Reviewerly/exHarmony huggingface dataset:
A Variational Bayesian Perspective on Massive MIMO Detection

Optimal data detection in massive multiple-input multiple-output (MIMO) systems requires prohibitive computational complexity. A variety of detection algorithms have been proposed in the literature, offering different trade-offs between complexity and detection performance. In this paper, we build upon variational Bayes (VB) inference to design low-complexity multiuser detection algorithms for massive MIMO systems. We first examine the massive MIMO detection problem with perfect channel state information at the receiver (CSIR) and show that a conventional VB method with known noise variance yields poor detection performance. To address this limitation, we devise two new VB algorithms that use the noise variance and covariance matrix postulated by the algorithms themselves. We further develop the VB framework for massive MIMO detection with imperfect CSIR. Simulation results show that the proposed VB methods achieve significantly lower detection errors compared with existing schemes for a wide range of channel models.
STRATEGI KETIDAKSANTUNAN DALAM VIDEO DEBAT PEMILIHAN PRESIDEN TAHUN 2019

The goal of this article is to identify and explain impoliteness strategies in the 2019 presidential election debate and to evaluate the role of impoliteness used in the 2019 presidential debate. Qualitative descriptive is the kind of method that used by the researcher. The data was gathered from four YouTube videos of the 2019 presidential election debates and analyzed using the distributional and identity methods. This study used the theory of impoliteness by Jonathan Culpeper (2005) which withhold politeness, sarcasm or mock politeness, negative impoliteness, positive impoliteness, dan bald on record impoliteness. Based on the research result, it is found negative impoliteness was the most frequent impoliteness strategy, accounting for 43%. The speaker demonstrated his superiority by accusing and condemning the flaws of others in this strategy.Keywords: Impoliteness, Impoliteness Strategies, Debate, Election
Accelerating the creation of instance segmentation training sets through bounding box annotation

Collecting image annotations remains a significant burden when deploying CNN in a specific applicative context. This is especially the case when the annotation consists in binary masks covering object instances. Our work proposes to delineate instances in three steps, based on a semi-automatic approach: (1) the extreme points of an object (left-most, right-most, top, bottom pixels) are manually defined, thereby providing the object bounding-box, (2) a universal automatic segmentation tool like Deep Extreme Cut is used to turn the bounded object into a segmentation mask that matches the extreme points; and (3) the predicted mask is manually corrected. Various strategies are then investigated to balance the human manual annotation resources between bounding-box definition and mask correction, including when the correction of instance masks is prioritized based on their overlap with other instance bounding-boxes, or the outcome of an instance segmentation model trained on a partially annotated dataset. Our experimental study considers a teamsport player segmentation task, and measures how the accuracy of the Panoptic-Deeplab instance segmentation model depends on the human annotation resources allocation strategy. It reveals that the sole definition of extreme points results in a model accuracy that would require up to 10 times more resources if the masks were defined through fully manual delineation of instances. When targeting higher accuracies, prioritizing the mask correction among the training set instances is also shown to save up to 80\% of correction annotation resources compared to a systematic frame by frame correction of instances, for a same trained instance segmentation model accuracy.
Entangling Billions of Motional Atoms and an Optical Loop at Ambient Condition

Abstract Quantum entanglement recognized by Einstein, Podolsky, Rosen and Schrodinger is the essence of quantum physics, and has become a real available resource in the laboratory over recent years. The observation of quantum entanglement in macroscopic matters has implications in the fundamental studies of quantum mechanics, as well as the implementations of quantum information technologies, such as quantum communication, enhanced sensing and distributed quantum computing. Here, we report the creation and storage of the heralded entanglement between two different types of broadband quantum memories located on separated platforms at room temperature: a single-photon entangled state delocalized between billions of motional atoms as a collective excitation and an all-optical loop as a flying qubit. The stored entangled state is subsequently retrieved after a programmable storage time, and is verified by measuring the nonclassical correlations, quantum interference, and concurrence of the mapped-out Raman-scattered photons. Our results show that quantum entanglement can be sustained in macroscopic matters at ambient condition, which pushes the fundamental researches of the transition boundary between quantum and classical worlds. Also, the two quantum memories constitute a hybrid memory-built-in quantum network, which highlights the potential of the cooperation between an stationary atomic ensemble and an all-optical loop as quantum nodes at ambient condition, and brings a significant step towards practical quantum networks.
TransforMatcher: Match-to-Match Attention for Semantic Correspondence

