Miracle-dz/UniFAR-FTUs huggingface dataset:
Literature Survey on Color Image Compression 1

The need for an efficient technique for compression of Images ever increasing because the raw images need large amounts of disk space seems to be a big disadvantage during transmission & storage. Even though there are so many compression technique which is faster, memory efficient and simple surely suits the requirements of the user. This paper consists of review of some of the color image compression techniques.
0406038 KCL-MTH-04-07 SPIN-04 / 08 ITP-04 / 14 UG-04 / 02 Non-extremal D-instantons

We construct the most general non-extremal deformation of the D-instanton solution with maximal rotational symmetry. The general non-supersymmetric solution carries electric charges of the SL(2, R) symmetry, which correspond to each of the three conjugacy classes of SL(2, R). Our calculations naturally generalise to arbitrary dimensions and arbitrary dilaton couplings. We show that for specific values of the dilaton coupling parameter, the non-extremal instanton solutions can be viewed as wormholes of non-extremal Reissner-Nordström black holes in one higher dimension. We extend this result by showing that for other values of the dilaton coupling parameter, the non-extremal instanton solutions can be uplifted to non-extremal non-dilatonic p-branes in p + 1 dimensions higher. Finally, we attempt to consider the solutions as instantons of (compactified) type IIB superstring theory. In particular, we derive an elegant formula for the instanton action. We conjecture that the non-extremal D-instantons can contribute to the R-terms in the type IIB string effective action. PACS number: 04.50. +h 04.65. +e 11.25. -w
JPEG 2000 and Digital Watermarking Technique Using in Medical Image

The Picture Archiving and Communication System(PACS) was introduced for computerization of the medical system and telediagnosis between the hospital. It is becoming possible to create, store, and transmit medical images via PACS. There has been a growing interest in protecting medical images with an enormous amount of information. To improve transmission speed among the hospitals, the medical image should be compression JPEG 2000 by high compression ratio. This paper proposes an algorithm that utilizes both JPEG 2000 and robust watermarking for protection and compression of the medical image. With the proposed algorithm, it takes considerably less time to do JPEG 2000 and watermarking than when they are done separately. Based on the experiment results, it takes 0.72 second for the proposed algorithm and 1.11 second when they are done separately. We confirmed that the proposed algorithm was faster than when they are done separately.
Monitoring urban sprawl and its trends with EO data. UrbEx, a prototype national service from a WWF-ESA joint effort

This paper reports the results of UrbEx (urban expansion monitoring) project of the European Space Agency (ESA) carried out in collaboration with the Word Wildlife Fund (WWF) as end-user. The project, which has been carried out by the Italian company advanced computer system (ACS), was aimed at developing a user-oriented information service for monitoring urban expansion at national scale. Such a service was tailored to the requirements of the user, WWF, for supporting the conservation activities of this international nongovernmental organization. From a methodological viewpoint, the system was based on a novel data fusion approach to land-cover classification. In particular, both Landsat and 35-days interferometric ERS images where used to achieve an accurate detection of the urban tissue over the whole Italian coastal area at two different times, 1995 and 2000. The final results validated by WWF demonstrate the capabilities of EO technology to provide reliable, synoptic and continuous information on the urban expansion at national, regional and even global scale.
Real-time moving object detection using a vehicle-mounted monocular rear-view fisheye camera

In this paper, we propose a moving object detection algorithm that enables to detect moving objects with collision risk in rear of the vehicle by using image sequences from a vehicle-mounted monocular rear-view fisheye camera. The proposed moving object detection algorithm detects corner points by using Harris corner detector and computes the optical flow vectors from two consecutive images corresponds to the detected corner points. By considering the feature of the vehicle movement that the vehicle goes straight in a short time interval, we find the focus of expansion (FOE) by using matched filter and divide the image into four sections around the FOE. The optical flow angle distribution of each section is analyzed to find pixels corresponds to the background components and robust background motion compensation method to the complex scene is suggested. Add to this, we propose false removal method that eliminates false positives by considering two features of the detection box for the moving object candidates; position and pixel intensity distribution. Simulation results show that our proposed algorithm achieves 97.19% of detection rate toward various detection target including pedestrians, bicycles, and cars. Furthermore, our proposed false removal algorithm performs an extremely low 2.7% of false rate toward false positives such as trees, shadows, and road markers.
Adaptive Background Estimation for Object Tracking

Adaptive Background Model Tracking people has received considerable attention by computer vision researchers. Interest is motivated by the broad range of potential applications such as per- sonal identification, human-machine interaction, and au- tomated surveillance. We previously proposed a method for extracting moving region by subtraction from back- ground images. Background images were estimated by using UD factorized Kalman Filter. In order to use a Kalman Filter, good initial parameters are needed for es- timation and were manually given in the previous work. In this paper we propose an improved method for esti- mation of background image and for automatic parame- ter initialization. We also show experimental results us- ing real images to test the performance of our proposed method.
Sealing OS processes to improve dependability and safety

