Reviewerly/exHarmony huggingface dataset:
HOPE: A Task-Oriented and Human-Centric Evaluation Framework Using Professional Post-Editing Towards More Effective MT Evaluation

Traditional automatic evaluation metrics for machine translation have been widely criticized by linguists due to their low accuracy, lack of transparency, focus on language mechanics rather than semantics, and low agreement with human quality evaluation. Human evaluations in the form of MQM-like scorecards have always been carried out in real industry setting by both clients and translation service providers (TSPs). However, traditional human translation quality evaluations are costly to perform and go into great linguistic detail, raise issues as to inter-rater reliability (IRR) and are not designed to measure quality of worse than premium quality translations. In this work, we introduce HOPE, a task-oriented and human-centric evaluation framework for machine translation output based on professional post-editing annotations. It contains only a limited number of commonly occurring error types, and use a scoring model with geometric progression of error penalty points (EPPs) reflecting error severity level to each translation unit. The initial experimental work carried out on English-Russian language pair MT outputs on marketing content type of text from highly technical domain reveals that our evaluation framework is quite effective in reflecting the MT output quality regarding both overall system-level performance and segment-level transparency, and it increases the IRR for error type interpretation. The approach has several key advantages, such as ability to measure and compare less than perfect MT output from different systems, ability to indicate human perception of quality, immediate estimation of the labor effort required to bring MT output to premium quality, low-cost and faster application, as well as higher IRR. Our experimental data is available at \url{https://github.com/lHan87/HOPE}.
Automatic evaluation of scientific abstracts through natural language processing

This work presents a framework to classify and evaluate distinct research abstract texts which are focused on the description of processes and their applications. In this context, this paper proposes natural language processing algorithms to classify, segment and evaluate the results of scientific work. Initially, the proposed framework categorize the abstract texts into according to the problems intended to be solved by employing a text classification approach. Then, the abstract text is segmented into problem description, methodology and results. Finally, the methodology of the abstract is ranked based on the sentiment analysis of its results. The proposed framework allows us to quickly rank the best methods to solve specific problems. To validate the proposed framework, oil production anomaly abstracts were experimented and achieved promising results.
Unsupervised Domain-Specific Deblurring using Scale-Specific Attention

In the literature, coarse-to-fine or scale-recurrent approach i.e. progressively restoring a clean image from its low-resolution versions has been successfully employed for single image deblurring. However, a major disadvantage of existing methods is the need for paired data; i.e. sharpblur image pairs of the same scene, which is a complicated and cumbersome acquisition procedure. Additionally, due to strong supervision on loss functions, pre-trained models of such networks are strongly biased towards the blur experienced during training and tend to give sub-optimal performance when confronted by new blur kernels during inference time. To address the above issues, we propose unsupervised domain-specific deblurring using a scale-adaptive attention module (SAAM). Our network does not require supervised pairs for training, and the deblurring mechanism is primarily guided by adversarial loss, thus making our network suitable for a distribution of blur functions. Given a blurred input image, different resolutions of the same image are used in our model during training and SAAM allows for effective flow of information across the resolutions. For network training at a specific scale, SAAM attends to lower scale features as a function of the current scale. Different ablation studies show that our coarse-to-fine mechanism outperforms end-to-end unsupervised models and SAAM is able to attend better compared to attention models used in literature. Qualitative and quantitative comparisons (on no-reference metrics) show that our method outperforms prior unsupervised methods.
Training Structured Neural Networks Through Manifold Identification and Variance Reduction

This paper proposes an algorithm (RMDA) for training neural networks (NNs) with a regularization term for promoting desired structures. RMDA does not incur computation additional to proximal SGD with momentum, and achieves variance reduction without requiring the objective function to be of the finite-sum form. Through the tool of manifold identification from nonlinear optimization, we prove that after a finite number of iterations, all iterates of RMDA possess a desired structure identical to that induced by the regularizer at the stationary point of asymptotic convergence, even in the presence of engineering tricks like data augmentation and dropout that complicate the training process. Experiments on training NNs with structured sparsity confirm that variance reduction is necessary for such an identification, and show that RMDA thus significantly outperforms existing methods for this task. For unstructured sparsity, RMDA also outperforms a state-of-the-art pruning method, validating the benefits of training structured NNs through regularization.
LongChecker: Improving scientific claim verification by modeling  full-abstract context

