Reviewerly/exHarmony huggingface dataset:
Joint Spatial-Temporal Optimization for Stereo 3D Object Tracking

Directly learning multiple 3D objects motion from sequential images is difficult, while the geometric bundle adjustment lacks the ability to localize the invisible object centroid. To benefit from both the powerful object understanding skill from deep neural network meanwhile tackle precise geometry modeling for consistent trajectory estimation, we propose a joint spatial-temporal optimization-based stereo 3D object tracking method. From the network, we detect corresponding 2D bounding boxes on adjacent images and regress an initial 3D bounding box. Dense object cues (local depth and local coordinates) that associating to the object centroid are then predicted using a region-based network. Considering both the instant localization accuracy and motion consistency, our optimization models the relations between the object centroid and observed cues into a joint spatial-temporal error function. All historic cues will be summarized to contribute to the current estimation by a per-frame marginalization strategy without repeated computation. Quantitative evaluation on the KITTI tracking dataset shows our approach outperforms previous image-based 3D tracking methods by significant margins. We also report extensive results on multiple categories and larger datasets (KITTI raw and Argoverse Tracking) for future benchmarking.
Automatic Landmark-guided Bijective Brain Image Registration by Composing Region-based Locally Diffeomorphic Warpings

Abstract As the size of the neuroimaging cohorts being increased to address key questions in the field of cognitive neuroscience, cognitive aging, and neurodegenerative diseases, the accuracy of the spatial normalization as an essential pre-processing step becomes extremely important in the neuroimaging processing pipeline. Existing spatial normalization methods have poor accuracy particularly when dealing with the highly convoluted human cerebral cortex and when brain morphology is severely altered (e.g. clinical and aging populations). To address this shortcoming, we propose to implement and evaluate a novel landmark-guided region-based spatial normalization technique that takes advantage of the existing surface-based human brain parcellation to automatically identify and match regional landmarks. To simplify the non-linear whole brain registration, the identified landmarks of each region and their counterparts are registered independently with large diffeomorphic (topology preserving) deformation via geodesic shooting. The regional diffeomorphic warping fields were combined by an inverse distance weighted interpolation technique to have a smooth global warping field for the whole brain. To ensure that the final warping field is diffeomorphic, we used simultaneously forward and reverse maps with certain symmetric constraints to yield bijectivity. We have evaluated our proposed method using both simulated and real (structural and functional) human brain images. Our evaluation shows that our method can enhance structural correspondence up to around 86%, a 67% improvement compared to the existing state-of-the-art method. Such improvement also increases the sensitivity and specificity of the functional imaging studies by about 17%, reducing the required number of subjects and subsequent costs. We conclude that our proposed method can effectively substitute existing substandard spatial normalization methods to deal with the demand of large cohorts and the need for investigating clinical and aging populations.
STUDENT'S LANGUAGE LEARNING STRATGIES IN LISTENING

This research was aimed to describe language learning strategies in listening used by a third semester student of English Education Study Program who gets high achievement in listening English comprehension. This research was a qualitative research. The subject of this research was a successful language learner from a third semester student. This research was a case study which used observation and interview as the instruments of data collecting. The observation was done using direct and indirect observation and for the interview was done using semi structured interview that supported by a narrative analysis. The result of data analysis indicated that the student who categorized as a successful language learner in listening comprehension used the four framework of listening strategies by Goh and Kaur (2017) as the representative of language learning strategies in listening which proposed by O’Malley and Chamot (1990). Based on the result, the student who gets high achievement in listening English comprehension employed four strategies namely directed/selective attention strategies, planning and evaluation strategies, problem solving strategies and mental translation strategies. The findings revealed that the student who categorized as a successful language learner in this research was not achieving the goal of learning instantly
LibGuides: Online Resources For Music Study: Listening

Subscription and open-access content for the study of both applied and academic music studies. Tips for Music Collection Listening
Construction and Application of Data Driven Analysis Model for Students' Mastery Level of Knowledge Points Based on Electronic Schoolbag

Students' mastery level of knowledge points is an important indicator reflecting their learning situations. Obtaining the mastery level of knowledge points is an important basis for realizing students' individualized learning and for improving students' learning situation and teachers' teaching quality. Based on the Rasch model, this research constructs the model for students' mastery level of knowledge points based on electronic schoolbag, and excavates and analyzes students' test scores understanding their mastery level of knowledge points. Through the application of this model on two monthly test scores of a middle school, the results confirmed that this model can well characterize students' mastery level of knowledge points. The construction and application the model for students' mastery level of knowledge points based on electronic schoolbag provides reference and ideas for students' individualized learning situation analysis and teacher's refined teaching implementation.
Detection of Hepatomegaly by Automated-Diagnosis Method based on Artificial Neural Network