In most modern operating systems, a process is a hardware-protected abstraction for isolating code and data. This protection, however, is selective. Many common mechanisms---dynamic code loading, run-time code generation, shared memory, and intrusive system APIs---make the barrier between processes very permeable. This paper argues that this traditional open process architecture exacerbates the dependability and security weaknesses of modern systems. As a remedy, this paper proposes a sealed process architecture, which prohibits dynamic code loading, self-modifying code, shared memory, and limits the scope of the process API. This paper describes the implementation of the sealed process architecture in the Singularity operating system, discusses its merits and drawbacks, and evaluates its effectiveness. Some benefits of this sealed process architecture are: improved program analysis by tools, stronger security and safety guarantees, elimination of redundant overlaps between the OS and language runtimes, and improved software engineering. Conventional wisdom says open processes are required for performance; our experience suggests otherwise. We present the first macrobenchmarks for a sealed-process operating system and applications. The benchmarks show that an experimental sealed-process system can achieve performance competitive with highly-tuned, commercial, open-process systems.
long noncoding rna aFaP1-as1, a potential novel biomarker to predict the clinical outcome of cancer patients: a meta-analysis

: A number of studies have demonstrated that the expression level of actin filament-associated protein 1 antisense RNA 1 (AFAP1-AS1) was upregulated in various cancers. High expression of AFAP1-AS1 is associated with an increased risk of metastasis and a poor prognosis in cancer patients. The electronic search was conducted in PubMed, EMBASE, Cochrane Library, China National Knowledge Infrastructure, and Wanfang database. We collected relevant articles to explore the association between the expression levels of AFAP1-AS1 and lymph node metastasis, distant metastasis, overall survival, relapse-free survival, and progression-free survival. A total of 1,017 patients from eight studies were finally included. The results showed that cancer patients with high AFAP1-AS1 expression suffered an increased risk of developing lymph node metastasis (odds ratio = 3.19, 95% confidence interval [CI]: 2.11–4.83, P , 0.00001) and distant metastasis (odds ratio = 3.05, 95% CI: 1.84–5.04, P , 0.0001). Moreover, we found that patients with high AFAP1-AS1 expression also had a poorer overall survival (hazard ratio [HR]: 1.98, 95% CI: 1.57–2.38, P = 0.000), a worse progression-free survival (HR: 1.73, 95% CI: 1.11–2.35, P = 0.000), and a shorter recurrence-free survival (HR: 1.96, 95% CI: 1.02–2.90, P = 0.000) than those with low AFAP1-AS1 expression. High expression of AFAP1-AS1 was associated with poor clinical outcome. AFAP1-AS1 might serve as a potential novel biomarker for indicating the clinical outcomes in human cancers.
Effect of Dexmedetomidine Alone for Intravenous Patient-Controlled Analgesia After Gynecological Laparoscopic Surgery

AbstractGynecological laparoscopic surgery is minimally invasive compared with open surgical approaches, but postoperative pain is generally undermanaged. Pain management strategies related to the procedure-specific efficacy are needed. Many studies have shown that dexmedetomidine (DEX) has opioid-sparing properties. It is not clear whether DEX used alone for intravenous patient-controlled analgesia (PCA) could reduce postoperative pain after an invasive procedure. We hypothesized that DEX alone would reduce postoperative pain in women patients undergoing an elective gynecological laparoscopic procedure.This CONSORT-prospective randomized controlled clinical study aimed to investigate the effects of DEX alone for intravenous PCA after gynecological laparoscopic operation. Forty women patients scheduled for elective gynecological laparoscopy were enrolled into the study at Shandong Cancer Hospital and Institute and randomly allocated into two groups (n = 20 each). In the DEX group (group D), the intravenous PCA protocol was DEX 0.25 &mgr;g/kg/h diluted to 100 mL in 0.9% saline. In the fentanyl group (group F), the PCA protocol was fentanyl 20 &mgr;g/kg diluted to 100 mL in 0.9% saline. The primary outcome was the mean pain score on a visual analogue scale (VAS) at 6 hours after the operation. The secondary outcomes included the Ramsay sedation score, the incidence of postoperative nausea and vomiting (PONV), satisfaction with pain control, and time to recovery of gastrointestinal function.There were no significant differences in the patients’ characteristics and intraoperative measurements (P > 0.05). No patients received rescue analgesic. The mean VAS scores at 6 hours post-operatively were not significantly different between the groups (P > 0.05). The incidence of PONV was less in group D than in group F (P < 0.05). The Ramsay sedation scores were not significantly between the groups (P > 0.05). Satisfaction with pain control was higher and time to recovery of gastrointestinal function was lower in group D (P < 0.05). DEX alone is effective for intravenous patient-controlled analgesia after gynecological laparoscopic surgery without a change in sedation and with fewer side effects, and this effect was associated with better satisfaction with postoperative pain control and earlier recovery of gastrointestinal function.
A random local-DRM path planning algorithm for dual manipulator mobile robots in changing environments

