Reviewerly/exHarmony huggingface dataset:
Rethinking Training Schedules For Verifiably Robust Networks

New and stronger adversarial attacks can threaten existing defenses. This possibility highlights the importance of certified defense methods that train deep neural networks with verifiably robust guarantees. A range of certified defense methods has been proposed to train neural networks with verifiably robustness guarantees, among which Interval Bound Propagation (IBP) and CROWN-IBP have been demonstrated to be the most effective. However, we observe that CROWN-IBP and IBP are suffering from Low Epsilon Overfitting (LEO), a problem arising from their training schedule that increases the input perturbation bound. We show that LEO can yield poor results even for a simple linear classifier. We also investigate the evidence of LEO from experiments under conditions of worsening LEO. Based on these observations, we propose a new training strategy, BatchMix, which mixes various input perturbation bounds in a mini-batch to alleviate the LEO problem. Experimental results on MNIST and CIFAR-10 datasets show that BatchMix can make the performance of IBP and CROWN-IBP better by mitigating LEO.
EBB: Progressive Optimization For Partial Domain Adaptation

Unsupervised domain adaptation (UDA) methods are generally proposed based on the assumption that the source domain and the target domain share an identical group of classes. However, in transfer learning tasks in reality, the target domain often has fewer data with missing classes. Partial domain adaptation (PDA) allows the source domain to have un-shared categories. Anchor points are used to describe the easily identified target samples. It is observed that the shared classes tend to have more anchor points compared with the unshared classes and we introduce a novel progressive optimization method named Ebb PDA tasks. Ebb could resist the negative transfer caused by the category gap and can be applied to any domain adaptation model. Ebb picks the anchor points by analyzing the features of the base model, and it uses the class-wise distribution of anchor points to estimate the category gap. Then Ebb minimizes the errors of shared classes and corrects the error samples caused by blind alignment. To verify the effectiveness of the method, we apply Ebb to three PDA image classification tasks based on three widely used data sets, i.e, Office-Home, Office-31 and ImageCLEF-DA while using three state-of-the-art methods as the base models. The results show that Ebb brings a significant improvement in all tasks and the models optimized by Ebb have stable performance under a wide range of category gaps.
The Role of Mobile Assisted Language Learning in Improving the Pronunciation of Students of English in the College of Education for Women at Al-Iraqia University

This research tackles the effect of mobile assisted language learning (henceforth, MALL) in improving the students’ pronunciation of English language, in particular the pronunciation of word stress. It aims at:Identifying the term MALL, its uses, advantages, and disadvantages.Exploring the effectiveness of MALL devices both as instructional tools and learning resources within and beyond classroom learning environments to develop language skills, particularly pronunciation of word stress.The experimental design is pretest_experiment_post_test. To conduct the study, the researcher made a test on the pronunciation of word stress, and applied it to 60 students from Al-Iraqia University, Collge of Education for Women, Department of English. These students were divided into two groups; the control group (30) students, and experimental group (30) students. Both groups were taught the same material, but using different methods.The results of the post-test showed the outperformance of the experimental group over the control group which indicates the good impact of MALL on developing the experimental group’ Pronunciation of word stress
Self-Supervised Disentangled Embedding For Robust Image Classification

Recently, the security of deep learning algorithms against adversarial samples has been widely recognized. Most of the existing defense methods only consider the attack influence on image level, while the effect of correlation among feature components has not been investigated. In fact, when one feature component is successfully attacked, its correlated components can be attacked with higher probability. In this paper, a self-supervised disentanglement based defense framework is proposed, providing a general tool to disentangle features by greatly reducing correlation among feature components, thus significantly improving the robustness of the classification network. The proposed framework reveals the important role of disentangled embedding in defending adversarial samples. Extensive experiments on several benchmark datasets validate that the proposed defense framework consistently presents its robustness against extensive adversarial attacks. Also, the proposed model can be applied to any typical defense method as a good promotion strategy.
PCNET: Parallelly Conquer the Large Variance of Person Re-Identification

Person re-identification has a wide range of applications, and many state-of-the-art methods are proposed to solve the problem under specific scenarios. However, it is still a challenging issue because of the large variance in practical applications, such as pose variations, misalignment, and image noises.In this paper, Parallelly Conquer Net (PCNet) is proposed to deal with large variance in a parallel manner. PCNet consists of three module: Pose Adaptation Module (PAM), Global Alignment Module (GAM), and Pixel-Wised Attention Module (PWAM). Each module is designed to deal with a sub-variance independently. Furthermore, the generated features are aggregated by parallel branches to utilize complementary information among them. Extensive experiments on three benchmarks (Market-1501, DukeMTMC-reID, and CUHK03) demonstrate the effectiveness of the method. The results show that PCNet can significantly improve the performance of person re-identification.
Flexible Application of Computer Technology in Improving Students’ Interest in English Learning

