Reviewerly/exHarmony huggingface dataset:
Сlassification culture of the Russia

Classification culture – a set of achievements of the country in the field of classification systems (CS): the study of their history and theory, the creation of own systems and the development of foreign experience, publications and options, practice of application. It is told about the achievements of Russia: works on the history of library CS, the directions of theoretical studies. For example, he CS history is well studied – from ancient times to the present day. Interesting CS were developed in the 18–19th century (for the largest and some university libraries). Schedules of the foreign CS – complete translation of the Colon Classification and the Dewey Decimal Classification into Russian was published. Published Russian UDC retranslation in 10 volumes. The country has CS, recognized by the international community as the National System of Russia (firstly published in the years 1961–1968 in the 30 books). Contemporary classification practice is distinguished by the presence of variants of schedules – full, medium and abridged, in book and machine-readable form. The experience of developing classification schedules for children's and school libraries is unique. The conclusion is made: Russia certainly has a high level of classification culture.
Power shrinkage—curvelet domain image denoising using a new scale-dependent shrinkage function

Image processing and analysis algorithms are at the heart of applications in various scientific fields, such as medical diagnosis, military imaging, and astronomy. However, images are typically exposed to noise contamination during their acquisition and transmission. In this paper, we explore recent advancements in image denoising using curvelet domain shrinkage and present a novel scale-dependent shrinkage function, which we call power shrinkage, to enhance restored image quality. Experimental results confirm our proposed method to perform better than classical thresholding and to outperform recent state-of-the-art approaches in denoising different types of noises including speckle, Poisson and additive white Gaussian noise.
Learning to Find Common Objects Across Few Image Collections

Given a collection of bags where each bag is a set of images, our goal is to select one image from each bag such that the selected images are from the same object class. We model the selection as an energy minimization problem with unary and pairwise potential functions. Inspired by recent few-shot learning algorithms, we propose an approach to learn the potential functions directly from the data. Furthermore, we propose a fast greedy inference algorithm for energy minimization. We evaluate our approach on few-shot common object recognition as well as object co-localization tasks. Our experiments show that learning the pairwise and unary terms greatly improves the performance of the model over several well-known methods for these tasks. The proposed greedy optimization algorithm achieves performance comparable to state-of-the-art structured inference algorithms while being ~10 times faster. The code is publicly available on this https URL.
Point-less: More Abstractive Summarization with Pointer-Generator Networks

The Pointer-Generator architecture has shown to be a big improvement for abstractive summarization seq2seq models. However, the summaries produced by this model are largely extractive as over 30% of the generated sentences are copied from the source text. This work proposes a multihead attention mechanism, pointer dropout, and two new loss functions to promote more abstractive summaries while maintaining similar ROUGE scores. Both the multihead attention and dropout do not improve N-gram novelty, however, the dropout acts as a regularizer which improves the ROUGE score. The new loss function achieves significantly higher novel N-grams and sentences, at the cost of a slightly lower ROUGE score.
Mach–Zehnder interferometer with quantum beamsplitters

A one-dimensional waveguide enables a single two-level emitter to route the propagation of a single photon, as to provide a quantum mirror or a quantum beamsplitter. Here we present a fully-quantum Mach-Zehnder interferometer (QMZ) for single-photon pulses comprised of two quantum beamsplitters. We theoretically show how nonlinearities of the QMZ due to photon-emitter detunings and to the spectral linewidth of the pulse contribute to the versatility of the device with respect to the classical-beamsplitters scenario. We employ a quantum dynamics framework to obtain analytical expressions for the photodetection probabilities and prove, in the monochromatic regime, the equivalence with a transfer-matrix approach.
JOOX Application as a Media to Improve Students’ Pronunciation

The newest students in university often have difficulty with pronunciation in English. This article is based on the study on students’ pronunciation of Wijaya putra Surabaya University in the first semester. The purpose of this study is to improve the sudents’ pronunciation achievement by using Joox as a media. The subjects were 15 students of wijaya Putra University in the first semester. The data of the study were collected by interview, observation and test. The design of this study was classroom action research which consist of planning, acting, observing and reflecting. In analyzing the data, researcher compared the result of all instruments (interview, observation and test) and concluded it. The result showed that Jooox had positive effect to improve students’ pronunciation achievement.
Experience in Teaching Science in Virtual Environment

