Reviewerly/exHarmony huggingface dataset:
Residual Neural Network and Wing Loss for Face Alignment Network

Face recognition has made great progress due to the development of convolutional neural network (CNN), and face alignment is an important part of recognition, it is easily affected by gestures and occlusion. In this paper, we propose Residual Neural Network and Wing Loss for Face Alignment Network (RWAN), which consists of a plurality of stages, each stage ameliorates the position of facial landmarks estimated from the previous stage. Unlike the traditional cascading model, the network model uses the Residual Neural Network, which is easily to optimize and can improve accuracy by adding considerable depth, the internal residual block uses shortcut to alleviate the gradient explosion problem caused by increasing depth in deep neural network. Enter the face images and extract features from the entire image by introducing landmark heatmaps to obtain more accurate positioning. Using wing loss in the loss function part not only focuses on the landmark of large error points, but also on the small and medium errors of landmarks, it aims to improve the training ability of deep neural network with small and medium range error. The problem of unbalanced data not only confuses classification tasks, but also affects the accuracy of the model when face samples of different attitudes are not balanced in face key detection tasks. a simple but effective data enhancement method is proposed to deal with the problem, which solves the problem of unbalanced data processing by randomly rotating the training samples, amplifying and the like. The experimental results obtained by our method on the 300W dataset indicate the advantages of this method.
PENERAPAN MODEL PEMBELAJARAN KOOPERATIF TIPE PICTURE AND PICTURE UNTUK MENINGKATKAN HASIL BELAJAR MATEMATIKA SISWA KELAS IVB SDN 136 PEKANBARU

This research is motivated by the low mathematics learning outcomes of fourth grade students of SD Negeri 136 Pekanbaru, with an average score of 68.25 and only 17 of 40 reach the minimum completeness criteria (KKM). This research is a classroom action research (CAR) conducted aimed at improving the mathematics learning outcomes of fourth graders by implementing a cooperative model of picture and picture type. The subjects of this study were 40 fourth grade students of class IVB in 2019/2020 school year. Data collection instruments in this study were teacher activity sheets and student activities and learning outcomes. The results of this study indicate that student learning outcomes increased by 13.26% from an average of 68.25 to 77.3 in cycle I. In the second cycle it increased to 24.54% with an average of 85. Teacher activity also increased from 75 .00% with enough category in the first cycle of the first meeting to be 96.43% very good category in the second meeting of the second cycle. The results showed that the application of the Cooperative model type Picture and picture could improve the mathematics learning outcomes of class IVB students of SD Negeri 136 Pekanbaru.
Surface Irradiance Distribution Mapping Model for Photovoltaic Power Station Based on Ground-based Sky Images

Solar irradiance is one of the most crucial factors affecting the photovoltaic (PV) power output. Accurate solar irradiance forecasting can help predict future PV power generation for grid managers, thus to mitigate the adverse effect of random PV power on the power grid. However, research on real-time surface irradiance distribution in a certain region still not be presented in previous studies, which can bring further convenience to solar power forecasting and power grid dispatching work. In this paper, a surface irradiance distribution mapping model for PV power station based on ground-based sky images is proposed. First, we process raw sky image with distortion rectification and background message dislodged. Then after cloud motion displacement calculation by hybrid Fourier phase correlation (HFPC) theory, we extract the gray level and position information of pixels in sky images to explore the mapping relationship between sky image and solar irradiance. Subsequently, an adaptive sky image-irradiance mapping model is built, trained, and updated according to real-time sky images and solar irradiance. Simulation results show that the proposed method in relation to forecast can achieve real-time surface irradiance distribution mapping at quite high accuracy.
An improved detection method for multi-scale and dense pedestrians based on Faster R-CNN

The major bottleneck of pedestrian detection lies on the different scales of pedestrians and confusing overlapped pedestrians. To solve these problems, an improved detection method based on Faster R-CNN is proposed in this work. On the one hand, the region proposals are selected according to the usual size-distribution of pedestrians, which can moderately improve the detecting accuracy for different-scale pedestrians. On the other hand, the Soft-NMS algorithm is employed when pruning the redundant detection boxes. It can help to reduce the miss rate caused by overlapped dense pedestrians. With such strategies, the detection performance for multi-scale pedestrians and dense pedestrians can be improved. Finally, experiments to verify the effectiveness of the proposed method are conducted over the challenging Caltech Dataset. It shows that the proposed method outperforms 3.5% better MAP than original Faster R-CNN at the expense of a minor increase in detecting time.
Corrections to “Medical Image Synthesis With Deep Convolutional Adversarial Networks” [Mar 18 2720-2730]

Presents corrections to grant and sponsorship information in the above named paper.
Spatial entanglement and state engineering via four-photon Hong–Ou–Mandel interference

