Reviewerly/exHarmony huggingface dataset:
G-SimCLR : Self-Supervised Contrastive Learning with Guided Projection via Pseudo Labelling

In the realms of computer vision, it is evident that deep neural networks perform better in a supervised setting with a large amount of labeled data. The representations learned with supervision are not only of high quality but also helps the model in enhancing its accuracy. However, the collection and annotation of a large dataset are costly and time-consuming. To avoid the same, there has been a lot of research going on in the field of unsupervised visual representation learning especially in a self-supervised setting. Amongst the recent advancements in self-supervised methods for visual recognition, in SimCLR Chen et al. shows that good quality representations can indeed be learned without explicit supervision. In SimCLR, the authors maximize the similarity of augmentations of the same image and minimize the similarity of augmentations of different images. A linear classifier trained with the representations learned using this approach yields 76.5% top-1 accuracy on the ImageNet ILSVRC-2012 dataset. In this work, we propose that, with the normalized temperature-scaled cross-entropy (NT-Xent) loss function (as used in SimCLR), it is beneficial to not have images of the same category in the same batch. In an unsupervised setting, the information of images pertaining to the same category is missing. We use the latent space representation of a denoising autoencoder trained on the unlabeled dataset and cluster them with k-means to obtain pseudo labels. With this apriori information we batch images, where no two images from the same category are to be found. We report comparable performance enhancements on the CIFAR10 dataset and a subset of the ImageNet dataset. We refer to our method as G-SimCLR.
Improvement of Short Text Clustering Based on Weighted Word Embeddings

The data sparseness problem in short text clustering will causes low clustering performance. One solution is to enrich short text according to the semantic relationship from external text corpus. A new one is neural network based text representation learning which is word embeddibngs. In this paper, we studied the methods of vector to represent a short text. But how to get a vector from word embeddings is a challenge job. One way is average sum of vectors, but it ignores the importance of the terms. TF-IDF weighted vectors is better way, but the sparseness of terms makes the local IDF not sufficient. We proposed a new method with TF-GIDF weighted vectors, which use global IDF to conquer the shortcoming. The experiments are set up to compare the new method with baselines and the results analysis shows that the proposed method outperforms baselines significantly.
Crowd Density Estimation Based on Face Detection Under Significant Occlusions and Head Pose Variations

Counting and detecting occluded faces in the crowd is a challenging task in computer vision. In this paper, we propose a new approach to crowd estimation based on face detection under significant occlusion and head pose variations. Most of the state-of-the-art face detectors are unable to detect occluded faces. To address the problem, a novel approach for training various detectors is described. In order to obtain a reasonable evaluation of our solution, we trained and tested the model on our substantially occluded data-set. Images of the face up to 90\({^\circ }\) out-of-plane rotation and the faces with 25%, 50%, and 75% occlusion level. In this study, we trained the proposed model on 48,000 images obtained from our data-set consisting of 19 crowd scenes. To evaluate the model, 109 images with the face counts ranging from 21 to 905 and with an average of 145 individuals per image are utilized. Detecting faces in the crowded scenes with the underlying challenges cannot be addressed using a single face detection method. In this paper, by incorporating different traditional machine learning (ML) and the state-of-the-art convolutional neural network (CNN) algorithms, a robust method for counting faces visible in the crowd is proposed. Utilizing a compact variant of the VGG neural network [], the proposed algorithm outperforms various state-of-the-art algorithms in detecting faces ‘in-the-wild’.
PENURUNAN KADAR KESADAHAN DENGAN MENGGUNAKAN FILTERPADA AIR TANAH DI DESA DOUDO KECAMATAN PANCENG KABUPATEN GRESIK METODE KOMPLEKSOMETRI

To complete the daily needs, people in Doudo Village Panceng Subdistrict,Gresik District use groundwater. Geographical conditions which are mountain limestonecausegroundwater in the area have a high hardness level. High hardness is causedbycontact with limestone that is passedbywater.The study aims to determine the level ofhardness in groundwater and a decrease in the level of hardness after the filtrationprocess using whatman filter paper number 1, whatman filter paper number 10, andkanebo.The research design used in thisresearch is true experiment research designandone way anova statistical test with a significant value 0,000. From the result of theresearch conducted, it was know that there was a decrease in hardness levels after thefiltration process.Hardness level of water sample without filtered is 415,291 mg/L. Thefiltration process using whatman filter paper number 1can reduce hardness levels by346,187 mg/L with a decrease of 19,961%.The filtration process using whatman filterpaper number 10 can reduce hardness levels by 367,948 mg/L with a decrease of12,867%. The filtration process usingkanebo filter can reduce the hardness levels by339,543 mg/L with a decrease of 22,309%.From the One Way Anova statistical test, itcan be seen that there is a significant effect of using filters on hardness levels.
Is Q-Learning Provably Efficient? An Extended Analysis

