Miracle-dz/UniFAR-FTUs huggingface dataset:
Game-based human computer interaction using gesture recognition for rehabilitation

This paper introduces a human computer interaction system by using hand gesture recognition to apply into game-based rehabilitation application. The basic concept of this project is to assist patient to carry out rehabilitation with hand gesture recognition, meanwhile therapist is able to follow recovery progress of patient through results. In this manner, a rehabilitation system created aimed to increase efficacy in patient treatment with interactive game. The system is applied in real time application to contribute as a function of a virtual mouse. Approach proposed including hand region segmentation, hand palm partition and finger counting. Computer mouse is configured to move according to the location of hand palm region, and perform function of ordinary mouse according to number of fingers detected. In the analysis, experiments is carried out to show how this system collaborate with the simple rehabilitation game in computer. Experiment evaluation as shown has successfully demonstrate the effectiveness and robustness in performing the mouse function and feasible to be implemented into rehabilitation games.
Natural Killer Cells Promote Early CD8 T Cell Responses against Cytomegalovirus

Understanding the mechanisms that help promote protective immune responses to pathogens is a major challenge in biomedical research and an important goal for the design of innovative therapeutic or vaccination strategies. While natural killer (NK) cells can directly contribute to the control of viral replication, whether, and how, they may help orchestrate global antiviral defense is largely unknown. To address this question, we took advantage of the well-defined molecular interactions involved in the recognition of mouse cytomegalovirus (MCMV) by NK cells. By using congenic or mutant mice and wild-type versus genetically engineered viruses, we examined the consequences on antiviral CD8 T cell responses of specific defects in the ability of the NK cells to control MCMV. This system allowed us to demonstrate, to our knowledge for the first time, that NK cells accelerate CD8 T cell responses against a viral infection in vivo. Moreover, we identify the underlying mechanism as the ability of NK cells to limit IFN-α/β production to levels not immunosuppressive to the host. This is achieved through the early control of cytomegalovirus, which dramatically reduces the activation of plasmacytoid dendritic cells (pDCs) for cytokine production, preserves the conventional dendritic cell (cDC) compartment, and accelerates antiviral CD8 T cell responses. Conversely, exogenous IFN-α administration in resistant animals ablates cDCs and delays CD8 T cell activation in the face of NK cell control of viral replication. Collectively, our data demonstrate that the ability of NK cells to respond very early to cytomegalovirus infection critically contributes to balance the intensity of other innate immune responses, which dampens early immunopathology and promotes optimal initiation of antiviral CD8 T cell responses. Thus, the extent to which NK cell responses benefit the host goes beyond their direct antiviral effects and extends to the prevention of innate cytokine shock and to the promotion of adaptive immunity.
Behavior Based Credit Card Fraud Detection Using Support Vector Machines

Along with the great increase of internet and e-commerce, the use of credit card is an unavoidable one. Due to the increase of credit card usage, the frauds associated with this have also increased. There are a lot of approaches used to detect the frauds. In this paper, behavior based classification approach using Support Vector Machines are employed and efficient feature extraction method also adopted. If any discrepancies occur in the behaviors transaction pattern then it is predicted as suspicious and taken for further consideration to find the frauds. Generally credit card fraud detection problem suffers from a large amount of data, which is rectified by the proposed method. Achieving finest accuracy, high fraud catching rate and low false alarms are the main tasks of this approach.
The Acetylcholinesterase Homology Region Is Essential for Normal Conformational Maturation and Secretion of Thyroglobulin*

Secretion of thyroglobulin (Tg, a large homodimeric glycoprotein) is essential to deliver Tg to its site of iodination for thyroxine biosynthesis. An L2263P missense mutation in Tg has been proposed as the molecular defect causing congenital goitrous hypothyroidism in cog/cog mice due to perturbed Tg homodimerization, resulting in its retention within the endoplasmic reticulum. The mutation falls within a carboxyl-terminal region of Tg with high structural similarity to the entirety of acetylcholinesterase (AChE), a secretory protein that also forms homodimers. We provide new evidence that authentic AChE and the cholinesterase-like domain of Tg share a common tertiary structure. Moreover, we find that a Tg truncation, deleted of the cholinesterase-like region (but not a comparably sized deletion of internal Tg regions), blocks Tg export. Appending to this truncation a cDNA encoding authentic AChE results in translation of a chimeric protein in which AChE is present in a native, enzymatically active (albeit latent) conformation, and this fully rescues Tg secretion. Introduction of the cog mutation inhibits AChE enzyme activity, and established denaturing mutations of AChE block secretion of the Tg. Additional studies show that the native structure of the AChE region functions as a “dimerization domain,” facilitating intracellular transport of Tg to the site of thyroid hormonogenesis.
Modeling the Evolution of Knowledge and Reasoning in Learning Systems

