Miracle-dz/UniFAR-FTUs huggingface dataset:
Performance of the entropy as an index of classification accuracy in latent profile analysis: A Monte Carlo simulation study

Latent Profile Analysis (LPA) is a latent variable modeling technique that identifies latent (unobserved) subgroups of individuals within a population based on continuous indicators. LPA has become a popular statistical method for modelling unobserved population heterogeneity in social and behavioral science. Entropy is a standardized index of model-based classification accuracy, with higher values indicating more precise assignment of individuals to latent profiles. In lots of conditions, the aim of substantial research was to assign individual to different latent subgroup. Therefore, Entropy was chosen to report as an index reflecting accuracy of class membership assignment. Unfortunately, very few methodological studies have examined the behavior of Entropy under the conditions where sample sizes, latent class separations, number of indicators, and number of classes are varying. Thus, the primary purpose of this study was to examine how Entropy will perform with different sample sizes, latent class separations, number of indicators, and number of classes. By using Monte Carlo simulation techniques, we generated artificial data to fit true models and evaluated the performance of Entropy and entropy-based indexes (CLC, ICL_BIC, sample adjusted ICL_BIC) under different modeling conditions. The simulation was repeated 100 times for each condition of the 120 combinations: sample sizes (50, 100, 500, 1000, 3000), latent class separations (0.5, 1.2, 3), number of indicators (4, 8, 12, 20), and number of latent classes (3, 5). The continuous indicators of the latent class are not allowed to correlate. Different mean levels on the observed variables are calculated by Mahalanobis distance (MD). The simulations and analyses of the sample data were conducted using the Monte Carlo facilities of Mplus7.4. For 3 latent classes, Entropy values round 0.76 and above are related to at least 90% correct assignment, and Entropy values round 0.64 and below are related to at least 20% classification error rate. When the latent classes is 5, Entropy value around 0.84 and above are related to at least 90% correct assignment. The Entropy value decreases and the classification error rate increases as sample size increases. Entropy performs well under small sample sizes (50-100) and more indicators conditions. Entropy consistently performs better when latent class separation is large (MD=3), and the result is quite consistent across the sample size and number of latent classes. The tendency of CLC, ICL_BIC, and sample adjusted ICL_BIC were similar, which increases as sample size increases, and it also increases under large class separation but the differences of Entropy caused by class separation were more noticeable. This simulation indicates that the Entropy values are strongly correlated with the correct class membership assignment, but it varies according to number of latent classes, sample sizes, latent class separation and number of indicators. Hence, it is hard to determine cutoff values for Entropy, the indicator of class assignment.
Health anxiety and healthcare costs in Japanese individuals: an Internet survey

Aim: Health anxiety, also known as hypochondriasis, is classifiable as an anxiety disorder. The aim of this study was to examine the relationship between health anxiety and healthcare costs. Method: Participants – 100 Japanese individuals from the general population with chronic health problems and 100 without chronic health problems – were recruited via the Internet. They completed self-report scales measuring health anxiety, state anxiety, depression, obsessionality, and a scale specifically developed for this study that measured the use of healthcare services and the personal costs of respondents' healthcare. Results: Health anxiety was associated with more incidents of inpatient care and greater healthcare expenditure. These associations remained significant even after controlling for state anxiety, depression, obsessionality, and the presence of chronic health problems. Conclusion: We conclude that health anxiety is related to personal as well as social costs in Japan.
On the Design and Implementation of an Innovative Smart Building Platform

Smart Home and Connected Living are prosperous topics not only for technologies but also for fashions and tastes. From lighting control to energy usage tracking are taken care of with the utilization of technologies of sensors and Internet of Things (IoT). This paper will present some conceptual design framework and experimental demonstrative works in the lab: a self-configurable mechanism for IoT smart device deployment, an air purification system has been constructed by monitoring indoor air quality index (IAQI), a Personalized Comfort Sensing System has been built on Modified Predicted Mean Vote (PMV) Model while a preliminary Intelligent Energy Management System is also derived and implemented.
Memetic Algorithm for a Multi-Objective Vehicle Routing Problem with Multiple Trips