Abstract Chinese students are inevitably influenced by their mother tongue when they learn English. Most people can understand, but dare not open their mouth to say, and even some simply do not understand. After the application of computer information technology in English teaching, English learners can learn independently through the network, and can break the barrier between regions for more extensive communication and learning under the Internet. For example, the application of micro-class, teachers will show the key content in the textbook through video, audio, so that students can understand and learn more easily, and at the same time greatly increase students’ interest in learning English. Nowadays, science and technology are changing with each passing day, and the application of information technology is particularly important in teaching. For example, now our country is in the onset of the new crown pneumonia infectious disease, the country takes into account the safety of students, postponed the start of school date, so that all primary and secondary students through the Internet teaching and teacher distance guidance of a new learning model. Many teachers in colleges and universities use various information means to carry out network teaching, which is really eight immortals across the sea. Using recording class, micro class, Tencent classroom live broadcast and other ways to carry out online teaching, students are refreshing, stimulate learning enthusiasm, improve the teaching effect. This kind of network teaching method can not only guarantee students’ learning time, but also improve teachers’ ability to use information technology. This paper describes how students use computer to learn English flexibly to enhance their interest in learning English.
Some Improved Strategies of YOLOv3 Algorithm

To enhance the representational ability of object detection network and make it learn features more comprehensively, thereby improving the accuracy of algorithm, some improved strategies are proposed. Based on widely used YOLOv3, the spatial pyramid pooling is embedded to strengthen the ability of feature extraction in the local area, and the integrated dilated convolution and dual attention are used to improve the quality of feature expression. Additionally, the regression loss is calculated by CIoU to improve the localization effect of bounding boxes. Experimental results on the MS COCO (test-dev 2017) dataset show that the mAP of improved model increases by 8.8%, while the detection speed of algorithm is maintained at the original level.
Partial Color Photo Processing Method for Components Based on Image Enhancement Technology

Computer vision is currently playing an increasingly important role in automatically identifying the character of the image processing technology as research hotbed in the field of smart computing, OCR, face recognition, fingerprinting, biometric recognition, and so forth. Content-based image recovery, video recovery, multimedia collection, watermarking, games, film stunts, virtual reality, e-commerce, and other apps are available all round. The color pictures of parts taken by industrial cameras depend on computer performance and the intricate environment, and in particular, on the whole resolution image display, a lot of CPU resources are needed. Some details cannot be shown completely at the same time. If the image is not sufficiently clearly visible, methods for image processing like improvement, noise reduction, and interpolation must be used to improve color photo clarity. This article, based on the OpenCV platform, uses frequency domain filters, median filters, Fourier transform, and other image improvement technologies to remove image noise in order to enhance the quality of local photos from industrial cameras’ components. Finally, clear and available image information is obtained in different experimental methods, which check the application of image enhancement technology to image rebuilding. Finally, the performance of the proposed method in terms of CPBD value, definition <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>Q</mi> </math> value, and operation time is compared, which shows that the proposed method has obvious advantages in the above performance.
Research on Multi-level Extension Optimal Decision Analysis of Network Digital Library Resources

Abstract Network digital libraries have exploded in recent years, with numerous and complicated evaluation indicators. The main body of evaluation is gradually showing the diversification characteristics of evaluation objects, and comprehensive evaluation standards. In order to better ensure the overall quality and construction level of network digital library resources, this research innovation incorporates multi-level extension optimization evaluation. Based on the network digital library, a multi-level extension optimization decision-making mechanism combining qualitative and quantitative is proposed. It has developed a new direction and provided new ideas for the resource quality and data evaluation of the future network digital library.
Image Aesthetics Assessment with Gradient Shake Regularization

Due to their large capacity, the convolutional networks are susceptible to prioritize memorization of the training examples over the problem understanding and generalization. This phenomenon is encountered mainly in the difficult problems, where classes are overlapping due to various reasons, including noisy annotations. In this paper we propose to regularize the training process of the convolutional neural networks by the injection of randomized perturbation within controlled amplitude of gradient of the loss function. The method is evaluated on aesthetic evaluation of images. The proposed solution is shown to improve the performance of baselines and to compare favorably with established strong solutions.
Inventory Information System on Goods Stock in PT Berkatmas Mulia Guna

