Miracle-dz/UniFAR-FTUs huggingface dataset:
An Integrated Relevance Feedback Method for CBIR using Histogram Values , Texture Descriptor and Interactive Boosting

Image retrieval is an important topic in the field of pattern recognition and artificial intelligence. There are three categories of image retrieval methods: text-based, content-based and semantic-based. In CBIR, images are indexed by their visual content, such as color, texture and shapes. Color and Texture information have been the primitive image descriptors in content based image retrieval systems. Many content-based image retrieval applications suffer from small sample set and high dimensionality problems. Relevance feedback is often used to alleviate those problems. In this paper, an integrating Relevance feedback for content based image retrieval based method is proposed for image mining based on analysis of color Histogram values and texture descriptor of an image and a novel interactive boosting framework to integrate user feedback into boosting scheme and bridge the gap between high-level semantic concept and low-level image features. For this purpose, three functions are used for texture descriptor analysis such as entropy, local range and standard deviation. To extract the color properties of an image, histogram values are used. The combination of the color and texture features of the image provides a robust feature set for image retrieval. Our method has advantage over the classic relevance feedback method in that the classifiers are trained to pay more attention to wrongfully predicted samples in user feedback through a reinforcement training process. It achieves more performance improvement from the relevance feedback than AdaBoost does because human judgment is accumulated iteratively to facilitate learning process.
Discrete-Time Block Models for Transmission Line Channels: Static and Doubly Selective Cases

Most methodologies for modeling Transmission Line (TL) based channels define the input-output relationship in the frequency domain (FD) and handle the TL resorting to a two-port network (2PN) formalism. These techniques have not yet been formally mapped into a discrete-time (DT) block model, which is useful to simulate and estimate the channel response as well as to design optimal precoding strategies. TL methods also fall short when they are applied to Time Varying (TV) systems, such as the power line channel. The objective of this paper is to establish if and how one can introduce a DT block model for the Power Line Channel. We prove that it is possible to use Lifting and Trailing Zeros (L&TZ) techniques to derive a DT block model that maps the TL-based input-output description directly in the time domain (TD) block channel model. More specifically, we find an interesting relationship between the elements of an ABCD matrix, defined in the FD, and filtering kernels that allow an elegant representation of the channel in the TD. The same formalism is valid for both the Linear Time Invariant (LTI) and the Linear TV (LTV) cases, and bridges communications and signal processing methodologies with circuits and systems analysis tools.
Self-Adaptive Artificial Neural Network for Change-Detection of Land Cover : An Unsupervised Approach

One of the main problems related to unsupervised change detection methods based on the “difference image” lies in the lack of efficient automatic techniques for discriminating between changed and unchanged pixels in the difference image. Such discrimination is usually performed by using empirical strategies or manual trial-and-error procedures, which affect both the accuracy and the reliability of the change-detection process. To overcome such drawbacks, in this paper, we propose an automatic technique for the analysis of the difference image. Such technique allows the automatic selection of the decision threshold. Due to the variability on background, different change types, and different light conditions and noise in images, the difference image, segmentation based on a single threshold usually performs poorly. To improve the segmentation we used a variable threshold. The threshold was adapted for each pixel of the difference image, through a self-adaptive network based on an unsupervised artificial neural network classifier. The adaptive system approach is attractive for classifications tasks due to its self-organizing, generalizable, and fault-tolerant characteristics. In contrast to the supervised neural network, the adaptive system does not rely on user-defined training data. The desired response is guided by an internal mechanism designed to solve the specific classification problem. The adaptive systems can be classified in terms of open-loop or closed-loop adaptation. The closed-loop systems has proven to be more powerful, especially for nonlinear process. We used closed-loop adaptation, based on both input and feedback from the output. The self-adaptive classifier employs a variable threshold in conjunction with an adaptation algorithm to segment a difference image. The classification is achieved through an iterative process in which the expected input is estimated from the system output and compared to the actual input. The comparison produces an error signal that controls the thresholding parameter. Experimental results confirm the effectiveness of proposed technique. Key-Words: Self-adaptive classifiers, unsupervised change-detection, neural networks, image processing, multitemporal images, remote sensing.
Automatic Framework for Semantic Search Engine Evaluation