This paper considers a variant of the vehicle routing problem where vehicles are allowed to take more than one route during the working day. A restricted fleet size is used in order to serve customers’ demand. Therefore, vehicles could exceed the time horizon. The objective is to optimize both maximum overtime and routing cost. A mathematical model has been proposed for the VRPM. A memetic algorithm has been developed to solve it by combining a genetic algorithm based on suitable encoding and genetic operators, with a local search procedure. The algorithm is run over a benchmark of the VRPM. Comparing with seven existing algorithms, our approach shows competitive performance and contributes many new best solutions.
Dynamic Profile Driven Code Version Selection

In this paper, we study the effectiveness of dynamic code version selection on Itanium R © 2 processors. Code version selection can improve the effectiveness of optimizations, adapting them to multiple input sets. In this performance potential study, we conduct experiments on dual-core Itanium R © 2 processors and examine the effectiveness of dynamic code version selection of loop scheduling, load prefetch and control speculation aggressiveness adjustment. We apply multiple versions of these optimizations to selected SPEC CPU2006 benchmarks at base optimization levels used for SPEC reporting. The experimental results demonstrate 2.0% to 15.7% improvement in performance from versioning optimizations on tested benchmarks. This paper also discusses the issues and solutions about the implementation of dynamic code version selection.
Noisy Radial Deblurring Using Natural Image Prior

Radial blurring, a challenging problem in image processing, is a special case of the space-variant motion blur. Previous methods mainly focus on space-invariant motion blurring, which could not be utilized directly in the space-variant motion deblurring. In this paper, we proposed a modified noisy radial deblurring method based on polar coordinate system, using the global and local image priors. The experiment results show that our proposed algorithm can denoise the additive noise and suppress the undesirable ring like ringing artifacts successfully, while the objective evaluations and subjective visual effects of the restoration images are improved obviously.
Y-branches: when you come to a fork in the road, take it

We study the effects of manipulating the architected direction of conditional branches. Through the use of statistical sampling, we find that about 40% of all dynamic branches and about 50% of mispredicted branches do not affect correct program behavior when forced down the incorrect path. We call such branches Y-branches. To further examine this unexpected phenomenon, we provide a characterization of the coding constructs that give rise to such branches. Examples of such coding constructs include short-circuits and ineffectual loop iterations. We provide a statistical breakdown of the frequency of these branches and their constructs. Finally, we suggest some techniques for exploiting this behavior, particularly when it results from short-circuit constructs.
Particle filter positioning and tracking based on dynamic model

In order to deal with mobile target tracking which contain non-linear and non-Gaussian problem, this paper presents a particle filter positioning and tracking algorithm based on dynamic model. This method can apply to any state-space model which is nonlinear system, and the accuracy can approach to best of all. The simulation showed that particle filter method can be used effectively to inhibit non-line-of-sight (NLOS) errors and can be combined with positioning and tracking model to get higher precision.
Runtime Adaptation for Autonomic Heterogeneous Computing

Heterogeneity is increasing at all levels of computing, certainly with the rise in general purpose computing with GPUs in everything from phones to supercomputers. More quietly it is increasing with the rise of NUMA systems, hierarchical caching, OS noise, and a myriad of other factors. As heterogeneity becomes a fact of life at every level of computing, efficiently managing heterogeneous compute resources is becoming a critical task. The focus of my dissertation is developing methods and systems to allow software to adapt to the heterogeneous hardware it finds at runtime. The goal is to make the complex functions of heterogeneous computing autonomic, handling load balancing, memory coherence and other performance critical factors in the runtime. The investigation began by studying heterogeneity caused by system topology and resource contention in MPI applications. Since then the focus has shifted to work-sharing across CPU and GPU resources for accelerated OpenMP, and automatically managing the hardware capability imbalances between these resources. Moving forward, I propose to produce a system extending upon both previous approaches to offer work-sharing, topology aware affinity management, as well as novel automated memory transformations to reduce communication and increase memory access efficiency.
Lexical gravity across varieties of English: An ICE-based study of n -grams in Asian Englishes

