Reviewerly/exHarmony huggingface dataset:
Investigating the logical inference capabilities of Knowledge Graph Embedding Models

Author(s): Choudhary, Chirag | Advisor(s): Singh, Sameer | Abstract: A knowledge graph represents factual information in the form of graphs, where nodes repre- sent real-world entities such as people, places and movies, and edges represent the relation- ships between these entities. Existing knowledge graphs are far from complete. Knowledge graph completion or link prediction refers to the task of predicting new relations (links) between entities by deriving information from the existing relations. A number of link pre- diction model have been proposed, several of which make probabilistic predictions about new links. These models can be rule-based methods derived from observed edges, latent represen- tation based embedding methods, or a combination of both. These methods must capture different kinds of relational patterns in the data, such as symmetry or inversion patterns to fully model the data. Rule-based methods explicitly learn these patterns, and provide an interpretable approach to predict new edges. With embedding based models, however, due to the nature of latent embeddings, it is difficult to understand what is being captured by these models. In this work, we explore the logical inference capabilities of knowledge graph embedding models. We experiment with various knowledge graph embedding models on syn- thetic datasets to identify specific properties of each model. The objective is to empirically validate the suitability of these models to learning different relational patterns that exist in real-world knowledge graphs.
Optimal Planning Solution's Simulation for the Active Expertise

The proof of constructiveness of previously obtained solution for the synthesis of optimal mechanism of Active Expertise is provide. Its implementation in the RDS modeling framework is described.
Measuring and Reducing the Cognitive Load for the End Users of Complex Systems

With the proliferation of complex computer systems, end users face a never-ending increase in the number of tasks, methods, inputs, passwords, usernames (and so on) when using online and standalone computer-based systems and applications. This paper examines a method and approach to measure how complex a system is to use, and how to reduce the complexity of such systems by minimising the requirement for human inputs as much as possible, in order to reduce the cognitive load for that user, or group of users. This paper addresses a study completed around using virtualised computer management systems interfaces of two well-known products AWS (Amazon Web Services), Oracle Cloud, and compares the complexity of the steps and interface for end users to a private cloud less well-known system called the IDE (Intelligent Design Engine). By using a set of derived formula, we examine how this can be applied to systems that have qualitative data feedback from the experiment process, and how to convert this effectively into quantitative data. This data is then analysed numerically using a unique approach to provide additional and meaningful results based of the original end user data.
Domain Adaptation on Graphs via Frequency Analysis

Classical machine learning algorithms assume the training and test data to be sampled from the same distribution, while this assumption may be violated in practice. Domain adaptation methods aim to exploit the information available in a source domain in order to improve the performance of classification in a target domain. In this work, we focus on the problem of domain adaptation in graph settings. We consider a source graph with many labeled nodes and aim to estimate the class labels on a target graph with few labeled nodes. Our main assumption about the relation between the two graphs is that the frequency content of the label function has similar characteristics. Building on the recent advances in frequency analysis on graphs, we propose a novel graph domain adaptation algorithm. Experiments on image data sets show that the proposed method performs successfully.
Information-structural Constraints on PP Topicalization from NPs

In this paper, we discuss PP extraction from complex NPs such as [Aus Tübingen]i haben [mehrere Studenten ti] am Forschungswettbewerb teilgenommen. (‘From Tübingen have several students in the research contest participated’). Extractions from complex NPs are difficult to capture since they are subject to various restrictions related to the syntactic status of the extracted PP, the semantic relation between the verb and the complex NP, and the type of verb involved. In this paper, we will concentrate on one particular kind of extraction, the PP topicalization from NPs. This PP topicalization has been shown to be dependent on an appropriate context. In order to account for the discourse properties of the construction, we present an account based on the Questions under Discussion (QUD) approach to information structure. Modeling QUDs is starting to emerge as a fruitful way to define an explicit hinge between the properties of the sentence and the nature of the discourse in which the sentence can function. The proposed QUD analysis is based on attested data from corpora, which is supplemented by experimental evidence.
Modeling Human Activities via Long Short Term Memory Networks

