Reviewerly/exHarmony huggingface dataset:
Inferring symmetry in natural language

We present a methodological framework for inferring symmetry of verb predicates in natural language. Empirical work on predicate symmetry has taken two main approaches. The feature-based approach focuses on linguistic features pertaining to symmetry. The context-based approach denies the existence of absolute symmetry but instead argues that such inference is context dependent. We develop methods that formalize these approaches and evaluate them against a novel symmetry inference sentence (SIS) dataset comprised of 400 naturalistic usages of literature-informed verbs spanning the spectrum of symmetry-asymmetry. Our results show that a hybrid transfer learning model that integrates linguistic features with contextualized language models most faithfully predicts the empirical data. Our work integrates existing approaches to symmetry in natural language and suggests how symmetry inference can improve systematicity in state-of-the-art language models.
APF-PF: Probabilistic Depth Perception for 3D Reactive Obstacle Avoidance

This paper proposes a framework for 3D obstacle avoidance in the presence of partial observability of environment obstacles. The method focuses on the utility of the Artificial Potential Function (APF) controller in a practical setting where noisy and incomplete information about the proximity is inevitable. We propose a Particle Filter (PF) approach to estimate potential obstacle locations in an input depth image stream. The probable candidates are then used to generate an action that maneuvers the robot towards the negative gradient of potential at each time instant. Rigorous experimental validation on a quadrotor UAV highlights the robustness and reliability of the method when robot's sensitivity to incorrect perception information can be concerning. The proposed perception and control stack is run onboard the UAV, demonstrating the computational feasibility for real-time applications and agile robots.
Implementing Agent-Based Systems via Computability Logic CL2

Computability logic(CoL) is a powerful computational model. In this paper, we show that CoL naturally supports multi-agent programming models where resources (coffee for example) are involved. To be specific, we discuss an implementation of the Starbucks based on CoL (CL2 to be exact).
THE EFFECT OF WORD WALL MEDIA ON THE STUDENTS’ ABILITY IN SIMPLE PRESENT TENSE MASTERY AT GRADE X OF SMAN 1 TANJUNG TIRAM

This Research aims to investigate The Effect of Word Wall Media  on Students Ability in Simple Present Tense Mastery. This Research was conducted by Experimental Research . The population of this Research was the students at grade X of SMA N 1 Tanjung Tiram . the total number of the population Two hundred fifty two students in seven class . there were 36 students chosen as the sample by using random sampling technique . The sample was divided into two group , namely Control Group and Experiment Group . The Experiment group was taught by using Word Wall Media and control group was taught without using Word Wall Media  . The instrument for collecting the data was Multiple choice [-test . The data was analyze by using t- test formula . It means that the alternative  hypothesis ( Ha ) is accepted and null hypothesis ( Ho ) is rejected . based on the finding of this research , it was found that there is a significant effect of Word Wall Media in Simple Present Tense Mastery at grade x of SMA Negeri 1 Tanjung Tiram. Key word : Simple Present Tense Mastery, Word Wall Media.
Evaluating Attribution Methods using White-Box LSTMs

Interpretability methods for neural networks are difficult to evaluate because we do not understand the black-box models typically used to test them. This paper proposes a framework in which interpretability methods are evaluated using manually constructed networks, which we call white-box networks, whose behavior is understood a priori. We evaluate five methods for producing attribution heatmaps by applying them to white-box LSTM classifiers for tasks based on formal languages. Although our white-box classifiers solve their tasks perfectly and transparently, we find that all five attribution methods fail to produce the expected model explanations.
It's the Best Only When It Fits You Most: Finding Related Models for Serving Based on Dynamic Locality Sensitive Hashing

In recent, deep learning has become the most popular direction in machine learning and artificial intelligence. However, preparation of training data is often a bottleneck in the lifecycle of deploying a deep learning model for production or research. Reusing models for inferencing a dataset can greatly save the human costs required for training data creation. Although there exist a number of model sharing platform such as TensorFlow Hub, PyTorch Hub, DLHub, most of these systems require model uploaders to manually specify the details of each model and model downloaders to screen keyword search results for selecting a model. They are in lack of an automatic model searching tool. This paper proposes an end-to-end process of searching related models for serving based on the similarity of the target dataset and the training datasets of the available models. While there exist many similarity measurements, we study how to efficiently apply these metrics without pair-wise comparison and compare the effectiveness of these metrics. We find that our proposed adaptivity measurement which is based on Jensen-Shannon (JS) divergence, is an effective measurement, and its computation can be significantly accelerated by using the technique of locality sensitive hashing.
Datweaving: Textiles as Data Materialization