How do reasoning systems that learn evolve over time? Characterizing the evolution of these systems is important for understanding their limitations and gaining insights into the interplay between learning and reasoning. We describe an inverse ablation model for studying how learning and reasoning interact: Create a small knowledge base by ablation, and incrementally re-add facts, collecting snapshots of reasoning performance of the system to measure properties of interest. Experiments with this model suggest that different concepts show different rates of growth, and that the density of facts is an important parameter for modulating the rate of learning.
A generic service system for knowledge-intensive service firms

Service systems are highly complex and often difficult to study, design and implement given their socio-technical-organisational nature. This complexity leads to a poor understanding as to how service systems can be innovative and competitive. Within the service sector, Knowledge-Intensive Service firms compete by the skills and knowledge of their employees to deliver online business services. However, current service systems for Knowledge-Intensive Service firms are either too static to describe the dynamic mechanism, or too visionary to use in real world applications, which makes activities such as designing, developing, and evolving adaptive service applications unpractical. In this paper, we present a generic service system architecture describing service characteristics with descriptors such as customer-centric, knowledge-driven, reusability, safeguards, interoperability, and Internet delivery channels. Finally, we propose an implementation framework to achieve secure and adaptive service delivery and a service life cycle describing the service production process.
Making Self-Regulation More Than Merely Symbolic: The Critical Role of the Legal Environment

Using data from a sample of U.S. industrial facilities subject to the federal Clean Air Act from 1993 to 2003, this article theorizes and tests the conditions under which organizations' symbolic commitments to self-regulate are particularly likely to result in improved compliance practices and outcomes. We argue that the legal environment, particularly as it is constructed by the enforcement activities of regulators, significantly influences the likelihood that organizations will effectively implement the self-regulatory commitments they symbolically adopt. We investigate how different enforcement tools can foster or undermine organizations' normative motivations to self-regulate. We find that organizations are more likely to follow through on their commitments to self-regulate when they (and their competitors) are subject to heavy regulatory surveillance and when they adopt self-regulation in the absence of an explicit threat of sanctions. We also find that historically poor compliers are significantly less likely to follow through on their commitments to self-regulate, suggesting a substantial limitation on the use of self-regulation as a strategy for reforming struggling organizations. Taken together, these findings suggest that self-regulation can be a useful tool for leveraging the normative motivations of regulated organizations but that it cannot replace traditional deterrence-based enforcement.
Scalability of local image descriptors: a comparative study

Computer vision researchers have recently proposed several local descriptor schemes. Due to lack of database support, however, these descriptors have only been evaluated using small image collections. Recently, we have developed the PvS-framework, which allows efficient querying of large local descriptor collections. In this paper, we use the PvSframework to study the scalability of local image descriptors. We propose a new local descriptor scheme and compare it to three other well known schemes. Using a collection of almost thirty thousand images, we show that the new scheme gives the best results in almost all cases. We then give two stop rules to reduce query processing time and show that in many cases only a few query descriptors must be processed to find matching images. Finally, we test our descriptors on a collection of over three hundred thousand images, resulting in over 200 million local descriptors, and show that even at such a large scale the results are still of high quality, with no change in query processing time.
Volume segmentation in medical image analysis: a survey

Volume segmentation is an important part of computer based medical applications for diagnosis and analysis of anatomical data. With rapid advances in medical imaging modalities and volume visualization techniques, computer based diagnosis is fast becoming a reality. These computer based tools allow scientists and physician to understand and diagnose anatomical structures by virtually interacting with them. Volume segmentation plays a critical role by facilitating automatic or semiautomatic extraction of the anatomical organ or region of interest. In this review we provide an introduction to various segmentation algorithms found in the literature. We classify the algorithms in to three categories: structural techniques, statistical technique and hybrid techniques. Under structural techniques we will review algorithms which take into consideration structural information for segmentation stochastic techniques are those which perform segmentation based on statistical analysis methods and under hybrid techniques we will review algorithms which make use of structural information in addition to statistical analysis.
Adapting to unknown sparsity by controlling the false discovery rate