We introduce the LongChecker system for scientific claim verification. Given a scientific claim and an evidence-containing research abstract, LongChecker predicts a veracity label and identifies supporting rationales in a multitask fashion based on a shared encoding of the claim and abstract. We perform experiments on the SciFact dataset, and find that LongChecker achieves state-of-the-art performance. We conduct analysis to understand the source of this improvement, and find that identifying the relationship between a claim and a rationale reporting a scientific finding often requires understanding the context in which the rationale appears. By making labeling decisions based on all available context, LongChecker achieves better performance on cases requiring this type of understanding. In addition, we show that LongChecker is able to leverage weakly-supervised in-domain data to facilitate few-shot domain adaptation for scientific claim verification.
Optimizing Edge Detection for Image Segmentation with Multicut Penalties

The Minimum Cost Multicut Problem (MP) is a popular way for obtaining a graph decomposition by optimizing binary edge labels over edge costs. While the formulation of a MP from independently estimated costs per edge is highly flexible and intuitive, solving the MP is NP-hard and time-expensive. As a remedy, recent work proposed to predict edge probabilities with awareness to potential conflicts by incorporating cycle constraints in the prediction process. We argue that such formulation, while providing a first step towards end-to-end learnable edge weights, is suboptimal, since it is built upon a loose relaxation of the MP. We therefore propose an adaptive CRF that allows to progressively consider more violated constraints and, in consequence, to issue solutions with higher validity. Experiments on the BSDS500 benchmark for natural image segmentation as well as on electron microscopic recordings show that our approach yields more precise edge detection and image segmentation.
The Partially Observable Asynchronous Multi-Agent Cooperation Challenge

Multi-agent reinforcement learning (MARL) has received increasing attention for its applications in various domains. Researchers have paid much attention on its partially observable and cooperative settings for meeting real-world requirements. For testing performance of different algorithms, standardized environments are designed such as the StarCraft Multi-Agent Challenge, which is one of the most successful MARL benchmarks. To our best knowledge, most of current environments are synchronous, where agents execute actions in the same pace. However, heterogeneous agents usually have their own action spaces and there is no guarantee for actions from different agents to have the same executed cycle, which leads to asynchronous multi-agent cooperation. Inspired from the Wargame, a confrontation game between two armies abstracted from real world environment, we propose the first Partially Observable Asynchronous multi-agent Cooperation challenge (POAC) for the MARL community. Specifically, POAC supports two teams of heterogeneous agents to fight with each other, where an agent selects actions based on its own observations and cooperates asynchronously with its allies. Moreover, POAC is a light weight, flexible and easy to use environment, which can be configured by users to meet different experimental requirements such as self-play model, human-AI model and so on. Along with our benchmark, we offer six game scenarios of varying difficulties with the built-in rule-based AI as opponents. Finally, since most MARL algorithms are designed for synchronous agents, we revise several representatives to meet the asynchronous setting, and the relatively poor experimental results validate the challenge of POAC. Source code is released in \url{http://turingai.ia.ac.cn/data\_center/show}.
Critical configurations for two projective views, a new approach

The problem of structure from motion is concerned with recovering 3-dimensional structure of an object from a set of 2-dimensional images. Generally, all information can be uniquely recovered if enough images and image points are provided, but there are certain cases where unique recovery is impossible; these are called critical configurations. In this paper we use an algebraic approach to study the critical configurations for two projective cameras. We show that all critical configurations lie on quadric surfaces, and classify exactly which quadrics constitute a critical configuration. The paper also describes the relation between the different reconstructions when unique reconstruction is impossible.
Ymir: A Supervised Ensemble Framework for Multivariate Time Series Anomaly Detection

We proposed a multivariate time series anomaly detection frame-work Ymir, which leverages ensemble learning and supervisedlearning technology to efficiently learn and adapt to anomaliesin real-world system applications. Ymir integrates several currentlywidely used unsupervised anomaly detection models through anensemble learning method, and thus can provide robust frontalanomaly detection results in unsupervised scenarios. In a super-vised setting, domain experts and system users discuss and providelabels (anomalous or not) for the training data, which reflects theiranomaly detection criteria for the specific system. Ymir leveragesthe aforementioned unsupervised methods to extract rich and usefulfeature representations from the raw multivariate time series data,then combines the features and labels with a supervised classifier todo anomaly detection. We evaluated Ymir on internal multivariatetime series datasets from large monitoring systems and achievedgood anomaly detection performance.
3D-VField: Learning to Adversarially Deform Point Clouds for Robust 3D  Object Detection