Stock Inventory Information System is a system used to enter inventory data for incoming and outgoing goods into the database, controlling inventory, and providing inventory information. So that there will be no errors in data input, data output, and report results based on the desired data. PT Berkatmas Mulia Guna is a company engaged in HVAC (Heating, Ventilation, and Air Conditioning) contractors. Includes new installation of HVAC machines and systems, maintenance and repair of HVAC systems. PT Berkatmas Mulia Guna also includes manufacturing HVAC machines such as AHU, FCU, Exhaust Fan, Supply Fan and Heating. A lot of data changes and a long process of stock data collection from suppliers and customers in the process of processing inventory data, will result in inaccurate data obtained. In inventory data collection, recording of incoming and outgoing goods still uses a manual process with excel which can result in duplicate data or duplicate data for processing inventory data. Based on this problem, the authors conducted a study entitled "Inventory Information System Stock Goods at PT. Berkatmas Mulia Guna ”which aims to make it easier to handle the stock inventory process, data entry and exit, delivery of goods, so that the inventory process of goods is faster and more precise. This application system development method uses the waterfall method which consists of six stages. The stages are System Analysis and Design, software requirements analysis, system design, coding, system testing and maintenance. In making this application using a desktop-based website programming language for the Windows operating system, and using the MySql database.
High-Level Modelling for Typed Functional Programming

Abstract There is currently no way to model the high-level structural design of a functional system. Given the strong links between functional programming and mathematics, it is hypothesised that the language of mathematics can provide insight into how a functional system might be modelled. The approach is successful and both philosophy and the language of mathematics are used to identify the necessary modelling concepts and briefly outline some modelling notation alongside a small case study.
MAWA: A Browser Extension for Mobile Web Augmentation

In recent years, the usage of mobile browsers has experienced an astonishing growth. Nowadays, most citizens use their mobile phones instead of their laptops to surf the Web for immediate availability. Nevertheless, Web design is performed considering laptops screen dimensions and websites are readjusted to mobile screen resolutions using Responsive Web Design. This conversion to smaller screen resolutions causes some drawbacks to mobile navigation. Web Augmentation is an effective methodology that allows end-users to customize third party websites according to their needs. This technique can mitigate the problems caused by small screen resolutions.This article introduces MAWA, a Firefox mobile browser extension for Web Augmentation designed to remove and move content in any website using a visual programming technique. The article presents the benefits introduced by MAWA in response to the drawbacks arising when websites are adapted from laptop screen resolutions to mobile dimensions. Evaluation is also introduced in this article where end-users with no programming knowledge have adapted four different websites using the extension. Results show that testers claim that drawbacks presented by mobile browsers are mitigated with the utilization of MAWA.
Research on Management Decision Optimization Algorithm Based on Information Entropy in Cloud Computing Environment

Abstract In the cloud computing environment, information entropy is a false judgment on the composition of information and data inaccuracy and decision-making. By using information entropy to optimize the data information, under the Internet, the analysis of network user management data in the cloud computing environment can be Quickly identify clustered data objects and reorganize management data in a cloud computing environment to improve the effectiveness of management decisions.
EFFECTIVENESS OF PLANNED TEACHING PROGRAMME ON KNOWLEDGE REGARDING ROAD TRAFFIC SAFETY AMONG SELECTED HIGH SCHOOLS STUDENTS OF VISNAGAR

INTRODUCTION: Road traffic injuries are tenth leading cause of death globally, responsible for around 1.3 million burden, the on general assembly declared a decade of action for road safety (2011- 2020) Recognizing the obstacle that road traffic injuries present to development effort, member states also included a specific target on road safety. In the 2003 agenda for sustainable development.Road safety is important for all road users. Every year more than one million people are injured in road accident in india. In recent years, the number of vehicles on road has increased, as has number of goods transported by road. This makes road safety even more important. METHOD AND MATERIAL: A study was conducted among 60 high schools students from 8th , 9th& 10th standard students studying at Visnagar.The sample were selected by using Probability simple random sampling technique. Date was collected by using the structured knowledge questionnaires. Data was analyzed by descriptive and inferential statistics.RESULT:The study findings shows that the mean post-test knowledge score(18.21±3.89)was higher than the mean pre test knowledge score (7.58±4.52). The calculated ‘t’value (14.32) was greater than the table value (1.67) at 0.05 level of significance.There is significance association between post test knowledge score and selected demographic variable such as gender, source of information, does anyone met with an accident in your home.CONCLUSION: The overall knowledge of road traffic safety measures was high school students among the study participants, So need to continuous reinforcement and reminding can bring the positive changes among students
Robust Measurement-Driven Cardinality Balance Multi-Target Multi-Bernoulli Filter

