Miracle-dz/UniFAR-FTUs huggingface dataset:
A novel method to represent repetitive and overwriting activities in motion history images

Motion History Images(MHI) are extensively being used to represent the history of temporal changes involved in the execution of an activity. Here the intensity of the pixels of a scalar valued image are varied, depending upon the motion history. MHI suffers from a very prominent drawback that when self-occluding or over-writing activities are encountered it fails to generate a vivid description of the course of that activity. This happens because when repetitive activities are performed, the current action overwrites or deletes the information of the previous action. In this paper, we have designed a methodology which has overcome this problem of conventional MHI. In our mechanism, we have used red, green and blue channels to represent human movement. Initially the pixel values of all the three channels are null. When any motion is detected at a particular pixel for the first time, then it is represented using MHIR. However, if a repetitive action is performed, then it is represented using MHIG and if overwriting action is thereafter continued for the second time, then MHIB is utilized. In this approach, the pixel intensities are varied in accordance with the basic MHI. Thus, using our methodology which uses multiple channels, MHI is not only capable of detecting the history of motion but is also made capable of representing overlapping repetitive actions.
Parent Work and Child Well-Being in Low-Income Families

Using data from the National Survey of America's Families, this paper finds that levels of parent work are not generally associated with positive child outcomes even when family income is considered. For all children, positive outcomes are more strongly associated with both family and child characteristics, such as education and mental health, than with patterns of parent work. Job characteristics, such as access to family leave and flexible work schedules, may have more of an effect on parenting and child well-being than levels of work.
Symmetry reduction of information inequalities

Information inequalities can be used to bound the fundamental limits of communication systems and data storage systems. Information inequalities, particularly Shannon-type inequalities, and the problem-specific constraints are usually either linear equalities or inequalities of joint entropies, and thus outer bounding the fundamental limit can be viewed and solved as a linear program (LP). However, for many practical engineering problems, the resultant LP is very large. It was shown previously that symmetry in these problems can be used to reduce the scale of the LP, however the precise amount of reduction was not well understood. In this work, we provide a generic method to pinpoint this reduction. In particular, three problems are studied: extremal pairwise cyclic entropy inequalities, the regenerating code problem, and the caching problem. By viewing the symmetry as an induced permutation group on certain set, Pólya counting theorem can be applied, which however requires identifying the cycle index of the induced permutation.
Species Relationships and Farina Evolution in the Cheilanthoid Fern Genus Argyrochosma (Pteridaceae)

Abstract Convergent evolution driven by adaptation to arid habitats has made it difficult to identify monophyletic taxa in the cheilanthoid ferns. Dependence on distinctive, but potentially homoplastic characters, to define major clades has resulted in a taxonomic conundrum: all of the largest cheilanthoid genera have been shown to be polyphyletic. Here we reconstruct the first comprehensive phylogeny of the strictly New World cheilanthoid genus Argyrochosma. We use our reconstruction to examine the evolution of farina (powdery leaf deposits), which has played a prominent role in the circumscription of cheilanthoid genera. Our data indicate that Argyrochosma comprises two major monophyletic groups: one exclusively non-farinose and the other primarily farinose. Within the latter group, there has been at least one evolutionary reversal (loss) of farina and the development of major chemical variants that characterize specific clades. Our phylogenetic hypothesis, in combination with spore data and chromosome counts, also provides a critical context for addressing the prevalence of polyploidy and apomixis within the genus. Evidence from these datasets provides testable hypotheses regarding reticulate evolution and suggests the presence of several previously undetected taxa of Argyrochosma.
The Chaotic Nature of Human Experience: An Alternative Approach to Determinacy in Understanding Emotions and Experience

