Reviewerly/exHarmony huggingface dataset:
Recommending the most confusing images to the annotators via confusion graph for the large-scale face dataset annotation

Image set annotation is an important task in the supervised training of the deep neural network. Manual and data-driven dataset annotation methods are commonly used approaches. Both of them have shortcomings, especially in the case of the dataset requiring professional knowledge, which leads to high cost with manual annotation methods and poorly diversified annotation samples with data-driven annotation methods. Although the recommendation annotation method based on cosine similarity using deep neural network features takes advantages of manual annotation and data-driven method, there are still problems such as low accuracy and click-through rate. In order to improve the recommendation accuracy and click-through rate, we propose a confusion graph recommendation annotation method, which builds a confusion graph based on the Largest Margin Nearest Neighbor (LMNN) distance among deep neural network features, to recommend the most confusing images to annotators. In this paper, we made ablation studies on the self-built child face dataset in terms of Precision, mAP (mean Average Precision), and CTR (click-through-rate). The experimental results show that the proposed method achieves superior performance, compared with the cosine similarity recommendation annotation method and the manual annotation method.
Characteristic of reviewer's assessments of individual learning performance: Analysis of instructors' assessments using IRT

The relationships between rating scores of instructors and student peers were analysed using an open data set and an item response theory (IRT) model. The characteristics of report rating behaviours of instructors and peers were compared, and the preference by peers of giving moderate ratings was confirmed. Also, the deviations in instructor ratings were illustrated using an IRT technique and examined. Finally, it was determined that the means of the peer's assessments matched the means of the estimated instructor's assessments with an IRT almost exactly.
Research of Different Methods of Data Transmission and their Problems in Remote Areas Using Magnetometric Survey

The goal of the work is to design routing algorithm taken into account overlapping contacts and to perform bundles scheduling. Created approach should be capable to be combined with existing algorithms of the selected protocol, as an example: Dijkstra's algorithm, in order to reach the highest performance for network in the presence of overlapping contacts. After optimal path selection bundle scheduling should be done (fragmentation, etc.). For bundle scheduling the current state of the network (maximum flow value in flow network) should be taken into account in order to prevent additional data losses, because in some cases, the size of data can be larger than network can forward and as a result certain part of data can be lost. Contact plan should guarantee the most reliable and up-to date information (frequent updates). The already existing approaches and variety of proposed approaches will help to achieve all the goals.
Analisis Daerah Rawan Kekeringan Kabupaten Wajo Provinsi Sulawesi Selatan

This study aims to analyze the drought prone areas in Wajo Regency. This research was conducted by the water balance method by Thornthwaite-Mather (1957), with ILACO, B.V. drought index criteria. (1981). The results showed that there were 3 levels of drought in the Wajo Regency area. In Wajo Regency, most of the moderate categories (medium water shortages) are 117 851.40 Ha or 47.137%, spread in Maniangpajo, Gilireng Selatan, Sajoangin, North Majauleng, Penrang, Takkalalla and Eastern Bola districts. High drought level is 82 877.74 Ha or 33.148%, spread in Belawa, Tanasitolo, southern Majauleng, Tempe, Sabangparu, Pammana, and Western Bola regions. There is also a low level of drought covering an area of 49291.15 Ha or 19.715% scattered in the Pitumpanua District, Keera, northernGilireng.This condition requires good water management to meet domestic and agricultural needs
Competências e habilidades do arquivista no mercado de trabalho

The book “Archival skills in the labor market” reflects the path of theoretical and practical experience acquired by the author, Dr. Eliana Maria dos Santos Bahia, during her doctorate at Universidad Carlos III de Madrid. The work was launched in Londrina at the XIX National Meeting of Research in Information Science (ENANCIB) in 2018. The author bring to Brazilian literature the theme about the skills and competencies of the archivist in the labor market.
Can We Use Small/Easy Problems to Tune Algorithms for Large/Difficult Problems?

