Miracle-dz/UniFAR-FTUs huggingface dataset:
Matching Red Links with Wikidata Items

This work tackles the problem of matching Wikipedia red links with existing articles. Links in Wikipedia pages are considered red when lead to nonexistent articles. In other Wikipedia editions could exist articles that correspond to such red links. In our work, we propose a way to match red links in one Wikipedia edition to existent pages in another edition. We solve this task in a context of Ukrainian red links and English existing pages. We created a dataset of 3 171 most frequent Ukrainian red links and a dataset of 2 957 927 pairs of red links and the most probable candidates for the correspondent pages in English Wikipedia. This dataset is publicly released1. We deﬁned the task as a Named Entity Linking problem. Red links are named entities and we link Ukrainian red links to English Wikipedia pages. In this work we provide a thorough analysis on the data and deﬁne its conceptual characteristics to exploit in entity resolution. These characteristics are graph properties (connections with the pages where red links occur and connections with the pages which occur in the same pages with red links) and word properties (title names). BabelNet knowledge base was applied to this task. We evaluated its powers in terms of F1 score (29 %) and regarded it as a baseline for our approach. To improve the results we introduced several similarity metrics based on mentioned red links characteristics. Combined in a linear model they resulted in F1 score 85 % which is our best result. In our thesis we also discuss bottlenecks and limitations of the current approach and outline the ideas for future improvements. To the best of our knowledge,we are the ﬁrst to state the problem and propose a solution for red links in Ukrainian Wikipedia edition. All the code for this project is publicly released on github.
Association of Childhood Asthma Control with Parental Socioeconomic status

Objective: To evaluate association of childhood asthma control with parental socioeconomic status.Patients and Methods: This cross sectional study was conducted in four hospitals of Rawalpindi and Islamabad from 1st February, 2017 to 31st January, 2018. Total 151 patients, aged 6 months to 15 years were enrolled in study through non-randomized, simple consecutive sampling. After informed consent doctor filled the structured proforma by taking short interview of parents about their socioeconomic status, drug compliance and level of asthma control of their child. Data was entered and analyzed on SPSS version 21.0. Chi-square test was applied for association among categorical variables. P-value less than 0.05 was considered significant.Results: Among total of 151 children, 37 (24.5%) children had well controlled asthma while 69 (45.7%) had partially controlled and 45 (29.8) had uncontrolled asthma. Children from low socioeconomic families had poor asthma control than children from high socioeconomic families.Conclusion: There is statistically significant association of poor asthma control with low parental education and income status. Therefore, more asthma education, frequent follow-ups and provision of free of cost medicines are needed in order to achieve good asthma control in such children.Key words: Asthma control, Childhood, Socioeconomic status
Privacy-preserving AI Services Through Data Decentralization

User services increasingly base their actions on AI models, e.g., to offer personalized and proactive support. However, the underlying AI algorithms require a continuous stream of personal data—leading to privacy issues, as users typically have to share this data out of their territory. Current privacy-preserving concepts are either not applicable to such AI-based services or to the disadvantage of any party. This paper presents PrivAI, a new decentralized and privacy-by-design platform for overcoming the need for sharing user data to benefit from personalized AI services. In short, PrivAI complements existing approaches to personal data stores, but strictly enforces the confinement of raw user data. PrivAI further addresses the resulting challenges by (1) dividing AI algorithms into cloud-based general model training, subsequent local personalization, and community-based sharing of model updates for new users; by (2) loading confidential AI models into a trusted execution environment, and thus, protecting provider’s intellectual property (IP). Our experiments show the feasibility and effectiveness of PrivAI with comparable performance as currently-practiced approaches.
A Distributed Link Scheduler for In-Band Full Duplex Wireless Networks

In a wireless network, a well designed link scheduler ensures links are activated frequently and experience minimal or no interference, meaning these links will have a high capacity. A challenging problem, however, is random or uncertain channel gains. Another challenge is determining whether a set of links can co-exist together, meaning they do not cause excessive interference to one another. These challenges have a direct impact on the number of activated links as well as their data rates. To this end, this paper proposes a reinforcement learning approach that enables each node to learn the best link to activate and data rate to use over random channel conditions. In addition, it allows nodes to learn the opportune time to use full-duplex or half-duplex transmissions. Our simulation results show our link scheduler results in an average throughput that is triple that of Carrier Sense Multiple Access (CSMA), and up to quadruple the average throughput of Time Division Multiple Access (TDMA). Moreover, our link scheduler remains superior when channel gains vary significantly from their average value.
On-Chip Interconnection Architecture of the Tile Processor

IMesh, the tile processor architecture's on-chip interconnection network, connects the multicore processor's tiles with five 2D mesh networks, each specialized for a different use. taking advantage of the five networks, the C-based ILIB interconnection library efficiently maps program communication across the on-chip interconnect. the tile processor's first implementation, the tile64, contains 64 cores and can execute 192 billion 32-bit operations per second at 1 Ghz.
Neurocognitive dysfunctions and functional state of patients after internal carotid endarterectomy