The emphasis on human experience and experience in the Design Research literature is growing along with the importance of human-centered approaches to design. However, the fundamental connection between emotions and experience is not well understood mostly due to the lack of research in the nature of human experience in the design research literature. Many approaches tend to determine the “rules” or “laws” that govern the emergence of emotions. These approaches are limited by their deterministic nature in evaluating the outcomes of experience and emotional responses to design. In this paper, we present an alternative stance to the deterministic approach in explaining emotions and human experience. We attempt to explain the emergence of emotions as a part of an indeterministic system of experiences. Additionally, we present an indeterministic framework that formalizes the nature of human experience, and a set of guiding principles suggesting directions for design practice.
Multilevel additive Schwarz preconditioner for nonconforming mortar finite element methods

Mortar elements form a family of special non-overlapping domain decomposition methods which allows the coupling of different triangulations across subdomain boundaries. We discuss and analyze a multilevel preconditioner for mortar finite elements on nonmatching triangulations. The analysis is carried out within the abstract framework of additive Schwarz methods. Numerical results show a performance of our preconditioner as predicted by the theory. Our condition number estimate depends quadratically on the number of refinement levels.
BaSE(Byte addressable Storage Engine) Transaction Manager

Non-Volatile Memory (NVM) is an emerging memory technology that combines the best properties of current hard disks and main memories by providing non-volatility, high density, high speed, and byte addressability. This provides an opportunity to redesign systems and their software stacks to improve performance and to reduce the complexity. Present-day database systems are designed and optimized for traditional disks and memory hierarchies. They are very complex because they handle varying levels of storage latencies, from CPU caches to hard disks. Our intention is to build a prototype storage engine that is optimized for NVM and which takes advantage of the collapsed memory hierarchy. We are developing this storage engine in an incremental way. In this paper, we describe a novel approach to optimize write-ahead logging (WAL) for NVM based systems. Most database systems use ARIES-style write-ahead logging to implement transactions. ARIES techniques are optimized for disk based systems and tuned for the sequential write performance of disks. We leverage the high speed, byte-addressable random access of NVM to design a high-performance logging mechanism. We discuss the bottlenecks of sequential logging, identify the challenges of distributed logging and propose a novel solution. We show that NVM-optimized logging improves performance
Virtual object manipulation on a table-top AR environment

We address the problems of virtual object interaction and user tracking in a table-top augmented reality (AR) interface. In this setting there is a need for very accurate tracking and registration techniques and an intuitive and useful interface. This is especially true in AR interfaces for supporting face to face collaboration where users need to be able to easily cooperate with each other. We describe an accurate vision-based tracking method for table-top AR environments and tangible user interface (TUI) techniques based on this method that allow users to manipulate virtual objects in a natural and intuitive manner. Our approach is robust, allowing users to cover some of the tracking markers while still returning camera viewpoint information, overcoming one of the limitations of traditional computer vision based systems. After describing this technique we describe its use in prototype AR applications.
VLSI Implementation of Parallel CRC Using Pipelining, Unfolding and Retiming

Abstract : Error detection is important whenever there is a non-zero chance of data getting corrupted. A Cyclic Redundancy Check (CRC) is the remainder, or residue, of binary division of a potentially long message, by a CRC polynomial. This technique is ubiquitously employed in communication and storage applications due to its effectiveness at detecting errors and malicious tampering. The hardware implementation of a bit-wise CRC is a simple linear feedback shift register. Such a circuit is very simple and can run at very high clock speeds, but it requires the stream to be bit-serial. This means that ‘n’ clock cycles will be required to calculate the CRC values for an n-bit data stream. This latency is intolerable in many high speed data networking applications where data frames need to be processed at high speed and hence implementation of CRC generation and checking on a parallel stream of data becomes desirable. This paper presents implementation of parallel Cyclic Redundancy Check (CRC) based upon DSP algorithms of pipelining, retiming and unfolding. The architectures are first pipelined to reduce the iteration bound by using novel look-ahead techniques and then unfolded and retimed to design high speed parallel circuits. This paper presents the comparison between the parallel implementation of CRC-9 and its serial implementation. It also shows that parallel implementation uses less number of clock cycles than the serial implementation of CRC-9 thereby increasing the speed of the architecture. This paper is implemented using Verilog hardware description language, simulated using Xilinx ISE tools and synthesized using Cadence tools.
Low-order Controller Design Using Coprime Factors and Linear Matrix Inequalities for Discrete SISO Systems