As 3D object detection on point clouds relies on the geometrical relationships between the points, non-standard object shapes can hinder a method's detection capability. However, in safety-critical settings, robustness on out-of-distribution and long-tail samples is fundamental to circumvent dangerous issues, such as the misdetection of damaged or rare cars. In this work, we substantially improve the generalization of 3D object detectors to out-of-domain data by taking into account deformed point clouds during training. We achieve this with 3D-VField: a novel method that plausibly deforms objects via vectors learned in an adversarial fashion. Our approach constrains 3D points to slide along their sensor view rays while neither adding nor removing any of them. The obtained vectors are transferrable, sample-independent and preserve shape smoothness and occlusions. By augmenting normal samples with the deformations produced by these vector fields during training, we significantly improve robustness against differently shaped objects, such as damaged/deformed cars, even while training only on KITTI. Towards this end, we propose and share open source CrashD: a synthetic dataset of realistic damaged and rare cars, with a variety of crash scenarios. Extensive experiments on KITTI, Waymo, our CrashD and SUN RGB-D show the high generalizability of our techniques to out-of-domain data, different models and sensors, namely LiDAR and ToF cameras, for both indoor and outdoor scenes. Our CrashD dataset is available at https://crashd-cars.github.io.
How Prefrontal Cortex Works

This chapter describes a unified theory of how the prefrontal cortex interacts with multiple brain regions to carry out the higher cognitive, emotional, and decision-making processes that define human intelligence, while also controlling actions to achieve valued goals. This predictive Adaptive Resonance Theory, or pART, model builds upon the foundation in earlier chapters. Prefrontal functions are often called <italic>executive functions.</italic> Executive functions regulate flexible and adaptive behaviors, notably in novel situations, while suppressing actions that are no longer appropriate, notably reflexive responses to current sensory inputs. Working memory is particularly involved in contextually appropriate behaviors. Prefrontal properties of desirability, availability, credit assignment, category learning, and feature-based attention are explained. These properties arise through interactions of orbitofrontal, ventrolateral prefrontal, and dorsolateral prefrontal cortices with inferotemporal cortex, perirhinal cortex, parahippocampal cortices; ventral bank of the principal sulcus, ventral prearcuate gyrus, frontal eye fields, hippocampus, amygdala, basal ganglia, hypothalamus, and visual cortical areas V1, V2, V3A, V4, MT, MST, LIP, and PPC. Model explanations include how the value of visual objects and events is computed, which objects and events cause desired consequences and which may be ignored as predictively irrelevant, and how to plan and act to realize these consequences, including how to selectively filter expected vs. unexpected events, leading to movements towards, and conscious perception of, expected events. Modeled processes include reinforcement learning and incentive motivational learning; object and spatial working memory dynamics; and category learning, including the learning of object categories, value categories, object-value categories, and sequence categories, or list chunks.
Pendampingan Pembuatan Bahan Ajar Atraktif Bagi Tenaga Pengajar Sebagai Penunjang Pembelajaran Siswa/Siswi SMA BP FAI UMJ Muhammadiyah Boarding School "Ki Bagus Hadikusumo"

The use of technology in education is a necessity that cannot be avoided. Information technology-based learning such as presentations using Microsoft Power Point is an interesting process for students. Teaching materials/materials delivered using Microsoft power point can help students understand the material being taught. Generation Z as a generation that is already familiar with technological advances, of course, is also familiar with this technology. This affects their learning process. Visual learning style by relying on visual acuity is a mainstay for generation Z in the learning process. Attractive media and teaching materials will help them understand the material being studied. High school students BP FAI UMJ Muhammadiyah Boarding School (MBS) "Ki Bagus Hadikusumo" are included in generation Z who like visual learning styles. Therefore, the teaching staff of SMA BP FAI UMJ Muhammadiyah Boarding School (MBS) "Ki Bagus Hadikusumo" needs to deepen the use of attractive learning media in delivering material so that students more easily understand the subject. In this mentoring activity, it is expected that the teaching staff can explore the features in Microsoft Power Point so that they can create attractive teaching materials that can be implemented in the teaching and learning process in class or online.
A Critical Overview of the Geometry of Fraud and a Model Proposal within the Framework of Situational Action Theory

Geometric metaphors have been used in fraud theory for more than 60 years. The most commonly used of these metaphors is the fraud triangle. The fraud triangle has been criticized severely for its scope, substitutability of its elements, perceptuality, individuality and one-sidedness. Different models developed afterwards tried to cover the shortcomings of the fraud triangle with additions and modifications, but none of them became as popular as the fraud triangle. In this study, by moving away from the use of geometric metaphors, which has become a traditional approach in explaining the structure and causality of fraud, a model based on Situational Action Theory (SAT) is proposed. The aim of the study is to reveal that fraud has a complex and multi-dimensional structure that cannot be explained with one-dimensional metaphors, and in this context, to develop a theoretical model expressed in a mathematical formula for measuring fraud risk.
Smart Pet Collars Possess Potential in the Chinese Market

