Miracle-dz/UniFAR-FTUs huggingface dataset:
Selection of Mother Wavelet for Image Compression on Basis of Image

Recently discrete wavelet transform and wavelet packet has emerged as popular techniques for image compression. This paper compares compression performance of Daubechies, Biorthogonal, Coiflets and other wavelets along with results for different frequency images. Based on the result, we propose that proper selection of mother wavelet on the basis of nature of images and improve the quality and compression ratio remarkably
Impact of neuropathy on the adherence to diabetes-related self-care activities: a cross-sectional study

Purpose The purpose of this study was to evaluate the impact of the presence and severity of neuropathy and depression on the patient’s adherence to diabetes-related self-care activities (DRSCA) in a cohort of patients with type 2 diabetes mellitus (T2DM). Patients and methods In this cross-sectional, noninterventional study, 198 patients with T2DM were enrolled according to a population-based, consecutive-case enrollment principle. In all patients, the adherence to DRSCA was evaluated using the Summary of Diabetes Self-Care Activities (SDSCA) questionnaire; a higher SDSCA score is associated with a better adherence. The presence and severity of neuropathy was assessed using the Michigan Neuropathy Screening Instrument (MNSI) and the severity of depression using the Patient Health Questionnaire-9 (PHQ-9). Results The presence of neuropathy was associated with a decreased SDSCA score (26 points vs 37 points; P<0.001), an increased severe depression prevalence (24.7% vs 4.3%; P<0.001), and an increased PHQ-9 score (12 points vs 7 points; P<0.001). The MNSI score was reverse correlated with SDSCA score (r=−0.527; P<0.001) and positively correlated with PHQ-9 score (r=0.495; P<0.001). The reverse correlation between MNSI score and SDSCA score was present for all the subcomponents of SDSCA questionnaire (diet, exercise, glycemic monitoring, and foot care). Conclusion The presence of neuropathy is associated with decreases in the quality of adherence to DRSCA in patients with T2DM and with increases in the symptomatology of depression. The significant, negative association between the severity of T2DM and the quality of disease self-management points to a possible loop-type relationship between these two components, being possible a reciprocal augmentation with negative consequences on the global management of the disease.
Genetic Algorithm Applications to Stochastic Thermal Power Dispatch

This paper presents a genetic algorithm (GA) based effective method for the optimal scheduling of thermal generation incorporating the uncertainties in the system production cost data. A stochastic model of system production cost equation is formulated, with production cost coefficients and generator outputs as random variables. Minimization of total operating cost for thermal units in the system subjected to recognized constrains is solved using simple genetic algorithm (SGA). The advantages of this method lies in its faster convergence towards the global solution. The algorithm gives fairly accurate results. The effectiveness of the method has been demonstrated by analyzing sample systems and the results are presented.
Peroxisomal Β-oxidation—a Metabolic Pathway with Multiple Functions

Fatty acid degradation in most organisms occurs primarily via the β-oxidation cycle. In mammals, β-oxidation occurs in both mitochondria and peroxisomes, whereas plants and most fungi harbor the β-oxidation cycle only in the peroxisomes. Although several of the enzymes participating in this pathway in both organelles are similar, some distinct physiological roles have been uncovered. Recent advances in the structural elucidation of numerous mammalian and yeast enzymes involved in β-oxidation have shed light on the basis of the substrate specificity for several of them. Of particular interest is the structural organization and function of the type 1 and 2 multifunctional enzyme (MFE-1 and MFE-2), two enzymes evolutionarily distant yet catalyzing the same overall enzymatic reactions but via opposite stereochemistry. New data on the physiological roles of the various enzymes participating in β-oxidation have been gathered through the analysis of knockout mutants in plants, yeast and animals, as well as by the use of polyhydroxyalkanoate synthesis from β-oxidation intermediates as a tool to study carbon flux through the pathway. In plants, both forward and reverse genetics performed on the model plant Arabidopsis thaliana have revealed novel roles for β-oxidation in the germination process that is independent of the generation of carbohydrates for growth, as well as in embryo and flower development, and the generation of the phytohormone indole-3-acetic acid and the signal molecule jasmonic acid.
Using hardware classification to improve PC-based OpenFlow switching

OpenFlow is a promising technology that offers a flexible flow-based forwarding scheme based on multiple fields in the packet headers of different protocol layers. It provides a feasibility to adopt new network protocols and innovations thanks to a more flexible forwarding compared to traditional IP forwarding. In this paper, we propose an architectural design to improve lookup performance of PC-based OpenFlow switching in Linux using a standard commodity network interface card based on the Intel 82599 Gigabit Ethernet controller. We introduce a fast data path based on caching of flow table entries in on-board classification hardware on the NIC. We describe our design, and we present an experimental evaluation of its performance. Our results show that the throughput of our fast path gives over 40 percent higher throughput compared to the regular software-based OpenFlow switching.
Development of 416kHz PZT driver for acoustics energy transfer applications