This paper develops a low-order controller design method for linear discrete time-invariant single-input, single-output systems. Using the coprime factors and the properties of discrete outer functions, the low-order controller design becomes a zero-placement problem which is convex. The solutions are obtained using the linear matrix inequality techniques. The proposed design method is used to solve for several optimal control problems, such as pole-placement design, model-matching design, and H∞ optimization problems.
Crystal Structure of the 5-Chloro Salicylamides: Three Different Types of the H-bonding Influenced Linear Chain Formation in the Solid State

Three N-substituted 5-chlorosalicylamides (4-chlorophenyl, 2a; benzyl, 2b; phenethyl 2c) differing in the length of the 'linker' between the benzene ring and the amide moiety were prepared in order to compare their supramolecular architecture. The intramolecular NH···O(H) hydrogen bond and the intermolecular C=O···H–O hydrogen bond were found in the crystal structure of 2a and 2c thus forming an infinite linear chain. Compound 2b had a different arrangement with the intramolecular C=O···H–O hydrogen bond and another intermolecular NH···O(H) hydrogen forming a linear infinite chain.
Triangular kernel nearest neighbor based clustering for pattern extraction in spatio-temporal database

To date, various fields of applications have utilized spatio-temporal databases not only to store data, but to support decision making. For example, in traffic accident analysis; it is required to have knowledge on the pattern of accidents resulting in death. Thus, in such analysis, clustering technique is desired to implement pattern extraction. This paper presents clustering of spatio-temporal database using kernel nearest neighbor approach. It is chosen due to its ability to determine the number of clusters automatically. There are various types of kernel functions exist in the literatures, but the issue of concern is how to determine an appropriate kernel function for this application. In this study, two commonly used kernel functions, namely Gaussian and triangular, are investigated. From various experiments conducted, both functions produce reasonable clusters, but the triangular kernel nearest neighbor based clustering (TKNN) provides better performance with smaller number of iteration compared to Gaussian kernel nearest neighbor based clustering (ILGC) and K-means. Thus, TKNN is good option in clustering spatio-temporal database.
TeamX: A Sentiment Analyzer with Enhanced Lexicon Mapping and Weighting Scheme for Unbalanced Data

This paper describes the system that has been used by TeamX in SemEval-2014 Task 9 Subtask B. The system is a sentiment analyzer based on a supervised text categorization approach designed with following two concepts. Firstly, since lexicon features were shown to be effective in SemEval-2013 Task 2, various lexicons and pre-processors for them are introduced to enhance lexical information. Secondly, since a distribution of sentiment on tweets is known to be unbalanced, an weighting scheme is introduced to bias an output of a machine learner. For the test run, the system was tuned towards Twitter texts and successfully achieved high scoring results on Twitter data, average F1 70.96 on Twitter2014 and average F1 56.50 on Twitter2014Sarcasm.
ANS: Aberrant Neurodevelopment of the Social Cognition Network in Adolescents with Autism Spectrum Disorders

Background Autism spectrum disorders (ASD) are characterized by aberrant neurodevelopment. Although the ASD brain undergoes precocious growth followed by decelerated maturation during early postnatal period of childhood, the neuroimaging approach has not been empirically applied to investigate how the ASD brain develops during adolescence. Methodology/Principal Findings We enrolled 25 male adolescents with high functioning ASD and 25 typically developing controls for voxel-based morphometric analysis of structural magnetic resonance image. Results indicate that there is an imbalance of regional gray matter volumes and concentrations along with no global brain enlargement in adolescents with high functioning ASD relative to controls. Notably, the right inferior parietal lobule, a role in social cognition, have a significant interaction of age by groups as indicated by absence of an age-related gain of regional gray matter volume and concentration for neurodevelopmental maturation during adolescence. Conclusions/Significance The findings indicate the neural correlates of social cognition exhibits aberrant neurodevelopment during adolescence in ASD, which may cast some light on the brain growth dysregulation hypothesis. The period of abnormal brain growth during adolescence may be characteristic of ASD. Age effects must be taken into account while measures of structural neuroimaging have been clinically put forward as potential phenotypes for ASD.
Automated Reasoning Support for SUMO/KIF