The multi-target tracking filter under the Bayesian framework has strict requirements on the prior information of the target, such as detection probability density, clutter density, and target initial position information. This paper proposes a novel robust measurement-driven cardinality balance multi-target multi-Bernoulli filter (RMD-CBMeMBer) for solving the multiple targets tracking problem when the detection probability density is unknown, the background clutter density is unknown, and the target’s prior position information is lacking. In RMD-CBMeMBer filtering, the target state is first extended, so that the extended target state includes detection probability, kernel state, and indicators of target and clutter. Secondly, the detection probability is modeled as a Beta distribution, and the clutter is modeled as a clutter generator that is independent of each other and obeys the Poisson distribution. Then, the detection probability, kernel state, and clutter density are jointly estimated through filtering. In addition, the correlation function (CF) is proposed for creating new Bernoulli component (BC) by using the measurement information at the previous moment. Numerical experiments have verified that the RMD-CBMeMBer filter can solve the multi-target tracking problem under the condition of unknown target detection probability, unknown background clutter density and inadequate prior position information of the target. It can effectively estimate the target detection probability and the clutter density.
Converting (n, n)-Multiple Secret Sharing into More Friendly Appearance using Chinese Remainder Theorem and Boolean Operations

This paper extends the (n, n) - Multiple Secret Sharing (MSS) into friendlier appearance. This paper modifies the (n, n)-MSS by utilizing the Chinese Remainder Theorem (CRT) and bitwise Boolean computation. It creates n shadow images from n secret images. This technique needs n shadow images in order to reveal n secret images. The proposed method produces a set of shadow images in the noise-like form (random image). It aims that malicious attacker or unauthorized party cannot recognize the content of shadow images. Later, the proposed method converts each shadow image in the noise-like form into more meaningful image (or more friendly appearance) using Boolean operation. The experiments reveals that the proposed method yields the lossless recovered secret images for n is odd and even number.
Research on Tibetan-Chinese Machine Translation Based on Multi-Strategy Processing

This article takes the low-resource nature of Tibetan-Chinese machine translation as the research object, acquires training data through a variety of strategies, and explores the problem of domain adaptability in Tibetan-Chinese materials and the problem of multi-granularity segmentation. Researched the Tibetan-Chinese machine translation method based on Transformer attention mechanism, studied the Tibetan-Chinese machine translation method with different segmentation granularity applied to both ends of encoder-decoder, evaluated multiple granular segmentation, corpus fusion of different fields and different types. The effect of corpus fusion is the experimental result with the highest BLEU score of 44.9 points.
The influence of Worldskills technologies on the formation of professional competence of college students

The international experience shows that the mechanism of the WorldSkills competitive movement contributes to the development of the vocational education system. The study aimed to consider the Russian competition movement Worldskills Russia (WSR), as part of the international movement in terms of research problems: what is the pedagogical potential of WSR technologies and how effectively it provides the formation of the necessary professional and general competencies of students of secondary vocational institutions in blended learning (for example, medical college). The subject of the analysis was Russian and foreign research related to the formation of the competition movement; special attention was paid on materials that reveal the use of WSR technologies in educational institutions of the secondary vocational education system. The questionnaire was used to get students’ vision of WSR, their motivation for participating in the WSR. It was determined that the development processes of WSR included: the formation of the legislative framework; the allocation and use of significant state budget funds for professional training of teachers with the receipt of certificates of WSR Experts; significant costs for conducting exams according to the WSR standard, aimed at increasing the competitiveness of specialists for the innovative economy of the Russian Federation. A survey of medical college students shows that the involvement of the student audience in professional competitions is not ubiquitous; only 24.6% of first-year students of the first-year vocational education system are informed about the WSR movement. Only 16.9% of students were motivated to participate in Worldskills competitions. The integration of Worldskills technologies into the educational process in the blended learning model, along with project activities, ensures the formation of the necessary professional and general competencies of students of secondary vocational institutions. Methodological guidelines for the practice of vocational education in preparation for Worldskills require the solution of a set of related pedagogical and didactic problems. The research results can be used in the educational process of the secondary vocational education system.