Miracle-dz/UniFAR-FTUs huggingface dataset:
Information in transposition tables

Due to transpositions a search tree can be considered as a search graph. The transpositions are stored with information about previous searches in a transposition table. Although the use of transposition tables is standard practice, it is still an open question how large the overall reduction is and especially which information has the largest impact on the reduction. The paper describes an experiment to distinguish between the reduction given the best move is stored or the value of the best move is stored. A second experiment compares storing the bound values for minimal-window search with storing the true values. It transpires that the highest reduction comes from re-using bounds because they adequately generate cutoos. Since we know that from a certain transposition-table size not much is to be gained from doubling the number of entries, it might be useful to store additional information to see whether this leads to a better result. Our rst experiments on the use of more information per entry versus more entries indicate that additional memory can better be used for storing additional information than for doubling the size of the transposition table. When searching for a move chess programs build large search trees. Since a position sometimes can be arrived at by several distinct move sequences the size of the search tree can be reduced considerably if previous results for a position
Fine-tune BERT with Sparse Self-Attention Mechanism

In this paper, we develop a novel Sparse Self-Attention Fine-tuning model (referred as SSAF) which integrates sparsity into self-attention mechanism to enhance the fine-tuning performance of BERT. In particular, sparsity is introduced into the self-attention by replacing softmax function with a controllable sparse transformation when fine-tuning with BERT. It enables us to learn a structurally sparse attention distribution, which leads to a more interpretable representation for the whole input. The proposed model is evaluated on sentiment analysis, question answering, and natural language inference tasks. The extensive experimental results across multiple datasets demonstrate its effectiveness and superiority to the baseline methods.
Methods for improving quality and efficiency in quantitative echocardiography: Aspects of using high frame rate

Ultralyd er i dag et standardverktoy innen hjertediagnostikk. Analyse av ventrikkelfunksjon gjores subjektivt ved a se pa graskala ultralydopptak av hjertet. Dette gjor dokumentasjon og opplaering v .
Parametric Registration for Mobile Phone Images

Image registration is a significant step in a wide range of practical applications and it is a fundamental problem in various computer vision tasks. In this paper, we propose a highly accurate and fast parametric registration method for mobile phone photos. The proposed algorithm is based on a fast and accurate elastic registration algorithm, the Local All-Pass (LAP) algorithm, which performs in a coarse-to-fine manner. At each iteration, the LAP displacement field is fitted by a parametric model. Thus the image registration problem is equivalent to finding a few parameters to describe the displacement field. The fitting step can be performed very efficiently by solving a linear system of equations. In terms of the fitting model, it is easy to change the type of models to do the parametric fitting for specific applications. Experimental results on both synthetic and real images demonstrate the high accuracy and computational efficiency of the proposed algorithm.
PREDICTION OF TRAFFIC ACCIDENT SEVERITY USING DATA MINING TECHNIQUES IN IBB PROVINCE, YEMEN

Traffic accidents are the leading causes beyond death; it is the concern of most countries that strive for finding radical solutions to this problem. There are several methods used in the process of forecasting traffic accidents such as classification, assembly, association, etc. This paper surveyed the latest studies in the field of traffic accident prediction; the most important tools and algorithms were used in the prediction process such as Back- propagation Neural Networks and the decision tree. In addition, this paper proposed a model for predicting traffic accidents based on dataset obtained from the Directorate General of Traffic Statistics, Ibb, Yemen.
Deep Learning-Based Obstacle Detection and Depth Estimation

This paper proposed a modified YOLOv3 which has an extra object depth prediction module for obstacle detection and avoidance. We use a pre-processed KITTI dataset to train the proposed, unified model for (i) object detection and (ii) depth prediction and use the AirSim flight simulator to generate synthetic aerial images to verify that our model can be applied in different data domains. Experimental results show that the proposed model compares favorably with other depth map prediction methods in terms of accuracy in the prediction of object depth for pre-processed KITTI dataset, while the unified approach can actually improve both (i) and (ii) at the same time.
Encrypting and Preserving Sensitive Attributes in Customer Churn Data Using Novel Dragonfly Based Pseudonymizer Approach