Abstract The phenomenon of entanglement is the basis of quantum information and quantum communication processes. Entangled systems with a large number of photons are of great interest at present because they provide a platform for streaming technologies based on photonics. In this paper we present a device which operates with four-photons and based on the Hong–Ou–Mandel interference. The presented device allows to maximize the degree of spatial entanglement and generate the highly entangled four-dimensional Bell states. Furthermore, the use of the interferometer in different regimes leads to fast interference fringes in the coincidence probability with period of oscillations twice smaller than the pump wavelength. We have a good agreement between theoretical simulations and experimental results.
HUBUNGAN ANTARA PENGUASAAN KOSAKATA DENGAN KEMAMPUAN MENYIMAK BAHASA JERMAN SISWA KELAS XI BAHASA

Abstract. Correlation between Vocabulary Mastery with listening ability in German language Class XI students SMAN 13 Bone. Thesis. Faculty of Languages and Literature. State University of Makassar.This study aims to obtain data and information about the relationship between vocabulary mastery and te ability to listen to German in class XI language at SMA 13 Bone.This research is descriptive research.The population of this study was all studens of class XI Languages totaling one Class with 28 students. The sample of this study was 28 students of class XI language selected through total sampling techniques.Research data was collected through a written test, and analyzed using product moment correlation.The results of this study indicate that there is a significant link between mastery of vocabulary with the listening ability of German language class XI student SMA Negeri 13 Bone Rh = (0.574) &gt;Rt = (0.374) with a significant level of 5%.
CONCEPTUAL FRAMEWORK OF THE MODEL OF FORMING INTERETHNIC TOLERANCE IN THE MULTICULTURAL ENVIRONMENT OF THE UNIVERSITY

The aim of this research is to study the state, dynamics and factors affecting the interethnic relations of young people in the field of education. This article discusses the conceptual foundations of the structural and content model of forming interethnic tolerance of young people in a multicultural environment of the university. Particular attention is paid to the study of new educational strategies and approaches that can create the desired socio-cultural and theoretical tolerance of representatives of other cultures, ethnic groups and religions. The research was a mixed method research and it made use of both quantitative and qualitative data. Secondary data for the article was derived from the scientific publications of other researchers and primary data was collected with the help of focus groups and indepth interviews.The 1000 participants of the research were carefully selected to represent nationals of other countries, culture and religion, to reduce the bias that sociological studies exhibit. The hierachy technique was used to analyze qualitative data. The authors present the results and analysis of sociological research for the analysis of interethnic tolerance of young people. It focuses on the psychological and pedagogical content of the phenomenon of interethnic tolerance, that can be taught to students in school. The authors offer evidence-based conclusions and recommendations on the organization of the process of forming interethnic tolerance of young people in a multicultural environment of the university.
Machine Translation Post-Editing Levels: Breaking Away from the Tradition and Delivering a Tailored Service

While definitions of full and light post-editing have been around for a while, and error typologies like DQF and MQM gained in prominence since the beginning of last decade, for a long time customers tended to refuse to be flexible as for their final quality requirements, irrespective of the text type, purpose, target audience etc. We are now finally seeing some change in this space, with a renewed interest in different machine translation (MT) and post-editing (PE) service levels. While existing definitions of light and full post-editing are useful as general guidelines, they typically remain too abstract and inflexible both for translation buyers and linguists. Besides, they are inconsistent and overlap across the literature and different Language Service Providers (LSPs). In this paper, we comment on existing industry standards and share our experience on several challenges, as well as ways to steer customer conversations and provide clear instructions to post-editors.
Exploring the techniques for object detection, classification, and tracking in video surveillance for crowd analysis

Video surveillance (observation) will be going through a massive variation in the most recent decade, and loads of research is experiencing around there.As Tracking of objects will be an essential portion of such frameworks, it gets essential to survey all state of art techniques & methods that exist to classification, detection of an object, & tracking for crowd analysis.This manuscript comprises of all models in the classified method.After object identification, classification is done to track that later.The Tracking of objects will be the technique to find the target in the following video frames.There are additionally a few difficulties in video observation that make it difficult like the object tracking on a few parameters such as problems of environment, illumination variation, pose variation, motion blur, matching of coordinates, & objects tracking, etc.Great characterization is deliberated for survey & complete review of all probable methods, so every perspective is subsequently enlightened in this article.
Reasoning on company takeovers during the COVID-19 crisis with knowledge graphs