Algorithm tuning is a thriving area of current research. Tuning an algorithm involves a set of problem instances, called the 'training set', together with a procedure that modifies the parameters of the algorithm to improve its overall performance on the training set. Tuning is invariably successful, improving subsequent performance on new problem instances. However, very little research has looked at the relationship between the composition of the training set and the algorithm's generalization performance. For example, if an algorithm is targeted at problems of a certain type, then we would expect that the training set should contain instances of the same type. Similarly, we would expect that to perform well on (say) large-scale problems, an algorithm needs to be tuned on large-scale instances. We explore such expectations, using subspaces of Traveling Salesperson Problem (TSP) instances as our testbed. In one experiment, we create TSP instances that vary in terms of a single parameter of spatial structure; in another experiment, the instances only vary in problem size (number of cities). In both experiments, a suite of algorithms is generated by tuning on each subspace, and each trained algorithm is then tested on every subspace. We find, in both experiments, that the prior expectations are clearly refuted. In particular, the difficulty of the training set (estimated by fitness-distance-correlation) seems to have no influence on the relationship between the training set and generalization performance (or, any such influence is swamped by other factors). Meanwhile, the best tuned algorithm for a given subspace is often one that was tuned on a different subspace; this includes several cases where the best algorithm for a given problem size was one tuned on a subspace of smaller problems. We conclude with consideration of the limitations of the current work, and speculate on the potential implications.
A Study on Streaming Education in UK Public Education

[연구목적] 본 연구는 영국의 공교육에 적용된 수준별 교육(streaming)의 실천 양상과 그 결과에 대한 검토를 통해 한국 교육에 대한 시사점을 도출하는데 목적이 있다. [연구방법] 이를 위해 영국에서 이루어진 수준별 교육에 대한 실증적인 연구결과와 더불어 다양한 논의들을 분석하였다. [연구결과] 분석결과, 영국의 수준별 교육은 상위 스트림 학생들의 학력향상에 도움이 되었고, 하위 스트림의 학력은 더욱 악화시킨 것으로 나타났다. 그리고 사회적 평등에 대한 문제와 학생들의 사회성을 약화시키는 문제도 발생하였다. [결론] 이러한 영국의 사례를 통해 얻을 수 있는 시사점은 합리성을 앞세운 수준별 교육보다는 통합된 교육을 통해 학생들 행동의 개선과 사회적인 성장을 이끌어 내는 노력이 필요하다는 것이다.[Purpose] This study aims to derive implications for Korean education through reviewing the practice and results of the Streaming to UK public education. [Methods] To this end, various discussions and researches were analyzed along with the empirical results of the Streaming in the UK. [Results] The results of the analysis showed that Streaming in the UK helped to improve the achievements of upper stream students, and worsened the achievements of lower streams. There was also the problem of social inequality and the weakening of students’ sociality. [Conclusion] The implications of this UK case show it is important to lead the improvement of student behavior and social growth through integrated education rather than Streaming education with rationality
Search Based Optimization Approach for Video Super Resolution

Video Super Resolution (SR) is used to enhance the resolution of low resolution videos in minimum cost as compare to hardware models and it should handle noisy and blurry videos. This paper represents efficient and robust video SR model which enhance the resolution of different videos. Input video is taken from readily available standard UCSD datasets; these videos are in RGB format which are initially converted to HSV format. To achieve high resolution the V-channel is used for enhancement.  Full search motion estimation is used find out matching macro blocks in a video frame. Cubic spline interpolation technique is used to find value of unknown pixel. Bilateral total variation is used for de-noising and de-blurring. Resolution factor is optimized using different optimization techniques like particle swarm optimization (PSO), Grey Wolf optimization (GWO), Whale optimization algorithm (WOA) and Lion algorithm (LA). Performance of proposed video SR is compared with existing methods and statistical analysis is done with PSNR, SSIM, SDME, ESSIM and BRISQUE parameters.
Video Demo: Unsupervised Learning of Depth and Ego-Motion from Cylindrical Panoramic Video

In this demonstration, we will present a video showing depth predictions for street-level 360° panoramic footage generated using our unsupervised learning model. Panoramic depth estimation is important for a range of applications in- cluding virtual reality, 3D modeling, and autonomous robotic navigation. We have developed a convolutional neural network (CNN) model for unsupervised learning of depth and ego-motion from cylindrical panoramic video. In contrast with previous works, we focus on cylindrical panoramic projection. Unlike spherical or cube map projection, cylindrical projection is fully compatible with traditional CNN layers while still supporting a continuous 360° horizontal field of view. We find that this increased field of view improves the ego-motion prediction accuracy for street-level video input. This abstract motivates our work in unsupervised structure-from-motion estimation, describes the video demonstration, outlines our implementation, and summarizes our study conclusions.
Research of Information Propagation Path and Distribution of Vulnerable Points Based on Class Cooperative Network

With rapid growth in the size of software, applications' code structure becomes more complex, attacks against vulnerable points can cause huge damage. To solve this problem, this paper analyses code structure of classes, builds cooperative network at class level to describe information propagation. On the basis of class cooperative network, this paper designs a method to exclude wrong information propagation paths, uses WS small-world network to build software model, analyses data association, finds network critical paths and vulnerability points. Experiments are launched to verify the results.
Research Guides: Ethnic and Cultural Diversity: Introduction