The new generation of semantic search engines tends to exploit more and deeper semantic content of documents and queries. As the WEB is emerging to Semantic Web, a new evaluation method is needed for improving the current Information Retrieval Systems. The upcoming generation of Information Retrieval Systems are more document content centric and it has been registered many developments in this field. The evaluation of search engines is crucial for its development between versioning, and shows the right direction to be followed for improvement. Evaluating search engines requires considerable human effort, as large collection sizes demand deep judgments. Manually evaluating search engines is expansive and subjective. In practice it is common to evaluate only on small data sets. Evaluation on small data sets does not cover many aspects of a search engine. In last years it has been increased the interest in automatic or semi-automatic systems for evaluation for its low cost. Previous works on search engine evaluation focused more on its effectiveness, reliability and performance. In this work a new approach for automatic evaluation of Semantic Search Engines is proposed. The proposed evaluation framework takes into account rather the content of the document then the link between the query and the judged document. In the dynamic environment as the WEB, the contents of documents are in a continuous change, and the links between query and right document is in a change as well. The proposed framework evaluates the actual content of the documents and is designed to be flexible, robust, and easy to apply to any type of search engine, independent of the indexed corpus.
An iteratively reweighted method for recovery of block-sparse signal with unknown block partition

In this paper, a new iteratively reweighted least squares method is proposed for recovery of block-sparse signals with unknown cluster patterns. In many practical applications, sparse signals have block-sparse structures with nonzero coefficients occurring in clusters, while the prior information of the cluster pattern is usually unavailable. To address this issue, we propose an element-overlapping log-sum functional to encourage the sparseness and the cluster pattern simultaneously. The algorithm is developed by iteratively minimizing a convex surrogate function that majorizes the original objective function, which results in an iteratively reweighted process that alternates between estimating the sparse signal and refining the weights of the surrogate function. Convergence of the iterations to a local minimum of the penalty function is also guaranteed. Numerical results are provided to illustrate the effectiveness of the proposed method.
Analysis technique of a wearable IoT health information on the MAPHIS

Background/Objectives: As u-health becomes common that monitors body condition real time in the ubiquitous environment, people are increasingly interested in promoting their health using biometric information identified by various health equipment.Methods/Statistical analysis: the concept of digital health has emerged that encompasses the followings: u-health that is expected to improve efficiency in medical service and monitor patients’ condition with wireless communication through convergence of ICT and health care industry; smart health(s-health) that manages their own workout, calorie intake and sporting activities with their smart device; and, mobile health (m-health) that uses wearable and mobile devices as a means of healthcare.Findings: In this study, we aim to develop a health care platform that receives diabetes information generated from various IoT based on remote inputs, stores, analyzes, processes and provides visualized information. The purpose of this study is to develop and test IoT-based diabetes health big-data platform for diabetes mellitus patients. To achieve this goal, we suggest the development result of service and contents oriented “An IoT-based diabetes health big-data Offloading platform” that comprehensively manages healthcare products created in many IoT-based diabetes information to build a personal health management system. We also developed android 4.x-based application so that the health management service and contents provided by a third party can be checked with the client PC as well as health management service and contents offered by web-based client application and third party can be operated in the mobile environment such as smartphone or tablet.Improvements/Applications: The results of this study are verified by applying it to patients with diabetes or suspected cases. In order to increase the efficiency of real-time processing, we used off-loading technology to utilize big data related to diabetes generated from wearable IoT device. The results of this study will be used for telemedicine in two hospitals in Malaysia after various laboratory verification procedures.
Some Aspects of Coherence, Genre and Rhetorical Structure – and Their Integration in a Generic Model of Text

Texts appear as syntagms, sequences of syntactically defined units (clauses, clause complexes, sentences), while, at the same time, forming a unified whole. The relation between the microand macro-levels of textual organization as well as the functioning of texts in their social context has been addressed by various models. Three such approaches will be discussed in this essay, associated with three different linguistic traditions: coherence analysis, genre theory, and rhetoric. I will argue that, despite their diverse origins, they are not incompatible and can be combined, representing, in fact, three aspects of textuality. Integration of these three aspects leads to the formulation of a generic coherence model, some important characteristics and requirements of which are discussed in the final part of this paper.
Design criteria for power factor preregulators based on Sepic and Cuk converters in continuous conduction mode

Sepic and Cuk converters used as power factor preregulators in continuous conduction mode are analyzed. The application of the average current mode control technique to obtain sinusoidal input current is investigated. It is shown that, unlike the usual boost topology, the gain of the internal current loop depends strongly on the instantaneous input voltage. Moreover, the presence of undamped complex poles and zeroes makes difficult the design of a stable control. The paper shows that stability can be achieved by using a suitable damping network, which properly shapes the current loop transfer function without introducing significant power losses. With this provision, a standard PI controller can be used to ensure the desired phase margin in any operating condition. Detailed design criteria of both power and control stages are given in the paper. Results of a 300 W prototype based on the Sepic topology are also reported. <<ETX>>
iDistanceM : A Real-time Smartphone-based Distance Measurement for the Americans with Disabilities Act Compliance Assessment Tool ( ADACAT )