When some country takes a disproportionate hit by a large-scale turmoil—just like Italy did during the COVID-19 pandemics—the share prices of its companies plunge Suddenly, it becomes feasible to attempt foreign takeovers of national assets, including those of strategic interest To avert this risk, the Government can veto transactions by summoning the so-called “Golden Powers” Or, it can work to proactively identify structural weaknesses in the control or shareholding chains of key companies, in order to reinforce them without resorting to special powers Sometimes, vulnerabilities and attacks hide in plain sight due to how complex and intertwined the network of mutual company shareholding is In this work, we show how to leverage Knowledge Graphs (KGs) as a representation and reasoning framework to analyze both reactive and proactive measures against takeover attempts, however intricate the setting where they take place We formally characterize a set of reasoning tasks that define when and if to employ Golden Powers, plus others that aim at pinpointing companies prone to attacks These criteria are exercised on the real network of all Italian companies, built for the occasion A rich set of experiments is provided, including on several large synthetic instances, to prove the robustness of our method Copyright © 2020 for this paper by its authors
A family of multi-concept program synthesisers in Alloy⁎

Program synthesis aims to mechanise the task of programming from the user intent (using pre and post condition, examples and sketches). There are many approaches (or concepts) in program synthesis that are usually implemented in isolation: deductive, syntax-based, inductive, etc. In this paper, we present a characterisation of program synthesis as model finding, using Alloy⁎. Such a characterisation unifies several of these concepts in a single model. Through model finding, we obtain a general framework for rapid development of a program synthesiser accommodating denotational semantics based synthesis, simultaneous deductive and inductive synthesis, software reuse, syntactic ingredients (the Alloy⁎ scope of entities), and a new one: a soft sketch (a set of commands that must appear in the synthesised program but in no particular order of execution). Our family of synthesisers produce general purpose programs in the Java language. As the Alloy⁎ synthesiser requires several rounds of user assistance to set scope, sketches, etc., particularly for complex problems, we integrated the model finder to a genetic algorithm module, where candidate solutions and user inputs are generated and mutated automatically. We carried out empirical evaluations on program synthesis successfully. As results, we verified that: (i) we can synthesise thirteen programs (Maj5, Maj8, IntSQRT, Max4, Modu, Fact, Fib, aMax, aDouble, aSum, eCount, aBubSort, aSelSort); (ii) inductive synthesis was faster than deductive synthesis; (iii) synthesis with reuse was faster; and (iv) Genetic Algorithm is better than user trial and error approach.
Approximation of the Diagonal of a Laplacian’s Pseudoinverse for Complex Network Analysis

The ubiquity of massive graph data sets in numerous applications requires fast algorithms for extracting knowledge from these data. We are motivated here by three electrical measures for the analysis of large small-world graphs G = (V, E) - i. e., graphs with diameter in O(log |V|), which are abundant in complex network analysis. From a computational point of view, the three measures have in common that their crucial component is the diagonal of the graph Laplacian’s pseudoinverse, L^+. Computing diag(L^+) exactly by pseudoinversion, however, is as expensive as dense matrix multiplication - and the standard tools in practice even require cubic time. Moreover, the pseudoinverse requires quadratic space - hardly feasible for large graphs. Resorting to approximation by, e. g., using the Johnson-Lindenstrauss transform, requires the solution of O(log |V| / e²) Laplacian linear systems to guarantee a relative error, which is still very expensive for large inputs.In this paper, we present a novel approximation algorithm that requires the solution of only one Laplacian linear system. The remaining parts are purely combinatorial - mainly sampling uniform spanning trees, which we relate to diag(L^+) via effective resistances. For small-world networks, our algorithm obtains a ± e-approximation with high probability, in a time that is nearly-linear in |E| and quadratic in 1 / e. Another positive aspect of our algorithm is its parallel nature due to independent sampling. We thus provide two parallel implementations of our algorithm: one using OpenMP, one MPI + OpenMP. In our experiments against the state of the art, our algorithm (i) yields more accurate approximation results for diag(L^+), (ii) is much faster and more memory-efficient, and (iii) obtains good parallel speedups, in particular in the distributed setting.
Enabling context aware data analysis for long-duration repetitive stooped work through human activity recognition in sheep shearing

There is evidence to suggest that changes in kinematics and neuromuscular control in activities that take place over long periods of time lead to increased injury risk. The collection of biometric data over long time periods could provide insight into these injuries. However, it is difficult to analyse long period biometric data for occupations as the analysis depends on the activity being performed, and it is not practical to manually label the amount of data required. A sufficiently accurate human activity recognition algorithm can provide a means to segment the activities and allow this analysis, but the classification must be robust to the inter-individual differences, as well as the intra-individual variations in movement over time that are the target of analysis. This work presents a person-independent human activity recognition algorithm for sheep shearing using a Hidden Markov Model with physical features that are identified to be relevant to spinal movement quality. The classifier achieved an F1 score of 96.47% in identifying the shearing task.
Reimagining Secret Sharing: Creating a Safer and More Versatile Primitive by Adding Authenticity, Correcting Errors, and Reducing Randomness Requirements