Formal ontologies play an increasingly important role in demanding knowledge representation applications like the Semantic Web. Although there exist extensive formal ontologies written in the Knowledge Interchange Format (KIF), such as for instance the Suggested Upper Merged Ontology (SUMO), automated reasoning support for SUMO has so far been limited. We propose a novel transformation scheme for a non-trivial subset of the KIF language that enables the meaningful application of model-generation automated reasoning systems. Our transformation features the handling of equality, the Unique Name Assumption and default values and it can be configured to guarantee the finiteness of the resulting models.
A New Possibilistic Clustering Algorithm with Its Application to Fault Diagnosis

In 2010, we proposed the improved unsupervised possibilistic clustering algorithm (IUPC) that can be run as an unsupervised clustering and overcome the weakness of the unsupervised possibilistic clustering algorithm (UPC) that it tends to generate coincident clusters. IUPC inherits the merits of UPC. In the meanwhile, IUPC solves the coincident clusters problem of UPC by limiting the feasible regions of different clusters disjoint, and it also give a more accurate solution since it uses a global optimi-zation technique-differential evolution algorithm (DE) to optim-ize the proposed model. However, IUPC also has a disadvantage of sensitivity to the initializations because its feasible region of solutions is determined by the fuzzy c-means clustering algorithm, whose clustering result heavily depends on de the initial centers. In this paper, a new clustering algorithm called modified im-proved unsupervised possibilistic clustering algorithm (MIUPC) is proposed to overcome such problem of IUPC. The proposed algorithm adopts the subtractive clustering algorithm to initialize the cluster centers of IUPC. MIUPC not only inherits the merits of IUPC but also avoids the problem of sensitive to the initializa-tions. The contrast experiments with UPC and IUPC show the effectiveness of MIUPC. The proposed algorithm is also applied to the fault diagnosis, and the results show its better performance.
Investigating The Acceptance and Use of Performance Information System (SIP Bdg Juara) on Bandung City

Improving the utilization of ICT is one of many solutions proposed by The Municipal Government of Bandung to overcome urban problems. In its entirity, the concept is well-known as Bandung Smart City. There are many Smart Government programs that have been implemented in Bandung. One of them is Performance Information System of Bandung Juara or known as SIP Bdg Juara. But the acceptance of SIP Bdg Juara are relatively low. Furthermore, not all of the citizens have thoroughly understood the system. Hence, it is important to find out the acceptance of this website. This research uses modified UTAUT 2 model which was taken from the UTAUT 2 Theory by Venkatesh et al. (2012) that consist of seven main variables; Performance Expectancy, Effort Expectancy, Social Influence, Facillitating Conditions, Hedonic Motivation, Price value, and Habit. Moreover, two moderating variables of Age and Gender were also involved. This research used SEM-PLS method to analyze the data. For the data collection, questionnaires were distributed to 400 residents living in the city of Bandung as the respondents whether they have or have not participated yet in SIP Bdg Juara website. Finally, this study found that the most influential factor of interest in using SIP Bdg Juara website is Price Value, followed by Hedonic Motivation and then Habit in which all of those three factors have positive influence in both types of latent variables. While the variables affecting Use Behavior related to the use of SIP Bdg Juara wesite are Habit and Behavioral Intention. These findings are expected to provide valuable insights for The Government in improving the success rate of ICT-based governance programs. Keywords: Bandung Smart City, Modified UTAUT 2 , SIP Bdg Juara, Techonology Acceptance
Differential response to a selective cannabinoid receptor antagonist (SR141716: rimonabant) in female mice from lines selectively bred for high voluntary wheel-running behaviour