Introduction. Carotid endarterectomy (CEA) is a method of treatment of carotid stenosis, which significantly reduces the risk of ischemic stroke. This procedure may affect the patient’s neurocognitive functioning. The aim of the study was to evaluate the occurrence of neurocognitive disorders and to determine the functional status of the patients undergoing CEA. Material and methods. The study group consisted of 102 people who underwent CEA. Studies were performed preoperatively and repeated on the fourth postoperative day. The following scales were used: the Mini-Mental State Examination (MMSE), Activities of Daily Living Scale (ADL), Instrumental Activities of Daily Living (IADL) and the Hamilton Depression Rating Scale (HAM-D). Results. The average number of points obtained by patients in the MMSE, both before and after surgery is 26 points to 30 that may be obtained, which shows a subtle cognitive impairment. Almost every patient showed fitness both in terms of basic (ADL), as well as complex (IADL) activities before and after surgery. Conclusions. Low or average level of cognitive performance is observed in most subjects, both before and after CEA. There are many different factors that can affect the cognitive functions. The functional status of patients stood without significant changes.
Modelación y Validación de Sistemas de Control de Unidades de Generación del Sistema Nacional Interconectado Ecuatoriano

The adequate modeling of control systems of a power system, particularly the control systems of generating units, is of fundamental importance for the realization of dynamic studies to emulate the responses of the power system as nearly as possible. This paper presents a methodology for the development of control models of generating units. The models are developed in DIgSILENT Simulation Language (DSL) of PowerFactory. Structured models are tested thoroughly cabinet for an isolated system. The purpose of these tests is to analyze the consistency of the results and parameter validation. The tested models are part of the Ecuadorian Power System (SNI) and their incorporation to a database for stability studies will allow improving the emulation of electromechanical transients in the system.
Voice-Based Malay Commands Recognition by Using Audio Fingerprint Method for Smart House Applications

Voice-based command recognition is commonly used in security systems, phones, household appliances and hardware designed for handicapped people. Most of the current research study the voice command recognition for the smart home in English. Lack of study for voice command recognition in Malay makes it challenging to apply the voice command services for the smart home in Malaysia. Also, voice recognition is a non-trivial task in natural language processing. This project is to identify the command used for smart home appliances using Malay and design the algorithm for this system. Then, the proposed algorithm will be deployed on a Raspberry Pi to see the performance of Malay command in accuracy and the suitability of the algorithm to be deployed on low cost embedded devices. Light, fan, and television had been chosen as electrical appliances to build the command. An algorithm that previously used to recognize songs, the robust quad algorithm, is used in this project for voice command recognition. The proposed method has two main processes, known as feature extraction and voice recognition. In the feature extraction process, the audio fingerprint will extract data from the command spectral peak. For voice recognition, audio fingerprint matching will be used to analyze the audio commands. The outcome of this project is when the voice command is given by the user by activate or deactivate the target home appliance. The second outcome is the background noise that affects the system is reduced by using robust quad algorithm and increase the accuracy of the system. The results of this project have shown that the proposed algorithm is suitable to be implemented on a Raspberry Pi and achieve a high recognition rate with 87%. In the presence of noise with 15 dB, the proposed algorithm can maintain the high recognition rate with 82%.
Designing for Trust: A Behavioral Framework for Sharing Economy Platforms

Trust is a fundamental prerequisite in the growth and sustainability of sharing economy platforms. Many of such platforms rely on actions that require trust to take place, such as entering a stranger’s car or sleeping at a stranger’s place. For this reason, understanding, measuring, and tracking trust can be of great benefit to such platforms, enabling them to identify trust behaviors, both online and offline, and identify groups which may benefit from trust-building interventions. In this work, we present the design and evaluation of a behavioral framework to measure a user’s propensity to trust others on Airbnb. We conducted an online experiment with 4,499 Airbnb users in the form of an investment game in order to capture users’ propensity to trust other users on Airbnb. Then, we used the experimental data to generate both explanatory and predictive models of trust propensity. Our contribution is a framework that can be used to measure trust propensity in sharing economy platforms via online and offline signals. We discuss which affordances need to be in place so that sharing economy platforms can get signals of trust, in addition to how such a framework can be used to inform design around trust in the short and long term.
Pediatric Psychopharmacology: Fast Facts