Abstract Aiming to strengthen classical secret-sharing to make it a more directly useful primitive for human endusers, we develop definitions, theorems, and efficient constructions for what we call adept secret-sharing. Our primary concerns are the properties we call privacy , authenticity , and error correction . Privacy strengthens the classical requirement by ensuring maximal confidentiality even if the dealer does not employ fresh, uniformly random coins with each sharing. That might happen either intentionally—to enable reproducible secretsharing— or unintentionally, when an entropy source fails. Authenticity is a shareholder’s guarantee that a secret recovered using his or her share will coincide with the value the dealer committed to at the time the secret was shared. Error correction is the guarantee that recovery of a secret will succeed, also identifying the valid shares, exactly when there is a unique explanation as to which shares implicate what secret. These concerns arise organically from a desire to create general-purpose libraries and apps for secret sharing that can withstand both strong adversaries and routine operational errors.
Real-time long-term tracker with tracking–verification–detection–refinement

Long-term tracking is one of the most challenging problems in computer vision. In this paper, we make full use of the Discriminative Correlation Filter (DCF), and propose a real-time long-term tracker by exploiting a joint tracking–verification–detection–refinement framework. We utilize a DCF which is updated aggressively to estimate translation and scale variation of the target. Subsequently, a passively updated DCF checks the reliability of the tracking result. Once the result is not reliable, we evoke the proposed optimized candidate detector to generate a small number of relatively high quality candidates. Finally, one DCF with an adaptive online learning rate is adopted to refine the predictions that the sparse candidates inferred. In addition, we employ a selection mechanism for the correlation responses to maintain reliable samples effectively. Extensive experiments show that the proposed method performs favorably against lots of state-of-the-art methods while running more than 30 frames per second on single CPU.
Thermal perception method of virtual chemistry experiments

With the aim of addressing the difficulty in identifying temperatures in virtual chemistry experiments, we propose a temperature-sensing simulation method of virtual chemistry experiments. We construct a virtual chemistry experiment temperature simulation platform, based on which a wearable temperature generation device is developed. The typical middle school virtual experiments of concentrated sulfuric acid dilution and ammonium nitrate dissolution are conducted to verify the actual effect of the device. The platform is capable to indicate near real-world experimental situations. The performance of the device not only meets the temperature sensing characteristics of human skin, but also matches the temperature change of virtual chemistry experiments in real-time. It is demonstrated that this temperature-sensing simulation method can represent exothermic or endothermic chemistry experiments, which is beneficial for students to gain understanding of the principles of thermal energy transformation in chemical reactions, thus avoiding the danger that may be posed in the course of traditional teaching of chemistry experiments effectively. Although this methoddoes not have a convenient enough operation for users, the immersion of virtual chemical experiments can be enhanced.
Motivating and Meeting Students’ Diverse Needs with Positive Classroom Design and Social-Emotional Learning

Classroom management has been a primary concern of teachers ever since we can remember. Excellent classroom managers are teachers who understand their own needs as well as the needs of their students. Teachers must get to know their students, where they come from, what motivates them, and what they need in order to best understand them. Through establishing a positive classroom environment, teachers can work to encourage and motivate students through promoting self-discipline while utilizing social-emotional learning.
English Get-Passives: Reassessing the Frequencies across Genres

This study investigates the get -passive in American English, with emphasis on its distribution in different text types and its semantic features characterized by co-occurring verbs. The data was drawn from the Corpus of Contemporary American English (COCA), i.e. the latest version with eight different genres. The findings indicate that the get-passive is a linguistic feature of informal English due to its highest frequency in spoken genres, e.g. TV and movie subtitles, and blogs. Furthermore, common verbs constituting the get -passive were explored and their meanings in context were analyzed. In agreement with previous studies, the semantic analysis of get -passives revealed a higher proportion of verbs expressing adversity, followed by those with positive and neutral meanings, respectively. The existence of non-adversative get -passive predicts a decline in the adversative type.
Privacy Preserving Detection of Path Bias Attacks in Tor

Abstract Anonymous communication networks like Tor are vulnerable to attackers that control entry and exit nodes. Such attackers can compromise the essential anonymity and privacy properties of the network. In this paper, we consider the path bias attack– where the attacker induces a client to use compromised nodes and thus links the client to their destination. We describe an efficient scheme that detects such attacks in Tor by collecting routing telemetry data from nodes in the network. The data collection is differentially private and thus does not reveal behaviour of individual users even to nodes within the network. We show provable bounds for the sample complexity of the scheme and describe methods to make it resilient to introduction of false data by the attacker to subvert the detection process. Simulations based on real configurations of the Tor network show that the method works accurately in practice.