Reviewerly/exHarmony huggingface dataset:
Human Body Parts Tracking: Applications to Activity Recognition

As cameras and computers became popular, the applications of computer vision techniques attracted attention enormously. One of the most important applications in the computer vision community is human activity recognition. In order to recognize human activities, we propose a human body parts tracking system that tracks human body parts such as head, torso, arms and legs in order to perform activity recognition tasks in real time. This thesis presents a real-time human body parts tracking system (i.e. HBPT) from video sequences. Our body parts model is mostly represented by body components such as legs, head, torso and arms. The body components are modeled using torso location and size which are obtained by a torso tracking method in each frame. In order to track the torso, we are using a blob tracking module to find the approximate location and size of the torso in each frame. By tracking the torso, we will be able to track other body parts based on their location with respect to the torso on the detected silhouette. In the proposed method for human body part tracking, we are also using a refining module to improve the detected silhouette by refining the foreground mask (i.e. obtained by background subtraction) in order to detect the body parts with respect to torso location and size. Having found the torso size and location, the region of each human body part on the silhouette will be modeled by a 2D-Gaussian blob in each frame in order to show its location, size and pose. The proposed approach described in this thesis tracks accurately the body parts in different illumination conditions and in the presence of partial occlusions. The proposed approach is applied to activity recognition tasks such as approaching an object, carrying an object and opening a box or suitcase.
NEW RE-RANKING APPROACH IN MERGING SEARCH RESULTS

When merging query results from various information sources or from different search engines, popular methods based on available documents scores or on order ranks in returned lists, its can ensure the fast response, but results are often inconsistent. Another approach is downloading contents of top documents for re-indexing and re-ranking to create final ranked result list. This method guarantees better quality but is resource-consuming. In this paper, we compare two methods of merging search results: a) applying formulas to re-evaluate document based on different combinations of returned order ranks, documents titles and snippets; b) Top-Down Re-ranking algorithm (TDR) gradually downloads, calculates scores and adds top documents from each source into the final list. We propose also a new way to re-rank search results based on genetic programming and re-ranking learning. Experimental result shows that the proposed method is better than traditional methods in terms of both quality and time.
Perancangan Aplikasi Perpustakaan Berbasis Web pada SMK Negeri 1 Rangkasbitung

The school library is one of supporting that could achieve the quality of education. It was because the library could be a source of learning in the school. Management of data processing, lending, and returning of library books could take a lot of fees and times. It was because using a ledger. Besides, the students having difficulties in order to get the books. It was because there is no technology information that provide the search engine toward a books. This research was focus on the library information system. The objective of this research were to facilitate officer in managing the library data and to make the students easy to find the books. This library application is website-based which is develop by using the PHP as programming language while MySQL as database management system. The method that used to conduct the data were structured interview, literature study, and observation which regard to the school library in making a library application. This study gives the result in the usability aspect the percentage is 79,5%, its mean that the library application could facilitate officer in processing data collection, borrowing and returning library books, moreover, the students could search the book easily.
The determination of identity and uniqueness of color laser printouts of Ricoh® brand by Adobe® Creative Cloud Photoshop® 2018

Printer forensics is a sub-branch of digital forensic science encompassing the identification of the source of any printout by different means. Over the world, still today, there is no digital forensic lab able to identify the source of the color laser printer seized in forgery crimes by a certain scientific method. Therefore, the determination for the identity and uniqueness of color laser printouts have become one of the most challenging recently investigated in digital forensic labs. In the present study, one type of the most common brand of color laser printers called Ricoh® was selected. Three defined steps, printing, scanning, and extracting, were applied to measure and calculate the obtained data for achieving our target. Adobe Creative Cloud Photoshop 2018 was used as a forensic tool for image processing. In our study, 400 color laser printout samples from 93 Ricoh® color laser printers with different serial numbers were investigated. The study results successfully present the basic coded dot matrix pattern (CDMP) that characterizes and corresponds to the Ricoh® color laser machines with a 100% accuracy ratio. The determination of identity and uniqueness of color laser printouts of Ricoh® brand was achieved with a high accuracy ratio (100%). Adobe Creative Cloud Photoshop 2018 was a versatile software applied as a steganalysis tool for extracting the steganography information embedded in all undertaken color laser printouts.
LAC: Locating and Applying Consistent and Repetitive Changes

