Reviewerly/exHarmony huggingface dataset:
Comparing Causal Discovery Methods using Synthetic and Real Data

&amp;lt;p&amp;gt;Unveiling causal structures, i.e., distinguishing cause from effect, from observational data plays a key role in climate science as well as in other fields like medicine or economics. Hence, a number of approaches has been developed to approach this. Recent decades have seen methods like Granger causality or causal network learning algorithms, which are, however, not generally applicable in every scenario. When given two variables X and Y, it is still a challenging problem to decide whether X causes Y, or Y causes X. Recently, there has been progress in the framework of structural causal models, which enable the discovery of causal relationships by making use of functional dependencies (e.g., only linear) and noise models (e.g., only non-Gaussian noise). However, each of them is coming with its own requirements and constraints. While the corresponding conditions are usually unknown in real scenarios, it is quite hard to choose the right method for every application in general.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;The goal of this work is to evaluate and to compare a number of state-of-the-art techniques in a joint benchmark. To do so, we employ synthetic data, where we can control for the dataset conditions precisely, and hence can give detailed reasoning about the resulting performance of the individual methods given their underlying assumptions. Further, we utilize real-world data to shed light on their capabilities in actual applications in a comparative manner. We concentrate on the case considering two uni-variate variables due to the large number of possible application scenarios. A profound study, comparing even the latest developments, is, to the best of our knowledge, so far not available in the literature.&amp;lt;/p&amp;gt;
Traffic light upgrade set to improve traffic flow

Works to upgrade the traffic signals at the junction of Burnside Road, East Kilbride Road and Dukes Road will begin on 21 September 2020
LSC-North Harris Library Research Guides: PSYC 2314 - Cross-Cultural Research Assignment (Prof. Hartgrove-Freile): Starting Point

The purpose of this research guide is to show students in Prof. Hartgrove-Freile's PSYC 2314 classes to complete Writing Assignment #1.
Disambiguating Confusion Sets as an Aid for Dyslexic Spelling

Spell checkers and other proofreading software are crucial tools for people with dyslexia and other reading disabilities. Most spell checkers automatically detect spelling mistakes by looking up individual words and seeing if they exist in the vocabulary. However, one of the biggest challenges of automatic spelling correction is how to deal with real-word errors, i.e. spelling mistakes which lead to a real but unintended word, such as when then is written in place of than. These errors account for 20% of all spelling mistakes made by people with dyslexia. As both words exist in the vocabulary, a simple dictionary lookup will not detect the mistake. The only way to disambiguate which word was actually intended is to look at the context in which the word appears. This problem is particularly apparent in languages with rich morphology where there is often minimal orthographic difference between grammatical items. In this paper, we present our novel confusion set corpus for Icelandic and discuss how it could be used for context-sensitive spelling correction. We have collected word pairs from seven different categories, chosen for their homophonous properties, along with sentence examples and frequency information from said pairs. We present a small-scale machine learning experiment using a decision tree binary classification which results range from 73% to 86% average accuracy with 10-fold cross validation. While not intended as a finalized result, the method shows potential and will be improved in future research.
Automatic Annotation of Werewolf Game Corpus with Players Revealing Oneselves as Seer/Medium and Divination/Medium Results

While playing the communication game “Are You a Werewolf”, a player always guesses other players’ roles through discussions, based on his own role and other players’ crucial utterances. The underlying goal of this paper is to construct an agent that can analyze the participating players’ utterances and play the werewolf game as if it is a human. For a step of this underlying goal, this paper studies how to accumulate werewolf game log data annotated with identification of players revealing oneselves as seer/medium, the acts of the divination and the medium and declaring the results of the divination and the medium. In this paper, we divide the whole task into four sub tasks and apply CNN/SVM classifiers to each sub task and evaluate their performance.
Is Bitcoin Helping In Improving the Global Economy

Bitcoin plays a major role in improving the global economy. It became one of the most popular and valued investment assets. Moreover, many people who ...
Flying cargo system with no operator

In recent years, UAVs have been used in various fields. Among them, the transportation of luggage has been used for the general society, leaving the area of research. However, in these aircraft, the dedicated operator at the company side sets the necessary information and skips the aircraft to the place requested by the user or a predetermined place, so that one individual can carry the baggage without the intervention of the operator. It does not correspond to exchanging. Therefore, in this research, we aim to develop an aircraft called an aerial bogie that was produced in previous research, and to produce an aircraft that can exchange luggage even with ordinary people without piloting skills. Therefore, in this research, we aim to develop an aircraft that can exchange luggage even with people without piloting skills.
Predicting Spammers in Twitter using a Sophisticated Schematic Mechanism