The presence of rapidly increasing visual data adds importance to the computer vision studies for automatic analysis and interpretation of content. Although the nervous and sensory systems in humans easily perform the processes such as understanding and recognizing activities that take place on a stage, these processes are among the most challenging research topics of computer vision. The activities vary according to the number of participants. For instance, a single person can perform activities consisting of various atomic actions. In the scenes with more than one person, interactions occur between people. Since interactions are mutual movements between multiple people, both temporal changes in the scene and the spatial structures need to be modeled for analysis. In this study, long short term memory networks and support vector machines, based on the positions and distances of human body joints, are trained for the automated classification of actions and interactions.
SYMPES Character Encoding Algorithm

In this paper, SYMPES Character Encoding Algorithm, which bring a new solution to secure communication, is described. Although SYMPES is not an encryption algorithm, it has an inherently trusted infrastructure. The character data is not send with SYMPES, it is only the indexes which are transmitted over the communication channel. In this way, due to the natural structure of the system, trusted communication infrastructure is provided and the data is compressed.
Digitalisering som pedagogiskt redskap : En kvalitativ studie om hur digitaliseringen kan användas som pedagogiskt redskap för att främja elevernas inlärning i skolarbetet

Our entire society is going through a digital development that never takes a break and the school is required to educate digitally competent citizens. To meet these challenges, it is necessary that ...
Förtroende i samband med influencer marketing : En kvalitativ studie om konsumenters förtroende gentemot influencers och företag

The purpose with this study is to increase the understanding behind consumers trust towards influencers and companies while these two collaborate with each other. Two questions were developed to in ...
LibGuides: MHIKNET: Your Library Card

A library card enables clients to borrow library materials, order literature searches, and make requests for copies of articles, which will be delivered to you. Library cards will be issued and mailed to MHIKNET clients.
The cache construction and intelligent imagination of airfare shopping engine

This paper mainly introduces the search engine optimization scheme used by airlines to check flight and fare information. The legacy shopping search engine based on IBE has two drawbacks. First, the query efficiency is low. Second, the ability to withstand pressure is weak.In order to solve these problems, based on the application of cloud computing technology, this paper proposes a data cache and data accuracy optimization scheme to build a new shopping cloud engine architecture.We will focus on how to cache data and how to improve the accuracy.The data shows that the cache accuracy has been improved from the initial 75.62% to a maximum of 98.68%. One airline used the optimized shopping search engine architecture proposed in this paper for a year, the retrieval efficiency increased by about 25.1 times, and the concurrent processing capacity increased by 10.5 times.
A Case Study on the Stress Resilience Training Program of Police Officers via the Mobile Application

이 연구는 경찰공무원의 직무스트레스 관리를 위한 기초자료를 제공하기 위하여 회복탄력성 증진 프로그램 중 하나인 명상 으로 구성된 모바일 어플리케이션을 활용한 사례연구를 통해 효과성을 실증적으로 검증하는 것이 목적이다. 이를 위하여 회복탄력성 제고 어플리케이션을 선정하고 20명의 경찰공무원(실험군 10명과 통제군 10명)을 대상으로 4주간의 모바일 회복탄력성 프로그램 적용 전과 후의 차이 검증을 통해 실질적으로 회복탄력성이 제고되고 선행연구에서 제시한 경찰직무와 관련된 스트레스인 감정노동, 외상 후 스트레스 장애, 일반적 직무스트레스, 직무소진이 감소되는지를 확인하였다. 통제군과 달리 실험군에서는 실험 전과 후의 회복탄력성, 일반적 직무스트레스, 직무소진이 통계적으로 유의미한 차이가 있는 것을 확인하였다. 4주간(1일 30분)의 모바일 어플리케이션 활용을 통한 회복탄력성 제고 활동으로 실질적으로 회복탄력성이 증가하였고 직무스트레스와 직무 소진이 감소하는 것을 확인하였다. 이러한 실험결과는 모바일 어플리케이션을 활용한 회복탄력성을 제고를 통해 경찰공무원의 직무와 관련된 스트레스를 관리할 수 있다는 것을 보여주는 것이다.The purpose of this study is to validate the effectiveness actually through case studies using mobile application consisted of meditation which is one of resilience improvement program in order to provide basic data for managing job stresses of police officers. For this, through selecting resilience raising application and validating the difference between before and after applying 4 weeks of mobile resilience program subjecting to 20 police officers (10 officers of test group and 10 officers of control group), check if resilience is actually raised and stresses related to police jobs suggested in previous studies such as emotional labor, post-traumatic stress disorder (PTSD), general job stress, and burnout are reduced. It is indicated that unlike in control group, in test group, resilience, general job stress, and job burnout before & after test have statistically significant difference. It is indicated that with resilience raising activity through using mobile application for 4 weeks (1 day and 30 minutes), resilience actually increases and job stress & job burnout are reduced. Such test result shows that through raising the resilience using mobile application, the stresses related to jobs of police officers can be managed.
Noise Flow: Noise Modeling with Conditional Normalizing Flows

