Reviewerly/exHarmony huggingface dataset:
Rethinking Robust Representation Learning Under Fine-Grained Noisy Faces

Learning robust feature representation from large-scale noisy faces stands out as one of the key challenges in high-performance face recognition. Recent attempts have been made to cope with this challenge by alleviating the intra-class conflict and inter-class conflict. However, the unconstrained noise type in each conflict still makes it difficult for these algorithms to perform well. To better understand this, we reformulate the noise type of each class in a more fine-grained manner as N-identities $$|$$ K $$^C$$ -clusters. Different types of noisy faces can be generated by adjusting the values of $$N$$ , $$K$$ , and $$C$$ . Based on this unified formulation, we found that the main barrier behind the noise-robust representation learning is the flexibility of the algorithm under different $$N$$ , $$K$$ , and $$C$$ . For this potential problem, we propose a new method, named Evolving Sub-centers Learning (ESL), to find optimal hyperplanes to accurately describe the latent space of massive noisy faces. More specifically, we initialize M sub-centers for each class and ESL encourages it to be automatically aligned to N-identities $$|$$ K $$^C$$ -clusters faces via producing, merging, and dropping operations. Images belonging to the same identity in noisy faces can effectively converge to the same sub-center and samples with different identities will be pushed away. We inspect its effectiveness with an elaborate ablation study on the synthetic noisy dataset with different $$N$$ , $$K$$ , and $$C$$ . Without any bells and whistles, ESL can achieve significant performance gains over state-of-the-art methods on large-scale noisy faces.
SELF-EDUCATION OF THE TEACHER OF GENERAL EDUCATION ORGANIZATION IN THE CONTEXT OF THE DEVELOPMENT OF LEGAL COMPETENCE

Introduction. The expanding legal relations in the general education environment along with the large scale updates of Russian legislation require an adequate and timely response from the entities involved in the preparation and development of pedagogical personnel. However, the legal training of teachers of general education organization starts lagging, which makes them use alternative ways of developing legal competence. The paper aims to determine the place of self-education in the development of legal competence of the teacher of general education organization. Materials and methods. The study is based on the comptency-focused approach, theoretical principles of self-education and continuous vocational education. Results. The specific features and resources of legal self-education for the teacher of general education organization are identified, and a professional self-education program, a modified technique by V. G. Maralov with the results of its testing are presented. Conclusion. The study has established that the creation of conditions for the development of the legal competence of the school teacher can affect their legal self-development ability. Keywords: teacher of a general education organization, development of legal competence, self-education
Improving Students' Speaking Skill Using Hello English Application as a Medium of Learning from Home

This study aims to improve stduents’ speaking skill using hello English application. The subjects of this study were the fifth-grade students of SDN Sidorejo 1 Mojokerto. The researcher used classroom action research to conduct this study. The research instruments were an observation sheet, questionnaire sheet, test, and documentation. This study was done in two cycles. The researcher procedures of this research consisted of four steps. They are planning, implementing, observing, and reflecting. The criteria of success were 75% of students got a score of 9.From the findings and discussion, the result is Hello English application can improve students’ speaking skills. It can be seen in the test of cycle 2. In cycle 2, the result of students’ speaking scores showed that 15 students passed and 2 of the students were failed. The mean scores of the test in cycle 2 were 9,05 and the percentage of improvement 88,23%. It is showed that in cycle 2, the students' scores passed the criterion of success 75%. Thus, based on the result of cycle 2 Hello English application can improve students’ speaking skills.
An efficient length-segmented inverted index-based set similarity query algorithm

Set Similarity query is extensively used in recommendation systems, near duplicate detection and many other fields. Existing approaches usually scan all relevant inverted lists or trie indexes, thus leading to low efficiency. To solve this problem, in this paper, an efficient length segmentation-based inverted index, LenSegII, is designed. LenSegII imposes a length segmentation table (LST) on inverted indexes by exploiting length filtering, thus logically dividing an inverted index into segments. Based on LenSegII, LenSC-SSQ, an efficient set similarity query algorithm is proposed. LenSC-SSQ only needs to access a certain range of inverted index segments and locates the segments efficiently by LST, thus a large number of unqualified sets can be filtered, which significantly decreases query time. Besides, LenSC-SSQ can improve space efficiency by using a more compact count array. Experiments on 2 real and 1 synthetic datasets show LenSC-SSQ outperforms the other competitors significantly and can reach a performance improvement up to 5.8X over ScanCount.
Wireless Communication Systems: Compression and Decompression Algorithms