Twitter is a champion among the most standard microblogging organizations, which is generally used to share news and updates through short messages restricted to 280 characters. Regardless, its open nature and considerable customer base are routinely abused by robotized spammers, content polluters, and other ineffectively proposed customers to execute various cybercrimes, for instance, cyberbullying, trolling, talk dispersal, and stalking. In like way, different approaches have been proposed by masters to address these issues. Most of these strategies depend on customer depiction and absolutely insulting shared coordinated efforts. In this task, we present a mutt approach for recognizing robotized spammers by amalgamating communitybased features with other component groupings, specifically metadata-, content-, and participation based features. The peculiarity of the proposed methodology lies in the depiction of customers dependent on their coordinated efforts with their enthusiasts given that a customer can evade incorporates that are related to his/her own one of a kind activities, in any case, maintaining a strategic distance from those reliant on the supporters is inconvenient.
Research Guides: Information Literacy: Home

An exploration of information literacy including research skills, the ability to find, digest and evaluate information and the intersection of digital literacy and IL paradigms such as the ACRL Framework.
ENGLISH INSTRUCTIONS MEDIA BOOK IN IMPROVING ENGLISH OF ELEMENTARY SCHOOL TEACHERS IN TANGERANG

Abstract This research was conducted to improve the teachers' English mastery, especially in elementary school teachers in Tangerang, Indonesia. The method of this research was a quantitative approach with a quasi-experimental design by using nonequivalent control group design (pretest-posttest), to compare the English skill levels before and after treatment through the English Instructions book. The researcher took samples of 40 teachers from several Elementary School teachers in Tangerang. The result of the research found that the average post-test score of the experimental group was 88.06 and the average post-test score of the control group was 79.40, which means that there was an increased score of 8.65 or 11%. The hypothesis of the research stated that H0 was rejected and Ha was accepted, it means that there were significant changes in improving the teachers' English skill of Elementary School using the English instructions book. Keywords: English Instructions Book, Elementary School
#MeToo, Men’s Sexual Violence against Women and the Chilling Effect of Defamation Lawsuits: A Feminist Critique of the Swedish Criminal Justice System

#MeToo became in 2017 a strong and successful movement in Sweden where women exercised their freedom of expression to question structural injustice and men’s sexual violence against women. The movement broke the silence and stigma about being victim of men’s sexual violence and helped fight impunity. However, some of the #MeToo women in Sweden who told their stories also pointed out their perpetrators. Consequently, five of those women were met with a chilling and silencing legal consequence: defamation lawsuits. When examining the interrelation between freedom of expression, men’s sexual violence against women, and defamation under Swedish law, it was found that to disseminate information about being victim of sexual violence and name your perpetrator, constituted the crime of defamation. Moreover, defamation constitutes a lawful restriction on freedom of expression under IHRL. It is meant to protect the reputation of an individual and is applied objectively and equally for all. However, because of deeply rooted discriminatory practices in the Swedish society, such restriction can impose a disproportionate interference with freedom of expression under Articles 19 and 26 in conjunction with Articles 2 and 3 of the ICCPR and Article 5 of the CEDAW. One of the most extreme expressions of such discriminatory practice is men’s sexual violence against women, which restrict women’s freedom of expression because of stigma, fear, and impunity. It is also caused by the inability of the Swedish criminal justice system to handle men’s sexual violence against women, and often women are reproduced in court in as untrustworthy, sexualized subjects that do not fit into the realm of the autonomous legal subject. Serious defamation cases often involve sexual violence, and they affect men and women differently. Women are often victims of being posed in sexualized context, while men are often accused of being sex criminals. The #MeToo defamation cases involve the latter, where the women who claimed they had been victims of sexual violence and had achieved no justice became the perpetrators of a defamation crime. The result of my research shows that the #MeToo cases constitutes a societal, structural, and legal complexity between freedom of expression, men’s sexual violence against women, and defamation. In the Swedish criminal justice system, because of its objective assessment, such complexity not only position women victims of sexual violence as criminals who have overstepped the boundaries of Swedish defamation law. Women are once again reproduced as a sexualized and untrustworthy subjects, whose freedom of expression is limited. As a result, it causes a discriminatory interference with Articles 19 and 26 in conjunction with 2 and 3 of the ICCPR and Article 5 of the CEDAW.
Performance Enhancement and Accuracy of Artificial Neural Networks Using Particle Swarm Optimization for Breast Cancer Prediction