Modeling and synthesizing image noise is an important aspect in many computer vision applications. The long-standing additive white Gaussian and heteroscedastic (signal-dependent) noise models widely used in the literature provide only a coarse approximation of real sensor noise. This paper introduces Noise Flow, a powerful and accurate noise model based on recent normalizing flow architectures. Noise Flow combines well-established basic parametric noise models (e.g., signal-dependent noise) with the flexibility and expressiveness of normalizing flow networks. The result is a single, comprehensive, compact noise model containing fewer than 2500 parameters yet able to represent multiple cameras and gain factors. Noise Flow dramatically outperforms existing noise models, with 0.42 nats/pixel improvement over the camera-calibrated noise level functions, which translates to 52% improvement in the likelihood of sampled noise. Noise Flow represents the first serious attempt to go beyond simple parametric models to one that leverages the power of deep learning and data-driven noise distributions.
Segmentation of Human Eye Pupil with Novel Grid Based Localization Computing

This paper discuss about grid computing which increase the performance of vision image segmentation. The segmentation process with grid computing identifies the challenging in computation task. Novel grid based method supports image processing technique to circle eye pupil in normal and defective eyes. The procedure of the proposed system use real eye images as data collection for input to grid computing. Image corner and line edge detection is replaced with grid based technique to achieve good segmentation result. Grid with different sizes classified to reach neighbors to form grid segment. Adjacency graph merge the regions of highlighted eye pupil. This grid location points of eye image is noted to identify the eye center localization. The experimental task proves the efficiency of automate segmentation compared with other algorithms.
“Is It Necessary to Take Postinor Too?”: The Use of Postinor-2 by Indonesian Youths and The Imagination of Efficacious Contraceptive Method

In this provisional paper, I investigate how Postinor-2, as an emergency contraceptive, mediates Indonesian youths’ anxieties of the ineffectualness of other birth control methods. By focusing on diversified questions concerning Postinor-2 by youths in Twitter, I argue that the use of Postinor-2 by Indonesian youth does not only based on the intention to prevent pregnancy, but also to diminish their uncertainties and doubts. An anxious statement such “although we already used a condom and counted my fertile days, is it necessary to take postinor too?” show that the lack of information about contraceptive methods influences youths’ use of Postinor-2. This lack of information mainly derived from the common assumption attached to contraception, in which contraceptive methods often perceived as only accessed by married, adult persons. This common assumption also affects youths’ purchase of Postinor-2. While legally Postinor-2 can only obtain with a medical prescription, many online drug stores sell Postinor-2 without requiring a prescription. Since youths purchase Postinor-2 without prescription, an interactive consultation with a medical doctor is absent. Instead of consulting with a doctor, youths who intend to obtain Postinor-2 gain their knowledge on it by reading the experience of other Postinor-2’s users, particularly in regard to its indication and contraindication.  In this paper, I formulate the preliminary analysis by incorporating the conceptual framework from the anthropological study of the body in order to understand how youths’ bodily use of Postinor-2 to overcome their anxiety by rendering them efficacious.
Gender Classification with GAL Network