The power transfer efficiency and power transfer capability are the most crucial performance indicator in acoustic energy transfer (AET) applications. As overall efficiency is dependent on the power conditioning at the transmitter and receiver side, it is required to develop a high efficiency power amplifier on the transmitter side in addition with a high efficiency power processor at the receiving end. This paper developed the complete PZT driver with π1a matching impedance circuit that applied to the transmitter unit of the AET system in order to transmit 80mW power on the operating frequency of 416kHz. The Proteus Software used as a simulation platform with peripheral interface controller (PIC16F877A) as pulse width modulation signal generator for MOSFET IRF510 gate in the circuit. As the promising result, the developed transmitter unit shown 73.68mW at 416kHz with 92.1% power conversion efficiency.
Music selectivity in the cortex is independent of extensive musical training

Speech and music perception are core functions of the human auditory system, but we know very little about the neural systems that process these complex auditory signals. While speech selectivity is an established feature of non-primary auditory cortex, clear neural selectivity for music has only recently been demonstrated (Norman-Haignere, Kanwisher, & McDermott, 2015). Here we ask whether music selectivity in the cortex requires extensive musical training, or whether it is present even in individuals with minimal musical training. To answer this question, we scanned 10 people with extensive musical training and 10 with almost none, and used the voxel decomposition methods of Norman-Haignere et al. (2015) to test whether the magnitude or anatomical extent of musicselective neural populations is influenced by musical training. Overall, we found no major differences in the patterns of fMRI responses to music stimuli between musicians and non-musicians, suggesting that music selectivity in the brain is not dependent on extensive musical training. These data raise the possibility that music selective brain responses could be a universal property of human auditory cortex.
Sentiment detection and visualization of Chinese micro-blog

Micro-blog has been increasingly used for the public to express their opinions, and for organisations to detect public sentiment about social events. In contrast to the effort and progress made in English-based micro-blog analysis, research on Chinese micro-blog received relatively little attention. In this paper we examine and identify the key problems of this field, focusing particularly on the characteristics of innovative words, emoticon elements and hierarchical structure of Chinese “Weibo”. Based on the analysis we propose and develop associated theoretical and technological methods to address these problems. These include the development of new sentiment word mining method based on three wording standards and point-wise metrics, a rule set model for analyzing sentiment features of different linguistic components, and the corresponding methodology for calculating sentiment on multi-granularity considering emoticon elements. We use original Chinese tweets from a dataset of Sina Weibo to test and evaluate our new word discovery and sentiment detection methods. Initial results show that our new diction can improve sentiment detection, and demonstrate that our multi-level rule set method is more effective by giving 10.2% and 1.5% higher average accuracy than two existing methods for Chinese micro-blog sentiment analysis. In addition, we exploit visualisation techniques to study the relationships between online sentiment and real life, which can help depict the correlation between public emotions and events.
Relation Extraction with Weak Supervision and Distributional Semantics

Abstract : Relation Extraction aims at detecting and categorizing semantic relations between pairs of entities in unstructured text. It benefits an enormous number of applications such as Web search and Question Answering. Traditional approaches for relation extraction either rely on learning from a large number of accurate human-labeled examples or pattern matching with hand-crafted rules. These resources are very laborious to obtain and can only be applied to a narrow set of target types of interest. This dissertation focuses on learning relations with little or no human supervision. First, we examine the approach that treats relation extraction as a supervised learning problem. We develop an algorithm that is able to train a model with approximately 1/3 of the human-annotation cost and that matches the performance of models trained with high-quality annotation. Second, we investigate distant supervision, a weakly supervised algorithm that automatically generates its own labeled training data. We develop a latent Bayesian framework for this purpose. By using a model which provides a better approximation of the weak source of supervision, it outperforms the state-of-the-art methods. Finally, we investigate the possibility of building all relational tables beforehand with an unsupervised relation extraction algorithm. We develope an effective yet efficient algorithm that combines the power of various semantic resources that are automatically mined from a corpus based on distributional semantics. The algorithm is able to extract a very large set of relations from the web at high precision.
Visual Prompts and Graphical Design: A Framework for Exploring the Design Space of 2-D Charts and Graphs