Labs are needed in science education, and schools usually lack appropriate conditions to provide active learning activities with experience. In schools, the lack of labs for science education is usual. The new technologies of 3D virtual worlds allow the user, through immersive virtual worlds, or metaverses, to experience situations similar to those possible in a real environment. A solution to the lack of labs is the virtual labs, which consist of virtual representations that reproduce the environment of a real lab. These are applications based on simulations, offering computational representations of reality. The simulation of concrete and real situations gives the student the opportunity to experiment, actively participating in the learning process by performing experiments that would otherwise not be possible due to safety issues (possible damage to students or the environment) or economy (cost of components involved and derived from the creation and operation of the lab). These virtual labs can be implemented on personal computers that exist in schools or even in the student's home. Users can work in the virtual lab, through their avatars, using experiments provided there, as well as acting collaboratively.
Live Demonstration: Hardware Implementation of Brain-Inspired Amygdala Model

This live demonstration presents a brain-inspired amygdala model. Amygdala is an area of the brain that is associated with fear conditioning, which is a type of classical conditioning. The model can learn preferences through humanrobot interactions by application of classical conditioning to the model. Additionally, to develop a high speed and low power system, we design a hardware of the amygdala model, and implemented the hardware into field programmable gate array.
Characterizing the invariances of learning algorithms using category theory

Many learning algorithms have invariances: when their training data is transformed in certain ways, the function they learn transforms in a predictable manner. Here we formalize this notion using concepts from the mathematical field of category theory. The invariances that a supervised learning algorithm possesses are formalized by categories of predictor and target spaces, whose morphisms represent the algorithm's invariances, and an index category whose morphisms represent permutations of the training examples. An invariant learning algorithm is a natural transformation between two functors from the product of these categories to the category of sets, representing training datasets and learned functions respectively. We illustrate the framework by characterizing and contrasting the invariances of linear regression and ridge regression.
Research on Semantic Information Retrieval Model of Bamboo & Rattan Domain Based on Query Extension

The existing domain semantic information retrieval models generally do not consider the wide range of domain knowledge involved, and lack the ability to query multi-level domain knowledge in this domain. Based on the semantic expression of the knowledge about bamboo & rattan via ontology, this paper mainly studies the measurement of semantic relevancy in the field of bamboo and rattan, and finally proposes a Semantic Information Retrieval Model in this field which is based on relevancy. Experimental results show that the semantic query extension based on the concept of similarity proposed in this paper can improve the recall rate and F value of retrieval model significantly.
Using Data Mining to Predict Sludge and Filamentous Microorganism Sedimentation

This study attempted to develop statistical regression models for predicting the settleability of activated sludge based on the quality of incoming sewage and on the identified dominant filamentous species.As part of the analyses conducted for the purpose, classification models are presented that enable identification of the respective filamentous microorganisms, based on the working parameters of the bioreactor and the quality of the influent.The study calculations demonstrated that the modeling methods based on artificial neural networks, random forests, and boost trees can be applied for the identification of filamentous microorganisms Microthrix parvicella, Nostocoida sp., and Thiotrix sp. in activated sludge chambers in the STP located in Sitkówka-Nowiny.The best predictive capacity, covering identification of the above-mentioned filamentous bacterial species in activated sludge chambers, was observed for statistical models obtained by the random forest method.
Pengenalan Penggunaan Software Perancangan Pesawat Di Industri Penerbangan Pada Siswa SMK Muhammadiyah I Temon Kulon Progo

The era of globalization with all its implications is one of the triggers of rapid changes that occur in various aspects of people's lives and if there is no serious effort to anticipate it, it will become a very serious problem. The field of education has a large responsibility, especially in the change of preparing strong human resources so that they are able to live in harmony with the changes themselves. This community service activity was held at Muhammadiyah I Vocational School Temon Kulon Progo with the aim of introducing the use of CATIA aircraft design software in the aviation industry. Implementing community service activities in the context of introducing aircraft design software in the aviation industry is carried out for 2 days in accordance with existing competencies in the relevant school curriculum. This activity is able to provide knowledge that can be used as a reference in improving student learning competencies.
Sasirangan Motifs Classification using Scale- Invariant Feature Transform (SIFT) and Support Vector Machine (SVM)