Path planning for mobile robots with high degree of freedom (DOF) is an extreme challenge. Although lots of algorithms have focused on the planning of fixed manipulators and mobile robots with low degree of freedom, seldom of them can be employed to deal with high DOF mobile agents. The unpredictable obstacles and too many freedoms increased computational complexity dramatically. In this paper, a novel and general real-time approach is introduced to solve this problem. The core of this approach can be divided into two phases. By locally employing Dynamic Roadmap Mapping, a mobile robot can seek a collision-free path locally without too much collision detection. And a hierarchy sampling strategy is employed to treat narrow passages. Based on the idea of Rapidly-Exploring Random Tree, a high-level guide is developed by randomly generating subgoals. These two phases collaborate to help generate a desired path. Experimental results show that our algorithm can find out a collision free path in real time for mobile robots with 15 DOF in complex environments in the presence of both stationary and changing obstacles.
High Sensitivity Carbon Nanotubes Flow-Rate Sensors and Their Performance Improvement by Coating

A new type of hot-wire flow-rate sensor (HWFS) with a sensing element made of a macro-sized carbon nanotube (CNT) strand is presented in this study. An effective way to improve repeatability of the CNT flow-rate sensor by coating a layer of Al2O3 on the CNT surface is proposed. Experimental results show that due to the large surface-to-volume ratio and thin coated Al2O3 layer, the CNT flow-rate sensor has higher sensitivity and faster response than a conventional platinum (Pt) HWFS. It is also demonstrated that the covered CNT flow-rate sensor has better repeatability than its bare counterpart due to insulation from the surrounding environment. The proposed CNT flow-rate sensor shows application potential for high-sensitivity measurement of flow rate.
Measuring individual and cultural differences in implicit trait theories.

A new measure of implicit theories or beliefs regarding the traitedness versus contextuality of behavior was developed and tested across cultures. In Studies 1 (N = 266) and 2 (N = 266), these implicit beliefs dimensions were reliably measured and replicated across U.S. college student samples and validity evidence was provided. In Study 3, their structure replicated well across an individualistic culture (the United States; N = 249) and a collectivistic culture (Mexico; N = 268). Implicit trait and contextual beliefs overlapped only modestly with implicit entity theory beliefs and were predicted by self-construals in ways that generally supported cultural psychology hypotheses. Implicit trait beliefs were fairly strongly endorsed in both cultures, suggesting that such beliefs may be universally held.
Spatio-Temporal Data Construction

On the route to a spatio-temporal geoscience information system, an appropriate data model for geo-objects in space and time has been developed. In this model, geo-objects are represented as sequences of geometries and properties with continuous evolution in each time interval. Because geomodeling software systems usually model objects at specific time instances, we want to interpolate the geometry and properties from two models of an object with only geometrical constraints (no physical or mechanical constraints). This process is called spatio-temporal data construction or morphological interpolation of intermediate geometries. This paper is strictly related to shape morphing, shape deformation, cross-parameterization and compatible remeshing and is only concerned with geological surfaces. In this study, two main sub-solutions construct compatible meshes and find trajectories in which vertices of the mesh evolve. This research aims to find an algorithm to construct spatio-temporal data with some constraints from the geosciences, such as cutting surfaces by faulting or fracturing phenomena and evolving boundaries attached to other surfaces. Another goal of this research is the implementation of the algorithm in a software product, namely a gOcad plug-in. The four main procedures of the algorithm are cutting the surfaces, setting up constraints, partitioning and calculating the parameterizations and trajectories. The software has been tested to construct data for a salt dome and other surfaces in regard to the geological processes of faulting, deposition and erosion. The result of this research is an algorithm and software for the construction of spatio-temporal data.
Towards Ontological Modelling of Historical Documents

In this presentation we describe a methodology we have adopted for coding the semantic structure of a historical document and the resulting semantic model. To do this, we adapted currently available methodologies for ontology engineering to the context of semantic document coding. Using Protégé-2000 we then used this methodology to develop a formal ontological model and finally to encode a historical document covering the evolution of the constitution of modern Iran. The resulting semantic model was then evaluated using Protégé-2000 by direct reference back to the set of competency questions and motivating scenarios used to develop the model. Our implementation was successful in answering these competency questions as well as in providing support for the selected scenarios. The implementation and the evaluation results are presented along with our proposed future work.
Random forest with data ensemble for saliency detection