Graphical presentations can be very effective in communicating large datasets and patterns, trends and relationships in them. Charts and graphs used in reporting data usually tend to highlight certain aspects and suppress others. In fact, a recent study of several hundred annual reports found that more than 30% of charts in these reports were designed to facilitate inferences favorable to the companies while hindering others. Unfortunately, many of the techniques used to achieve these effects may not be obvious to the average user. One solution to this problem is to make design choices explicit to the user. This paper presents a data analysis interface that educates users by enabling them to explore the visualization space and modifying chart design parameters. This interface is based on an analysis of a corpus of charts and graphs and uses knowledge about a variety of techniques for emphasizing specific trends and/or values shown in 2-D charts and graphs.
Direct manipulation of programming: How should we design interfaces?

ion. There is a strong tendency to apply these notions to any problem area that we tackle, even if they are not appropriate. Our programming notations tend to be linear in nature, there is one thread of control through the entire program. In user interfaces there are typically multiple threads of control. This is very hard to describe in a textual notation. Many claims have been made for the textual description of user interfaces. Textual notations are more powerful than the existing graphical notations (a general programming language can do anything that is computable). There are user interfaces that can be described by programs that cannot be produced by existing graphical techniques. But, do we need this generality, and will it be effectively used? The problem with using programming languages is that vast amounts of code must be written in order to produce the user interface. Thousands of lines of code may be required to produce a user interface that could be constructed in 30 minutes with a graphical tool. Recently rule based techniques have been proposed as a way of designing user interfaces. These techniques can be characterized as a textual description with a high level of abstraction. This is a very nice approach (otherwise 1 wouldn’t be using it), since it allows us to capture some of the expertise of good designers. We can develop rules for the selection of interaction techniques, details of their graphicalion. This is a very nice approach (otherwise 1 wouldn’t be using it), since it allows us to capture some of the expertise of good designers. We can develop rules for the selection of interaction techniques, details of their graphical presentation (fonts and line styles, for example), and their placement on the screen. This removes control from the user interface designer, since he or she has no control over the rules that fire in the design of the user interface. It could be argued that the designer could edit the rule base in order to have some control over the design process, but this is not a viable solution. A realistic rule base will have a large number of rules (thousands? ) and there will be subtle interactions between these rules. As a result. , it may be very difficult to modify the rule base without introducing undesirable side
Vector quantization, density estimation and outlier detection on cricket dataset

This study aims to apply unsupervised machine learning algorithms on Cricket players' career statistics dataset. K-means clustering algorithm is used to find the natural grouping that exists within the cricket players using player's batting average, strike rate, bowling average, economy etc. as input features - in this case players are grouped into 3 groups. Further separate probability density models are fitted for batsmen, bowlers and all-rounding players using appropriate player's performance metrics as input features and using these models, outstanding players are identified. Similar method is used to identify match winning players, where the differences between player's performance metrics and team's average performance metrics are used as input features. The results obtained from this study seem to correlate with expert generated results where they used point based system to rank the players. This kind of statistical analysis of sports data plays a vital role in team planning and exploiting opponents' weakness.
Application of MCMC Algorithm in Interest Rate Modeling

Interest rate modeling is a challenging but important problem in financial econometrics. This work is concerned with the parameter estimation of the short term interest models. In light of a recent development in Markov Chain Monte Carlo simulation techniques based on Gibbs sampling, numerical experimentations are carried out for finding an effective and convergent Beyesian estimation scheme. The optimal degree of data augmentation is probed on basis of sensitivity analysis in searching of maximum A-posteriori probability density. Our method is calibrated with both US Treasury bills and basic loan rates from Japanese market.
Assessment of the Risk Factors of Coronary Heart Events Based on Data Mining With Decision Trees

Coronary heart disease (CHD) is one of the major causes of disability in adults as well as one of the main causes of death in the developed countries. Although significant progress has been made in the diagnosis and treatment of CHD, further investigation is still needed. The objective of this study was to develop a data-mining system for the assessment of heart event-related risk factors targeting in the reduction of CHD events. The risk factors investigated were: 1) before the event: a) nonmodifiable-age, sex, and family history for premature CHD, b) modifiable-smoking before the event, history of hypertension, and history of diabetes; and 2) after the event: modifiable-smoking after the event, systolic blood pressure, diastolic blood pressure, total cholesterol, high-density lipoprotein, low-density lipoprotein, triglycerides, and glucose. The events investigated were: myocardial infarction (MI), percutaneous coronary intervention (PCI), and coronary artery bypass graft surgery (CABG). A total of 528 cases were collected from the Paphos district in Cyprus, most of them with more than one event. Data-mining analysis was carried out using the C4.5 decision tree algorithm for the aforementioned three events using five different splitting criteria. The most important risk factors, as extracted from the classification rules analysis were: 1) for MI, age, smoking, and history of hypertension; 2) for PCI, family history, history of hypertension, and history of diabetes; and 3) for CABG, age, history of hypertension, and smoking. Most of these risk factors were also extracted by other investigators. The highest percentages of correct classifications achieved were 66%, 75%, and 75% for the MI, PCI, and CABG models, respectively. It is anticipated that data mining could help in the identification of high and low risk subgroups of subjects, a decisive factor for the selection of therapy, i.e., medical or surgical. However, further investigation with larger datasets is still needed.
Noise sources and calculation techniques for switched capacitor filters