This paper presents a solution for gender classification problem using GAL network which is a model of neural networks. This network is trained and tested on a publicly available Stanford dataset including 400 images (200 female and 200 male). Before classification process, primarily Principal Component Analysis (PCA) is applied on 128×128 pixel face images to reduce dimension and the dimension is reduced by about 99%; then GAL, an incremental neural network for supervised learning, is used to classify the data as male or female. Ten-fold cross validation is used for determining the accuracy rate of the classification. This paper has a novelty in the way of applying GAL to face images for gender classification. Test results show that 96% classification performance is obtained with GAL.
Learning to Follow Verbal Instructions with Visual Grounding

We present a visually grounded deep learning model towards a virtual robot that can follow navigational instructions. Our model is capable of processing raw visual input and natural text instructions. The aim is to develop a model that can learn to follow novel instructions from instruction-perception examples. The proposed model is trained on data collected in a synthetic environment and its architecture allows it to work also with real visual data. We show that our results are on par with the previously proposed methods.
Dynamic Scaling of EEG Fluctuations of Patients with Learning Disorders Based on Artificial Intelligence

This work models the dynamics of time series fluctuations of patients with learning disorders, specifically with reading-writing problems, applying fractal geometry, rough interface growth theory and Artificial Intelligence. From the EEG of children diagnosed with reading-writing problems, we obtain data of the brain activity of these children with which time series of fluctuations (standard deviations, $$\upsilon \left( t,\tau \right) $$ ) for each of the 19 channels distributed in different regions of the cerebral cortex. The self-affinity of the time series of fluctuations (treated as interfaces in motion) is characterized by the scaling behavior of the structure functions by one hand $$\sigma \propto \left( \delta _{t}\right) ^{\zeta }$$ , with $$\zeta $$ as the local or roughness exponent and the other hand $$\sigma \propto \left( \tau \right) ^{\beta }$$ , with $$\beta $$ as the fluctuation growth exponent. These findings guide us to propose the existence of a dynamic scaling behavior similar to that of Family-Vicsek for the kinetic roughening of a moving interface. In addition these findings are implemented in an Internet of Things (IoT) Network.
Dependent Pearl: Normalization by realizability.

For those of us who generally live in world of syntax, semantic proof techniques such as reducibility, realizability or logical relations seem somewhat magical despite -- or perhaps due to -- their seemingly unreasonable effectiveness. Why do they work? At which point in proof is the real done? Hoping to build a programming intuition of these proofs, we implement a normalization argument for simply-typed lambda-calculus with sums: instead of a proof, it is described as a program in a dependently-typed meta-language. The semantic technique we set out to study is Krivine's classical realizability, which amounts to a proof-relevant presentation of reducibility arguments -- unary logical relations. Reducibility assigns a predicate to each type, realizability assigns a set of realizers, which are abstract machines that extend lambda-terms with a first-class notion of contexts. Normalization is a direct consequence of an adequacy theorem or fundamental lemma, which states that any well-typed term translates to a realizer of its type. We show that adequacy theorem, when written as a dependent program, corresponds to an evaluation procedure. In particular, a weak normalization proof precisely computes a series of reduction from input term to a normal form. Interestingly, choices that we make when we define reducibility predicates -- truth and falsity witnesses for each connective -- determine evaluation order of proof, with each datatype constructor behaving in a lazy or strict fashion. While most of ideas in this presentation are folklore among specialists, our dependently-typed functional program provides an accessible presentation to a wider audience. In particular, our work provides a gentle introduction to abstract machine calculi which have recently been used as an effective research vehicle.
사법분야 조약에 대한 인도네시아의 접근방식에 대한 연구

Indonesia is Korea’s 2nd largest Investment country amongst ASEAN Countries. It stands a 4th place in terms of population and economic growth rate in...
Reservoir-size dependent learning in analogue neural networks

