks recently has made super-resolution researches achieve great progress. However, most of the existing researches only consider the super-resolution of several integer scale factors. In this paper, we propose a dual-scale convolutional neural network (DSCNN) to solve super-resolution problem of arbitrary scale factor. The magnifying module of the proposed DSCNN is designed with two chains. First, the large- scale chain learns the feature mappings from the image blocks of the low resolution (LR) to those of the high resolution (HR). The LR image blocks are the magnified image blocks with the desired size via bicubic interpolation. Second, the small-scale chain learns the feature mappings from the down-sampling image blocks to the magnified image blocks. Compared to the existing SR networks, DSCNN has two advantages: (1) it can handle the super-resolution images with arbitrary scale factors, and (2) it dynamically predicts the values rather than the weights of the interpolated pixels of HR images. The extensive experiments on widely used benchmark datasets show the superiority of the proposed DSCNN to the state-of-the-art SR methods in terms of both numerical results and visual quality.
Paralleling Collision Detection on Five-Axis Machining

With the rapid growth of the Fourth Industrial Revolution (or Industry 4.0), five-axis machining has played an important role nowadays. Due to the expensive cost of five-axis machining, how to solve the collision detection for five-axis machining in real-time is very critical. In this paper, we present a parallel method to detect collision for five-axis machining. Moreover, we apply the bounding volume hierarchy technique with two-level bounding volume represent the surface or solid of the object to reduce triangle meshes inside each axis of the five-axis machine tool, and then matching the operating range limit of the five-axis machine tool itself, delete the no colliding triangle mesh. Additionally, we also propose some optimization with loop unrolling and prefetching techniques to improve performance of collision detection. Our approach can reduce the execution time significantly by computing six separating axes in plan and eleven separating axis in non-plan between two triangle meshes based on the characteristic of GPUs (Graphics Processing Units) for program acceleration. Our proposed work consists of kinematic analysis and interpolation for axes to save the numerous collision detection for five-axis machining computations. In this experiment, the result shows that using the proposed approach above can achieve approximately 37.1 times speedup than that of CPU.
Recommendation System Based on Semantic Web Approach

Recently, most countries suffer a decrease the crop productivity due to weather conditions, so many kinds of research are suggested for growth and enhance crop productivity with different methodologies and techniques. The Semantic Web (SW) approach is proposed in this work to build a recommendation system for helping the farmers to take an adequate decision of recommended fertilizer in fast time by specific parameters (e.g., temperature, humidity, moisture, soil type, crop type, nitrogen, potassium, and phosphorus). This proposed approach is based on some essential technologies such as Ontology, Web Ontology Language (OWL), Resource Description Framework (RDF), and RDF Query Language (SPARQL). In this work, our goal is to present another technique to improve the quality of service for farmers with more intelligence in order to increase their crop productivity. The results showed that the capability of the recommendation system based on SW was effective.
FPGA Implementation of an Image Watermarking Scheme based on Intensity Level Matching

The aim of this work is to develop FPGA architecture of such an image watermarking scheme that can overcome the tradeoffs among the major three qualities - imperceptibility, robustness, and payload. This proposed scheme, developed in spatial domain, can be performed to embed gray-scale watermark image into any single color-plane of a cover object. Embedding has been performed in such a manner that the watermark pixels of any particular shade can be inserted into cover image pixels having the same or contiguous shades. This way, the visual transparency of the embedded information has been improved. Efficiency of this proposed scheme has been assessed through some image quality metrics, and compared to the same of some new age image watermarking techniques. The comprehensive analysis asserts that this watermarking system is able to meet its goal successfully.
Extending Music Notation as a Programming Language for Interactive Music

This work describes a novel approach for composing and performing interactive music by extending the traditional staff notation to the programming language domain. The proposed syntax aims to describe the interaction between humans and computers in live-electronics music performance. Thus, both performers and machines will understand this new notation, creating a cohesive music representation for performance that is both human-readable and technology-independent. This paper starts by describing some critical issues related to live-electronics that make it challenging to build repertoire around this genre. Next, the proposed approach is detailed, along with some syntax examples. Finally, the last section describes the evaluation of the proposed approach, including a description of the software implementation and a set of short interactive-pieces.
Self-Localization Using Two Steps Correction Based on Registration Characteristic of 3D-map and Camera Image

For the purpose of Self-Localization by 3D-maps and camera images, we have worked on the realignment task that has bigger error than conventional DNN that matches between 3D-maps and camera images. To resolve this task, we payed attention the registration characteristics of CalibNet that realign them focusing on the axis of height of the vehicle. We devised the error given to training data for CalibNet, and we can train the network that can registration focusing on the side and depth axes of the vehicle. Combining with CalibNet and our network and doing two steps correction, we achieved that improving error by 1.4 times in average of each axes of the vehicle when the center error of 3D maps and camera images is max 55[cm].
Diagnostics of the formation level of self-organization skills among university students

The study of students’ self-organization skills is relevant due to the significance of the issue of forming students’ ability to independently organize their educational and future professional activities in the process of training at the university. The purpose of the article: to analyze the results of a diagnostic study of the formation of self-organization skills in psychology students. Methods and methodology of research: the scale “Situation Analysis” of the “Diagnostic of Self-Organization Features-39” questionnaire, the A.V. Karpov’s reflexivity questionnaire, questions of a specially developed questionnaire, the indicators “Goal Setting” and “Planning” of the “Diagnostic of Self-Organization Features-39” questionnaire, the indicator of prospective reflexivity, as well as the questions of the developed questionnaire. The research base consists of 33 first-, third-, and fifth-year psychology students. We present quantitative data on the indicators of students’ self-organization skills. We identify the problem areas in the formation of self-organization skills, value motivation for this process, and the cognitive component of self-organization. The significance and advantage of using the spiral model in the context of the formation of the student's self-organization skills in the process of professional training at the university are substantiated. Each new round leads to the development and improvement of these skills in students: analysis of the necessary skills, self-diagnosis, planning and skills of purposeful implementation of activities, adjustment of this process on the basis of constant reflection. Thus, the process of developing the student's self-organization skills is a repetitive chain of implementing these skills and actions with the improvement and development of the degree of proficiency in these skills, their systematization on the basis of a value attitude and self-reflection.