Sasirangan is one of the traditional cloth from Indonesia. Specifically, it comes from South Borneo. It has many variations of motifs with a different meaning for each pattern. This paper proposes a prototype of Sasirangan motifs classification using four (4) type of Sasirangan motifs namely Hiris Gagatas, Gigi Haruan, Kulat Kurikit, and Hiris Pudak. We used primary data of Sasirangan images collected from Kampung Sasirangan, Banjarmasin, South Kalimantan. After that, the images are processed using Scale-Invariant Feature Transform (SIFT) to extract its features. Furthermore, the extracted features vectors obtained is classified using the Support Vector Machine (SVM). The result shows that the Scale- Invariant Feature Transform (SIFT) feature extraction with Support Vector Machine (SVM) classification able to classify Sasirangan motifs with an overall accuracy of 95%.
PROBLEMS OF PSYCHO-HYGIENE IN THE STAFF OF THE PENITENTIARY SYSTEM

In the article specific, psycho-traumatic conditions of the professional activity of the staff of the penitentiary system are considered. They are associated with the periodically arising threat to life and health, honor and dignity, in the protection and convoy of convicted criminals. The purpose of the study is to identify the problems of psycho-hygienic training of officers of the penitentiary system for performing their professional tasks. The subject of the study is the psycho-hygienic training of officers of the penitentiary system. The subject of the research is the ways of psycho-hygienic training of officers of the penitentiary system. Under the psycho-hygienic training of officers of the penitentiary system, official activity is understood as the formation of a stable psychological state. It ensures effective professional activity. Psychological readiness to perform professional activities is a factor in the effective service of officers of the penitentiary system. Simultaneously - this is an important condition of psycho-hygiene. Psychological readiness needs to be diagnosed during the admission to the service and developed in the process of adaptation during the initial preparation. There is a psychological selection of candidate's compliance with the required conditions of the service. But, most young officers have poor psychological preparedness to perform professional tasks. It is proposed to strengthen the psycho-hygienic aspects of the educational process; to assess the level of psycho-hygienic training of officers of the penitentiary service at the stage of their professional selection; organize the psychotherapeutic support of officers of the penitentiary system during the initial training with the aim of developing their professionally important qualities as the basis for psycho-hygienic training; consider psycho-hygienic training for performance as one of the determining factors in the overall assessment of officers of penitentiary institutions.
Image Identification of Encrypted JPEG Images for Privacy-Preserving Photo Sharing Services

We propose an image identification scheme for double-compressed encrypted JPEG images that aims to identify encrypted JPEG images that are generated from an original JPEG image. To store images without any visual sensitive information on photo sharing services, encrypted JPEG images are generated by using a block-scrambling-based encryption method that has been proposed for Encryption-then-Compression systems with JPEG compression. In addition, feature vectors robust against JPEG compression are extracted from encrypted JPEG images. The use of the image encryption and feature vectors allows us to identify encrypted images recompressed multiple times. Moreover, the proposed scheme is designed to identify images re-encrypted with different keys. The results of a simulation show that the identification performance of the scheme is high even when images are recompressed and re-encrypted.
Extreme Learning Machine and Particle Swarm Optimization for Inflation Forecasting

Inflation is one indicator to measure the development of a nation.If inflation is not controlled, it will have a lot of negative impacts on people in a country.There are many ways to control inflation, one of them is forecasting.Forecasting is an activity to find out future events based on past data.There are various kinds of artificial intelligence methods for forecasting, one of which is the extreme learning machine (ELM).ELM has weaknesses in determining initial weights using trial and error methods.So, the authors propose an optimization method to overcome the problem of determining initial weights.Based on the testing carried out the purposed method gets an error value of 0.020202758 with computation time of 5 seconds.
CoAP: Echo, Request-Tag, and Token Processing

This document specifies enhancements to the Constrained ApplicationProtocol (CoAP) that mitigate security issues in particular use cases.The Echo option enables a CoAP server to verify the freshness of arequest or to force a client to demonstrate reachability at itsclaimed network address; it is now the recommeded way to mitigateamplification attacks. The Request-Tag option allows the CoAP serverto match block-wise message fragments belonging to the same request.The update to the client Token processing requirements of RFC 7252forbids non-secure reuse of Tokens to ensure binding of responses torequests when CoAP is used with security.
Cluster based Hybrid Approach to Task Scheduling in Cloud Environment

Cloud computing technology enables sharing of computer system resources among users through internet.Many numbers of users may request for sharable resources from a cloud.The sharable resources must be effectively distributed among requested users with in a less amount of time.Task scheduling is one of the ways of handling the user requests effectively in a cloud environment.There were many existing biologically inspired optimization techniques worked with task scheduling problems.The proposed paper is aimed at clubbing clustering techniques with biologically inspired optimization algorithms for deriving better results.A new hybrid methodology KPSOW (K-means with PSO using weights) has been proposed in the paper, which makes use of the strengths of both the Kmeans and PSO algorithms with the inclusion of weights concept.The results have shown that KPSOW has made considerable changes in reducing the makespan and improves the utilization of computing resources in the cloud.
A modified Particle Swarm Optimization algorithm linking dynamic neighborhood topology to parallel computation