Breast cancer is the one of leading causes of death among the women in many parts of the world.  According  to Global Cancer Observatory (GCO) data from WHO (2018) show that approximately 58,256 (16,7%) cancer cases were  found in Indonesia out of a total of 348,809 cancer cases. The number of breast cancer patients throughout the world reached 42.1 per 100,000 population on average death rate of 17 per 100,000 inhabitants.Various ways have been used to find effective methods in the early detection of breast cancer. A prediction of breast cancer in early stage is very important in the medical world, which allows them to develop strategic programs that will help diagnose and reduce mortality rates from breast cancer. Performance enhancement and accuracy of artificial neural networks using particle swarm optimization is an effective solution for breast cancer prediction. The accuracy result was found 70% for training data and 96.1% for 30% prediction in this study. Previous studies only used the backpropagation algorithm to predict breast cancer and the result was 94.17%. Compared with previous study, there is an increase of 1.93% in combining  Backpropagation with Particle Swarm Optimization.
Splitting a Hybrid ASP Program

Hybrid Answer Set Programming (Hybrid ASP) is an extension of Answer Set Programming (ASP) that allows ASP-like rules to interact with outside sources.The Splitting Set Theorem is an important and extensively used result for ASP.The paper introduces the Splitting Set Theorem for Hybrid ASP, which is for Hybrid ASP the equivalent of the Splitting Set Theorem, and shows how it can be applied to simplify computing answer sets for Hybrid ASP programs most relevant for practical applications.
Which Dependency Parser to Use for Distributional Semantics in a Specialized Domain

We present a study whose objective is to compare several dependency parsers for English applied to a specialized corpus for building distributional count-based models from syntactic dependencies. One of the particularities of this study is to focus on the concepts of the target domain, which mainly occur in documents as multi-terms and must be aligned with the outputs of the parsers. We compare a set of ten parsers in terms of syntactic triplets but also in terms of distributional neighbors extracted from the models built from these triplets, both with and without an external reference concerning the semantic relations between concepts. We show more particularly that some patterns of proximity between these parsers can be observed across our different evaluations, which could give insights for anticipating the performance of a parser for building distributional models from a given corpus
Built-in software quality in Agile development

AbstractWaterfall and Agile are the two most popular software development methodologies. Waterfall as the traditional one, is a progressive method. The progress flows in one direction, and upon the ...
Adding a Syntactic Annotation Level to the Corpus of Contemporary Romanian Language

In this paper we present an experiment of augmenting the Corpus of Contemporary Romanian Language (CoRoLa) with the syntactic level of annotations, which would allow users to address queries about the syntax of Romanian sentences, in the Universal Dependency model. After a short introduction of CoRoLa, we describe the treebanks used to train the dependency parser, we show the evaluation results and the process of upgrading CoRoLa with the new level of annotations. The parser displaying the best accuracy with respect to recognition of heads and relations, out of three variants trained on manually built treebanks, was chosen. Keywords: Syntactic annotation, treebank, corpus, maltparser
Analyzing the Financial Development and Economic Growth in West Africa: With Specific Reference on Financial Economics

The financial sector development in developing countries and emerging markets is part of the private sector development strategy to stimulate economic growth and poverty reduction. It could be defined that, the financial sector is the set of institutions, instruments, and markets. This study analyzes the financial development and economic growth in West Africa with the main focus on financial development. The data were obtained from the world bank open data page website to estimate the financial development using dynamic panel data approach. The results show that official development assistance and broad money statistically has a significant impact on financial development in West Africa. It is therefore imperative that policymakers should implement policies that may attract official development assistance and broad money through regional stability, infrastructural improvement and creditors right reinforcement. It is also important to introduce reforms in the financial sector which may lead to a wide range of financial development. The coefficient of the speed of adjustment is small that is, it indicates a slow impact on the adjustment.
Planning and Control of Multiple Mobile Robots for Intralogistics: an optimization-based reordering strategy

In this thesis we consider multiple Automated Guided Vehicles (AGVs) navigating a common workspace to fulfill intralogistics tasks, typically formulated as the Multi-Agent Path Finding (MAPF) problem. To keep plan execution deadlock-free, one approach is to construct an Action Dependency Graph (ADG) which encodes the ordering of AGVs as they proceed along their routes. Using this method, delayed AGVs occasionally require others to wait for them at intersections, thereby affecting the plan execution efficiency. If the workspace is shared by dynamic obstacles such as humans or third party robots, AGVs can experience large delays. A common mitigation approach is to re-solve the MAPF using the current, delayed AGV positions. However, due to its inherent complexity, solving the MAPF is time-consuming, making this approach inefficient, especially for large AGV teams. To address this challenge, we present a novel concept called a Switchable Action Dependency Graph (SADG) which is used as the basis for a shrinking and receding horizon control scheme to repeatedly modify an acyclic ADG to minimize route completion times of each AGV using an optimization based approach. Our control strategies persistently maintain an acyclic ADG, necessary for deadlock-free plan execution. The proposed control strategies are evaluated in a simulation environment and show a reduc- tion in route completion times when a fleet of AGVs is subjected to random delays. Finally, the methods are also implemented using ROS and validated in the Gazebo simulation envi- ronment to illustrate practical feasibility when applied to real systems.
PENGARUH PEMANFAATAN PLATFORM SOSIAL MEDIA PADA ERA DIGITAL TERHADAP PRESTASI MAHASISWA