This work extends the analysis of the theoretical results presented within the paper Is Q-Learning Provably Efficient? by Jin et al. We include a survey of related research to contextualize the need for strengthening the theoretical guarantees related to perhaps the most important threads of model-free reinforcement learning. We also expound upon the reasoning used in the proofs to highlight the critical steps leading to the main result showing that Q-learning with UCB exploration achieves a sample efficiency that matches the optimal regret that can be achieved by any model-based approach.
창업교육 프로그램에 관한 동향분석 연구

The recession in the national economy has increased the youth unemployment rate, and as a result, the enthusiasm for entrepreneurs, prospective entrepreneurs and college students has increased, and the government''s entrepreneurship support policy for job creation has also increased. It is important to have a foundation for a successful startup. The government is also expanding the scope of education and training institutes, efficiently utilizing professional manpower and facilities of colleges and junior colleges, and preparing educational programs to help start and start businesses. For successful start-ups, entrepreneurship education must be conducted, and in Korea, most of the entrepreneurship training courses are conducted without knowing what the actual start-up needs are. The purpose of this study is to develop a b beauty entrepreneurship education program through analysis of the research trend of the entrepreneurship e education program.Since the start of the entrepreneurship education course, 88 domestic thesis on entrepreneurship education programs, which have been researched and published for 22 years from 1998 to 2019, are analyzed by research field, research subject, and research subject by research period and research period. The trends are summarized. As a result, there have been many studies since 2013 when the government actively started supporting projects, and in academic studies, there have been many studies on the entrepreneurship common program that fused each field with the creation of the entrepreneurship graduate school in 2004. In addition, many papers aimed at elementary, middle and high school students and college students were shown as preliminary founders and founders. The theme of the study was the result of a number of papers on performance factors after 2013, when the government''s start-up support policy increased. The failure rate of en ent en entrepreneurship is high and uncertainty about success is the result of the need for a systematic and gr granular entrepreneurship education program in many areas of the industry. Entrepreneurship can be learned th through education and learning many of the desirable qualities and abilities that the entrepreneur must ha have. I hope that research on entrepreneurship education programs will become more active in the future. Key words: Entrepreneurship Education, Entrepreneurship Education Program, Entrepreneurship Curriculum, Entrepreneurship, Research Trends
An Online Linguistic Analyser for Scottish Gaelic

This paper describes the Gaelic Linguistic Analyser, a new resource for the Scottish Gaelic language. The GLA includes a tagger, a lemmatiser and a parser, which were developed largely on the basis of existing resources. This tool is available online as the first component of the Scottish Gaelic Toolkit.
REQUIREMENTS ANALYSIS METHOD OF INFORMATION SECURITY MANAGEMENT SYSTEMS

The process of analyzing the requirements for information security management systems is considered. The obligation to comply with the requirements of the international standard ISO/IEC 27001 is shown. This provides confidence to stakeholders in the proper management of information security risks with an acceptable level. This is due to the internal and external circumstances of influencing the goal and achieving the expected results of organizations. In addition, the identification of stakeholders, their needs and expectations from the development of information security management systems are also considered. It is established that now the main focus is on taking into account the requirements for the process of developing these systems or to ensure information security in organizations. The transformation of the needs, expectations and related constraints of stakeholders into an appropriate systemic solution has been overlooked. These limitations have been overcome through the method of analyzing the requirements for information security management systems. Its use allows, based on the needs, expectations and related constraints of stakeholders, to identify relevant statements in established syntactic forms. There is need to check each of them for correctness of formulation and compliance with the characteristics of both the individual requirement and the set of requirements. For their systematization, establishment of relations the graphic notation SysML is applied. In view of this, the requirement is considered as a stereotype of a class with properties and constraints. Relationships are used to establish relationships between requirements. Their combination is represented by a diagram in the graphical notation SysML and, as a result, allows you to specify the requirements for information security management systems. In the prospects of further research, it is planned to develop its logical structure on the basis of the proposed method.
원자력시설 해체 프로젝트관리정보시스템 개발 및 연계