The measurement kit of the ADA-CAT (Americans With Disabilities Act-Compliance Assessment Toolkit) consists of measurement instruments, such as a measuring tape, a sound level meter, a light level meter, and a magic slope block. Perhaps the most important tool is the measuring tape for length and height measurement as distances are used so prolifically in the ADA assessment process. Currently, ultrasonic sensors are widely used for distance measurement all around the world. In this paper measuring the height and length of an obstacle by using an ultrasonic sensor and a smartphone through Wi-Fi communication is presented. To the best of our knowledge we are the first to use a smartphone with an ultrasonic sensor for measuring length and height through a Wi-Fi network. We have validated our system by conducting two different experiments and found a high degree of accuracy in length measurement. Compared to other existing smartphonebased measurement tools, our application demonstrated the lowest absolute error.
A Cross-Layer Approach in Sensing and Resource Allocation for Multimedia Transmission over Cognitive UWB Networks

We propose an MAC centric cross-layer approach to address the problem of multimedia transmission over cognitive Ultra Wideband (C-UWB) networks. Several fundamental design issues, which are related to application (APP), medium access control (MAC), and physical (PHY) layer, are discussed. Although substantial research has been carried out in the PHY layer perspective of cognitive radio system, this paper attempts to extend the existing research paradigm to MAC and APP layers, which can be considered as premature at this time. This paper proposed a cross-layer design that is aware of (a) UWB wireless channel conditions, (b) time slot allocations at the MAC layer, and (c) MPEG-4 video at the APP layer. Two cooperative sensing mechanisms, namely, AND and OR, are analyzed in terms of probability of detection (), probability of false alarm (), and the required sensing period. Then, the impact of sensing scheduling to the MPEG-4 video transmission over wireless cognitive UWB networks is observed. In addition, we also proposed the packet reception rate- (PRR-) based resource allocation scheme that is aware of the channel condition, target PRR, and queue status.
Software-Defined Networking for Smart Grid Resilience: Opportunities and Challenges

Software-defined networking (SDN) is an emerging networking paradigm that provides unprecedented flexibility in dynamically reconfiguring an IP network. It enables various applications such as network management, quality of service (QoS) optimization, and system resilience enhancement. Pilot studies have investigated the possibilities of applying SDN on smart grid communications, while the specific benefits and risks that SDN may bring to the resilience of smart grids against accidental failures and malicious attacks remain largely unexplored. Without a systematic understanding of these issues and convincing validations of proposed solutions, the power industry will be unlikely to embrace SDN, since resilience is always a key consideration for critical infrastructures like power grids. In this position paper, we aim to provide an initial understanding of these issues, by investigating (1) how SDN can enhance the resilience of typical smart grids to malicious attacks, (2) additional risks introduced by SDN and how to manage them, and (3) how to validate and evaluate SDN-based resilience solutions. Our goal is also to trigger more profound discussions on applying SDN to smart grids and inspire innovative SDN-based solutions for enhancing smart grid resilience.
SPACE CENTER UNIVERSITY OF CENTRAL FLORIDA ENHANCEMENT OF THE GROUND PROCESSING SCHEDULING SYSTEM ( GPSS )

A scheduling process that maintains, repairs and confi gures a space shuttle orbiter must satisfy temporal, resource and configuration constraints. The GPSS, whi ch stands for ground processing scheduling system, has been successfully used to schedule tasks that prepare an orbiter for its next launch. The objective of this project is to enhance and to im prove the GPSS. The GPSS uses an iterative repair method that starts with a rough schedule, which satis fies only the temporal constraints. Then it repairs the resource and configuration constraint violations one by one until no more violations exist or no further improvements can be made. The conflict resolution, whi ch repairs all the resource and the configuration constraint violations, forms the bulk of the cognitive and computational complexity of the system. We started with a study of the solution space of the G PSS which will help us to develop algorithms and strategies to improve the efficiency of the deconfli ction process. To our knowledge there has been no previous effort to obtain this solution space. We probed t h existing GPSS and constructed its solution spaces. The total number of iterations during the decon fli tion process depends on the quality of the initial schedule better the quality the smaller the number of iterations. The quality of the initial schedule can be improved by applying a genetic algorithm on an abstract problem generated from the original problem. We have designed and developed a genetic algorithm and interfaced with the GPSS to generate a feasible schedule that satisfies temporal and resource constraints. Further, the computational efficiency of the deconflic tion portion of the GPSS can be improved by using better heuristic strategies and better cost estimators instead of actually computing the cost for the heurist ic table. The heuristic table used in the GPSS is very sta tic, nd the normalization process is somewhat arbitrary. We have designed and developed an evolution ary learning technique and interfaced it with the GPSS so as to learn the appropriate ways of combining t he heuristic parameters and thereby reduce conflicts faster.
Self-esteem and Positive Psychology

