Reviewerly/exHarmony huggingface dataset:
Graph Edit Networks

While graph neural networks have made impressive progress in classification and regression on graphs, few approaches to date perform time series prediction on graphs and those that do are mostly limited to edge changes. We suggest that graph edits are a more natural interface for graph-to-graph learning. In particular, graph edits are general enough to describe any graph-to-graph change, not only edge changes, they are sparse, making them easier to understand for humans and more efficient computationally, and they are local, avoiding the need for pooling layers in graph neural networks. In this paper, we propose a simple linear layer - the graph edit network - which takes node embeddings as input and generates a sequence of graph edits that transform the input graph to the output graph. Theoretically, we show that a mapping between the node sets of two graphs is sufficient to construct training data for a graph edit network and that an optimal mapping yields edit scripts that are almost as short as the graph edit distance between the graphs. We further provide a proof-of-concept empirical evaluation on several graph dynamical systems, which are difficult to learn for baselines from the literature.
PODS: Policy Optimization via Differentiable Simulation

Current reinforcement learning (RL) methods use simulation models as simple black-box oracles. In this paper, with the goal of improving the performance exhibited by RL algorithms, we explore a systematic way of leveraging the additional information provided by an emerging class of differentiable simulators. Building on concepts established by Deterministic Policy Gradients (DPG) methods, the neural network policies learned with our approach represent deterministic actions. In a departure from standard methodologies, however, learning these policy does not hinge on approximations of the value function that must be learned concurrently in an actor-critic fashion. Instead, we exploit differentiable simulators to directly compute the analytic gradient of a policy's value function with respect to the actions it outputs. This, in turn, allows us to efficiently perform locally optimal policy improvement iterations. Compared against other state-of-the-art RL methods, we show that with minimal hyper-parameter tuning our approach consistently leads to better asymptotic behavior across a set of payload manipulation tasks that demand high precision.
Digital Currency and Economic Crises: Helping States Respond

The current crisis, at the time of writing, has had a profound impact on the financial world, introducing the need for creative approaches to revitalizing the economy at the micro level as well as the macro level. In this informal analysis and design proposal, we describe how infrastructure for digital assets can serve as a useful monetary and fiscal policy tool and an enabler of existing tools in the future, particularly during crises, while aligning the trajectory of financial technology innovation toward a brighter future. We propose an approach to digital currency that would allow people without banking relationships to transact electronically and privately, including both internet purchases and point-of-sale purchases that are required to be cashless. We also propose an approach to digital currency that would allow for more efficient and transparent clearing and settlement, implementation of monetary and fiscal policy, and management of systemic risk. The digital currency could be implemented as central bank digital currency (CBDC), or it could be issued by the government and collateralized by public funds or Treasury assets. Our proposed architecture allows both manifestations and would be operated by banks and other money services businesses, operating within a framework overseen by government regulators. We argue that now is the time for action to undertake development of such a system, not only because of the current crisis but also in anticipation of future crises resulting from geopolitical risks, the continued globalization of the digital economy, and the changing value and risks that technology brings.
Survey on Machine Learning Based Video Analytics Techniques

Video information has turned into the biggest wellspring of information expended all inclusive. Because of the fast development of applications which are related to video applications and requests of boosting for greater surpassing video administrations, video information volume has expanding violently around the world, which is the serious challenge for media processing, capacity and transmission. Video coding by packing recordings into a lot littler size is also key arrangements; in any case, its advancement has turned out to be soaked somewhat while the pressure proportion consistently develops over the most recent three decades. Machine inclining calculations, particularly those utilizing profound realizing, which are equipped for finding learning from unstructured huge information and giving information driven forecasts, give new chances to further updating video coding advancements. In this survey, we try to express an audit on AI based video encoding streamlining, expecting to furnish specialists with a solid establishment and rouse future improvements for information driven video coding. Initially, we investigate the portrayals furthermore, information about redundant videos. Besides, we audit the improvement of video coding models and key prerequisites. Hence, we exhibit a foundational review on the ongoing advances also difficulties related regarding AI based video coding enhancements from following key viewpoints, such as high effectiveness, low unpredictability and also high visual quality. Their work processes, delegate plans, exhibitions, focal points and disservices are broke down in detail. At long last, the difficulties and openings are recognized, which may furnish the scholastic and mechanical networks with preparation and potential bearings for eventual research.
Kohesi dan Koherensi dalam Teks Eksplanasi Karangan Siswa Kelas VIII SMPN 3 Colomadu

