Miracle-dz/UniFAR-FTUs huggingface dataset:
Digital Dental X-Ray Image Segmentation and Feature Extraction

The process of analysis of such images is important in order to improve quantify medical imaging systems. It is significant to analysis the dental x-ray images we need features of image. In this paper we present a method for segmentation and feature extraction of dental x-ray images. The proposed method has been implemented by using level-set method for segmentation after image enhancement and illustrate contour for teeth to complete the segmentation step. Furthermore, we extracted multiple features of dental x-ray images using texture statistics techniques by gray-level co-occurrence matrix. Extracted data can perform to obtain the teeth measurements for automatic dental systems such human identification or dental diagnosis systems. Preparatory experiments show the significance of the proposed method to extract teeth from an x-ray image. DOI: http://dx.doi. org/10.11591/telkomnika.v11i6. 2655 Full Text: PDF
Approximate Strategic Reasoning through Hierarchical Reduction of Large Symmetric Games

To deal with exponential growth in the size of a game with the number of agents, we propose an approximation based on a hierarchy of reduced games. The reduced game achieves savings by restricting the number of agents playing any strategy to fixed multiples. We validate the idea through experiments on randomly generated local-effect games. An extended application to strategic reasoning about a complex trading scenario motivates the approach, and demonstrates methods for game-theoretic reasoning over incompletely-specified games at multiple levels of granularity. Motivation Consider the task of selecting among a large set of strategies to play in an 8-player game. Through careful judgment you manage to narrow down the candidates to a reasonable number of strategies (say 35). Because the performance of a strategy for one agent depends on the strategies of the other seven, you wish to undertake a game-theoretic analysis of the situation. Determining the payoff for a particular strategy profile is expensive, however, as your observations of prior game instances are quite limited, and the only operational description of the game is in the form of a simulator that takes a non-negligible time (say 10 minutes) to produce one outcome. Moreover, since the environment is stochastic, numerous samples (say 12) are required to produce a reliable estimate for even one profile. At two hours per profile, ex
A Reconfigurable Motor for Experimental Emulation of Stator Winding Inter-Turn and Broken Bar Faults in Polyphase Induction Machines

The advantages and demerits of a 5-hp reconfigurable induction motor, which was designed for experimental emulation of stator winding inter-turn and broken rotor bar faults, are presented in this paper. It was perceived that this motor has the potential of quick and easy reconfiguration to produce the desired stator and rotor faults in a variety of different fault combinations. Accordingly, this would eliminate the need to permanently destroy machine components such as stator windings or rotor bars when acquiring data from a faulty machine for fault diagnostic purposes. Experimental results under healthy and various faulty conditions will be presented in this paper, including issues associated with rotor bar-end ring contact resistances, to demonstrate the benefits and drawbacks of this motor for acquiring large amounts of fault signature data.
Fine-grained German Sentiment Analysis on Social Media

Expressing opinions and emotions on social media becomes a frequent activity in daily life. People express their opinions about various targets via social media and they are also interested to know about other opinions on the same target. Automatically identifying the sentiment of these texts and also the strength of the opinions is an enormous help for people and organizations who are willing to use this information for their goals. In this paper, we present a rule-based approach for German sentiment analysis. The proposed model provides a fine-grained annotation for German texts, which represents the sentiment strength of the input text using two scores: positive and negative. The scores show that if the text contains any positive or negative opinion as well as the strength of each positive and negative opinions. To this aim, a German opinion dictionary of 1,864 words is prepared and compared with other opinion dictionaries for German. We also introduce a new dataset for German sentiment analysis. The dataset contains 500 short texts from social media about German celebrities and is annotated by three annotators. The results show that the proposed unsupervised model outperforms the supervised machine learning techniques. Moreover, the new dictionary performs better than other German opinion dictionaries.
Under Consideration for Publication in Theory and Practice of Logic Programming Surviving Solver Sensitivity: an Asp Practitioner's Guide

Answer set programming (ASP) is a declarative programming formalism that allows a practitioner to specify a problem without describing an algorithm for solving it. In ASP, the tools for processing problem specifications are called answer set solvers. These systems often require significant computational effort to succeed. Furthermore, they offer different heuristics, expose numerous parameters, and it is well known that their running time is sensitive to the configuration used. Portfolio solvers and automatic algorithm configuration systems are recent attempts to automate the problem of manual parameter tuning, and to mitigate the burden of identifying the right solver configuration. The approaches taken in portfolio solvers and automatic algorithm configuration systems are orthogonal. This paper evaluates these approaches, separately and jointly, in the context of real-world ASP application development. It outlines strategies for their use in such settings, identifies their respective strengths and weaknesses, and advocates for a methodology that would make them an integral part of developing ASP applications.
Operon Conservation and the Evolution of trans-Splicing in the Phylum Nematoda