Diagnosis sonography (ultrasonography) is an ultrasoundbased diagnostic imaging technique which is used for visualizing internal body structures including tendons, muscles, joints, vessels and internal organs for possible pathology.In Bangladesh, there is no formal training for the sonographer, that's why sometimes there is a great possibility to make mistakes to find out the exact disease.Sonography is done manually.Sometimes the disease suggested by doctor and the diagnosis report that is done by the sonographer are not the same, for this reason the patient may be confused about his or her actual disease.To solve all these problems an automatic diagnosis tool is needed to get the required result of the required patient.Here, proposed an artificial neural network based model for automated diagnosis of hepatomegaly from ultrasound images to get an automated result which is appropriate for anyone.
Modal Matters in Interpretability Logics

This paper from 2008 is the first in a series of three related papers on modal methods in interpretability logics and applications. In this first paper the foundations are laid for later results. These foundations consist of a thorough treatment of a construction method to obtain modal models. This construction method is used to reprove some known results in the area of interpretability like the modal completeness of the logic ${\textbf{IL}}$. Next, the method is applied to obtain new results: the modal completeness of the logic ${\textbf{IL}}{\sf M_0}$, and modal completeness of ${\textbf{IL}}({\sf W^*})$.
Quantum algorithms for computational geometry problems

We study quantum algorithms for problems in computational geometry, such as POINT-ON-3-LINES problem. In this problem, we are given a set of lines and we are asked to find a point that lies on at least $3$ of these lines. POINT-ON-3-LINES and many other computational geometry problems are known to be 3SUM-HARD. That is, solving them classically requires time $Ω(n^{2-o(1)})$, unless there is faster algorithm for the well known 3SUM problem (in which we are given a set $S$ of $n$ integers and have to determine if there are $a, b, c \in S$ such that $a + b + c = 0$). Quantumly, 3SUM can be solved in time $O(n \log n)$ using Grover's quantum search algorithm. This leads to a question: can we solve POINT-ON-3-LINES and other 3SUM-HARD problems in $O(n^c)$ time quantumly, for $c&lt;2$? We answer this question affirmatively, by constructing a quantum algorithm that solves POINT-ON-3-LINES in time $O(n^{1 + o(1)})$. The algorithm combines recursive use of amplitude amplification with geometrical ideas. We show that the same ideas give $O(n^{1 + o(1)})$ time algorithm for many 3SUM-HARD geometrical problems.
Pooling Method On PCNN in Convolutional Neural Network

Abstract The pooling method aggregates the points in the neighborhood in Convolutional Neural Network(CNN). It can not only reduce the dimension, but also improve the results, so that the results are not easy to over-fit. However, the common pooling methods have the problems of single feature and lack of self-adaptability. In order to solve this problem, the Pulse Coupled Neural Network (PCNN) is introduced and a pooling method based on PCNN is proposed. The algorithm learns the weights of each eigenvalue from the convoluted neighborhood sub-region by PCNN and fuses them to get the final pooling result. The experimental results on image recognition datasets MNIST, CIFAR-100 show that the proposed PCNN-based pooling method has better recognition effect and improves the performance of CNN compared with the existing pooling methods.
Low-light-level image colorization based on Laws’ texture feature descriptor

Since low-light-level images are generally grayscale images that lack color information, it is necessary to colorize these images to enhance human vision for night vision scene. In this paper, a low-light-level image colorization method is proposed based on Laws’ texture feature descriptor. Laws’ filter can analyze the texture features of the image, which are the pointers we used to find the corresponding color information for low-light-level gray images from the reference color image. The experimental results demonstrate that our colorization method can make night vision images have colors closer to natural perception and help observers better understand night vision scenes.
LibGuides: Covid-19 online resources for remote learning: Copyright during Covid 19

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Sparse reconstruction of guided wavefield from limited measurements using compressed sensing