In our earlier work on three Asian Englishes and British English, we showed how lexico-syntactic co-occurrence preferences for three argument structure constructions revealed differences between varieties that correlated well with Schneider’s (2003, 2007) model of evolutionary stages. Here, we turn to lexical co-occurrence preferences and investigate if and to what degree n-grams distinguish between different modes and varieties in the same components of the International Corpus of English. Our approach to n-grams differs from previous work in that we neither use raw frequencies nor (problematic) MI-values but the newly proposed measure of lexical gravity (cf. Daudaravicius a Marcinkevicienė 2004), which takes type frequencies into consideration. We show how lexical gravity can be extended to handle n-grams with n ≥ 3 and apply this method to our n-gram data; in addition, we suggest a new concept for describing the tendency of a word to occur in significant n-grams: lexical stickiness.
Analytic Redundancy, Possible Conflicts, and TCG-based Fault Signature Diagnosis applied to Nonlinear Dynamic Systems

Abstract The FDI and DX communities have developed complementary approaches that exploit structural relations in the system model to find efficient solutions for the residual generation and residual evaluation steps in fault detection and isolation in dynamic systems. This paper compares three different structural techniques, two from the DX community and one from the FDI community. To simplify our comparison, we start with a common modeling approach that employs bond graphs. We describe the residual generation methods used by the three approaches, and apply them to a standard three tank configuration to demonstrate their diagnostic ability for continuous, nonlinear systems.
Complex matched filter for line detection

In this work we introduce a new filter that extracts lines of particular widths. By using 1D model of line and edge profiles we demonstrate the problem of line extraction and propose the kernels and technique for processing of responses. We demonstrate the stability of the proposed filter in the presence of background gradients. Though the area of possible applications is broader, we use biometric palm images and an algorithm similar to Non-Halo Complex Matched Filtering to demonstrate that the proposed approach can extract only palm features (palm vein and palm print patterns) discarding any other irrelevant information, such as lighting conditions, contours, etc., simplifying further processing of the feature data.
Observer-based active impedance control of a knee-joint assistive orthosis

In this study, a new active impedance control method of a knee joint orthosis is proposed by using a nonlinear observer to estimate the human joint torque. The proposed method is an alternative to the sensor-based impedance controller in which the human joint torque is estimated by using the electromyography (EMG) sensor, force/torque sensors, etc. The use of the nonlinear observer can efficiently overcome the shortcomings of the EMG and force/torque sensors, such as complexity of use, encumbrance, modeling, sensitivity to noise, high cost, etc. Additionally, a main goal of the proposed active impedance control is to decrease the impedance of the human-orthosis system to a desired level that the wearer can easily achieve. To achieve this goal, a virtual desired impedance model is designed to estimate human intention movements; a nonlinear disturbance observer based sliding mode controller is proposed to track the human estimated movements. Two experiments were carried out to verify the performance of the proposed nonlinear observer and sliding mode controller, as well as the effectiveness of the active impedance assistance strategy. The results show that the proposed method can effectively decrease the impedance of the human-orthosis system according to the desired impedance model. At the same time, the required human muscle torque for ensuring the knee joint flexion/extension movement can be also significantly decreased with the active impedance assistance.
Effects of sampling standardization on estimates of Phanerozoic marine diversification