Nowadays, smart devices spreads around the world and internet of things (IoT) starts to include all kinds of data: text, image, audio, and video. Yet, limited bandwidth and storage centers had made limitations in sending and receiving the data. As a result, various data compression algorithms are being introduced from time to time to decrease the sizes of the data and facilitate sending them. Mainly, there are two types of compression: lossy and lossless compression. In this chapter, an overview of various data compression algorithms is presented for each data type, comparisons between different algorithms will be elaborated to show their best usage, and a conclusion will be made to show the differences between algorithms to show which one to be used. Data compressions has many applications.
CamPressID: Optimizing Camera Configuration and Finger Pressure for Biometric Authentication

To protect sensitive information on smartphones, state-of-the-art (SoA) studies exploit the built-in camera to capture PPG signals from fingertips as a hard-to-forge biometric. However, those studies do not provide a comprehensive analysis to optimize the camera parameters and finger pressure, leading to distorted and unstable PPG signals that degrade the authentication performance. To overcome these limitations, we propose the CamPressID framework. First, we analyze various camera parameters and optimize their configuration to obtain PPG signals with a high signal-to-noise ratio. Second, we investigate different finger pressures to identify the best pressure for every subject, in order to avoid signal distortion. To evaluate the performance of CamPressID, we collect a diverse dataset with 58 subjects. Our evaluation results show that CamPressID can improve the average balanced accuracy (BAC) by 10%. Moreover, the BAC reaches 90%, which is similar to the accuracy reported in the SoA using a dedicated PPG sensor for authentication.
Evaluating the Usability of Augmented Reality (AR) Applications: The Impact of The Device Used

Augmented Reality (AR) has become increasingly popular in recent years in the realms of training, manufacturing, gaming, office work, and even a social platform. This technology has shown promise as different types of devices support AR, such as mobile phones, head-mounted displays (HMDs), desktop PCs, etc. As applications are deployed on multiple AR device types, it is important to assess the usability of these applications on all possible devices to assess the cross-channel user experience. This paper describes two case studies that show usability issues with different types of devices. Overall, using known interactions with devices instead of implementing new interactions was more important for mobile devices, comfort is more of a concern for HMDs, and common "rules of thumb" (such as making sure the form of buttons communicates function) is a concern for both application types.
On Georgian Text Processing Toolkit Development

This invited talk presents both recent advances and previous work on text processing and information retrieval for the Georgian Language.
Design of Blockchain Application Framework for Claims Platform of Flight Delay Insurance

Flight delay insurance is a kind of insurance to settle claims for passengers due to flight delay or cancellation caused by natural disasters, bad weather, mechanical failure and other reasons. Because flight delay insurance has the characteristics of cumbersome operation process of traditional insurance products, complex product design, manual claim audit and risk management, how to provide undeniable and traceable evidence for the attributes involved in the claim settlement of aviation delay insurance events, and how to improve the execution efficiency of insurance contracts and improve passengers' claim experience are technical problems that need to be optimized urgently. Based on the establishment of the claim platform of flight delay insurance, the introduction of blockchain technology into insurance claim can effectively give play to the technical advantages of blockchain and improve the efficiency and benefits of claim platform.
Traditional Mongolian Script Standard Compliance Testing Based on Deep Residual Network and Spatial Pyramid Pooling

Mongolian is a language spoken in Inner Mongolian, China. Traditional Mongolian script standard compliance testing is very important and fundamental work. This paper proposes a classification network for traditional Mongolian script standard compliance testing. The network uses the spatial pyramid pooling mechanism, it can accept images of different sizes in the convolutional neural network. It can effectively solve multi-scale problems of traditional Mongolian words. Experimental results show the SSP-ResNet-18-Single can effectively recognize traditional Mongolian images, and the recognition average accuracy can reach 98.60%. This network achieves good performance in Mongolian word recognition compare with the current mainstream word recognition network. The dataset has been publicly available.
SANAL PAZAR UYGULAMALARININ KULLANILABİLİRLİĞİNİN DUYGU ANALİZİ TEMELLİ DEĞERLENDİRİLMESİ

Businesses will be able to keep their customers in their hands by facilitating the use of the applications they offer to their customers in the e-commerce environment or by eliminating the errors that occur on the application, and they will be in an advantageous position compared to their competitors. The subject of this research is to investigate the usability dimensions of virtual marketplace applications through customer comments. In the research, it was tried to remove the differences in terms of users of the applications of the companies that are competing with each other in the e-commerce sector. The applications were examined in terms of eight dimensions in terms of usability dimensions: ease of use, feedback and meeting expectations, satisfaction, simplicity or simplicity, compatibility and security, performance and speed, error correction and visibility. According to the aforementioned usability dimensions, a machine learning set was created and the total data was estimated and the sentiment analysis method was applied to the total data. Thus, it was seen that the most positive dimension of the users was feedback and the most negative dimension was satisfaction.
Research on athlete's wrong movement prediction method based on multimodal eye movement recognition