The nematode Caenorhabditis elegans is unique among model animals in that many of its genes are cotranscribed as polycistronic pre-mRNAs from operons. The mechanism by which these operonic transcripts are resolved into mature mRNAs includes trans-splicing to a family of SL2-like spliced leader exons. SL2-like spliced leaders are distinct from SL1, the major spliced leader in C. elegans and other nematode species. We surveyed five additional nematode species, representing three of the five major clades of the phylum Nematoda, for the presence of operons and the use of trans-spliced leaders in resolution of polycistronic pre-mRNAs. Conserved operons were found in Pristionchus pacificus, Nippostrongylus brasiliensis, Strongyloides ratti, Brugia malayi, and Ascaris suum. In nematodes closely related to the rhabditine C. elegans, a related family of SL2-like spliced leaders is used for operonic transcript resolution. However, in the tylenchine S. ratti operonic transcripts are resolved using a family of spliced leaders related to SL1. Non-operonic genes in S. ratti may also receive these SL1 variants. In the spirurine nematodes B. malayi and A. suum operonic transcripts are resolved using SL1. Mapping these phenotypes onto the robust molecular phylogeny for the Nematoda suggests that operons evolved before SL2-like spliced leaders, which are an evolutionary invention of the rhabditine lineage.
The effect of high-dose simvastatin on triglyceride-rich lipoprotein metabolism in patients with type 2 diabetes mellitus Published, JLR Papers in Press, October 28, 2005.

Statins decrease triglycerides (TGs) in addition to decreasing low density lipoprotein-cholesterol. Although the mechanism for the latter effect is well understood, it is still unclear how TG decrease is achieved with statin therapy. Because hypertriglyceridemia is common in obese patients with type 2 diabetes mellitus, we studied triglyceride-rich lipoprotein triglyceride (TRL-TG) turnover in 12 such subjects using stable isotopically labeled glycerol. The diabetic subjects were studied after 12 weeks of placebo and after a similar course of therapy with simvastatin (80 mg daily) in a single-blind design. The results were compared with those from six nonobese nondiabetic control subjects. Simvastatin therapy reduced serum TGs by 35% in the diabetic subjects. Compared with the control subjects, TRL-TG secretion was almost 2-fold higher in the diabetic subjects (45.4 ± 4.9 vs. 24.4 ± 1.9 μmol/min; P < 0.002) and was unaffected by simvastatin therapy. However, TRL-TG clearance was significantly increased in the diabetic subjects during simvastatin treatment compared with placebo (0.25 ± 0.03 vs. 0.16 ± 0.02 pools/h; P < 0.002). This change was accompanied by a 49% increase in preheparin plasma lipase activity (P < 0.03) and a 21% increase in postheparin LPL activity (P < 0.01). Together, these findings provide strong evidence that the effect of statins on serum TGs is related to an increase in LPL activity, resulting in accelerated delipidation of TRL particles. The effect of high-dose simvastatin on triglyceride-rich lipoprotein metabolism in patients with type 2 diabetes mellitus.
Analysis of Clustering Technique in Android Malware Detection

Mobile computing is an important field in information technology, because of the wide use of mobile devises and mobile applications. Clustering gives good results with information retrieval (IR), It aims to automatically put similar applications in one cluster. In this paper, we evaluate clustering techniques in Android applications. We explain how we can apply clustering techniques in malware detection of Android applications. We also use machine learning techniques in auto detection of malware applications in the Android market. Our evaluation is given by clustering two categories of Android applications: business, and tools. We have extracted 18,174 Android's application files in our evaluation using clustering. We extract the features of the applications from applications' XML-files which contains permissions requested by applications. The results gives a positive indication of using unsupervised machine learning techniques in malware detection in mobile applications using a combination of the application information and xml Android Manifest files.
Ischemic depolarization monitoring: evaluation of protein synthesis in the hippocampal CA1 after brief unilateral ischemia in a gerbil model.

OBJECT The authors investigate whether depolarization monitoring is an accurate index of ischemic damage in a gerbil model of unilateral ischemia and assess the effects of brief cerebral ischemia on protein synthesis in this model. METHODS The authors evaluate the relationship between the duration of ischemic depolarization caused by unilateral carotid artery occlusion and ischemia-induced neuronal damage in the CA1 subregion 7 days after ischemia. When the depolarization period exceeded 210 seconds, some neuronal damage was detected, and almost complete neuronal damage was observed when the period exceeded 400 seconds. Uptake of [14C]valine was evaluated in ischemic and nonischemic CA1 subregions. Disturbances in protein synthesis were seen in all animals subjected to sublethal ischemia (< or = 210-second depolarization) after a 10-minute recirculation, and after 2 and 6 hours of recirculation in animals with 90 seconds or more of depolarization. Inhibition of protein synthesis was proportional to the length of the depolarization period. After 1 and 3 days of recirculation, protein synthesis returned to near normal, and some animals with depolarizations greater than 180 to 210 seconds showed an increase in protein synthesis. Protein synthesis in all animals returned to normal levels after 7 days of recirculation. CONCLUSIONS In this study the authors demonstrate that monitoring of ischemic depolarization is a useful method to predict neuronal damage in the hippocampal CA1 in this model, and they identify subtle changes in protein synthesis after brief ischemia. Sublethal ischemia was divided into three categories by its depolarization period (< 90 seconds, 90-180 seconds, and > 180-210 seconds) with regard to changes in protein synthesis.
An evaluation of dynamic object tracking with 3D LIDAR