Approximately 60% of people kept or have kept pets, and a significant problem is the loss of pets.Thus, our group decided to design a product with a localization function such as a smart pet collar to address the pet loss issue.In order to verify whether such a product is feasible in the Chinese market, we have done a feasibility study.In Particular, we did a survey and interview.The data illustrated that more than 80% of them were willing to buy a smart collar for their pets.Moreover, according to this survey, we identified our target customers and set a detailed customer profile through an interview.In addition, we discovered that there are a few pet collars aimed to locate pets in the Chinese market.Therefore, this product has desirability and potential in China, and we can promote smart pet collars in the Chinese market.The early development of our smart pet collar included positioning functions.The GPS device is designed to use the existing button-shaped anti-lost tracker, and the button in the middle can connect the tracker to the equipped app so that the pet owner can check the exact location of the pet at all times.In addition, the security level of pet collars is very high.Only the face ID and fingerprint of the pet owner can unlock the collar.If a stranger wants to open the collar maliciously, an alarm will be triggered.When our positioning collar products began to have some sales and profit, we added the health monitoring technique into the collar to address the problem of pets' health issues: pet owners can constantly monitor the pet's heartbeat, breathing, exercise volume, and sleep conditions, etc.Moreover, a health report will be generated every week.
ANOMALIES IN THE IMPLEMENTATION OF METHODS OF TEACHING MATHEMATICS AND THEIR CONSEQUENCES

The article deals with the negative trends in the development of school mathematics education in the context of the relationship of inductive and deductive methods of teaching educational material. It is stated that the dominance of deduction in teaching of mathematics in high school and its separation from inductive explanations to students of mathematical concepts, definitions, rules, formulas indicates a complete disregard for pedagogical experience and K.F. Lebedyntsev and D. Polya’s methodical research. The historical causes of excessive application of the deductive method of teaching mathematics are revealed: the centuriesold practice of imitating the method of “Beginnings” by Euclid; historically short (about four centuries) period of induction method formation in scientific research; even shorter period of pedagogical induction applications practice. In order to more fully revealing the pedagogical significance of inductive methods of teaching mathematics, a brief analysis of the methodical features of lectures course on algebra, developed and delivered by I. Newton to the students of Cambridge University, was carried out.It is found out that this course has a strong inductive orientation and revealed this genius English scientist’s conviction that teaching mathematics is an art in which solving examples and problems is more important and useful than rules. It is emphasized that this I. Newton’s conviction was significantly ahead of other Western European mathematicians. The purposefulness and persistence of national mathematicians-teachers M.G. Kurganov, F.I. Busse, P.S. Guriev, O.M. Strannolyubsky, S.I. Shohor-Trotsky, K.F. Lebedyntsev in substantiating the pedagogical significance of the inductive method of teaching mathematics and its practical implementation were noted. The important general didactic significance of F.I. Busse’s views on the implementation of learning consciousness principle has been revealed. Examples of M.M. Luzin’s high school education and modern practice of training mathematics teachers are given. They show that the lack of inductive explanations of the main thing in the content of the lesson turns deductive learning into formal-deductive, accompanied by the substitution of understanding by thoughtless mechanical memorization of educational material. The exceptional relevance of the practical implementation of the methodical heritage of K.F. Lebedyntsev and D. Polya’s in the context of solving the problem of improving the quality of school mathematical education is substantiated. The most important task of of the considered problem – determinating the practical ways of its decision – is formulated.
Peer review declaration

All papers published in this volume of Journal of Physics: Conference Series have been peer reviewed through processes administered by the Editors. Reviews were conducted by expert referees to the professional and scientific standards expected of a proceedings journal published by IOP Publishing. • Type of peer review : Single blind • Conference submission management system : By email received from the organizing and editorial committee of the conference. The correspondence authors make the submission by email. • Number of submissions received : 32 • Number of submissions sent for review : 32 • Number of submissions accepted : 20 • Acceptance Rate (Number of Submissions Accepted / Number of Submissions Received X 100) : 62.5% • Average number of reviews per paper : 2 reviews per paper • Total number of reviewers involved : 10 • Any additional info on review process : All papers are plagiarism checked by Turnitin software • Contact person for queries : Ely Dannier V. Niño Foundation of Researchers in Science and Technology of Materials (FORISTOM) Email: info@foristom.org Please submit this form along with the rest of your files on the submission date written in your publishing agreement. The information you provide will be published as part of your proceedings.