Global diversity curves reflect more than just the number of taxa that have existed through time: they also mirror variation in the nature of the fossil record and the way the record is reported. These sampling effects are best quantified by assembling and analyzing large numbers of locality-specific biotic inventories. Here, we introduce a new database of this kind for the Phanerozoic fossil record of marine invertebrates. We apply four substantially distinct analytical methods that estimate taxonomic diversity by quantifying and correcting for variation through time in the number and nature of inventories. Variation introduced by the use of two dramatically different counting protocols also is explored. We present sampling-standardized diversity estimates for two long intervals that sum to 300 Myr (Middle Ordovician-Carboniferous; Late Jurassic-Paleogene). Our new curves differ considerably from traditional, synoptic curves. For example, some of them imply unexpectedly low late Cretaceous and early Tertiary diversity levels. However, such factors as the current emphasis in the database on North America and Europe still obscure our view of the global history of marine biodiversity. These limitations will be addressed as the database and methods are refined.
Performance and Preliminary Calibration of the Hopkins Ultraviolet Telescope on the Astro-2 Mission

An improved version of the Hopkins Ultraviolet Telescope (HUT) made its second flight on the Astro-2 mission aboard the Space Shuttle Endeavour from 1995 March 2-18. The longer mission duration and greatly improved pointing stability relative to Astro-1 made possible 385 observations of 265 celestial targets at far-ultraviolet wavelengths. Observing efficiency exceeding 60% over 14 days of science operations yielded 205 hr of on-source integration time, a factor of 5.1 increase over Astro-1. We describe changes to the instrument following Astro-1 and the in-flight photometric calibration, which is based on a comparison of our observations of the hot DA white dwarf HZ 43 with a model atmosphere whose parameters were derived from optical observations. The peak effective area is 24.1 cm2 at 1160 Å, where the inverse sensitivity is 7.09 × 10-13 ergs cm-2 count-1. This is an improvement by a factor of 2.3 over Astro-1, largely attributable the installation of new optics coated with ion-sputtered silicon carbide. Observations of several other white dwarfs indicate that the calibration is accurate to about 5%, after correction for modest, but significant, time-dependent degradation during the mission. The spectral resolution varied from 2 to 4 Å over the first-order wavelength range of 820-1840 Å. The wavelength scale is established to better than 1 Å. As on Astro-1, dark counts and scattered light were extremely low. Airglow line intensities were much lower because of the lower level of solar activity. When all factors are considered together, HUT performance on Astro-2 was a full order of magnitude better than that achieved on the highly successful Astro-1 mission.
New Nonasymptotic Channel Coding Theorems for Structured Codes

New nonasymptotic random coding theorems (with error probability E and finite block length n) based on Gallager parity check ensemble and general parity check ensembles are derived in this paper. The resulting nonasymptotic achievability bounds, when combined with nonasymptotic equipartition properties developed in this paper, can be easily computed. Analytically, these nonasymptotic achievability bounds are shown to be asymptotically tight up to the second order of the coding rate as n goes to infinity with either constant or subexponentially decreasing E in the case of Gallager parity check ensemble, and to imply that low density parity check (LDPC) codes be capacity-achieving in the case of LDPC ensembles. Numerically, they are also compared favorably, for finite n and E of practical interest, with existing nonasymptotic achievability bounds in the literature.
Mis-operation Resistant Searchable Homomorphic Encryption