The current domestic policy flow towards nuclear decommissioning have difficult maintaining existing nuclear technologies and capabilities. Knowledge management has become an important project management execution process. The decommissioning research department of the Korea Atomic Energy Research Institute, which conducts decommissioning research and technology development, also attempted to develop a decommissioning project management information system using this knowledge management method. The decommissioning project management information system consisted of scope management, configuration management, organization and manpower management, schedule management, cost management, risk management, and stakeholder management. In this paper, we developed a document management system to perform the decommissioning of nuclear power plants in the process of constructing a project management system for dismantling nuclear facilities. The decommissioning project management system will be completed in stages by linking the history management platform and the process optimization platform based on the document management system.
Status and challenges of FAIR data principles for a long-term repository

&amp;lt;p&amp;gt;The data center of the National Science Foundation&amp;amp;#8217;s Seismological Facility for the Advancement of Geoscience (SAGE), operated by IRIS Data Services, has evolved over the past 30 years to address the data accessibility needs of the scientific research community.&amp;amp;#160; In recent years a broad call for adherence to FAIR data principles has prompted repositories to increased activity to support them. As these principles are well aligned with the needs of data users, many of the FAIR principles are already supported and actively promoted by IRIS.&amp;amp;#160; Standardized metadata and data identifiers support findability. Open and standardized web services enable a high degree of accessibility. Interoperability is ensured by offering data in a combination of rich, domain-specific formats in addition to simple, text-based formats. The use of open, rich (domain-specific) format standards enables a high degree of reuse.&amp;amp;#160; Further advancement towards these principles includes: an introduction and dissemination of DOIs for data; and an introduction of Linked Data support, via JSON-LD, allowing scientific data brokers, catalogers and generic search systems to discover data. Naturally, some challenges remain such as: the granularity and mechanisms needed for persistent IDs for data; the reality that metadata is updated with corrections (having implications for FAIR data principles); and the complexity of data licensing in a repository with data contributed from individual PIs, national observatories, and international collaborations.&amp;amp;#160; In summary, IRIS Data Services is well along the path of adherence of FAIR data principles with more work to do. We will present the current status of these efforts and describe the key challenges that remain.&amp;lt;/p&amp;gt;
Concentrated Multi-Grained Multi-Attention Network for Video Based Person Re-Identification

Occlusion is still a severe problem in the video-based Re-IDentification (Re-ID) task, which has a great impact on the success rate. The attention mechanism has been proved to be helpful in solving the occlusion problem by a large number of existing methods. However, their attention mechanisms still lack the capability to extract sufficient discriminative information into the final representations from the videos. The single attention module scheme employed by existing methods cannot exploit multi-scale spatial cues, and the attention of the single module will be dispersed by multiple salient parts of the person. In this paper, we propose a Concentrated Multi-grained Multi-Attention Network (CMMANet) where two multi-attention modules are designed to extract multi-grained information through processing multi-scale intermediate features. Furthermore, multiple attention submodules in each multi-attention module can automatically discover multiple discriminative regions of the video frames. To achieve this goal, we introduce a diversity loss to diversify the submodules in each multi-attention module, and a concentration loss to integrate their attention responses so that each submodule can strongly focus on a specific meaningful part. The experimental results show that the proposed approach outperforms the state-of-the-art methods by large margins on multiple public datasets.
Metacognitive Reading Strategies in the Filipino ESL Classroom: Use and Instruction

This article reports on a study that examined the use and instruction of metacognitive reading strategies in Mindanao rural secondary schools. Using mixed-methods research, through the use of a survey questionnaire and semi-structured interview, student (N = 408) and teacher (N = 20) participants reported utilizing problem-solving more frequently than global and support types of reading strategies. The findings also revealed that, in terms of global strategies, there was no significant difference between students' strategy use and teachers' instruction. Rural Mindanao students who learned English as a second language faced various challenges in reading. The teachers reported that these challenges partially explained why many of their students were unmotivated and frustrated readers. Interestingly, this study also disclosed a number of teachers' lack of awareness of metacognitive reading strategies, which has implications for pre-and in-service teacher development programs.
You Only Look as Much as You Have To

Active vision considers the problem of choosing the optimal next viewpoint from which an autonomous agent can observe its environment. In this paper, we propose to use the active inference paradigm as a natural solution to this problem, and evaluate this on a realistic scenario with a robot manipulator. We tackle this problem using a generative model that was learned unsupervised purely from pixel-based observations. We show that our agent exhibits information-seeking behavior, choosing viewpoints of regions it has not yet observed. We also show that goal-seeking behavior emerges when the agent has to reach a target goal, and it does so more efficiently than a systematic grid search.
InterCriteria Analysis of the Financial System in the EU Countries