With miscellaneous information accessible in public depositories, consumer data is the knowledgebase for anticipating client preferences. For instance, subscriber details are inspected in telecommunication sector to ascertain growth, customer engagement and imminent opportunity for advancement of services. Amongst such parameters, churn rate is substantial to scrutinize migrating consumers. However, predicting churn is often accustomed with prevalent risk of invading sensitive information from subscribers. Henceforth, it is worth safeguarding subtle details prior to customer-churn assessment. A dual approach is adopted based on dragonfly and pseudonymizer algorithms to secure lucidity of customer data. This twofold approach ensures sensitive attributes are protected prior to churn analysis. Exactitude of this method is investigated by comparing performances of conventional privacy preserving models against the current model. Furthermore, churn detection is substantiated prior and post data preservation for detecting information loss. It was found that the privacy based feature selection method secured sensitive attributes effectively as compared to traditional approaches. Moreover, information loss estimated prior and post security concealment identified random forest classifier as superlative churn detection model with enhanced accuracy of 94.3% and minimal data forfeiture of 0.32%. Likewise, this approach can be adopted in several domains to shield vulnerable information prior to data modeling.
Scalable Power Impact Prediction of Mobile Sensing Applications at Pre-Installation Time

Today's smartphone application (hereinafter ‘app’) markets do not provide information on power consumption of apps, which is essential for users. Continuous sensing apps make this problem more severe because significant power is consumed without the users’ awareness. We propose PowerForecaster to break through such an exhaustive cycle. It provides users with personalized estimation of sensing apps' power cost at pre-installation time. It is challenging to provide such estimation in advance because the actual power cost of a sensing app varies depending on user behavior such as physical activities and phone use patterns. To address this, we develop a novel power emulator as a core component of PowerForecaster. It achieves accurate, personalized power estimation by reproducing users' behaviors and emulating the target app's power use. We optimize the system to make the power emulation fast and its trace collection energy efficient. We further address the problem of dealing with large-scale emulation requests from worldwide deployment. We develop a novel selective emulation approach to minimize the server-side resource cost. We performed extensive experiments and the experimental results show that PowerForecaster achieves the power estimation accuracy of 93.4 percent and saves on 60 percent of the emulator instance usage.
Deep learning for image-based classification of OAM modes in laser beams propagating through convective turbulence

Free-space optical communications are highly sensitive to distortions induced by atmospheric turbulence. This is particularly relevant when using orbital angular momentum (OAM) to send information. As current machine learning techniques for computer vision allow for accurate classification of general images, we have studied the use of a convolutional neural network for recognition of intensity patterns of OAM states after propagation experiments in a laboratory. The effect of changes in magnification and level of turbulence were explored. An error as low as 2.39% was obtained for a low level of turbulence when the training and testing data came from the same optical setup. Finally, in this article we suggest data augmentation procedures to face the problem of training before the final calibration of a communication system, with no access to data for the actual magnification and level of turbulence of real application conditions.
User Centered Design: Design and Development Methodology of Seed Planting Tools

Gejlek or Galah is a conventional wood-made seed planting tools aid common used by corn farmers. The limited functions and heavy weight of the existing seed planting caused low productivity and long duration in planting activity. The aim of the research is to design and to develop seed planting tools which able to accommodate user needs. User-Centered Design (UCD) methods used to design product focused on customer satisfaction. 15 respondents involved in this research to measure productivity, practicality, reliability and ergonomic. Validity test represent by F-value produce result test in between 0.354 to 0.625 where Ftest > Ftab means reject H0 or seed planting tools design able to fulfil user needs and different from previous design. User test show how work load against pulse measurement improve 26.44% and Nordic Body Map decrease by average 25% which mean there are significant improvement in terms of work load and ergonomics. It helps increase work productivity.
Toward Detecting Malicious Activities in Online Social Network through User Behavior

: Social networks consist of context-sensitive and relational data while also including a considerable amount of malicious content. It has turned out to be troublesome to clarify the genuine semantic estimation of distributed substance for the identification of client practices. Without comprehension the logical foundation, an integrated social media content analysis platform that leverages three levels of features, i.e., user-generated content, social graph connections, and user profile activities, to analyze and detect anomalous behaviors that deviate significantly from the norm in large-scale social networks. Several types of analyses have been conducted for a better understanding of the different user behaviors in the detection of highly adaptive malicious users. In the proposed method, propose a directed graphs, to detect fraudulent users in OSNs. In this, given a training dataset we estimate the posterior probability distribution for each user and uses it to predict a user's label.
Altered Cortical Morphometry in Pre-manifest Huntington’s Disease: Cross-sectional Data from the IMAGE-HD Study