Writing activities in schools today have led to many problems. For example, the difficulty of writing or pouring ideas into writing is one of the problems that occur with students. Therefore, the need for knowledge about cohesiveness and coherence to create a coherent and whole discourse. This study aims to describe: 1) the cohesiveness of the results of the students of SMPN 3 Colomadu, 2) the coherence of the results of the students of SMPN 3 Colomadu. The method used in this research is descriptive qualitative with data collection techniques are listening and note taking, and the data analysis technique uses a referential equivalent method and the aggregate method. The results of this study indicate that 1) There is a cohesiveness of discourse which includes: a) markers ofgrammatical cohesion consisting of references, substitutions, ellipsis, and conjunctions. b) markers of lexical cohesion consisting of synonyms, collocations, and reps. 2) There is a coherence of discourse which includes: a) signed coherence consisting of additives, causality, chronology, temporal, intensity, and contrast. b) unmarked coherence consisting of detailed coherence. The dominant unified discourse is the use of cohesion markers.Keywords: Cohesion, Coherence, Explanation Text
GINA, Big Data, and the Future of Employee Privacy

Threats to privacy abound in modern society, but individuals currently enjoy little meaningful legal protection for their privacy interests. We argue that the Genetic Information Nondiscrimination Act (GINA) offers a blueprint for preventing employers from breaching employee privacy. GINA has faced significant criticism since its enactment in 2008: commentators have dismissed the law as ill-conceived, unnecessary, and ineffective. While we concede that GINA may have failed to alleviate anxieties about medical genetic testing, we assert that it has unappreciated value as an employee-privacy statute. In the era of big data, protections for employee privacy are more pressing than protections against genetic discrimination. Instead of failed legislation, GINA could represent the future of employment law.
Automated Polyp Detection System in Colonoscopy using Object Detection Algorithm based on Deep Learning

In Korea, colon cancer is increasing due to westernized eating habits. Colonoscopy is being used to reduce deaths from colon cancer and studies of CADx(Computer-aided Diagnosis) are being developed to improve accuracy. Due to the nature of medical data, it was difficult to collect a lot of data, so data was increased 25 times using AutoAugment’s CIFAR-10 policy, and YOLOv4(You Only Look Once), a real-time object detection algorithm, was used to detect lesions. A new object detection algorithm, YOLOv4, use new eight features such as Weighted-Residual-Connections, Cross-Stage-Partial-connections, Cross mini-Batch Normalization and Self-Adversarial-Training. The performance of augmented data had a maximum mAP of 27.44 higher than the original data. The average IoU(Intersection over Union) was 11.44 higher than the original data. When the IoU value is 0.5, the F1-scores of the original data and the augmented data are 0.9 and 0.97 respectively.
A hybrid grid-based many-objective optimisation algorithm for software defect prediction

How to apply limited test resources to detect error module is one of the challenges of software defect prediction problem. To solve the problem, a many-objective software defect prediction model is proposed by considering the probability of detection and false alarm rate, the Balance value and F-measure as defect prediction objectives. At the same time, a hybrid grid-based many-objective optimisation algorithm is designed to solve the model. In the designed algorithm, the adaptive dominant region operator is introduced into the grid-based many-objective optimisation algorithm to improve the performance of algorithm in balancing dynamically the convergence and diversity of population. The simulation results show that the proposed algorithm has better performance in solving many-objective the software defect prediction problem.
MENINGKAT KEMAMPUAN MENULIS TEKS BERITA DENGAN MODEL PEMBELAJARAN KOOPERATIF TIPE STAD ( STUDENT TEAM ACHIEVEMENT DIVISIONS ) OLEH SISWA KELAS VIII-A SMP DHARMA PANCASILA MEDAN TAHUN PELAJARAN 2018/2019

This study aims to find out the increasing learning outcomes of the ability to write news by using the cooperative model STAD type in students VIII-A Middle School Pancasila Dharma Medan Learning Year 2018/2019. The basic problem is the low writing ability of students. This research approach uses descriptive qualitative in the form of CAR. The subjects of this study were Indonesian Language students and teachers of class VIII-A SMP Dharma Pancasila Medan. The research data is in the form of photos, RPP, syllabus, learning results and notes on interview results. Data collection techniques by means of tests, interviews, and observations. The procedure of this study uses the Kemmis Mc Taggart design, namely planning, implementation, observation, and reflection. The results found were an increase in the learning outcomes of the ability to write news texts by using the cooperative type STAD model after going through two cycles with details of an increase of 38.46% which completed 84.61%.
Type-Based Analysis of Logarithmic Amortised Complexity