This paper evaluates experimentally approaches to tracking dynamic objects using 3D LIDAR data such as that obtained from a Velodyne HDL-64. Whilst many algorithms exist for tracking objects in 2D laser data, only recently have algorithms which model the 3D appearance of tracked objects been proposed. The benets of these algorithms with respect to model free centroid based methods have not been fully examined, and therefore the key contribution of this paper is to analyse the behaviour of dierent implementations of these two broad approaches. Through a quantied comparison of simple tracking methods for 3D LIDAR data validated against manually labelled tracks it was found that centroid tracking outperformed 3D appearance model based techniques. In the experiments, which focused upon pedestrian tracking in particular, centroid tracking achieved up to 95% correct data associations compared to 60% for the appearance model based methods.
Probabilistic multi-class multi-kernel learning: on protein fold recognition and remote homology detection

MOTIVATION The problems of protein fold recognition and remote homology detection have recently attracted a great deal of interest as they represent challenging multi-feature multi-class problems for which modern pattern recognition methods achieve only modest levels of performance. As with many pattern recognition problems, there are multiple feature spaces or groups of attributes available, such as global characteristics like the amino-acid composition (C), predicted secondary structure (S), hydrophobicity (H), van der Waals volume (V), polarity (P), polarizability (Z), as well as attributes derived from local sequence alignment such as the Smith-Waterman scores. This raises the need for a classification method that is able to assess the contribution of these potentially heterogeneous object descriptors while utilizing such information to improve predictive performance. To that end, we offer a single multi-class kernel machine that informatively combines the available feature groups and, as is demonstrated in this article, is able to provide the state-of-the-art in performance accuracy on the fold recognition problem. Furthermore, the proposed approach provides some insight by assessing the significance of recently introduced protein features and string kernels. The proposed method is well-founded within a Bayesian hierarchical framework and a variational Bayes approximation is derived which allows for efficient CPU processing times. RESULTS The best performance which we report on the SCOP PDB-40D benchmark data-set is a 70% accuracy by combining all the available feature groups from global protein characteristics but also including sequence-alignment features. We offer an 8% improvement on the best reported performance that combines multi-class k-nn classifiers while at the same time reducing computational costs and assessing the predictive power of the various available features. Furthermore, we examine the performance of our methodology on the SCOP 1.53 benchmark data-set that simulates remote homology detection and examine the combination of various state-of-the-art string kernels that have recently been proposed.
Go Beyond Raw Trajectory Data: Quality and Semantics

Past decades have witnessed extensive studies from both academia and industries over trajectory data, which are generated from a diverse range of applications. Existing literature mainly focuses on raw trajectories with spatio-temporal features such as location, time, speed, direction and so on. Recently, the pervasive use of smart mobile devices like smart phones, watches and bands have brought about more generation of trajectory by personal users (instead of companies or organizations) and from online space (instead of physical space), where individuals can decide when and where to log on and share their locations with others. The more discentralized and contextualized trajectory sources have brought some unique challenges for database management with respect to the quality and semantics of trajectories data. With more applications and services relying on trajectory data analysis, it is necessary for us to think about how these new issues will affect the traditional way that trajectories are digested and processed. In this paper we will elaborate on these challenges and introduce our recent progress in the respective directions. The message we try to deliver is that raw trajectories themselves no longer satisfy the requirement of today’s mainstream applications. To really release the power of trajectory-based applications, we should go beyond the raw trajectory data by enhancing their quality and semantics, which calls for novel computing architectures, paradigms and algorithms with sufficient capabilities to manage and analyse the enhanced trajectory data.
Evolution and Development of Neural Networks Controlling Locomotion, Gradient-Following, and Obstacle-Avoidance in Artificial Insects

This paper describes how the SGOCE paradigm has been used to evolve developmental programs capable of generating neural networks that control the behavior of simulated insects. This paradigm is characterized by an encoding scheme, by an evolutionary algorithm, and by an incremental strategy that are described in turn. The additional use of an insect model equipped with 6 legs and two antennae made it possible to generate control modules that allowed to successively add gradient-following and obstacle-avoidance capacities to walking behavior. The advantages of this evolutionary approach, together with directions for future work, are discussed. Keywords— SGOCE Paradigm, Recurrent Neural Networks, Leaky Integrators, Genetic Programming, Animats.
Improving Window Manipulation and Content Interaction on High-Resolution, Wall-Sized Displays