As a software system evolves, constant changes are usually made in the code. Similar changes to code fragments often occur in multiple locations. It is very tedious and error prone for developers to manually update similar changes in multiple locations. To address this problem, we propose an approach for Locating and Applying Consistent and Repetitive Changes (LAC) as an Eclipse plug-in. LAC analyzes the code fragments to detect change anomalies, such as omissions or inconsistent edits, and automatically repairs these anomalies by applying required changes. Our static analysis technique to detect anomalies is carried out by inferring change patterns. The evaluation results in a user study show that automatically applying repetitive changes in identified locations using LAC correctly matches with that of manually updated ones, which significantly decreases manual efforts and error-prone edits.
Disabled Children in the Context of Russian Digital Economy Challenges

Abstract The digital society having gained quite a momentum in the Russian Federation in the recent decade has brought along some new trends in “problems-shaping” not only for the producing, but also for the non-producing strata of the population. The new “non-producing classes” get formed in a situation of new historical conditions the coordinates of which cover the entire global world, and it can be spoken definitely that problems of the Russian society are determined, among other things, by trends of the “world information community”, with the former being a part of it nowadays. The paper outlines the main vectors of the problem of socialization of the disabled children in the contemporary Russian society within the context of information economy forming. Particularities of integration of children having disabilities are shown in the logic of development of the world social inequality processes based on the “digital divide” approaches. Proceeding from the Russian statistical materials, some suppositions are made about possible negative development scenarios of the present-day cohort of children having functional limitations who can become outsiders of the digital revolution and man the strata of non-producing classes if there are no state programs, education strategies, pedagogical personnel and focused work with parents.
Application investigation and technological development of intelligent product in college English teaching

Abstract Through quantitative and qualitative research methods, this paper investigated the application of intelligent products in College English teaching in China and proposed its future technological development prospect based on application problem and both teachers’ and students’ practical needs. The survey shows that kekenet.com and Chinese Universities MOOC Network are the most popular intelligent products used by teachers and students due to effective intelligent monitor and rich resource support function etc. The application problems and product disadvantages include lack of intelligent monitoring and recording system, lack of error identification and correction function, without learning process evaluation and individualized learning guidance etc. The future improvements will be adding listening error analysis in listening intelligent product, adding speech identification and comparison function in speech teaching software, creating translation teaching platform and adding word-in-use technique for vocabulary teaching product etc.
Blockchain Private Key Storage Algorithm Based on Image Information Hiding

The private key is the unique credential of the blockchain asset and can be used to sign and authenticate transactions. Losing the private key also loses the control of the account, so the leakage of the private key is one of the major factors affecting the security of the blockchain. In order to improve the security of blockchain private key storage technology in transactions, this paper proposes a blockchain private key storage algorithm based on image information hiding. In order to improve the camouflage ability and reduce the bit error rate, the algorithm adds random numbers and error correction code to the original private key to provide double protection. After generating the watermark information, the watermark information is embedded into the digital image by means of discrete wavelet transform and quantized index modulation to realize the process of information hiding. Finally, it is proved that this algorithm has good transparency and robustness, can hide the private key information visually, and extract it accurately.
MODERN CONTENT AND DEVELOPMENT TRENDS OF PEDAGOGICAL TUTORSHIP IN THE RUSSIAN GENERAL EDUCATION SYSTEM

The article considers the essence and characteristics of modern pedagogical tutorship and identifies its development trends. The author analyses the peculiarities of tutorship in successful regional education systems where the emphasis is made on new subjectivity (a child’s active position causes a teacher’s active position). The researcher identifies relevant forms of pedagogical experience presentation and distribution within the framework of the municipal educational forum as a key area of teachers’ professional communication. The paper considers a potential for the development of the traditional forms of tutorship in education.
A Simple Pooling-Based Design for Real-Time Salient Object Detection

We solve the problem of salient object detection by investigating how to expand the role of pooling in convolutional neural networks. Based on the U-shape architecture, we first build a global guidance module (GGM) upon the bottom-up pathway, aiming at providing layers at different feature levels the location information of potential salient objects. We further design a feature aggregation module (FAM) to make the coarse-level semantic information well fused with the fine-level features from the top-down pathway. By adding FAMs after the fusion operations in the top-down pathway, coarse-level features from the GGM can be seamlessly merged with features at various scales. These two pooling-based modules allow the high-level semantic features to be progressively refined, yielding detail enriched saliency maps. Experiment results show that our proposed approach can more accurately locate the salient objects with sharpened details and hence substantially improve the performance compared to the previous state-of-the-arts. Our approach is fast as well and can run at a speed of more than 30 FPS when processing a $300 \times 400$ image. Code can be found at http://mmcheng.net/poolnet/.
A framework for automatically visualizing and recommending efficient routes