A wavefield sparse reconstruction technique based on compressed sensing is developed in this work to dramatically reduce the number of measurements. Firstly, a severely underdetermined representation of guided wavefield at a snapshot is established in the spatial domain. Secondly, an optimal compressed sensing model of guided wavefield sparse reconstruction is established based on l1-norm penalty, where a suite of discrete cosine functions is selected as the dictionary to promote the sparsity. The regular, random and jittered undersampling schemes are compared and selected as the undersampling matrix of compressed sensing. Thirdly, a gradient projection method is employed to solve the compressed sensing model of wavefield sparse reconstruction from highly incomplete measurements. Finally, experiments with different excitation frequencies are conducted on an aluminum plate to verify the effectiveness of the proposed sparse reconstruction method, where a scanning laser Doppler vibrometer as the true benchmark is used to measure the original wavefield in a given inspection region. Experiments demonstrate that the missing wavefield data can be accurately reconstructed from less than 12% of the original measurements; The reconstruction accuracy of the jittered undersampling scheme is slightly higher than that of the random undersampling scheme in high probability, but the regular undersampling scheme fails to reconstruct the wavefield image; A quantified mapping relationship between the sparsity ratio and the recovery error over a special interval is established with respect to statistical modeling and analysis.
SISTEM INFORMASI PELAPORAN TRANSAKSI DI BANK BTN SYARIAH BERBASIS JAVA

Bank BTN Syariah is one of the Sharia Business Units of Bank Tabungan Negara (Persero) Tbk engaged in sharia-based financial services, in carrying out its banking activities there are still several transaction reporting systems that are still done manually, so the level of employee registration errors is very high and less effective. The purpose of this study is to design a transaction reporting system using the Java-based programming language and this design method uses the Research and Development (R&D) method. The results built on this information system collection of data related to data on receiving and borrowing daily transaction reports making it easier for administrators to manage daily transaction reporting.Keywords: Design, BTN Syariah, Daily Transaction Reporting
Characteristics Classification of Mobile Apps on Apple Store Using Clustering

This research is interested in the user ratings of Apps on Apple Stores.The purpose of this research is to have a better understanding of some characteristics of the good Apps on Apple Store so Apps makers can potentially focus on these traits to maximize their profit.The data for this research is collected from kaggle.com, and originally collected from iTunes Search API, according to the abstract of the data.Four different attributes contribute directly toward an App's user rating: rating_count_tot, rating_count_ver, user_rating and user_rating_ver.The relationship between Apps receiving higher ratings and Apps receiving lower ratings is analyzed using Exploratory Data Analysis and Data Science technique "clustering" on their numerical attributes.Apps, which are represented as a data point, with similar characteristics in rating are classified as belonging to the same cluster, while common characteristics of all Apps in the same clusters are the determining traits of Apps for that cluster.Both techniques are achieved using Google Colab and libraries including pandas, numpy, seaborn, and matplotlib.The data reveals direct correlation from number of devices supported and languages supported to user rating and inverse correlation from size and price of the App to user rating.In conclusion, free small Apps that many different types of users are able to use are generally well rated by most users, according to the data.
Representation and Learning Methods for Situation Evaluation in RoboCup Soccer Simulation

In this paper, representations of a situation evaluation model and their learning methods in RoboCup soccer simulation 2D are reviewed. A soccer game is commonly known as an example of multi-agent systems in an uncertain and dynamic environment. First, this paper discusses the similarities and differences between the RoboCup soccer simulation and other benchmark tests of game AI research. Next, a search method based on action chains is presented as a mechanism of action selection by a soccer player. This method is based on the look-ahead of field situations by the combination of a search tree and a situation evaluation model, which is the case in the chess and shogi programs. Then, various methods of constructing the situation evaluation models that are used in the generation of action chains by the machine learning framework are reviewed along with example models that have been proposed.
Entropy Coders Based on the Splitting of Lexicographic Intervals

We propose a technique that performs entropy coding by splitting lexicographic intervals. The main characteristics of our technique are that it is: based on automata quite similar to Mealy machines; fast in encoding and decoding; able to achieve arbitrarily low redundancy; designed to require a small number of states; able to decode forwards (making it suitable for streaming); able to handle skewed probability distributions; intended for stationary memoryless sources; a kind of variable-to-fixed coding; and able to handle finite source alphabets of arbitrary sizes.
Rain on your radar : Engaging with weather data visualizations as part of everyday routines

This chapter discusses visualizations of weather data, used to communicate short-term precipitation predictions to lay audiences. Focusing on the example of Buienradar, a popular Dutch weather forecast website and app, it investigates how people engage with such representations on a daily basis, how they interpret them, and how their readings of them affect their actions and decisions, shaping their day-to-day routines. The research is based on semi-structured interviews with users with different demographic profiles. Aside from establishing usage patterns or preferences and readerly strategies, the chapter also considers people’s own evaluations of their conduct in relation to the Buienradar service, and more broadly, their reflections on the significance of weather data visualizations to their lives.
Development of speaking material in english for nursing