The noise response of switched capacitor networks (SCNs) is reviewed with emphasis on simplifying approximations suitable for SPICE noise simulation. The techniques developed cover all op-amp noise sources, as well as capacitor switching noise. The close agreement between predicted and measured noise responses for several monolithic SCNs bears out the validity of these simulation techniques.
An Input-Output Construction of Finite State  $\rho/\mu$ Approximations for Control Design

We consider discrete-time plants that interact with their controllers via fixed discrete alphabets. For this class of systems, and in the absence of exogenous inputs, we propose a general, methodical approach for constructing a sequence of finite state approximate models starting from finite length sequences of input and output signal pairs. We explicitly derive conditions under which the proposed construct, used in conjunction with a particular generalized structure, satisfies desirable properties of ρ/μ approximations thereby leading to nominal deterministic finite state machine models that can be used in certified-by-design controller synthesis. We also show that the cardinality of the minimal disturbance alphabet that can be used in this setting equals that of the sensor output alphabet. Finally, we show that the proposed construct satisfies a relevant semi-completeness property.
Sampling-Based Motion Planning Using Uncertain Knowledge

Sampling-based algorithms have dramatically improved the state of the art in robotic motion planning. However, these approaches still make significant assumptions that limit their applicability for real-world planning. This work describes how one of these assumptions: that the world is perfectly known, can be removed. We propose a predictive roadmap planner that incorporates uncertainty directly into the planning process. This enables the planner to identify configuration space paths that minimize the risk due to uncertainty and, when necessary, directs sensing to reduce uncertainty. Experimental results in several domains indicate that the predictive roadmap is adept at planning despite uncertainty in its perception of the workspace.
Modular-Based Classifier for Phoneme Recognition

This paper proposes a modular-based classifier for the problem of phoneme recognition. This is carried out by the use of a two-level classification approach including, high and low levels. We propose a new concept called phoneme family. To obtain phoneme families, we employ k-mean clustering method. A given unknown phoneme is first classified into a phoneme family at high level classification. Then, the exact label of the phoneme is determined at low level classification. We have used a combined framework of statistical and neural network based classifiers. Encouraging results are obtained by applying the proposed method on TIMIT database and its performance is compared against other methods
Increased fetal nuchal translucency at 11–14 weeks

Nuchal translucency (NT) is the sonographic appearance of a subcutaneous collection of fluid behind the fetal neck. The measurement of fetal NT thickness at the 11–14‐week scan has been combined with maternal age to provide an effective method of screening for trisomy 21; for an invasive testing rate of 5%, about 75% of trisomic pregnancies can be identified. When maternal serum free‐β human chorionic gonadotrophin (β‐hCG) and pregnancy‐associated plasma protein‐A (PAPP‐A) at 11–14 weeks are also taken into account, the detection rate of chromosomal defects is about 90%. Increased NT can also identify a high proportion of other chromosomal abnormalities and is associated with major defects of the heart and great arteries, and a wide range of skeletal dysplasias and genetic syndromes. In monochorionic twins, discordancy for increased NT is an early marker of twin‐to‐twin transfusion syndrome (TTTS). As with the introduction of any new technology into routine clinical practice, it is essential that those undertaking the 11–14‐week scan are adequately trained and their results are subjected to rigorous audit. Copyright © 2002 John Wiley & Sons, Ltd.
On the application of genetic algorithms for test case prioritization: a systematic literature review

We conducted a Systematic Literature Review (SLR) to investigate the effectiveness of genetic algorithms for test case prioritization. The search string retrieved 120 test case prioritization papers, but after we read them in full, we identified that genetic algorithm was used in seven primary studies. One paper does not provide any experimental data. Based on the results of these six papers, we conclude that there is evidence that genetic algorithm-based techniques are effective for test case prioritization and the field is still open for further research.
Resource Reservation Protocol

to obtain differing qualities of service (QoS) for their data flows. Such a capability recognizes that different applications have different network performance requirements. Some applications, including the more traditional interactive and batch applications, require reliable delivery of data but do not impose any stringent requirements for the timeliness of delivery. Newer application types, including videoconferencing, IP telephony, and other forms of multimedia communications require almost the exact opposite: Data delivery must be timely but not necessarily reliable. Thus, RSVP was intended to provide IP networks with the capability to support the divergent performance requirements of differing application types.