Abstract. Due to an increasing traffic density in urban environments, the traffic management as well as the society needs to face various problems including congestion, air pollution or a higher probability of accidents. Therefore, it is getting more important to make road users aware of efficient route alternatives to obtain a better distribution of the traffic flow. Since the time for making route decisions is usually limited, the visualization of the information, which should be conveyed, needs to be prepared in a very clear and easily understandable format. In this approach, we propose a framework for automatically visualizing route efficiency based on various environmentally relevant scenarios. Our methods used to create the map visualizations are based on human perception of space, in order to communicate routes and traffic-related situations more intuitively. That is, humans are assumed to mentally abstract the geographic space using various types of distortions rather than perceiving the environment in its actual shape. Based on these concepts, we argue that a perception-based representation of the route, as well as the visual communication of temporary disturbances may not only simplify the navigation process, but also supports an awareness for the current traffic dynamics, which thus may influence route choice behavior towards a more altruistic behavior. In this paper, we further present two algorithmic approaches for automatically abstracting the geometry of a route in more detail, using cartographic generalization techniques - to present the road network in a way, how it might be perceived based on a certain traffic situation.
Terrain Referenced Navigation Using a Multilayer Radial Basis Function-Based Extreme Learning Machine

A high-resolution digital elevation model (DEM) is an important element that determines the performance of terrain referenced navigation (TRN). However, the higher the resolution of the DEM, the bigger the memory size needed for storing it. It is difficult to secure such large memory spaces in small, low-priced unmanned aerial vehicles. In this study, a high-precision terrain regression model to fit the DEM is generated using the extreme learning machine technique based on the multilayer radial basis function. The TRN results using the proposed method are compared with existing studies on various DEM fitting methods. This study verifies that the proposed method obtains improved fitting accuracy and TRN performance over existing DEM fitting methods such as bilinear interpolation, SVM for regression, and bi-spline neural network, without the DEM storage space.
Rate of Distribution of Breast Lump in Different Quadrants with Their Cytological Findings in Both Sex

Background: The breast lump is a localized swelling, protuberance, bulge, or bump in the breast that feels different from the breast tissue around it or the breast tissue in the same area of the other breast.&#x0D; Objective: The purpose of the present study was to find out the frequency of breast lump in different quadrants and their cytological findings.&#x0D; Methodology: The present study was carried out at the Department of Pathology, Chittagong Medical College, Bangladesh one-year period from May 2005 to June 2006. This study was cross sectional which included 110 cases of breast lump. The patients with breast lump were received from indoor and outdoor patient Department of Surgery, Chittagong Medical College, Bangladesh. The patient presents with definite palpable lump, irrespective of age and sex were included in this study.&#x0D; Result: This study shows only 1.8% male were found central quadrant individual breast. In female maximum (46.4%) were found in upper &amp; outer quadrant individual breast. Right sided of affected breast in highest percentage of upper &amp; outer quadrant individual breast (21.8%). Also shows left side of affected breast in highest percentage of upper &amp; outer quadrant individual breast (24.5%). It was observed that maximum (34.5%) upper &amp; outer quadrant individual breast was seen in benign lesion and (3.6%) were suspicious. But maximum (12.7%) were show multiple in malignant lesion.&#x0D; Conclusion: In conclusion highest frequency of patients are reported in upper outer quadrant breast lump followed by multiple, central, lower &amp; inner and lower &amp; outer. It also observed that the upper outer quadrant is the most commonly affected quadrant in benign and multiple quadrant the malignant breast neoplasms.&#x0D; Journal of National Institute of Neurosciences Bangladesh, 2019;5(1): 69-71
ANALISIS DAN PERANCANGAN SISTEM INFORMASI MANAJEMEN SURAT MASUK DAN SURAT KELUAR PADA DINAS KEPENDUDUKAN DAN PENCATATAN SIPIL KOTA JAMBI