Exercise is a naturally rewarding behaviour in human beings and can be associated with feelings of euphoria and analgesia. The endocannabinoid system may play a role in the perception of neurobiological rewards during and after prolonged exercise. Mice from lines that have been selectively bred for high voluntary wheel running (high runner or HR lines) may have evolved neurobiological mechanisms that increase the incentive salience of endurance-type exercise. Here, we test the hypothesis that endocannabinoid signalling has been altered in the four replicate HR lines as compared with four nonselected control lines. After 18 days of acclimation to cages with attached wheels, we injected mice with rimonabant (SR141716), a selective cannabinoid CB1 receptor antagonist. During the time of normal peak running, each mouse received, in a randomized order, intraperitonial injection of rimonabant (0.1 or 3.0 mg/kg) or vehicle, over 9 days. Drug response was quantified as wheel revolutions, time and speed 10–70 min postinjection. Rimonabant decreased running in all mice; however, female HR mice differentially decreased running speed and distance (but not time) as compared with control females. We conclude that altered endocannabinoid signalling plays a role in the high wheel running of female HR mice.
The Development of a Self-Report Questionnaire on Coping with Cyberbullying: The Cyberbullying Coping Questionnaire

The negative effects and the continuation of cyberbullying seem to depend on the coping strategies the victims use. To assess their coping strategies, self-report questionnaires (SRQs) are used. However, these SRQs are often subject to several shortcomings: the (single and topological) categorizations used in SRQs do not always adequately differentiate among various coping responses, in addition the strategies of general SRQs fail to accurately measure coping with cyberbullying. This study is therefore aimed to develop a SRQ that specifically measures coping with cyberbullying ( i.e. , Cyberbullying Coping Questionnaire; CCQ) and to discover whether other, not single and topological, categorizations of coping strategies can be found. Based on previous SRQs used in the (cyber)bullying ( i.e. , traditional and cyberbullying) literature ( i.e. , 49 studies were found with three different SRQs measuring coping with traditional bullying, cyberbullying or (cyber)bullying) items and categorizations were selected, compared and merged into a new questionnaire. In compliance with recommendations from the classical test-theory, a principal component analysis and a confirmatory factor analysis were done, and a final model was constructed. Seventeen items loaded onto four different coping categorizations: mental-, passive-, social-, and confrontational-coping. The CCQ appeared to have good internal consistency, acceptable test-retest reliability, good discriminant validity and the development of the CCQ fulfilled many of the recommendations from classical test-theory. The CCQ omits working in single and topological categorizations and measures cognitive, behavioral, approach and avoidance strategies.
A framework for adaptive Monte Carlo procedures

Abstract Adaptive Monte Carlo methods are recent variance reduction techniques. In this work, we propose a mathematical setting which greatly relaxes the assumptions needed by for the adaptive importance sampling techniques presented in [Arouna, Monte Carlo Methods Appl. 10: 1–24, 2004, Arouna, The Journal of Computational Finance 7: Winter 2003/2004, Su and Fu, Journal of Computational Finance 5: 27–50, 2002, Vázquez-Abad and Dufresne, Accelerated simulation for pricing asian options: 1493–1500, IEEE Computer Society Press, 1998]. We establish the convergence and asymptotic normality of the adaptive Monte Carlo estimator under local assumptions which are easily verifiable in practice. We present one way of approximating the optimal importance sampling parameter using a randomly truncated stochastic algorithm. Finally, we apply this technique to some examples of valuation of financial derivatives.
Segmentation of liver tumor with local C-V level set

In this paper, we proposes a local C-V level set algorithm integrated with local OTSU method for the segmentation of liver lesion. We first construct a local liver lesion image, then pre-segment the lesion with OTSU method to get the initial contour, finally evolve the active contour with local C-V level set method to get the final contour of lesion. Experiments showed this method can extract liver tumor efficiently.