The paper presents an analysis of the financial systems of the EU member states which is an actual problem. The paper showcases a new method of analyzing and grading of the connection between the criteria defining the state of the financial systems. The presented multicriteria decision making method is based on two fundamental concepts: Intuitionistic Fuzzy Sets (IFSs) and Index Matrices (IMs). We have named it `InterCriteria' decision making approach. The data for the financial systems of the EU member states is sourced from the World Economic Forum's Global Competitiveness Reports for 2019. The assessment was made using 12 main drivers, so called Pillars. The main goal is to assess the competitiveness of each EU country.
OCR Challenges for a Latvian Pronunciation Dictionary

This paper covers the devlopment of a custom OCR solution based on the Tesseract open source engine developed for digitization of a Latvian pronunciation dictionary where the pronunciation data is described using a large variety of diacritic markings not supported by standard OCR solutions. We describe our efforts in training a model for these symbols without the additional support of preexisting dictionaries and illustrate how word error rate (WER) and character error rate (CER) are affected by changes in the dataset content and size. We also provide an error analysis and postulate possible causes for common pitfalls. The resulting model achieved a CER of 2.07%, making it suitable for digitization of the whole dictionary in combination with heuristic post-processing and proofreading, resulting in a useful resource for further development of speech technology for Latvian.
Lifelong Learning Dialogue Systems: Chatbots that Self-Learn On the Job

Dialogue systems, also called chatbots, are now used in a wide range of applications. However, they still have some major weaknesses. One key weakness is that they are typically trained from manually-labeled data and/or written with handcrafted rules, and their knowledge bases (KBs) are also compiled by human experts. Due to the huge amount of manual effort involved, they are difficult to scale and also tend to produce many errors ought to their limited ability to understand natural language and the limited knowledge in their KBs. Thus, the level of user satisfactory is often low. In this paper, we propose to dramatically improve this situation by endowing the system the ability to continually learn (1) new world knowledge, (2) new language expressions to ground them to actions, and (3) new conversational skills, during conversation or "on the job" by themselves so that as the systems chat more and more with users, they become more and more knowledgeable and are better and better able to understand diverse natural language expressions and improve their conversational skills. A key approach to achieving these is to exploit the multi-user environment of such systems to self-learn through interactions with users via verb and non-verb means. The paper discusses not only key challenges and promising directions to learn from users during conversation but also how to ensure the correctness of the learned knowledge.
The LogBarrier adversarial attack: making effective use of decision boundary information

Adversarial attacks for image classification are small perturbations to images that are designed to cause misclassification by a model. Adversarial attacks formally correspond to an optimization problem: find a minimum norm image perturbation, constrained to cause misclassification. A number of effective attacks have been developed. However, to date, no gradient-based attacks have used best practices from the optimization literature to solve this constrained minimization problem. We design a new untargeted attack, based on these best practices, using the established logarithmic barrier method. On average, our attack distance is similar or better than all state-of-the-art attacks on benchmark datasets (MNIST, CIFAR10, ImageNet-1K). In addition, our method performs significantly better on the most challenging images, those which normally require larger perturbations for misclassification. We employ the LogBarrier attack on several adversarially defended models, and show that it adversarially perturbs all images more efficiently than other attacks: the distance needed to perturb all images is significantly smaller with the LogBarrier attack than with other state-of-the-art attacks.
The Acmeological Culture as an Invariant of Professionalism of a Socionomic Specialist

The acmeological culture is regarded as a leading system-forming factor in achieving a person’s acme. It ensures the self-development of a modern specialist, the ability to create and transmit acmeological values. It has been noted in the article that the acmeological culture of a socionomic specialist is a person’s newly formed structure which is revealed in their willingness and readiness to promote personal and professional growth and achievements, to foster optimal self-actualization at the level of personal professionalism and activity. It is provided by developmental competence. Acmeology has been described as an important feature of human vital activity. Well-formed acmeological literacy and acmeological competence, which includes the acmeological orientation and acmeological potential, are considered to be the initial stages in the development of the acmeological culture. The factors (acmeological conditions and components) that can contribute to or impede the formation of the acmeological culture within the framework of acmesinergetic professional development/self-development and improvement/self-improvement have been highlighted. An acmeological experiment was conducted to identify the characteristics of the formation of the acmeological culture of future socionomic specialists. A comparative analysis of the results of the recording and forming stages of the acmeological experiment has revealed a significant increase in the level of acmeological culture of the participants.