We introduce a novel amortised resource analysis couched in a type-and-effect system. Our analysis is formulated in terms of the physicist's method of amortised analysis, and is potential-based. The type system makes use of logarithmic potential functions and is the first such system to exhibit *logarithmic amortised complexity*. With our approach we target the automated analysis of self-adjusting data structures, like splay trees, which so far have only manually been analysed in the literature. In particular, we have implemented a semi-automated prototype, which successfully analyses the zig-zig case of *splaying*, once the type annotations are fixed.
Semantic relation graph reasoning network for visual question answering

In order to answer semantically-complicated questions about an image, a Visual Question Answering (VQA) model needs to fully understand the visual scene in the image, especially the dynamic interaction between different objects. This task inherently requires reasoning the visual relationships among the objects of image. Meanwhile, the visual reasoning process should be guided by the information of the question. In this paper, we proposed a semantic relation graph reasoning network, the process of semantic relation reasoning is guided by the cross-modal attention mechanism. In addition, a Gated Graph Convolutional Network (GGCN) constructed based on cross-modal attention weights that novelly injects the semantic interaction information between objects into their visual features, and the features with relational awareness are produced. In particular, we trained a semantic relationship detector to extract the semantic relationship between objects for constructing the semantic relation graph. Experiments demonstrate that proposed model outperforms most state-of-the-art methods on the VQA v2.0 benchmark datasets.
V International Conference «Information Technologies in Earth Sciences and Applications for Geology, Mining And Economy. Ites&amp;Mp-2019»

The materials presented at the Conference describe the results of recent years in the following areas: Open access to scientific data and knowledge in Earth Sciences; Data peculiarities in Earth Sciences: new concepts and methods, tools for their collection, integration and processing in different information systems, including systems with intensive use of data; Data mining and mathematical simulation of natural processes in Earth Sciences. Evolution of classical GIS-applications in Earth Sciences; Application to Critical Raw Materials (CRM); social aspects of mining (e.g., the Social Licence to Operate [SLO]); predictive mapping and applications to exploration, landuse and search for extensions of known deposits; Intelligent data analysis, elicitation of facts and knowledge from scientific publications. Thesauruses, ontologies and conceptual modeling. Semantic WEB, linked data. Services. Content semantic structuring. Applications for geosciences, e.g., Ontology-based Dynamic Decision Graphs for Expert systems and decision-aid tools; Application of methods and technologies of the remote sensing in Earth Sciences: from satellites to unmanned aerial vehicles; Information technologies for demonstration and popularization of scientific achievements in Earth Sciences; Applications: environmental risks including mining wastes, natural hazards, water resource management, etc.
Document Representations to Improve Topic Modelling

Each and every day, huge amount of information are collected from web applications. So it is difficult to understand or detect what the whole information is all about. To detect, understand and summarise the whole information, it requires some specific tools and techniques like topic modelling, which helps to analyze and identify the crisp of the data. This paper implements the sparsity based document representation to improve Topic Modeling, it organizes the data with meaningful structure by using machine learning algorithms like LDA (Latent Dirichlet Allocation) and OMP (Orthogonal Matching Pursuit) algorithms. It identifies a documents belongs to which topic as well as similarity between documents in an existing dictionary. The OMP(Orthogonal Matching Pursuit) algorithm is the best algorithm for sparse approximation With better accuracy. OMP(Orthogonal Matching Pursuit) algorithm can identify the topics to which the input document[Y] is mostly related to across a large collection of text documents present in a dictionary.
Institutional perspective on writing for international publication

Abstract The paper focuses on the institutional background of the publishing practices of Polish scholars, with special emphasis on publications in English. The University of Silesia in Katowice represents a Polish Higher Education Institution which places significant emphasis on international publications. On the basis of two data collection instruments, a report on point-winning publications (2017–2020) and a survey conducted among 197 faculty members, the author portrays the publication-related situation of the university and its most important determinants. Besides the facts pertaining to the distribution of point-winning publications among disciplines and languages, the article address such important issues as the authors’ strategies employed in the preparation of English language manuscripts, language-related issues articulated by the reviewers, and reasons for not publishing in English. A connection is made between the institutional context in which scholarly texts are composed and the strategic choices made by the authors in the process of manuscript drafting.
An improved algorithm for ship detection in SAR images based on CNN