Saliency detection is one of the most active research area in computer vision. Since L. Itti et al. [1] suggested computational model of visual attention, numerous detection algorithms have been proposed. However, most of modern saliency detection methods are based on superpixels which make detection results have abrupt edges inside the salient part. In this paper, we propose pixel-wise detection algorithm that makes more natural detection result. It makes our algorithm excel in describing detailed part of salient objects. Furthermore, we utilize the ensemble of not only random forest but also the data itself. Our algorithm achieves comparable performance with state of the art detection results.
Decision Support System for Cotton Bales Blending Using Genetic Algorithm

Traditionally the commercial value of cotton is accessed on the basis of subjective grading. The grade of cotton is decided on the basis of color, trash and ginning preparation, staple length and strength. Characteristics like length variation, fineness and maturity are also taken into account. [14] It is necessary for the cotton spinning mills to produce good quality yarn and that too at a competitive price. The cost and quality of yarn is greatly dependent on the cost and quality of cotton. So, wise selection of cotton and control on the cost at different stages of spinning, weaving, processing and marketing is very essential. It is a common practice in textile mills to mix cottons of different varieties. Hence, it is imperative to select the right cottons for the same, since it is one of the major factors affecting the total yarn cost as well as the final yarn quality. The objective of this research is to design a decision support system which will optimize the cotton bales blending/mixing so as to reduce the cost of overall cotton cost subject to quality constraint. This paper presents a method of optimization based on genetic algorithms. The Genetic Algorithms are a versatile tool, which can be applied as a global optimization method to problems of mixing, because they are easy to implement to non-differentiable functions and discrete search spaces. [13] The test runs were performed with minimal attention to tuning of the genetic algorithm parameters. In most cases, better performance is possible simply by running the algorithm longer or by varying the selection method, population size or mutation rate.
Constructing a TpB Questionnaire: Conceptual and Methodological Considerations

outcomes (behavioral beliefs), beliefs about the normative expectations of others and motivation to comply with these expectations (normative beliefs), and beliefs about the presence of factors that may facilitate or impede performance of the behavior and the perceived power of these factors (control beliefs). In their respective aggregates, behavioral beliefs produce a favorable or unfavorable attitude toward the behavior; normative beliefs result in perceived social pressure or subjective norm; and control beliefs give rise to perceived behavioral control. In combination, attitude toward the behavior, subjective norm, and perception of behavioral control lead to the formation of a behavioral intention. As a general rule, the more favorable the attitude and subjective norm, and the greater the perceived control, the stronger should be the person’s intention to perform the behavior in question. Finally, given a sufficient degree of actual control over the behavior, people are expected to carry out their intentions when the opportunity arises. Intention is thus assumed to be the immediate antecedent of behavior. However, because many behaviors pose difficulties of execution that may limit volitional control, it is useful to consider perceived behavioral control in addition to intention. To the extent that perceived behavioral control is veridical, it can serve as a proxy for actual control and contribute to the prediction of the behavior in question. The following figure is a schematic representation of the theory.
Reliability-Aware Optimization of Approximate Computational Kernels with Rely

Emerging high-performance architectures are anticipated to contain unreliable components (e.g., ALUs) that offer low power consumption at the expense of soft errors. Some applications (such as multimedia processing, machine learning, and big data analytics) can often naturally tolerate soft errors and can therefore trade accuracy of their results for reduced energy consumption by utilizing these unreliable hardware components. We present and evaluate a technique for reliability-aware optimization of approximate computational kernel implementations. Our technique takes a standard implementation of a computation and automatically replaces some of its arithmetic operations with unreliable versions that consume less power, but may produce incorrect results with some probability. Our technique works with a developer-provided specification of the required reliability of a computation – the probability that it returns the correct result – and produces an unreliable implementation that satisfies that specification. We evaluate our approach on five applications from the image processing, numerical analysis, and financial analysis domains and demonstrate how our technique enables automatic exploration of the trade-off between the reliability of a computation and its performance.
Fuzzy Rules and Regularization Theory

Regularization theory and stochastic processes have the advantage that a{priori information is expressed directly in terms of the function values of interest. Classical regularization functionals are convex and possess a unique minimum. On the other hand, convex functionals can only implement AND{like combinations of approximation conditions. The paper discusses the empirical measurement of general, also non{convex a{priori information and its technical implementation by fuzzy logical{like combinations of quadratic building blocks.