Huntington’s disease (HD) is an inherited progressive neurodegenerative disease mainly associated with subcortical striatal atrophy. There is also strong evidence showing cerebral atrophy and cortical thinning; however, limited research has investigated altered patterns of cortical folding in this disease. Here, we investigated cortical morphometry via both gyrification index (GI, a measure of cortical folding) and cortical thinning. The localized GI was examined using a novel GI, namely LB-GI. As part of a cross-sectional study, pre-manifest (pre-HD) individuals (n = 29) and matched controls (n = 29) underwent T1-MRI using data from the IMAGE-HD study. Compared to controls, pre-HD individuals demonstrated significantly lower GI in the left superior parietal and the right superior temporal regions and greater cortical thinning in the bilateral pre-central and the superior frontal gyri and left caudal middle frontal gyrus, as well as the superior parietal region. For the first time, we report evidence of abnormal localized cortical folding in pre-HD. We also provide evidence that cortical folding impacts different regions of the cortical surface more so than cortical thickness. As a result, we propose a potential new biological marker that may increase our understanding of the neuropathology of HD. Greater understanding of brain changes could inform new therapeutic approaches and target points for clinical trials.
Mathematical Models for Evaluation of the Higher Education System Functions with DEA Approach

The purpose of this research is to develop the Data Envelopment Analysis (DEA) methodology for modeling of the assessment of the regional higher education systems effectiveness. The importance and topicality of this study is based on the increasing role of universities in the economic development of regions and countries in recent decades as well as the need to develop approaches for assessing the university effectiveness, and using mathematical models and methods for these goals. The novelty of the research is the formation of the DEA model and its application to the analysis of regional higher education systems’ effectiveness. The hypothesis of uneven development of regional higher education systems was tested from the standpoint of functional approach; the higher education systems’ effectiveness has been calculated and the ranking of Russian regions was performed by different DEA models. As a result of the DEA modeling, a quantitative effectiveness assessment was carried out, and a set of Russian regions was ranked according to three basic university functions: education, science, and regional partnership. Conclusions about the level of effectiveness and development strategy of regional higher education systems in Russia have been drawn.
SimCorrMix: Simulation of Correlated Data with Multiple Variable Types Including Continuous and Count Mixture Distributions

The SimCorrMix package generates correlated continuous (normal, non-normal, and mixture), binary, ordinal, and count (regular and zero-inflated, Poisson and Negative Binomial) variables that mimic real-world data sets. Continuous variables are simulated using either Fleishman’s thirdorder or Headrick’s fifth-order power method transformation. Simulation occurs at the component level for continuous mixture distributions, and the target correlation matrix is specified in terms of correlations with components. However, the package contains functions to approximate expected correlations with continuous mixture variables. There are two simulation pathways which calculate intermediate correlations involving count variables differently, increasing accuracy under a wide range of parameters. The package also provides functions to calculate cumulants of continuous mixture distributions, check parameter inputs, calculate feasible correlation boundaries, and summarize and plot simulated variables. SimCorrMix is an important addition to existing R simulation packages because it is the first to include continuous mixture and zero-inflated count variables in correlated data sets.
Performance Standards for Antimicrobial Susceptibility Testing; Eighteenth Informational Supplement

The supplemental information presented in this document is intended for use with the antimicrobial susceptibility testing procedures published in the following Clinical and Laboratory Standards Institute (CLSI) approved standards: M2-A9—Performance Standards for Antimicrobial Disk Susceptibility Tests; Approved Standard—Ninth Edition; and M7-A7—Methods for Dilution Antimicrobial Susceptibility Tests for Bacteria That Grow Aerobically; Approved Standard—Seventh Edition. The standards contain information about both disk (M2) and dilution (M7) test procedures for aerobic bacteria. Clinicians depend heavily on information from the clinical microbiology laboratory for treatment of their seriously ill patients. The clinical importance of antimicrobial susceptibility test results requires that these tests be done under optimal conditions and that laboratories have the capability to provide results for the newest antimicrobial agents. The tabular information presented here represents the most current information for drug selection, interpretation, and quality control using the procedures standardized in M2 and M7. Users should replace the tables published earlier with these new tables. (Changes in the tables since the most current edition appear in boldface type.) Clinical and Laboratory Standards Institute. Performance Standards for Antimicrobial Susceptibility Testing; Seventeenth Informational Supplement. CLSI document M100-S17 (ISBN 1-56238-625-5). Clinical and Laboratory Standards Institute, 940 West Valley Road, Suite 1400, Wayne, Pennsylvania 19087-1898 USA, 2007. The data in the interpretive tables in this supplement are valid only if the methodologies in M2-A9—Performance Standards for Antimicrobial Disk Susceptibility Tests; Approved Standard—Ninth Edition; and M7-A7—Methods for Dilution Antimicrobial Susceptibility Tests for Bacteria That Grow Aerobically; Approved Standard—Seventh Edition are followed. January 2007 M100-S17
Web-based Multi-layered Exploration of Annotated Image-based Shape and Material Models