Aiming at the problem of low accuracy of ship detection in SAR images, we propose an improved detection method based on RetinaNet. This method introduces channel-wise attention mechanism into the backbone feature extraction network, in order to automatically obtains the importance of each feature channel by means of learning, and then enhances the useful features according to this importance and restrains the features that are not of much use to the detection task. In order to improve the capability of multi-scale detection, this method also introduces an efficient weighted bidirectional feature fusion network—BiFPN, which adjusts the proportion of each feature by learning the importance of features of different scales. In addition, we propose a training method to expand the complex background samples in the data set to improve the classification performance of the network to the targets and complex background. Training and testing with open SAR image ship detection datasets, the detection results show that this method can significantly improve the precision and recall rate.
Peningkatan Kesiapan Sekolah Siaga Bencana melalui Pelatihan Siswa Kader Sekolah Siaga Bencana di SMPN 1 Cimalaka

IMPROVING DISASTER PREPAREDNESS SCHOOL READINESS THROUGH TRAINING OF DISASTER PREPARED SCHOOL CADRE STUDENTS AT SMPN 1 CIMALAKA. Natural disasters are natural phenomena that occur directly or indirectly to disturb human life. Disasters can happen anywhere and anytime. Therefore it is necessary to prepare the community in facing disasters, including the school community. Disaster preparedness education needs to be carried out immediately considering that most Indonesians live in disaster-prone areas. The school community is one of the main stakeholders for preparedness against natural disasters. The purpose of this community service is to provide training for students of SMPN I Cimalaka to be prepared as cadres of disaster prepared schools. After the training, 45 students of disaster preparedness cadres at SMPN I Cimalaka have received training for 3 days of disaster preparedness. And the results of the evaluation showed that the participants' knowledge and skills were all good. Disaster preparedness cadre activities are quite active, namely by disseminating disaster preparedness to fellow students in extracurricular activities such as OSIS, Scouts and PMR. In addition, disaster preparedness cadres are also often involved in disaster recognition training by schools.
Edge-Aware Image Conditioning with a Siamese Neural Network

Summary Siamese neural networks have proved powerful at learning discriminative features for tasks such as face verification. We present a novel application, called Edge-aware Filtering, which employs a deep Siamese network ranking similarity between seismic image patches. Once the network is trained, we capitalize on the learned discriminative features to achieve within-image stacking power endowed with edge awareness. We show on field data examples that the learned representations lead to superior filtering performance at various discontinuity scales compared to 3D anisotropic diffusion and structure-oriented filtering.
Combating Sexist Hate Speech The Work of the Council of Europe

This paper presents the Council of Europe current work on sexist hate speech and sexism. After an overview on sexist hate speech, its different forms, the factors contributing to it and the connection between sexist hate speech and freedom of expression, this contribution presents relevant Council of Europe standards and activities (including the new Committee of Ministers Recommendation on preventing and combating sexism), extracting from them the key elements that should guide governments, media, civil society and other relevant stakeholders in their action against sexism. A ‘checklist’ of indicators and actions to eliminate sexist hate speech is presented as a conclusion.
Real-time people detection based on top-view TOF camera

This paper proposes a real-time people detection system based on time-of-flight (TOF) depth cameras, which monitors the flow of people in public places, such as subway entrances and exits and shopping mall passages. The proposed system mainly includes preprocessing, contour recognition, Neural Networks recognition, tracking and counting. It makes full use of the top-view depth information, avoids the problem of strabismus, and reduces the amount of calculation. At the same time, compared with the contour template matching algorithm, the accuracy is improved. This algorithm can improve the calculation speed while ensuring accuracy and robustness. Experiments show that the proposed system can run on the CPU platform at a speed of 20ms per frame. It also achieves high-precision head detection and counting, and the accuracy rates of single person and double person can reach 100% and the accuracy rates of the multi-person can reach 97%.
Point diffraction interferometer with tilted waves for measuring complex surfaces

Recently, much attention has been paid to optical components with complex surfaces because they can significantly optimize the aberrations of optical systems. However, a complex surface introduces several difficulties for precise measurement. To address this challenge, we propose tilted-wave point diffraction interferometer (TPDI) for measurements of complex surfaces. TPDI combines the advantages of tilted-wave interferometry and point diffraction interferometry, thus enabling high-precision measurements of complex surfaces; moreover, this method has demonstrated good efficiency and versatility. We adopt a fiber array (FA) to generate diffractive tilted waves to compensate for the local gradient of the test specimen. Unlike the classical PDI, which focuses more on the beam quality of the on-axis diffractive wave, the proposed system requires better quality for both the on-axis and off-axis diffractive waves. Therefore, the main parameters of the FA that may introduce measurement errors are analyzed, including the end face roughness of the array, the quality of diffractive wavefronts, and the initial optical path differences. Finally, the measurement result of the high-order hyperboloid verifies that our TPDI is effective and its precision of testing a complex surface is better than λ / 37 (rms, λ = 632.8 nm).