Interaction with high-resolution wall-sized (Powerwall) displays can be a tedious and difficult task due to large display areas and small target sizes. To overcome this, we developed techniques that reduce the precision required to manipulate windows and select data. The manipulation layer speeds up the common tasks of moving and resizing application windows by overlaying them with large, transparent target areas. The Power-Lens magnifies target sizes by automatically appearing once the cursor reaches the region of interest. Two experiments evaluated these techniques against conventional desktop-style interfaces. Experiment 1 showed the window manipulation layer to speed up the tasks of moving and resizing a window by 24% and 27%, respectively. Experiment 2 showed the Power-Lens to speed up the selection of 5 × 5 pixel targets by 18%. Together, our new techniques help to make interaction more fluid on Powerwall displays.
Robust Estimation for Discrete-Time Markovian Jump Linear Systems

This technical note deals with recursive robust estimation of Markov jump linear systems subject to unobserved chain state. It is developed an augmented system modeled via norm bounded uncertainties. Upper and lower bounds of an uncertain quadratic cost function are computed in order to define the variances of the augmented system. Numerical examples are provided to show the effectiveness of the proposed estimators.
The determination of absorbed dose in a proton beam for purposes of charged-particle radiation therapy.

Four methods are described by which absorbed dose has been measured in a proton beam extracted from the 160-MeV Harvard cyclotron. The standard dosimetry, used to determine doses for patient treatments, is based upon an absolute measurement of particle flux using a Faraday cup. Measurements have also been made using a parallel-plate ionization chamber; a thimble ionization chamber carying a /sup 60/Co calibration traceable to NBS; and a tissue-equivalent calorimeter. The calorimeter, which provides an independent check of the dosimetry, agreed with the standard dosimetry at five widely different depths within a range from 0.8 to 2.6%.
Geometric Dirichlet Means Algorithm for topic inference

We propose a geometric algorithm for topic learning and inference that is built on the convex geometry of topics arising from the Latent Dirichlet Allocation (LDA) model and its nonparametric extensions. To this end we study the optimization of a geometric loss function, which is a surrogate to the LDA's likelihood. Our method involves a fast optimization based weighted clustering procedure augmented with geometric corrections, which overcomes the computational and statistical inefficiencies encountered by other techniques based on Gibbs sampling and variational inference, while achieving the accuracy comparable to that of a Gibbs sampler. The topic estimates produced by our method are shown to be statistically consistent under some conditions. The algorithm is evaluated with extensive experiments on simulated and real data.
Campus Sexual Assault: A Systematic Review of Prevalence Research From 2000 to 2015

Sexual assault is a pervasive problem on university and college campuses in the United States that has garnered growing national attention, particularly in the past year. This is the first study to systematically review and synthesize prevalence findings from studies on campus sexual assault (CSA) published since 2000 (n = 34). The range of prevalence findings for specific forms of sexual victimization on college campuses (i.e., forcible rape, unwanted sexual contact, incapacitated rape, sexual coercion, and studies’ broad definitions of CSA/rape) is provided, and methodological strengths and limitations in the empirical body of research on CSA are discussed. Prevalence findings, research design, methodology, sampling techniques, and measures, including the forms of sexual victimization measured, are presented and evaluated across studies. Findings suggest that unwanted sexual contact appears to be most prevalent on college campuses, including sexual coercion, followed by incapacitated rape, and completed or attempted forcible rape. Additionally, several studies measured broad constructs of sexual assault that typically include combined forms of college-based sexual victimization (i.e., forcible completed or attempted rape, unwanted sexual contact, and/or sexual coercion). Extensive variability exists within findings for each type of sexual victimization measured, including those that broadly measure sexual assault, which is largely explained by differences in sampling strategies and overall study designs as well as measures of sexual assault used in studies. Implications for findings and recommendations for future research on the prevalence of college-based sexual victimization are provided.
Framework for patient-ventilator asynchrony during long-term non-invasive ventilation

Episodes of patient-ventilator asynchrony (PVA) occur during acute and chronic non-invasive positive pressure ventilation (NIV). In long-term NIV, description and quantification of PVA is not standardised, thus limiting assessment of its clinical impact. The present report provides a framework for a systematic analysis of polygraphic recordings of patients under NIV for the detection and classification of PVA validated by bench testing. The algorithm described uses two different time windows: rate asynchrony and intracycle asynchrony. This approach should facilitate further studies on prevalence and clinical impact of PVA in long-term NIV.