Gollihue and Xiong-Gum propose a set of understandings about data and data visualization that push against the overdetermination of the electronic screen in the processing and representation of data. They argue that 1) data is coded into made things, and 2) those made things are storage media that allow access to a structure of information. This webtext explores the materialization of data through old media textiles.
PANDANGAN MAHASISWA PENDIDIKAN MATEMATIKA: APAKAH SISWA AKAN MUDAH BELAJAR DENGAN PERMAINAN EDUKASI?

The development of mobile games at this time can be utilized in the field of mathematics education. One of them by using educational games in mathematics learning. Teachers as instructors need to innovate in using learning media. The purpose of this study was to determine the views of students of mathematics education about the ease of students when learning to use math education games. This research is a quantitative descriptive study and the instrument used is a questionnaire about mathematics educational games. The study was conducted in the city of Mataram, West Nusa Tenggara with research subjects who are mathematics education students. There are 48 students; 11 males and 37 females. The results of the study showed that 45 students students agreed that mathematics education games made it easy for students to learn mathematics. As for some of the reasons raised, among others, educational games can increase students' understanding, interest, and enthusiasm for learning in mathematics.
#MeToo Pakistani Moment: Social Media for Taboo Activism

Social Media platforms are providing opportunities to the users to talk about the stigmatized issues of the society. Recently sexual harassment, abuse and assault were highlighted around the globe by using hash tag Me Too (#metoo). Women all around the world described their stories of sexual abuse and harassment via twitter. It was an attempt to raise awareness and talk about this issue of sexual abuse and harassment, which was otherwise considered a taboo in a typical society like Pakistan. In case of Pakistan, sexual harassment, assault and abuse of women were never in limelight till the Me Too movement. Pakistani women from different walks of life came forward and shared their stories on twitter. Me Too movement generated debate on the issue of sexual abuse in Pakistan and there were talks in mainstream media also to deal it. Pakistani politicians, civil society activists, media personalities and common people were very charged for a short period of time on this issue of sexual. Unfortunately, soon these voices were forgotten. There were voices in favor of those who raised their voice against sexual abuse but there were also people who blamed the victims for remaining silent that many years and put the blame on the victims. This paper will try to examine the rise and fall of Me Too movement in Pakistan through content analysis. It will also try to look at the possibilities and challenges created through Me Too movement for Taboos Activism in Pakistan.
｢閱讀促進學習｣教學法在中文第二語言課堂的漢字 教學之應用及成效

Due to the variety and complexity of Chinese characters in terms of form, pronunciation and meaning, many non-Chinese speaking (NCS) students failed to master the formation of characters and sentences. It might be worth mentioning that English language and some NCS students’ first languages such as Pilipino, Urdu and Nepali, are all based on alphabetic writing system. A lack of understandings of font and word meaning makes NCS students feel particularly difficult when writing Chinese characters. In view of the difficulties of NCS students in writing Chinese characters and the achievements of predecessors in applying Reading to Learn (R2L) pedagogy, this paper attempts to explore the application and effectiv eness of R2L pedagogy in teaching Chinese characters to NCS students during Chinese classes. The participants were a group of 15 secondary one students who are learning Chines e as a second language, and their Chinese teacher. Using a qualitative research approach, interviews, lesson study and text analysis were conducted to explore whether R2L was consi dered and in fact effective for the students to acquire Chinese characters. The results of the study showed that the students with different Chinese levels significantly improved their writing after they received R2L pedagogy teaching. Based on the results, the researchers put forward relevant suggestions on the R2L teaching strategies that Chinese teachers might apply when teaching NCS students.
A Conglomerate of Multiple OCR Table Detection and Extraction

Information representation as tables are compact and concise method that eases searching, indexing, and storage requirements. Extracting and cloning tables from parsable documents is easier and widely used, however industry still faces challenge in detecting and extracting tables from OCR documents or images. This paper proposes an algorithm that detects and extracts multiple tables from OCR document. The algorithm uses a combination of image processing techniques, text recognition and procedural coding to identify distinct tables in same image and map the text to appropriate corresponding cell in dataframe which can be stored as Comma-separated values, Database, Excel and multiple other usable formats.
《아시연설가(我是演說家)》우승자와 준우승자의 레토릭 지수 비교 분석