This guide was created for the purpose of learning about and understanding different cultures, how cultural differences shape our lives and to help us understand the world we live in.
Knowledge Representation Learning Based On SoftMax

Knowledge Graph (KG) embedding has begun to be widely used in various fields, such as search engine, semantic search, and knowledge inference. In the traditional model, because an effective time period of knowledge is not taken into account, it leads to a wrong knowledge construction. And their robustness is not excellence after adding new knowledge elements. In most existing KG representation learning models, repeated calculations are performed by synthesizing a negative sample set with the correct sample set, which also leads to an increase in the amount of calculation. At the same time, because there are many-to-many situations for entities and relationships in the KG, the vector representation obtained when using the previous model for representation learning is not unique. So it leads to an increase in computational complexity. Therefore, in this paper we propose a knowledge representation learning model based on softmax, which calculates the most suitable representation vector by the conditional probability of the entity and relation vector. The experimental results show that our model has improved significantly in the knowledge completion compared with the traditional method.
Deformable hourglass network with face region normalization for robust face alignment

Stacked hourglass (HG) networks have been successfully applied to face alignment. However, due to the complex geometry of the facial appearance, the HG model still lacks the robustness of aligning faces in large poses. In this paper, a two-step method is proposed for robust face alignment. First, by using a convolutional neural network (CNN) to directly output the transformation parameters, the conventional procedure of normalizing the face region by performing Procrustes analysis based on the detected landmarks and the mean shape is simplified. In this way, faces with different poses can be converted to a canonical state, which is more advantageous for subsequent face alignment. Second, motivated by recent deformable convolutional networks, we propose a modulated deformable residual block and replace the plain counterparts in the HG model, resulting in deformable hourglass networks (DHNs). The DHN yields large performance improvements over original HG model while having the almost same amount of parameters and bringing minor additional computation costs. Depending on the synergistic effect of two innovations, the proposed method achieves better performance in comparison to the state-of-the-art methods on challenging benchmark datasets.
Acceptable Privacy for XAND Blockchain

Recently, the privacy of blockchain transactions has taken center stage. Privacy is an essential feature of crypto-transactions. We have seen privacy-preserving cryptocurrencies like Monero with Cryptonote protocol and Zerocash with Zk-SNARKs. However, the privacy of transactions comes at the cost of the transactions not being visible at all to any regulatory bodies. In most developed or developing countries, the Government is democratic, and it is generally a good idea for the Government to be able to prevent illicit activities like foreign exchange frauds, black markets, criminal funding, and drug trafficking. The XAND blockchain aims to resolve this problem by allowing some authorized participants to view the transactions, while at the same time maintaining the privacy of the transactions from other regular users, including the validators.
Collaborative Regression Analysis Algorithm for Multi Organizational Coupling Feature to Ensure Privacy: LARS Based on Federal Learning

With the development of big data, emphasis on data privacy and security has become a global trend. To address data privacy and security issues, Google has proposed a federal learning framework designed to make effective and accurate cross-organizational use of data within the industry. Suppose two organizations, A and B, each maintaining a private record of different sets of features for a common entity. If there is no coupling between the features, the processing is relatively simple. Assuming strong coupling between features, how to deal with it is relatively complicated, both to ensure privacy and to obtain coupling relationships, such as inner product calculation results. We designed and implemented a framework that guarantees privacy and efficient coupling. The experimental results show that the scheme is feasible, the data privacy is guaranteed, the feature coupling between different organizations is calculated, the result is accurate, and the execution time is acceptable.
Film Box Office Prediction Based On Factor Analysis

Predicting the film box office will help the cinema's film arrangement and can also provide reference for investors. The year of 2017 is a milestone in the history of Chinese film. This paper analyses the data from147 films that obtained higher box office in 2017. The selected indicators include the premiere box office, 360 index, days of film Screening, ratings on movie.Douban.com, etc. Finally, we take factor analysis and linear regression to predict and analyze film box office.
Mobile Recommendation Method for Fusing Item Features and User Trust Relationship