Establishing correspondences between images remains a challenging task, especially under large appearance changes due to different viewpoints or intra-class variations. In this work, we introduce a strong semantic image matching learner, dubbed TransforMatcher, which builds on the success of transformer networks in vision domains. Unlike existing convolution- or attention-based schemes for correspondence, TransforMatcher performs global match-to-match attention for precise match localization and dynamic refinement. To handle a large number of matches in a dense correlation map, we develop a light-weight attention architecture to consider the global match-to-match interactions. We also propose to utilize a multi-channel correlation map for refinement, treating the multi-level scores as features instead of a single score to fully exploit the richer layer-wise semantics. In experiments, TransforMatcher sets a new state of the art on SPair-71k while performing on par with existing SOTA methods on the PF-PASCAL dataset.
PROFESSIONAL SELF-DETERMINATION OF HIGH SCHOOL STUDENTS IN THE CONDITIONS OF MASTERING THE REGIONAL AGRICULTURAL SCHOOL ADDITIONAL EDUCATIONAL PROGRAM

Statement of the problem. Professional self-determination and choice of profession by high school students are becoming especially relevant in the era of ongoing changes in the development of the world and modern Russia. The importance of human capital and its improvement is increasing. The problem solved in this article is related to the state request for: – implementation of the Federal State Educational Standard aimed at the formation of professional orientation of high school students in specific fields of activity; – implementation of the All-Russian project of early professional orientation of students from grades 6–11 of the Ticket to the Future general education organizations of the Success of Every Child federal project of the Education national project, aimed at development of awareness and readiness for professional self-determination of students of grades 6–11; – the need to train future competent specialists and nature users with systemic and environmental thinking, functional literacy, knowledge in the field of IT and biotechnology; – the use of new most effective methods for professional self-determination of high school students in the field of agriculture. The purpose of the article is to develop an educational program in an institution of additional education in the field of agriculture aimed at professional self-determination of high school students. The research methodology (materials and methods) includes systematic approach and scientific, pedagogical, and psychological approaches, cartographic, empirical, and statistical methods. Research results. The results of the study indicate the following: the implementation of a new educational program is associated with the development of students’ interest in agricultural professions, through their involvement in solving agroecological problems of sustainable development of the Russian Federation and the Krasnoyarsk Territory. When implementing an educational program in the field of agriculture, students are maximally immersed in theory and practice (a modular form of organizing an educational process), with inclusion in various roles: analyst, researcher, expert, manager, developer, and PR. This allows students not only to understand their role in the future production process, but also to develop systemic and environmental thinking, the basics of major competencies, the ability to set a goal and achieve it, plan, organize, and perform appropriate work, take responsibility for decision-making, bring the work started to the end. Conclusions. The article deals with the issues of professional self-determination and choice of profession by high school students of the basic school. Methodological and organizational measures have been carried out to develop and implement an educational program in the field of agriculture on the basis of an additional education institution. The outcome of the implementation of the educational program in the field of agriculture are metasubject and personal results of students.
Comparison of BIM and Conventional Method to Analyze Dimension and Volume of Abutment

The development of the industrial era 4.0 is rapid, one of which is the BIM (Building Information Modeling) method for surveying activities, namely 3D Scanning. The 3D Scanning method, which can obtain millions of points in point clouds in one scan, is expected to provide higher accuracy and more efficient and effective operating time than conventional methods. Conventional methods, which still apply human plotting, require more time and human resources because they have to move from one point to another, and their accuracy is highly dependent on the quality of human resources. This research was conducted at the Interchange Project of the Batang Industrial Estate, Central Java, and aimed to compare the dimensions, volume, and shop drawings of the 3D Scanning method with the conventional method and the advantages of the 3D Scanning and Modeling method. The results obtained in this study are that there is an average dimension difference of 0.007 m from the shop drawing image, then there is a difference in the average coordinate point of 0.028 m which causes the Abutment position to shift from the shop drawing image. BIM is one method for get the volume of structures on Interchange Project with careful results and faster process. The calculated volume is based on shop drawings. The results showed that the difference between the conventional BIM method in the volume of concrete was 1.156%, and in the granular heap, there was a difference of 1.292%. The advantages of these 2 BIM methods are how they operate, which has started to implement an automation system and requires less time than conventional methods.
Implementation of innovations in higher education institutions

The article shows the importance of human capital in the innovative university model. Education is considered a special commodity and a socially significant service that determines the technological leadership of the State and the well-being of its citizens. The article justifies the necessity of State financing of higher education, presents a model of financing scientific and innovative activities, as well as shows the current situation and the place of university science in the development and implementation of innovations. The contradictions, existing at universities in scientific and innovative work are formulated and analyzed, indicating the directions of removing these contradictions.
Effect of Gender, Pose and Camera Distance on Human Body Dimensions Estimation