In order to solve the problems of large prediction error, long time consumption and large amount of interference data in the prediction results of traditional methods, an athlete's wrong movement prediction method based on multimodal eye movement recognition is proposed. Firstly, the spectral clustering algorithm is used to mine the wrong movements. Secondly, the least square method is used to improve the support vector machine, and the improved support vector machine is used to classify athletes' wrong movements according to the statistical characteristics of athletes' wrong movements. Finally, based on the classification results and the historical data of athletes' wrong movements, the trend of athletes' wrong movements is predicted by the multimodal eye movement recognition method to complete the prediction of athletes' wrong movements. The experimental results show that the method causes small prediction error, consumes short prediction time and has a low proportion of interference data.
Multimodal recommendation of messenger channels

Collaboration platforms, such as GitHub and Slack, are a vital instrument in the day-to-day routine of software engineering teams. The data stored in these platforms has a significant value for data-driven methods that assist with decision-making and help improve software quality. However, the distribution of this data across different platforms leads to the fact that combining it is a very time-consuming process. Most existing algorithms for socio-technical assistance, such as recommendation systems, are based only on data directly related to the purpose of the algorithms, often originating from a single system.
Design of epidemic detection system in railway station based on FPGA

Since the outbreak of COVID-19 in 2020, wearing masks and displaying green codes in and out of public places has become a habit. The identity verification of the existing railway station is mainly based on face recognition. Due to the outbreak and persistence of the epidemic, people often need to call out the green code on their mobile phones for staff verification, which takes a lot of time. At the same time, the existing face recognition equipment requires the inbound personnel to take off their masks, which also increases the infection risk of the inbound personnel. In order to reduce the above infection risk and speed up people’s entry and exit speed, we have designed a system that can identify people wearing masks and judge whether they are confirmed or suspected cases at the same time. Firstly, the system measures the passenger’s body temperature through the infrared temperature measurement module, carries out face detection and recognition at the same time, and queries the recognition results in the database to judge whether the passenger is diagnosed or in close contact. When the passenger is normal, it is allowed to pass, otherwise it is not allowed to pass, and updates the relevant data in the cloud database. The system uses Yolo algorithm as the face detection algorithm, and then carries out face recognition through FaceNet network, so as to judge its identity and query the relevant information of the person in the cloud database. After testing, the iterative loss rate of the system is basically below 0.1 and the accuracy is basically stable above 99%. Considering that we need to use it on embedded devices and the amount of calculation operation of deep learning algorithm is large, and FPGA can well build circuits according to the needs of the model because of its reconfigurability, and FPGA can realize hardware acceleration because it can run in parallel, so we finally choose to deploy the model to FPGA to complete face recognition.
Analysis And Designing of STF Controller

The goal of a fuzzy controller is to account for the system's changing properties. It serves the same function as the fuzzy logic controller. To fulfil this purpose, the perfect FLC design is now needed; unfortunately, such a controller has yet to be produced since the fuzzy controller is made up of control rules that are imprecisely designed using traditional controller approaches. The parameters of a fuzzy control system are regularly modified via trial and error. To solve these challenges, we need a mechanism that automatically modifies system settings after analysing fuzzy control response A FLC is a collection of established control rules that are often constructed with the use of expert knowledge. It is common to specify the membership functions of the linked input and output language variables. Several performance measures, including peak overshoot, settling time, rise time, integral absolute error and integral-of-time multiplied absolute error, as well as responses due to step set-point change and load disturbance, are compared between the proposed self-tuning FLCs and their corresponding conventional FLCs. The suggested approach outperforms its conventional equivalent in each case.
The Effects of Sincerity and Empathy on Communication Ability of Nursing Student Who Experienced a Non-face-to-face Semester Due to COVID-19

The purposes of this study was to examine the effects of sincerity and empathy on the communication ability of nursing students who experienced non-face-to-face classes. The data was collected by questionnaires from 127 sophomores who attended non-face-to-face classes for one year as nursing students at two universities in G city. The data were analyzed with the SPSS/WIN 22.0 program. The result of the study, the average score of communication ability was 3.14. The higher the sincerity(β=.29, p&lt;.001), the higher the major satisfaction of the non-face-to-face class(β=.16, p=.017), academic performance(β=.17, p=.022), cyber interaction, and interpersonal relationship were higher(β=.14, p=.037), and the explanation power was 43.5%(F=18.51, p&lt;.001). Based on the results of this study, it is necessary to develop a systematic program that can promote communication ability and sincerity of nursing students who have experienced non-face-to-face classes interactions and interpersonal relationships in cyberspace.
Learning Visual Tempo for Action Recognition