We introduce a novel versatile approach for letting users explore detailed image-based shape and material models integrated with structured, spatially-associated descriptive information. We represent the objects of interest as a series of registered layers of image-based shape and material information. These layers are represented at multiple scales, and can come out of a variety of pipelines and include both RTI representations and spatially-varying normal and BRDF ﬁelds, eventually as a result of fusing multi-spectral data. An overlay image pyramid associates visual annotations to the various scales. The overlay pyramid of each layer can be easily authored at data preparation time using widely available image editing tools. At run-time, an annotated multi-layered dataset is made available to clients by a standard web server. Users can explore these datasets on a variety of devices, from mobile phones to large scale displays in museum installations, using JavaScript/WebGL2 clients capable to perform layer selection, interactive relighting and enhanced visualization, annotation display, and focus-and-context multiple-layer exploration using a lens metaphor. The capabilities of our approach are demonstrated on a variety of cultural heritage use cases involving different kinds of annotated surface and material models.
Nonzero-sum Adversarial Hypothesis Testing Games

We study nonzero-sum hypothesis testing games that arise in the context of adversarial classification, in both the Bayesian as well as the Neyman-Pearson frameworks. We first show that these games admit mixed strategy Nash equilibria, and then we examine some interesting concentration phenomena of these equilibria. Our main results are on the exponential rates of convergence of classification errors at equilibrium, which are analogous to the well-known Chernoff-Stein lemma and Chernoff information that describe the error exponents in the classical binary hypothesis testing problem, but with parameters derived from the adversarial model. The results are validated through numerical experiments.
Embedding Lexical Features via Tensor Decomposition for Small Sample Humor Recognition

We propose a novel tensor embedding method that can effectively extract lexical features for humor recognition. Specifically, we use word-word co-occurrence to encode the contextual content of documents, and then decompose the tensor to get corresponding vector representations. We show that this simple method can capture features of lexical humor effectively for continuous humor recognition. In particular, we achieve a distance of 0.887 on a global humor ranking task, comparable to the top performing systems from SemEval 2017 Task 6B (Potash et al., 2017) but without the need for any external training corpus. In addition, we further show that this approach is also beneficial for small sample humor recognition tasks through a semi-supervised label propagation procedure, which achieves about 0.7 accuracy on the 16000 One-Liners (Mihalcea and Strapparava, 2005) and Pun of the Day (Yang et al., 2015) humour classification datasets using only 10% of known labels.
Context Rich Hybrid Navigation Using WiFi and Geomagnetic Sensors in Smartphones and Map Generation Using Lidar

Navigation systems perform a huge role in traveling component of life. Most importantly it helps people to get to places even in foreign or unfamiliar environments. This research introduces a way of mapping environments with less effort, which shows that mapping an indoor environment is an easy task that could be performed by any tech savvy individual. This has been done by examining several projects and researches conducted by various personnel and organizations including NASA. It has become clear that the technology ‘LIDAR,’ is clearly feasible for the requirement of indoor map generation. A software was later built to accommodate the device which is built using LIDAR and to give the user a better experience in map generation. The software helps to overcome the limitations that are imposed by the device. The overall product with the device and software integrated provides an ideal low-budget solution for the users. The proposed system service features three highly desirable properties, namely accuracy, scalability, and crowdsourcing. IPS is implemented with a set of crowdsourcing-supportive mechanisms to handle the collective amount of raw data, filter incorrect user contributions and exploit Wi-Fi data from diverse mobile devices. Furthermore, it uses a big-data architecture for efficient storage and retrieval of localization and mapping data. In this research, the service relies on the sensitive data collected by smartphones (Wi-Fi signal strength and geomagnetic measurements) to deliver reliable indoor geolocation information.
Nonlinear Modified Concurrent Equalizer

Wireless and optical communication systems not infrequently are disrupted by intersymbol interference (ISI). Wireless communications are degraded by multipath propagation in the wireless channel, while optical communications are degraded due to chromatic and polarization mode dispersion. For both cases, equalization techniques are applied to circumvent the problem. In this context, this paper proposes a novel concurrent blind equalizer, based on the nonlinear modified constant modulus algorithm (NMCMA) and on the soft direct decision (SDD) algorithm. The proposed nonlinear modified concurrent equalizer (NMCE) combines the NMCMA sharp decision regions with the SDD fast convergence, resulting in improved performance. The NMCE is compared with the NMCMA and with the constant modulus algorithm CMA-SDD equalizers under static and dynamic multipath scenarios. Results show that the proposed solution presents fast convergence, lower steady-state mean squared error (MSE), and reduced symbol error rate (SER) when compared with the NMCMA and the CMA-SDD equalizers, even in dynamic propagation scenarios.