WW Norton Co, United States, 2006. Paperback. Condition: New. Language: English . Brand New Book ***** Print on Demand *****. This new title in the Fast Facts series, full of up-to-date and authoritative information, is a critical resource for all health care professionals working with children and adolescents.Psychiatrists, prescribing psychologists, psychotherapists, pediatricians, family practice physicians, pediatric neurologists, nurse practitioners, allied mental health professionals, and trainees in these professions are provided with: General introductory reviews of the principles of pediatric psychopharmacology and related patient assessment issues;Detailed discussions of psychiatric medications organized by drug class;A comprehensive review of child and adolescent psychiatric disorders.Rich background information and clear explanations of clinical directions for the prescriber and nonprescriber alike. Authored by two child and adolescent psychiatrists, Pediatric Psychopharmacology provides empirically-based, state-of-the-art knowledge as well as clear practical guidance regarding assessment, prescription, monitoring, and discontinuation of medications.
English to Korean transliteration using Sequence to Sequence model

음차 표기 모델은 그림 과 같 Sequence-to-Sequence 1 이, 입력된 문자열을 함으로써 문자열 자체가 encoding 하나의 입력 값이 되는 효과를 갖도록 한다. 하나의 문 자열이 하나의 입력 값이 됨으로써 조합이 달라질 때마 다 입력 값이 바뀌므로 변별력을 높이고 길이와 관계없 , 이 하나의 입력 값으로 취급하기 때문에 관계에서의 N:M 입력 출력 문제를 보완할 수 있다 null , null [5]. 기본적인 은 시퀀스 길이가 길어질 수록 경사 RNN Cell 사라짐 문제 가 있기 때문에 (Vanishing Gradient) RNN 을 로 적용하는 방법이나 을 적용 Cell LSTM Cell GRU Cell 하는 방법을 이용하여 선택적으로 기억을 수용 문제점 , 을 보완하였다 이번 실험에서는 을 적용하였다 . GRU Cell [6]. 그림 구조 1. Sequence-to-Sequence
Photodynamic therapy in periodontology: a systematic review

There are limited clinical trials that deal with the influence of photodynamic therapy (PDT) in addition to conventional scaling and root planing (SRP), due to the clinical and biological parameters of periodontitis. The scope of this review is to evaluate the interest for photodynamic therapy (PDT) over the last 10 years, as an alternative to traditional methods, more comfortable for all patients. Randomized controlled trials, systematic reviews, in the last 10 years were identified, all of them having PDT compared to conventional non-surgical treatment as FMD and medication. Data on changes in clinical and microbiological parameters were extracted. Screening, data abstraction and quality assessment were conducted. There were clinical heterogeneities among included studies. PDT as an independent treatment or as an adjunct to SRP did not demonstrate major statistically or clinically significant advantages. Combined therapy of PDT + SRP indicated a probable efficacy in CAL gain or probing depth reduction.
Treatment Adherence in Patients with Hypertension: A Cross Sectional Study from Southeast of Iran

Community and Family Medicine Department, School of Medicine, Kerman University of Medical Sciences, Kerman, Iran Social Determinants of Health Research Center, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran Corresponding author: Social Determinants of Health Research Center, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran. Tel: +98-3433257313, Email: ahmadipour@kmu.ac. ir
Digital Hardware Implementation of Artificial Neurons Models Using FPGA

This paper present the digital implementation of multiply-accumulate (MAC) circuit of artificial neuron using FPGA (Field Programmable Gate Array) including three types of nonlinear activation functions: hardlims, satlins and tansig. A VHDL hardware description Language codes are used to implement the neuron using XC3S500E-FG320 Xilinx FPGA device. The simulation results obtained with Xilinx Foundation 8.2i software are presented. The results are analyzed in terms of usage percentage of chip resources and maximum working frequency.
Wavelet-Based Homomorphic De-Speckling Technique With Directional Weighted-Averaging Stage

In this paper, a new homomorphic wavelet-based de-speckling technique is proposed. The technique combines a spatial domain processing with another transform domain processing in a homomorphic environment. The spatial domain nonlinear processing is based on replacing each log-transformed pixel value of the speckled image by a weighted-average of the logtransformed pixel values in the neighborhood of that pixel. The weighted averaged logtransformed image is then subjected to wavelet thresholding for further nonlinear transform domain processing. An exponential operation on the filtered output is used to simulate the final homomorphic antilog-transformation stage and to obtain the de-speckled image. Quantitative and qualitative measures prove the superiority of the proposed technique for the applications of image classification and recognition in ultrasonic and SAR systems.
Design principles of multi-axis, large magnitude force sensors based on stress fields for use in human and robotic locomotion