This paper aims to find out how rhetorical the rhetoric effect is in the speeches of the winners and runners-ups in the final round of the fifth final title. The subjects of this paper are the speeches of the winners and runner-ups who won the 5th and 10th finalist finals of “I Am a Speaker”, which aired on Beijing TV on March 6, 2019. These speeches have images as well as texts, so we will look at the rhetorical expressions in the text and the speech and gesture language of the speakers. In addition, photographs presented as background data on stage when the winner and the runner-up each speak will be included in the analysis.In this paper, we will apply the “Rhetorical Sensitivity Scale”, which quantifies the ability of persuasion as a methodology, and sets up the evaluation items based on the traditional theory of rhetoric and then analyzes two speeches. The traditional theory of rhetoric can be divided into five areas and three persuasive elements. The five areas include idea, disposition, expression, memory, and action delivery. The three persuasion elements are Ethos, Logos, and Pathos.In order to pursue objectivity as much as possible, this paper will proceed with both text analysis with verbal expression and video analysis with field situations at the time of speech.
The Reciprocal Relationship among the Value of Information, Training and Development of Human Resources in Institutions

In nowaday, after the major changes witnessed by the world in various field especially in information , communication and management fields , affected By the extensive hops that resulted from what is known as “ information revolution or “ information explosion” during the last three decades of the last century. With the advent of the third millennium, the twenty- first century witnessed great openness among societies in various areas of life, and as a result, the term “Globalization” has emerged in various administrative, economic, social , cultural fields and others . All this, imposes the administrative apparatus to be eager to compete with other, through his quest to find all means that enable him to compete and keep up with others, benefiting from all techniques and information technology that offer great potential and abilities , perhaps, the most notably are computers and accessories with all its related applications, as information networks, management information systems , decision support and expert systems(1). The first and the most highlighted methods and tools of confrontation is preparing a qualified and competent element for this task, and the most prominent means to achieve this , is to adopt the method of scientific training based on accurate and comprehensive information, which in turn, based on actual needs required in terms of : training types, training levels, mechanisms and timing that contribute to develop a successful training program that meets the basic objectives(2) . This is the goal of this study .
It's the Journey Not the Destination: Building Genetic Algorithms Practitioners Can Trust

Genetic algorithms have been developed for decades by researchers in academia and perform well in engineering applications, yet their uptake in industry remains limited. In order to understand why this is the case, the opinions of users of engineering design tools were gathered. The results from a survey showing the attitudes of engineers and students with design experience with respect to optimisation algorithms are presented. A survey was designed to answer two research questions: To what extent is there a pre-existing sentiment (negative or positive) among students, engineers, and managers towards genetic algorithm-based design? and What are the requirements of practitioners with regards to design optimisation and the design optimisation process? A total of 23 participants (N = 23) took part in the 3-part mixed methods survey. Thematic analysis was conducted on the open-ended questions. A common thread throughout participants responses is that there is a question of trust towards genetic algorithms within industry. Perhaps surprising is that the key to gaining this trust is not producing good results, but creating algorithms which explain the process they take in reaching a result. Participants have expressed a desire to continue to remain in the design loop. This is at odds with the motivation of a portion of the genetic algorithms community of removing humans from the loop. It is clear we need to take a different approach to increase industrial uptake. Based on this, the following recommendations have been made to increase their use in industry: an increase of transparency and explainability of genetic algorithms, an increased focus on user experience, better communication between developers and engineers, and visualising algorithm behaviour.
Dissecting the components and factors of Neural Text Generation.

Neural text generation metamorphosed into several critical natural language applications ranging from text completion to free form narrative generation. Generating natural language has fundamentally been a human attribute and the advent of ubiquitous NLP applications and virtual agents marks the need to impart this skill to machines. There has been a colossal research effort in various frontiers of neural text generation including machine translation, summarization, image captioning, storytelling etc., We believe that this is an excellent juncture to retrospect on the directions of the field. Specifically, this paper surveys the fundamental factors and components relaying task agnostic impacts across various generation tasks such as storytelling, summarization, translation etc., In specific, we present an abstraction of the imperative techniques with respect to learning paradigms, pretraining, modeling approaches, decoding and the key challenges. Thereby, we hope to deliver a one-stop destination for researchers in the field to facilitate a perspective on where to situate their work and how it impacts other closely related tasks.
A spatial model checker in GPU (extended version)