Self-esteem is a widely used concept in many fields and social science and it refers to general feelings of self-worth or self-value. Self-esteem is one of the top three covariates in personality and social psychology research, along with gender and negative affectivity. Researches have shown that there is indeed a strong correlation between happiness and self-esteem, and self-esteem has held a significant place in positive psychology. The definitions of self-esteem vary and there are several types of self-esteem. Among many forms of self-esteem, authentic self-esteem and it's implications for well-being or for living the good life has been interested in positive psychology. (Korean J Str Res 2007;15:205 ∼ 214)
Fast Automated Estimation of Variance in Discrete Quantitative Stochastic Simulation November 2010

Quantitative stochastic simulation is an important tool in assessing the performance of complex dynamic systems such as modern communication networks. Because of the proliferation of computers and devices that use and rely on networks such as the internet, assessing the performance of these networks is important to ensure future reliability and service. The current methodology for the analysis of output data from stochastic simulation is focused mainly on the estimation of means. Research on variance estimation focuses mainly on the estimation of the variance of the mean, as this is used to construct confidence intervals for the estimated mean values. To date, there has been little research on the estimation of variance of auto correlated data, such as those collected during steady-state stochastic simulation. This research investigates different methodologies for estimation of variance of terminating and steady-state simulation. Results from the research are implemented in the simulation tool Akaroa2.
Barriers, Motivations, and Preferences for Physical Activity Among Female African American Older Adults

According to the Centers for Disease Control and Prevention, less than 11% of adults more than the age of 65 meet the 2008 Physical Activity Guidelines for Americans. Among minority populations, only 5% of non-Hispanic Black older adults met the guidelines. Given our limited understanding of psychosocial and environmental factors that affect physical activity participation in these groups, the purpose of our focus groups was to investigate barriers, motivators, and preferences of physical activity for community-dwelling African American older adults. Three focus groups were conducted with female African American older adults (N = 20). Questions posed to each focus group targeted motivations and barriers toward physical activity as well as their preferences for physical activity. The motivations included perceived health benefits of physical activity, social support, and enjoyment associated with engagement in physical activity. Prominent barriers included time and physical limitations, peer pressure and family responsibilities, and weather and poor neighborhood conditions. Group activities involving a dance component and novel exercises such as tai-chi or yoga were preferred choices. These findings should be taken into consideration when designing and implementing research or community physical activity programs for female African American older adults.
Association of High Myopia with Crystallin Beta A4 (CRYBA4) Gene Polymorphisms in the Linkage-Identified MYP6 Locus

Background Myopia is the most common ocular disorder worldwide and imposes tremendous burden on the society. It is a complex disease. The MYP6 locus at 22 q12 is of particular interest because many studies have detected linkage signals at this interval. The MYP6 locus is likely to contain susceptibility gene(s) for myopia, but none has yet been identified. Methodology/Principal Findings Two independent subject groups of southern Chinese in Hong Kong participated in the study an initial study using a discovery sample set of 342 cases and 342 controls, and a follow-up study using a replication sample set of 316 cases and 313 controls. Cases with high myopia were defined by spherical equivalent ≤ -8 dioptres and emmetropic controls by spherical equivalent within ±1.00 dioptre for both eyes. Manual candidate gene selection from the MYP6 locus was supported by objective in silico prioritization. DNA samples of discovery sample set were genotyped for 178 tagging single nucleotide polymorphisms (SNPs) from 26 genes. For replication, 25 SNPs (tagging or located at predicted transcription factor or microRNA binding sites) from 4 genes were subsequently examined using the replication sample set. Fisher P value was calculated for all SNPs and overall association results were summarized by meta-analysis. Based on initial and replication studies, rs2009066 located in the crystallin beta A4 (CRYBA4) gene was identified to be the most significantly associated with high myopia (initial study: P = 0.02; replication study: P = 1.88e-4; meta-analysis: P = 1.54e-5) among all the SNPs tested. The association result survived correction for multiple comparisons. Under the allelic genetic model for the combined sample set, the odds ratio of the minor allele G was 1.41 (95% confidence intervals, 1.21-1.64). Conclusions/Significance A novel susceptibility gene (CRYBA4) was discovered for high myopia. Our study also signified the potential importance of appropriate gene prioritization in candidate selection.