Our ability to purposefully move across varied terrain requires us to have knowledge of the interactions our feet have with the external environment. However, existing sensing methods are inadequate to address the many unique demands of legged locomotion (i.e. fragile structures, incapable of handling large impact forces and noise caused by inertial loads during stride). This research is a study of how best to replicate the role of skin mechanoreceptors that enable our biological counterparts to perform dynamic maneuvers, and to develop innovative sensors that would empower the next generation of agile robots and smart shoes. The thesis introduces new design principles and methodologies for developing multi-axis, large magnitude force sensors based on stress fields to achieve these goals. Fabrication methods are presented for a monolithic elastomeric footpad that is biologically inspired, allowing it to measure large magnitude forces in both normal and shear axes while being compact, lightweight, impact robust, dust tight, and waterproof. The key principle that enables this is termed Stress Field (SF) based force sensing. Instead of funneling the load path directly through a few sensors in traditional force sensing methods, SF based force sensing allows the sampling of the stress distribution over the entire footpad surface through an array of piezoresistive sensor elements. The force estimator is constructed in two steps. First, linear regression fits the sensor readings to normal and shear forces. Then, machine learning is used as a nonlinear function approximator on the residual to further refine the force estimator to achieve greater accuracy. To enable these SF force sensor to be reproduced or customized for different needs, guidelines are provided in the form of simple design principles based on biological receptive fields, as well as an analytical model for cylindrical sensor types. For more complex sensor geometries, a material model of the elastomer is experimentally characterized, and Finite Element Analysis (FEA) can be used to determine the optimal configurations of these sensor arrays for different sensing needs. To show the feasibi-
Asymptotic Behavior of Sequence Models

In this paper we study the limiting dynamics of a sequential process that generalizes Pólya’s urn. This process has been studied also in the context of language generation, discrete choice, repeat consumption, and models for the web graph. The process we study generates future items by copying from past items. It is parameterized by a sequence of weights describing how much to prefer copying from recent versus more distant locations. We show that, if the weight sequence follows a power law with exponent α ∈ [0, 1), then the sequences generated by the model tend toward a limiting behavior in which the eventual frequency of each token in the alphabet attains a limit. Moreover, in the case α > 2, we show that the sequence converges to a token being chosen infinitely often, and each other token being chosen only constantly many times.
DETECTION & EXTRACTION OF HUMAN SKIN USING CONCEPT OF FUZZY LOGICS

— In today’s life security is a high concern. As crime is increasing day by day therefore security and surveillance become the need of any society. Our paper is based on automatic detection & extraction of skin part of any human face. In this paper a very robust algorithm for detection & extraction of human skin using fuzzy logics has been proposed. Primary color space model human acquire is RGB color space model as it is so handy and easy to use but it has a negative characteristic that each of its component i.e. R component G component & B component have different values for each coordinate which become so complicated to handle and also sometimes it doesn’t provide the required information i.e. it doesn’t detect the correct area of skin. Due to these reasons we are going to use HSV/HSI color space model. In this paper we are focusing only about skin detection as only by skin detection one can get the information that either any intruder enters in the premises or not.
Integration of Remote Sensing and GIS to Extract Plantation Rows from A Drone-Based Image Point Cloud Digital Surface Model

Automated feature extraction from drone-based image point clouds (DIPC) is of paramount importance in precision agriculture (PA). PA is blessed with mechanized row seedlings to attain maximum yield and best management practices. Therefore, automated plantation rows extraction is essential in crop harvesting, pest management, and plant grow-rate predictions. Most of the existing research is consists on red, green, and blue (RGB) image-based solutions to extract plantation rows with the minimal background noise of test study sites. DIPC-based DSM row extraction solutions have not been tested frequently. In this research work, an automated method is designed to extract plantation row from DIPC-based DSM. The chosen plantation compartments have three different levels of background noise in UAVs images, therefore, methodology was tested under different background noises. The extraction results were quantified in terms of completeness, correctness, quality, and F1-score values. The case study revealed the potential of DIPC-based solution to extraction the plantation rows with an F1-score value of 0.94 for a plantation compartment with minimal background noises, 0.91 value for a highly noised compartment, and 0.85 for a compartment where DIPC was compromised. The evaluation suggests that DSM-based solutions are robust as compared to RGB image-based solutions to extract plantation-rows. Additionally, DSM-based solutions can be further extended to assess the plantation rows surface deformation caused by humans and machines and state-of-the-art is redefined.
Improving Worker Engagement Through Conversational Microtask Crowdsourcing

The rise in popularity of conversational agents has enabled humans to interact with machines more naturally. Recent work has shown that crowd workers in microtask marketplaces can complete a variety of human intelligence tasks (HITs) using conversational interfaces with similar output quality compared to the traditional Web interfaces. In this paper, we investigate the effectiveness of using conversational interfaces to improve worker engagement in microtask crowdsourcing. We designed a text-based conversational agent that assists workers in task execution, and tested the performance of workers when interacting with agents having different conversational styles. We conducted a rigorous experimental study on Amazon Mechanical Turk with 800 unique workers, to explore whether the output quality, worker engagement and the perceived cognitive load of workers can be affected by the conversational agent and its conversational styles. Our results show that conversational interfaces can be effective in engaging workers, and a suitable conversational style has potential to improve worker engagement.