Let us consider a scenario that a data holder (e.g., a hospital) encrypts a data (e.g., a medical record) which relates a keyword (e.g., a disease name), and sends its ciphertext to a server. We here suppose not only the data but also the keyword should be kept private. A receiver sends a query to the server (e.g., average of body weights of cancer patients). Then, the server performs the homomorphic operation to the ciphertexts of the corresponding medical records, and returns the resultant ciphertext. In this scenario, the server should NOT be allowed to perform the homomorphic operation against ciphertexts associated with different keywords. If such a mis-operation happens, then medical records of different diseases are unexpectedly mixed. However, in the conventional homomorphic encryption, there is no way to prevent such an unexpected homomorphic operation, and this fact may become visible after decrypting a ciphertext, or as the most serious case it might be never detected. To circumvent this problem, in this paper, we propose mis-operation resistant homomorphic encryption, where even if one performs the homomorphic operations against ciphertexts associated with keywords ω' and ω, where ω -ω', the evaluation algorithm detects this fact. Moreover, even if one (intentionally or accidentally) performs the homomorphic operations against such ciphertexts, a ciphertext associated with a random keyword is generated, and the decryption algorithm rejects it. So, the receiver can recognize such a mis-operation happens in the evaluation phase. In addition to mis-operation resistance, we additionally adopt secure search functionality for keywords since it is desirable when one would like to delegate homomorphic operations to a third party. So, we call the proposed primitive mis-operation resistant searchable homomorphic encryption (MR-SHE). We also give our implementation result of inner products of encrypted vectors. In the case when both vectors are encrypted, the running time of the receiver is millisecond order for relatively small-dimensional (e.g., 26) vectors. In the case when one vector is encrypted, the running time of the receiver is approximately 5 msec even for relatively high-dimensional (e.g., 213) vectors.
Dynamic maximum tree depth: a simple technique for avoiding bloat in tree-based GP

We present a technique, designated as dynamic maximum tree depth, for avoiding excessive growth of tree-based GP individuals during the evolutionary process. This technique introduces a dynamic tree depth limit, very similar to the Koza-style strict limit except in two aspects: it is initially set with a low value; it is increased when needed to accommodate an individual that is deeper than the limit but is better than any other individual found during the run. The results show that the dynamic maximum tree depth technique efficiently avoids the growth of trees beyond the necessary size to solve the problem, maintaining the ability to find individuals with good fitness values. When compared to lexicographic parsimony pressure, dynamic maximum tree depth proves to be significantly superior. When both techniques are coupled, the results are even better.
Robust multi-bit watermarking for HDR images in the Radon-DCT domain

High Dynamic Range (HDR) imaging is rapidly emerging as an innovative approach for digitally representing and reproducing real scenes with a wide range of intensity levels, which can be hardly managed by current standard methods for image capture and display. In order to protect the intellectual property of HDR images, a novel blind multi-bit watermarking method exploiting both their peculiar characteristics and the properties of the Radon-Discrete Cosine Transform (RDCT) for the embedding process, is here proposed. The performed experimental tests demonstrate the possibility of marking HDR images without significantly altering their perceptual characteristics, while guaranteeing the recovery of the embedded information even from Low Dynamic Range (LDR) images obtained through the application of different tone-mapping operators (TMOs).
Deductive verification of probabilistic real-time systems

Many people have studied formal specification and verification methods of real-time systems all over the world. We can specify real-time systems using timed automata, and verify them using model-checking. Especially, recently, probabilistic timed automata and their model-checking have been developed in order to express the relative likelihood of the system exhibiting certain behavior. Moreover, model-checking and probabilistic timed simulation verification methods of probabilistic timed automata have been developed. In this paper, we propose probabilistic timed transition systems by generalizing probabilistic timed automata, and propose deductive verification rules of probabilistic real-time linear temporal logic over probabilistic timed transition systems. As our proposed probabilistic timed transition system is a general computational model, we have developed general verification methods.
Image segmentation based on two -dimension Fuzzy Tsallis-Entropy

Image processing bears some fuzziness in nature, as a effective mathematical tool for handling the ambiguity, Fuzzy set theory is introduced in the paper to define a new kind of fuzzy entropy, namely Two-dimension Fuzzy Tsallis Entropy(TFTE) and applied in image segmentation following the maximum entropy principle. To overcome the huge calculational burden when generalizing one-dimension entropy to two-dimension, the particle swarm optimization (PSO) algorithm was employed to accelerate the search of the optimal threshold. The validity and effectiveness of the presented method is illustrated by experiments and the application of Tsallis Entropy is generalized to fuzzy fields.