Human Body Dimensions Estimation (HBDE) is a task that an intelligent agent can perform to attempt to determine human body information from images (2D) or point clouds or meshes (3D). More specifically, if we define the HBDE problem as inferring human body measurements from images, then HBDE is a difficult, inverse, multi-task regression problem that can be tackled with machine learning techniques, particularly convolutional neural networks (CNN). Despite the community's tremendous effort to advance human shape analysis, there is a lack of systematic experiments to assess CNNs estimation of human body dimensions from images. Our contribution lies in assessing a CNN estimation performance in a series of controlled experiments. To that end, we augment our recently published neural anthropometer dataset by rendering images with different camera distance. We evaluate the network inference absolute and relative mean error between the estimated and actual HBDs. We train and evaluate the CNN in four scenarios: (1) training with subjects of a specific gender, (2) in a specific pose, (3) sparse camera distance and (4) dense camera distance. Not only our experiments demonstrate that the network can perform the task successfully, but also reveal a number of relevant facts that contribute to better understand the task of HBDE.
Development of the forms of educational and research activities in school education for the formation of students’ ethnocultural competence

Issues of organization of educational and research activities in the conditions of modern school education draw attention of contemporary researchers. One of the methodological issues is the organization of educational and research activities of schoolchildren in the study of extant “echoes” of traditional folklore holidays and modern spoken folklore genres. The article describes the sequence of organizing work on the study of folklore and algorithms for collecting, processing, and storing texts of spoken folklore in the school curriculum. In addition, the paper introduces the idea of a “native land corner” as a form of extracurricular (local history) activities at school. The study provides the methodology of the organization of the Corner, its rubrics, and topics. The authors consider the peculiarities of the study of local history study of the Small Motherland as part of the Fatherland to a global scale (the native microcosm – the region – Russia – the world).
FORMING THE READYNESS OF FUTURE TEACHERS MATHEMATICS TO CREATIVE PEDAGOGICAL ACTIVITIES

The principal steps of forming students’ readiness to their creative pedagogical activities by means of solving creative pedagogical problems (tasks) are revealed in the article.
XYtracker: a new approach to estimate fault rupture extent in real time for large earthquakes

Abstract We present a methodology for estimating fault geometry and utilizing the distance to the fault for the shaking estimation to improve the accuracy of real-time shaking estimates for large earthquakes. Most of the earthquake early warning system currently estimates the seismic intensity with the ground-motion prediction equations (GMPE) as a function of the hypocenter distance. However, using the fault distance computed from a finite source model can improve the accuracy of the shaking intensity estimation for large earthquakes. This study proposes a novel methodology, XYtracker, to estimate the surface projection of the fault extent and real-time seismic intensity. For large earthquakes, high-frequency ground motions tend to saturate over the magnitude range and strongly correlate with fault distance. As a result, this work can achieve the fault extent using seismic intensity and GMPE. We considered three types of fault models: point-source, line-source, and rectangle-source model. We found the most probable model parameters for each model by minimizing the residual sum of squares between the observed and estimated seismic intensities. The Akaike Information Criterion selected the most probable model among them. The strong motion data set of the 2008 Wenchuan, 2011 Tohoku, and 2016 Kumamoto earthquakes was used to test our methodology. The new method for estimating the fault geometry can obtain the ongoing rupture length and direction using the strong motion data. The model selection scheme with the Akaike Information Criterion selected the finite-source model to explain the shaking distribution. Results revealed that this new approach performed well in estimating the fault dimension. The method can promote the accuracy of the seismic intensity estimation for future large earthquakes, including the subduction earthquakes. Graphical Abstract
THE YOUNG GENERATION PERSPECTIVE ON THE USE OF INDONESIAN LANGUAGE IN THEIR ERA

Indonesian is the personality of each and every Indonesian occupant. Language itself is fundamental for human life. With language we can talk with each other, considering that individuals are social creatures who can't get by without each other's help. Regardless, the usage of language should be recognized calmly. The use and explanation ought to be seen suitably so there are no misinterpretations or other unwanted things. The improvement of the times and globalization have affected the presence of lingos which are correct now starting to lose their validity. One model is the use of work related conversation as a normal word until its realness (the principal word) is bit by bit disregarded. As well as the use of obscure vernaculars that are more interesting to the millennial age.
FORMATION STRATEGY COGNITIVE MEANINGS AS A FACTOR PROFESSIONAL TRAINING TEACHERS OF ART DISCIPLINES

In article processes of an intensification of vocational training of the teacher of art in a context of modem strategy of training are considered It is accented oft expediency of comprehension of value of strategy of fomiation cognitive senses a universal principle of cognitive activity; the principles classified to an operational attribute are allocated; groups of these principles are considered in quality constrm strategy cognitive development of the person.
An Overview of Chatbots using ML Algorithms in Agricultural Domain