The implementation of artificial neural networks in hardware substrates is a major interdisciplinary enterprise. Well suited candidates for physical implementations must combine nonlinear neurons with dedicated and efficient hardware solutions for both connectivity and training. Reservoir computing addresses the problems related with the network connectivity and training in an elegant and efficient way. However, important questions regarding impact of reservoir size and learning routines on the convergence-speed during learning remain unaddressed. Here, we study in detail the learning process of a recently demonstrated photonic neural network based on a reservoir. We use a greedy algorithm to train our neural network for the task of chaotic signals prediction and analyze the learning-error landscape. Our results unveil fundamental properties of the system's optimization hyperspace. Particularly, we determine the convergence speed of learning as a function of reservoir size and find exceptional, close to linear scaling. This linear dependence, together with our parallel diffractive coupling, represent optimal scaling conditions for our photonic neural network scheme.
Rancang Bangun Sistem Pakar Diagnosa Penyakit Ayam Bangkok

Abstract: Expert System is a knowledge-based program that offers quality solutions for problems within a certain domain. The implementation of expert systems is widely used for commercial purposes, because expert systems are seen as a way to store expert knowledge in certain areas in computer programs, so that they can make informed decisions and think intelligently. Using Android-based methods, this program is expected to represent experts in diagnosing Kip-Bangkok disease. Expert Systems Applications diagnosis chicken bangkok provide maximum convenience for the owner of the chickens Bangkok, so owners do not have chicken bangkok meet with specialists in person, the owner of the chicken bangkok open the computer and use this expert system application instead of the disease in poultry media consultation of experts in chickens bangkok. The expert system is used as a decision support and is used as a tool for someone who has no information about the types of diseases and symptoms in bangkok chicken, as well as preventative advice. From this problem this disease can be known on the basis of the symptoms found in chicken in Bangkok and to prevent deaths by giving appropriate prevention advice.&#x0D; &#x0D; Keywords: Expert system, Application, Diagnosis of the Chicken Disease of Bangkok
Extended Abstract: When Is Persona Design Accountable?

Recently, there has been reason to question whether persona research in industry actually meets the ultimate standard of accountability-i.e., providing actionable results. In this session, the speaker will reflect critically on four cases where teams created personas for actual clients (Blue Cross & Blue Shield of South Carolina, the Clemson-Area Bosom Buddies Breast Cancer Support Group, the CAAH Web Design Team, and the Communications Director for the School of Architecture). Two of these projects led to extremely successful results; two of them did not. In this interactive session, the audience participants will examine the potential causes of these outcomes and enumerate the causes.
A relation between log-likelihood and cross-validation log-scores

It is shown that the log-likelihood of a hypothesis or model given some data is equal to an average of all leave-one-out cross-validation log-scores that can be calculated from all subsets of the data. This relation can be generalized to any k-fold cross-validation log-scores.
CRPN(Customer-oriented Risk Priority Number): SNS 오피니언 마이닝을 활용한 고객 의견 기반의 RPN 평가 기법

Purpose: The purpose of this study is to propose a new Risk Priority Number(RPN) evaluation method which analyzes value of product functions by mining customer opinions in Social Network Service(SNS).Methods: A traditional RPN is measured by three evaluation standards (Severity, Occurrence, Detection) which are analyzed by manufacturing engineers and researchers. On the other hand, these standards are analyzed by customers’ viewpoints through SNS opinion mining in this research. In order to extract customer feedbacks from textual data sets, the methodology in this paper implies natural language processing, hereby collecting product related data sets and analyzing the opinions automatically. An emotional polarity of an opinion indicates severity, while the number of negative opinion shows occurrence, and the entire number of customer opinion refers to detection.Results: The results of this study are as follows; As a result of the CRPN evaluation, it is confirmed that the features evaluated as risky are highly likely to be improved in the next series. Therefore, CRPN is an effective risk assessment model that reflects customer feedback.Conclusion: Reflecting customer feedback is a useful tool for risk assessment of the product as well as for developing new products and improving existing products.