Abstract Learning material is important for students, unfortunately finding the appropriate material is not easy. One of the cause is the online and commercial materials cannot cover the students need and curriculum. This research aims to develop the appropriate speaking materials for nursing students. This study was Research and Development (RND). The population and sample was 105 nursing students that divided into two groups, experimental and control group. The experimental group used the speaking material developed, and the control group used the commercial material. The data come from pre-test, post-test, questionnaire and observation. The findings are there are four aspects in five units that should be developed, such as topic, language, organization, design, and exercise. Moreover, the students speaking also analyzed based on fluency, coherence, grammatical, accuracy and pronunciation. The result of calculation in the experimental group has the significant value with p value &lt;0,05 and in the control group, the p value is &gt; 0,05. Therefore, it can be concluded that 1) Based on the effectiveness analysis of five units, the low category from 4% become 0% and the high category from 71% become 87%. 2) Based on the judges, the judges strongly agree the speaking material that has been developed strongly appropriate for the students. It can be seen from the score from English lecturer is 4,65, from expert judges is 4,39 and from stakeholder is 4,57. 5) There is a significant improvement after using speaking material after developed. Keywords: speaking material, learning material, English for Nursing.
Strengthening the construction of digital course, Promoting the innovation of teaching mode in University

Abstract Digital resources are the necessary conditions for the implementation of educational information. Nan Yang Polytechnic insists on the development of digital curriculum resources and teaching reform, To highlight the characteristics of professional courses and skills oriented, Based on the digital campus network platform, Taking the learning activities as the main line, the development of a “dynamic” network course, The digital curriculum resources with good interactive and sustainable development have been constructed, and the representative teaching model. It can effectively improve the quality and effect of classroom teaching, and accelerate the process of digital teaching.
HATE CRIMES, THE HATE SPEECH PHENOMENON, PRACTICE OF THE EUROPEAN COURT OF HUMAN RIGHTS AND THE RUSSIAN APPROACH TO DETERMINING EXTREMIST ACTIVITY

The subject of the research is criminal law rules that provide for criminal liability for hate crimes and the judicial decisions of the European Court of Human Rights on hate crimes. The purpose of the article is to confirm or refute the hypothesis that a unified approach to the definition of the legal concept of hate speech and the limits of its application is nec-essary. This approach must be based on the legal positions of the European Court of Hu-man Rights The research methodology includes analysis and interpretation of court decisions of the European Court of Human Rights, as well as a dialectical approach to the analysis of vari-ous points of view to the definition of extremist activity. The main results and scope of their application. The relevance of the research proposed for publication is due to the lack of uniform practice of applying the articles of the Russian Criminal Code on so-called "hate crimes" by Russian courts and the presence of signifi-cant contradictions in the positions of the European Court of Human Rights and the state position of the Russian Federation in defining key concepts in this area that are extremely important for criminal procedure and administrative activities. The paper considers scien-tific and practical attempts to define "hate crimes" in the global and regional human rights systems, basic recommendations of the UN on countering such crimes, and offers an interpretation of the term hate speech in relation to the related criminological concept of hate crime. The text provides statistical data describing the level of such crime and the practice of the ECHR in this area, mentions a list of criteria according to which "hate crimes" can be motivated by language differences, gender, sexual orientation and other characteristics, as well as criteria that distinguish hate speech from freedom of expres-sion, and suggests decriminalization of part 1 of article 282 of the Russian Criminal Code. Conclusions. It is necessary to unify the concepts of "hate crimes" (and the practice of their application) in the direction of, in particular, reducing the number of decisions of the European Court of Human Rights against the Russian Federation and increasing the level of legal protection of both the individual citizen of the Russian Federation and freedom of speech and expression.
A survey on operating systems, LFS and BLFS

Recently, government organizations, specialists have been attracted to the movement of open-source software and on top of them, the Linux open-source and free operating system has caught the most attention. So much so that in this movement even the biggest companies like Google, Oracle have stepped forward and are developing open-source software, particularly Linux. This paper states the stepwise development of a Linux based operating system with the Linux from Scratch (LFS) method. First, we take a look at different operating systems, and the need to develop another Linux-based system. The advantages and disadvantages of these operating systems are presented. And further, we analyze a step by step method from “scratch” to a complete Linux-based operating system.