The agricultural sector plays a vital part in a country's economic growth.It has already made a major contribution to advanced countries' economic growth.The impact it hason less-developed countries' economic development is vitally important.The farmers involved in agricultural activities lack the resources to stay updated with the information related to the latest advancements in technologies and farming practices.Existing human-involved operations such as Kissan Call Center (KCC), even though capable of delivering expected results, has its own drawbacks.Hence there is a need for an automated chatbot system that can function as a substitute to KCC.A chatbot system is a system that delivers domainspecific knowledge to its users.Such a system in the field of agriculture is very helpful in keeping the farmers updated.In this paper, existing works on such question-answer systems focusing entirely on works involving machine learning techniqueshave been reviewed.Suggestionsto improve the overall usability of the existing systemshave also been made.
A Fast Hardware Pseudorandom Number Generator Based on xoroshiro128

The Graphcore Intelligence Processing Unit contains an original pseudorandom number generator (PRNG) called xoroshiro128aox, based on the F2-linear generator xoroshiro128. It is designed to be cheap to implement in hardware and provide high-quality statistical randomness. In this paper, we present a rigorous assessment of the generator's quality using standard statistical test suites and compare the results with the fast contemporary PRNGs xoroshiro128+, pcg64 and philox4x32-10. We show that xoroshiro128aox mitigates the known weakness in the lower order bits of xoroshiro128+ with a new 'AOX' output function by passing the BigCrush and PractRand suites, but we note that the function has some minor non uniformities. We focus our testing with specific tests for linear artefacts to highlight the weaknesses of both xoroshiro128 PRNGs, but conclude that they are hard to detect, and xoroshiro128aox otherwise provides a good trade off between statistical quality and hardware implementation cost.
WeDef: Weakly Supervised Backdoor Defense for Text Classification

Existing backdoor defense methods are only effective for limited trigger types. To defend different trigger types at once, we start from the class-irrelevant nature of the poisoning process and propose a novel weakly supervised backdoor defense framework WeDef. Recent advances in weak supervision make it possible to train a reasonably accurate text classifier using only a small number of user-provided, class-indicative seed words. Such seed words shall be considered independent of the triggers. Therefore, a weakly supervised text classifier trained by only the poisoned documents without their labels will likely have no backdoor. Inspired by this observation, in WeDef, we define the reliability of samples based on whether the predictions of the weak classifier agree with their labels in the poisoned training set. We further improve the results through a two-phase sanitization: (1) iteratively refine the weak classifier based on the reliable samples and (2) train a binary poison classifier by distinguishing the most unreliable samples from the most reliable samples. Finally, we train the sanitized model on the samples that the poison classifier predicts as benign. Extensive experiments show that WeDefis effective against popular trigger-based attacks (e.g., words, sentences, and paraphrases), outperforming existing defense methods.
Flipchart Learning Media at TK Muyang Mersa Takengon Regency

This research is motivated by the lack of learning motivation of children at Muyang Mersa Kindergarten, Linge District, Central Aceh Regency. The lack of value for children's learning motivation is evidenced by the results of children's learning which shows 18.78 with categories that still need guidance. This study aims to determine how the application of flipchart learning media in Muyang Mersa Kindergarten, Linge District, Central Aceh Regency. The method used in this research is classroom action research (CAR) with Kurt Lewin's model. Comparison of activity results after using Flipchart media the average value of pre-cycle children reached 18.78, in cycle I reached 21.64 and cycle II was 23.87, the percentage of children's learning completeness using Flipchart media in cycle I reached 40% and cycle II 59% occurred. 19% increase after using Flipchart media a significant improvement. Conclusion Flipchart media in increasing children's learning motivation increased by a total score of 41.7% with a very well developed category (BSB) Then in the second cycle it increased again with a total score of 58.3% with a very well developed category (BSB). The teacher provides picture media in the form of a calendar-sized flipchart, chooses the appropriate color to make it look attractive to the child, the teacher places the flipchart learning media in the right position so that it is clearly visible to the child after that the teacher also uses simple language.
PSYCHOLOGICAL AND PEDAGOGICAL CONDITIONS OF ADAPTATION FIRST-YEAR STUDENTS TO EDUCATIONAL PROCESS TEACHER OF OGICH UNIVERSITY

The article substantiates psychological-pedagogical conditions of adaptation period of first-year students to teaching and educational process at the establishment of higher education. The author submits for consideration and shows the essence of the adaptation process, reveals the factors conditioning difficulties of successful adaptation for first-year students to the system of higher education, gives recommendations o f organizing work with first-year students aiming at the overcoming of psychological barrier in the "teacher-student" system.