The purpose of this study was to determine the use of social media platforms on AMSS Padang student achievement. The research method used in this research is quantitative descriptive method. Respondents in this study were active AMSS students in the academic year 2019/2020 who took 60 subjects. The research technique used was a census. Data analysis techniques use simple regression analysis. The result of the research is obtained sig 0.01 then <0.05 so that Ho is rejected so there is an influence between the use of social media platforms on learning presatsi. This shows that there is an influence between the use of social media platforms and learning achievement
LibGuides: Curbside Services and Requesting Library Items: Step 3: Pick Up

This guide will outline the process on how to request library items and schedule a curbside pickup once ready.
A model for corporate social responsibility ranking on iron ore mining companies in Yazd province

Growth and development of various institutions and organizations as well as a rising trend in competitions among these entities have made them reflect on their organizational interests to survive in the business world. Accordingly; any small decisions or actions occurring in one of these centers, regardless of their direct effects on the desired sectors, can gradually have direct and indirect, hidden and obvious, as well as tangible and intangible impacts on all sectors in society and consequently lead to a series of actions and reactions in all levels and layers. Thus, the purpose of this study was to explain and rank factors affecting corporate social responsibility (CSR) of iron ore mining companies in Yazd Province, Iran. To this end, CSR indicators and dimensions were extracted using content analysis of interviews with expert groups from a total number of 9 iron ore mining companies in Yazd Province, Iran. The given dimensions included safety and health as well as legal, ethical, environmental, philanthropic, and economical. Then, relations between the indicators were determined via fuzzy cognitive map, and subsequently, they were ranked through FC Mapper. In the end, the intensity of the impact of the indicators on each other was calculated through Mic Mac.
Preparing for a Career That You Cannot Envision [The Way Ahead]

For my column in IEEE Potentials careers and graduate education theme issue last year, I shared common questions employers ask references when considering future employees. This year, the world has changed a bit. Employers are still hiring high-tech workers, so most IEEE Student Members are in a much better situation than many of their fellow citizens. Still, numerous companies are slowing down their hiring. Many students lost the summer internships they had been counting on. Students and recent graduates are reevaluating the job landscape.
CNN Model Optimization Cheme and Applications

CNN has made great contributions to computer vision. The original traditional computer vision algorithm was optimized and replaced by deep learning. The learning capabilities of deep learning have brought us great convenience, improved efficiency and speed, and accuracy. The core of this aspect is focused on the image, including image segmentation, image generation, image detection and so on. One of these important technologies is based on convolutional neural networks. In this article, we analyze the principles of the convolutional neural network and the directions that the optimization model can choose. And make further breakthroughs in applications, exploring the convenience of using CNN to detect and recognize gestures in photos. The applicability of the application is compared by comparing the photographing effects before and after posture detection using CNN. Other applications brought by CNN will be briefly explained in this article, but the main innovation is in photo pose detection.
Bend or PIN: Studying Bend Password Authentication with People with Vision Impairment.

People living with vision impairment can be vulnerable to attackers when entering passwords on their smartphones, as their technology is more 'observable'. While researchers have proposed tangible interactions such as bend input as an alternative authentication method, limited work have evaluated this method with people with vision impairment. This paper extends previous work by presenting our user study of bend passwords with 16 participants who live with varying levels of vision impairment or blindness. Each participant created their own passwords using both PIN codes and BendyPass, a combination of bend gestures performed on a flexible device. We explored whether BendyPass does indeed offer greater opportunity over PINs and evaluated the usability of both. Our findings show bend passwords have learnability and memorability potential as a tactile authentication method for people with vision impairment, and could be faster to enter than PINs. However, BendyPass still has limitations relating to security and usability.
Research on Workpiece Image Registration based on SIFT Algorithm

With the development of digital image processing and computer vision technology, image registration technology has become an extremely important technology in the field of image processing. In recent years, many scholars at home and abroad have carried out in-depth research on image registration, and proposed a variety of effective image registration methods. The most classical one is scale invariant feature transform (SIFT) proposed by Lowe. SIFT features keep good invariance under the conditions of image rotation, angle transformation, affine transformation and scale scaling. As an important part of image registration, feature matching has always been a hot topic. The main work of this paper is image registration based on SIFT algorithm, which aims to search and match feature points.