User based collaborative filtering recommendation method does not consider the impact of user preferences on the user’s similarity in the non-common score items, and the lack of traditional similarity measurement methods in sparse user score data. This paper proposed a hybrid recommendation method combining similar relationship and trust relationship of mobile users, using the EMD distance method of user preference on similar items to compute the preference similarity relation among the users, and fusing mobile user trust and similar user preferences for the target user’s non-scoring items to be scored prediction. Experimental results on public data sets show that, compared to the traditional collaborative filtering recommendation algorithm based on users, this method has a lower MAE error value and higher P@N value, effectively alleviate the data sparsity and improve the performance of the recommendation system.
GAMBARAN HASIL PEMERIKSAAN LEUKOSIT URINE MENGGUNAKAN METODE MIKROSKOPIK DENGAN ALAT AUTOMATIK MEDITRON JUNIOR

Microscopic examination of breathless urine to determine the presence of abnormalities in the kidneys and urinary tract. The urine used is urine needed. Sediment experiments are carried out using an objective (10X) called the small visual field or LPK. Besides that, objective (40X) which is called the big field of vision or LPB is also used. This study is motivated by researchers intending to conduct research on the massive assessment of leukocytes in the urine using microscopic methods with automatic tools. Having conducted research aimed at studying the results of microscopic leukocyte examination of microscopic methods with these automatic devices, use laboratory observational methods. The results of the study on 20 samples obtained were all normal. Conclusions Based on the results of the study showed the results of urine leukocyte examination using microscopic methods with automatic devices obtained the results were all normal. It is necessary to be able to use other examinations to complete data on various examination methods in order for researchers to provide accurate and more accurate examination results.
Google’s AI System Can Detect Breast Cancer Better Than Doctors

An artificial intelligence system by Google reportedly surpasses expert radiologists in accurately interpreting mammograms from screening programs.
Classification using Support Vector Machine to Detect Cyberbullying in Social Media for Myanmar Language

As a growth of the technological world, web technologies and social networking emerged and played an important role in telecommunication. People misuse the social network as a new weapon to make a person attack unable to find the identity of the attacker. Due to the illegal action, the technological world seems to face new challenges and new risks like cyberbullying. This paper proposes a supervised method for detection of Myanmar cyberbullying in social media by using Support Vector Machine (SVM) classifier. The proposed method includes three main steps: data preprocessing, word segmentation, and classification. In the first step, we extract the posts written in Myanmar text from social media. We break the posts and sentences into syllables into words by using the Longest Syllable Matching approach along with a dictionary as the second step. For the third step, we apply Support Vector Machine classifier to detect cyberbullying in social media whether the bullying words or not. Consequently, the experimental result shows that our method obtains 0.7540 classification accuracy in terms of F-score.
Two Views on the 2010 Moscow Metro Bombings: Corpus-Based Contrastive Keyword Analysis

The Internet has become an extremely important means of human communication. However, it is also used by extremists and terrorists who employ it to promote their ideas and recruit new members. One of the tasks facing academia and governments is detection of extremism/terrorism related content and counteracting a corresponding ideology. Analysis of linguistic features of texts from extremist sources can help to solve this task. The aim of the research is a corpus-based keyword analysis of Russian-language posts from extremist forum and texts by common Internet users on the same topic (comments on the 2010 Moscow Metro Bombings). We also used a corpus of blogs from LiveJournal as a reference corpus for deriving keywords, which is standard practice. We performed keyword analysis by means of WordSmith Tools software package and used qualitative, manual discourse analysis to identify a number of differences between texts from extremist forum and texts by common people on the same topic on lexical level.
An Enhanced Structured-Light Modulation System for Defect Detection of Specular Surface

The structured-light modulation analysis technique can be used to detect the defects on the specular surface. Based on this techinque, an enhanced modulation system is proposed in this paper, which gets better detection results.
Modeling Big Data and Further Analysis

The big data concept was introduced with the description and enumeration of online data sources. The importance of the big data was highlighted to help professionals and beginners to plan field work and research. The techniques that apply numerical methods to analyze huge dataset set were shown with adequate examples. The process of developing indigenous environmental model using computational techniques was explained with a C++ exercise.
Towards a Rigorous Software Architecture Documentation Process: A Demonstration with the Real-time Immersive Network Simulation Environment (RINSE)

Despite numerous empirical studies and wide-spread, practical experience demonstrating the importance of rigorous documentation in software engineering, many developers continue to treat it as an “after thought.” Documentation, particularly of software architecture, should be an integral process of any development group, whether entrepreneurial, academic, or corporate. In this article the authors develop and apply the software engineering principles of Len Bass, Paul Clements, and Rick Kazman for software architecture documentation. The case study involves a relatively large-scale, academic development project aimed at supporting large-scale network security preparedness and training exercises, involving hundreds of players and a modeled network composed of hundreds of networks.