The variation of visual tempo, which is an essential feature in action recognition, characterizes the spatiotemporal scale of the action and the dynamics. Existing models usually use spatiotemporal convolution to understand spatiotemporal scenarios. However, they cannot cope with the difference in the visual tempo changes, due to the limited view of temporal and spatial dimensions. To address these issues, we propose a multi-receptive field spatiotemporal (MRF-ST) network in this paper, to effectively model the spatial and temporal information. We utilize dilated convolutions to obtain different receptive fields and design dynamic weighting with different dilation rates based on the attention mechanism. In the proposed network, the MRF-ST network can directly obtain various tempos in the same network layer without any additional learning cost. Moreover, the network can improve the accuracy of action recognition by learning more visual tempo of different actions. Extensive evaluations show that MRF-ST reaches the state-of-the-art on the UCF-101 and HMDB-51 datasets. Further analysis also indicates that MRF-ST can significantly improve the performance at the scenes with large variances in visual tempo.
A Support System for Collecting/Selecting Topics in Chat Using Context/Physiological Information

People who are not engaging conversationalists often experience awkward silences in their conversations. The reason is that they have no topic that they deem sufficiently interesting at that moment to talk about; consequently, they cannot keep their conversation going. To solve this problem, this paper proposes a system that helps people create their own diary, select a topic from it, and converse smoothly on that topic. The results of comparative experiments conducted indicate that the proposed system enables users to write more topics than a simple diary system by presenting activity information when they go out. Furthermore, the diary created using the proposed system enables them to converse about the contents for a longer time with ease. In addition, by presenting candidate topics, the users could recollect topics faster and felt that the topics could be changed smoothly into next ones when talking for a long time.
A Study on SWOT-AHP Analysis for Marine Transportation Education using Metaverse Platform in Post Covid-19 Era

In this paper, as non-face-to-face education is expending due to COVID-19, non-face-to-face education using online is continued for maritime transportation education, access to the platform is difficult due to the aging of trainees etc. we have deduced a development plan in the field of maritime transportation education using the metaverse platform. First, it is necessary to develop and distribute equipment that can be conveniently used in the field in order to promote the transition to education using the metaverse platform in maritime traffic education. Second, it is necessary to improve the system for accreditation of legal education, expand support for dissemination so that a metaverse platform that can receive universal maritime traffic education can be provided free of time and space constraints. Third, it is necessary to prepare a plan to build an ecosystem tailored to the needs specialized in the field of maritime transport education.
Improving the Accuracy of 3D Measurement Using a Stereo Camera by Marking with a Line Laser

In this paper, we propose a method to improve the accuracy of 3D measurement using a stereo camera by marking objects with a line laser. Stereo cameras are often used for 3D measurement. However, stereo cameras have a problem that the accuracy of 3D measurement is affected by the amount of texture. Therefore, we develop a new measurement system that combines a stereo camera and a line laser. By using a line laser to mark arbitrary points on the object and measuring the marked points, we improve the accuracy of 3D measurement with a stereo camera regardless of the amount of texture on the object.
Steganography-Free Zero-Knowledge

We revisit the well-studied problem of preventing steganographic communication in multi-party communications. While this is known to be a provably impossible task, we propose a new model that allows circumventing this impossibility. In our model, the parties first publish a single message during an honest non-interactive pre-processing phase and then later interact in an execution phase. We show that in this model, it is indeed possible to prevent any steganographic communication in zero-knowledge protocols. Our solutions rely on standard cryptographic assumptions.
Penerapan Model Select and Assambel pada Pembelajaran Menulis Teks Anekdot pada Mata Pelajaran Bahasa Indonesia Kelas X SMA Negeri 1 Wewewa Selatan

This study aims to improve the anecdotal writing skills of class X SMA Negeri 1 Wewewa Selatan for the 2019/2020 academic year by applying the select and assamble model. Through the select and assamble learning model, it can be seen both in process and product. The research model used is experimental. The procedure for implementing the action and implementing the action at the research location is divided into two cycles. The success criteria achieved by students in this study were seen from a change for the better in writing anecdotes. The results showed the following. First, the use of select and assamble models in the quality of the learning process to write anecdotes. There are still students who do not understand how to write anecdotal texts and there are still many who lack focus. Second, learning to write anecdotes with the select assamble model can improve the results of anecdotal writing skills.Based on the results of the above research, it is known that the use of the select and assamble model is successful and can improve the anecdote writing skills of class X SMA Negeri 1 Wewewa Selatan.