Currently in the making or filing letters to the Department of Population and Civil Registrar of JambiCity there are still some obstacles such as making incoming mail reports, outgoing mail reports, recapreports, as well as when checking the report in certain period, searching documents that have been storedis still difficult and takes longer time because those old documents must be rechecked and reopened, andoftentimes the difficulty comes when we need to make a final recap of the report during a period whenthey are needed the most. Therefore, the author provide solutions in the form of information systemsmanagement to manage incoming and outgoing mail. This is helpful to facilitate employees in processingdata of incoming and outgoing mail and simplify the searching and the reports making processes. In thisresearch, the author uses UML (Unified Modeling Language) tool, Prototype model and VB.NETlanguage as a tool for making the design. In which the expected results are in the form of system designthat can make the searching and reports making processes easier and faster
Using syntax for improving phrase-based SMT in low-resource languages

Abstract Data driven approaches for machine translation, such as statistical and neural machine translation, suffer from sparsity when dealing with low-resource languages. In these cases, using other sources of information including linguistic information could alleviate the problem. In this article, we focus on the problem of word ordering in translation from a high-resource to a low-resource language and try to improve the quality by using syntactic information from the high-resource side. We propose some syntactic features based on Tree Adjoining Grammar (TAG) to be employed in a phrase-based SMT model in order to improve the word ordering. In this work, a set of synchronous TAG rules is extracted and used to estimate the probability of the phrase orders suggested by the phrase-based model. The main idea of the article is to handle the word ordering by using the extended domain of locality property of TAG and abstracting the long distance dependencies into a local view, which is a TAG elementary tree. The experiments on English–Persian and English–German translation showed that, by combining the proposed TAG-based reordering features with lexical and hierarchical reordering models, we gain significant improvements over the baseline and in comparison with a neural reordering model and a pre-reordering model.
NOAA-LSTM: A New Method of Dialect Identification

Dialect Identification (DID) is a particular case of general Language Identification (LID). Due to the high similarity of dialects and similar phonetic features in adjacent areas, DID is a more challenging problem. Long Short-Term Memory (LSTM) networks tend to be used and do well in LID tasks in recent years, but do not have a good performance on DID tasks. In this paper, NOAA (New One-against-all) binary classifier based on OAA (One-against-all) binary classifier obtained proposed, and a new dialect recognition method was combining NOAA with LSTM networks was offered under the guidance of Chinese humanities. The new approach achieves better performance on a DID task than a single LSTM network. The experiment was conducted based on six major dialects in China, and trained under the acoustic characteristics of log Mel-scale filter banks energies (FBANK). Experimental results on six dialects recognition tasks indicate that the accuracy of the new method is higher than that of a single LSTM network.
Research on Data Mining Algorithm Based on Pattern Recognition

With the advent of the era of big data, people are eager to extract valuable knowledge from the rapidly expanding data, so that they can more effectively use these massive storage data. The traditional data processing technology can only achieve basic functions such as data query and statistics, and cannot achieve the goal of extracting the knowledge existing in the data to predict the future trend. Therefore, along with the rapid development of database technology and the rapid improvement of computer’s computing power, data mining (DM) came into existence. Research on DM algorithms includes knowledge of various fields such as database, statistics, pattern recognition and artificial intelligence. Pattern recognition mainly extracts features of known data samples. The DM algorithm using pattern recognition technology is a better method to obtain effective information from massive data, thus providing decision support, and has a good application prospect. Support vector machine (SVM) is a new pattern recognition algorithm proposed in recent years, which avoids dimension disaster by dimensioning and linearization. Based on this, this paper studies the DM algorithm based on pattern recognition, and proposes a DM algorithm based on SVM. The algorithm divides the vector of the SV set into two different types and iterates through multiple iterations to obtain a classifier that converges to the final result. Finally, through the cross-validation simulation experiment, the results show that the DM algorithm based on pattern recognition can effectively reduce the training time and solve the mining problem of massive data. The results show that the algorithm has certain rationality and feasibility.
Conditional Independence Testing using Generative Adversarial Networks

We consider the hypothesis testing problem of detecting conditional dependence, with a focus on high-dimensional feature spaces. Our contribution is a new test statistic based on samples from a generative adversarial network designed to approximate directly a conditional distribution that encodes the null hypothesis, in a manner that maximizes power (the rate of true negatives). We show that such an approach requires only that density approximation be viable in order to ensure that we control type I error (the rate of false positives); in particular, no assumptions need to be made on the form of the distributions or feature dependencies. Using synthetic simulations with high-dimensional data we demonstrate significant gains in power over competing methods. In addition, we illustrate the use of our test to discover causal markers of disease in genetic data.