The tool voxlogica merges the state-of-the-art library of computational imaging algorithms ITK with the combination of declarative specification and optimised execution provided by spatial logic model checking. The analysis of an existing benchmark for segmentation of brain tumours via a simple logical specification reached state-of-the-art accuracy. We present a new, GPU-based version of voxlogica and discuss its implementation, scalability, and applications.
FaiR-N: Fair and Robust Neural Networks for Structured Data

Fairness in machine learning is crucial when individuals are subject to automated decisions made by models in high-stake domains. Organizations that employ these models may also need to satisfy regulations that promote responsible and ethical A.I. While fairness metrics relying on comparing model error rates across subpopulations have been widely investigated for the detection and mitigation of bias, fairness in terms of the equalized ability to achieve recourse for different protected attribute groups has been relatively unexplored. We present a novel formulation for training neural networks that considers the distance of data points to the decision boundary such that the new objective: (1) reduces the average distance to the decision boundary between two groups for individuals subject to a negative outcome in each group, i.e. the network is more fair with respect to the ability to obtain recourse, and (2) increases the average distance of data points to the boundary to promote adversarial robustness. We demonstrate that training with this loss yields more fair and robust neural networks with similar accuracies to models trained without it. Moreover, we qualitatively motivate and empirically show that reducing recourse disparity across groups also improves fairness measures that rely on error rates. To the best of our knowledge, this is the first time that recourse capabilities across groups are considered to train fairer neural networks, and a relation between error rates based fairness and recourse based fairness is investigated.
CC-Loss: Channel Correlation Loss For Image Classification

The loss function is a key component in deep learning models. A commonly used loss function for classification is the cross entropy loss, which is a simple yet effective application of information theory for classification problems. Based on this loss, many other loss functions have been proposed,~\emph{e.g.}, by adding intra-class and inter-class constraints to enhance the discriminative ability of the learned features. However, these loss functions fail to consider the connections between the feature distribution and the model structure. Aiming at addressing this problem, we propose a channel correlation loss (CC-Loss) that is able to constrain the specific relations between classes and channels as well as maintain the intra-class and the inter-class separability. CC-Loss uses a channel attention module to generate channel attention of features for each sample in the training stage. Next, an Euclidean distance matrix is calculated to make the channel attention vectors associated with the same class become identical and to increase the difference between different classes. Finally, we obtain a feature embedding with good intra-class compactness and inter-class separability.Experimental results show that two different backbone models trained with the proposed CC-Loss outperform the state-of-the-art loss functions on three image classification datasets.
Security and Operational considerations for manufacturer installed keys and anchors

This document provides a nomenclature to describe ways in whichmanufacturers secure private keys and public trust anchors in devices.RFCEDITOR: please remove this paragraph. This work is occuring inhttps://github.com/mcr/idevid-security-considerations
IMPLEMENTASI WEIGHTED PRODUCT METHOD PADA PEMILIHAN PESERTA DIKBANGUM POLDA BENGKULU

ABSTRACT To improve the quality of existing police resources, motivation must be given for achievement in participating in educational activities. The selection system was carried out both by the Bengkulu Regional Committee according to instructions and directions given by the National Police Headquarters. So that in accordance with the motto proclaimed by the National Police Headquarters namely Clean, Transparent, Accountable and Humanist. (Bersih, Transparan, Akuntabel dan Humanis).  The selection system used is still using MicroSoft Excel as its assistance software, especially the acceptance of the Police Academy, Police and Private Officers. For other selections, it is also possible to use a special program created. Whereas the determination of the best DikBangUm participants will be better if it is processed using good management techniques and rules in the form of a decision support system (DSS) used by the Weighted Product Method (WPM) which is considered the most appropriate to be applied. Keywords : DSS,WPM, Dikbangum
Unit-Selection Based Facial Video Manipulation Detection

Advancements in video synthesis technology have caused major concerns over the authenticity of audio-visual content. A video manipulation method that is often overlooked is inter-frame forgery, in which segments (or units) of an original video are reordered and rejoined while cut-points are covered with transition effects. Subjective tests have shown the susceptibility of viewers in mistaking such content as authentic. In order to support research on the detection of such manipulations, we introduce a large-scale dataset of 1000 morph-cut videos that were generated by automation of the popular video editing software Adobe Premiere Pro. Furthermore, we propose a novel differential detection pipeline and achieve an outstanding frame-level detection accuracy of 95%.