In this paper, a novel approach is considered, based on Particle Swarm Optimization (PSO) technique, using two concepts: evolutionary neighborhood topology associated to parallel computation for complex optimization problems.The idea behind using dynamic neighborhood topology is to overcome premature convergence of PSO algorithm, by well exploring and exploiting the search space for a better solution quality.Parallel computation is used to accelerate calculations especially for complex optimization problems.The simulation results demonstrate good performance of the proposed algorithm in solving a series of significant benchmark test functions.
New book classification based on Dewey Decimal Classification (DDC) law using tf-idf and cosine similarity method

Classification new book is needed in facilitating students and lecturers to find books. The law used is Dewey Decimal Classification (DDC) classification. The application of the DDC classification requires a high level of accuracy and concentration in grouping books into appropriate classes. Errors that occur in the form of discrepancies in the provision of class books. Performance can be improved by the existence of an information system that can help classify classes in books according to DDC law. The process of giving classes to books by looking for the highest similarity between titles and synopsis of books with each DDC dictionary class. Adjusting to the process of giving classes to books at the University of Jember Library, the title, synopsis and DDC dictionary are processed using the text mining method. Text mining produces data in the form of basic words from the title, synopsis and DDC dictionary. The number of occurrences of each word is useful for measuring how important a word is in a document. The method that is suitable for calculating the importance of a word in a document is the method of weighting Term Frequency-Inverse Document Frequency (TF-IDF). The results of the TF-IDF weighting are used to find the highest similarity between the title and the synopsis with the class in the DDC dictionary. The appropriate method in calculating the similarity of two documents is Cosine Similarity. The biggest similarity value between the title and synopsis with the DDC dictionary using Cosine Similarity method is made a priority in determining the class of books. The results of the application of the method in the system there are 20 data books resulting in book classes in DDC 000 class there are 3 books, DDC 100 class is 1 book, DDC class 200 there is 1 book, DDC 300 class there are 6 books, DDC 400 class there are 4 books, DDC 500 class is 1 book, DDC 600 class there are 2 books and DDC 700 class there are 2 books. Testing book classification information system produces accuracy percentage of 35 %.
Research on related technologies of vision target tracking based on discrete differential algorithm for deep learning

In order to promote the development of visual target tracking technology, this paper uses discrete differential algorithm in computer to assist analysis. First, the development status of target tracking technology is expounded, constructing the target tracking model by using different image sequen ces. In order to enhance the efficiency of target tracking, discrete differential algorithm is added. The algorithm can reduce the complexity of the computation and adopt the traditional optical flow evaluation method and level set frame tracking method to perfect. An optimization and update scheme is proposed from the algorithm flow and evaluation model, and an effective evaluation model is established by effectively evaluating the state of the visual tracking target model. In the test of target tracking model, through the comparison of different targets and the efficiency of data calculation, it is proved that the model can be layered for the target, and the addition of discrete differential algorithm effectively solves the problem of scale self-adaptation, which deserves further popularization.
Two-Layer Near-Lossless HDR Coding Using Zero-Skip Quantization with Backward Compatibility to JPEG

We propose an efficient two-layer near-lossless coding method using an extended histogram packing technique with backward compatibility to the legacy JPEG standard. The JPEG XT, which is the international standard to compress HDR images, adopts a two-layer coding method for backward compatibility to the legacy JPEG standard. However, there are two problems with this two-layer coding method. One is that it does not exhibit better near-lossless performance than other methods for HDR image compression with single-layer structure. The other problem is that the determining the appropriate values of the coding parameters may be required for each input image to achieve good compression performance of near-lossless compression with the two-layer coding method of the JPEG XT. To solve these problems, we focus on a histogram-packing technique that takes into account the histogram sparseness of HDR images. We used zero-skip quantization, which is an extension of the histogram-packing technique proposed for lossless coding, for implementing the proposed near-lossless coding method. The experimental results indicate that the proposed method exhibits not only a better near-lossless compression performance than that of the two-layer coding method of the JPEG XT, but also there are no issue regarding the combination of parameter values without losing backward compatibility to the JPEG standard.