We attempt to recover an n-dimensional vector observed in white noise, where n is large and the vector is known to be sparse, but the degree of sparsity is unknown. We consider three different ways of defining sparsity of a vector: using the fraction of nonzero terms; imposing power-law decay bounds on the ordered entries; and controlling the lp norm for p small. We obtain a procedure which is asymptotically minimax for l r loss, simultaneously throughout a range of such sparsity classes. The optimal procedure is a data-adaptive thresholding scheme, driven by control of the False Discovery Rate (FDR). FDR control is a relatively recent innovation in simultaneous testing, ensuring that at most a certain fraction of the rejected null hypotheses will correspond to false rejections. In our treatment, the FDR control parameter qn also plays a determining role in asymptotic minimaxity. If q = lim qn ∈ [0,1/2] and also qn > γ/log(n) we get sharp asymptotic minimaxity, simultaneously, over a wide range of sparse parameter spaces and loss functions. On the other hand, q = lim qn ∈ (1/2,1], forces the risk to exceed the minimax risk by a factor growing with q. To our knowledge, this relation between ideas in simultaneous inference and asymptotic decision theory is new. Our work provides a new perspective on a class of model selection rules which has been introduced recently by several authors. These new rules impose complexity penalization of the form 2 � log( potential model size / actual model size ). We exhibit a close connection with FDR-controlling procedures under stringent control of the false discovery rate.
Robotics based simultaneous localization and mapping of an unknown environment using Kalman Filtering

In this paper, Robotic application using a microcontroller ATmega2560 based kit mounted with low cost IR sensors for mapping and localization of unknown area is accurately carried out by using Kalman Filtering to determine or measure different parameters. This paper highlights the comparison between the accuracies of output data obtained from IR sensor using Average Technique as well as Kalman Filter. Various cases depending on robot movement in forward and backward direction and wheel rotation in clockwise and anticlockwise direction, are considered in this paper to calculate position encoder resolution. Also probabilistic estimation of actual position of robot is carried out using different techniques i.e Probability Density Function (P.D.F) for verifying the uncertainty in its position and found to be very close to actual position. Simultaneous Localization And Mapping (SLAM) is the only way a robot can navigate through an unknown environment with the use of minimum sensors and get reliable output of the same in identifying an unknown area. Using this method the robot not only create a map more accurate than GPS maps but also localize itself to determine its next position according to the map created more accurately at lower cost. Primarily used for creating a map of an unknown location, this concept can also be used to perform Qualitative Analysis of a given area by equipping the robot with appropriate sensors.
The Potential Role of Contraction-Induced Myokines in the Regulation of Metabolic Function for the Prevention and Treatment of Type 2 Diabetes

Skeletal muscle represents the largest organ in the body, comprises 36–42% of body weight, and has recently been recognized as having an endocrine function. Proteins expressed and released by muscle that have autocrine, paracrine, and endocrine bioactivities have been termed myokines. It is likely that muscle contraction represents the primary stimulus for the synthesis and secretion of myokines to enable communication with other organs such as the liver, adipose tissue, brain, and auto-regulation of muscle metabolism. To date, several hundred myokines in the muscle secretome have been identified, a sub-population of which are specifically induced by skeletal muscle contraction. However, the bioactivity of many of these myokines and the mechanism through which they act has either not yet been characterized or remains poorly understood. Physical activity and exercise are recognized as a central tenet in both the prevention and treatment of type 2 diabetes (T2D). Recent data suggest humoral factors such as muscle-derived secretory proteins may mediate the beneficial effects of exercise in the treatment of metabolic diseases. This mini-review aims to summarize our current knowledge on the role of contraction-induced myokines in mediating the beneficial effects of physical activity and exercise in the prevention and treatment of T2D, specifically glucose and lipid metabolism. Future directions as to how we can optimize contraction-induced myokine secretion to inform exercise protocols for the prevention and treatment of T2D will also be discussed.
The Sensitivity of Children to Electromagnetic Fields

In today's world, technologic developments bring social and economic benefits to large sections of society; however, the health consequences of these developments can be difficult to predict and manage. With rapid advances in electromagnetic field (EMF) technologies and communications, children are increasingly exposed to EMFs at earlier and earlier ages. Consistent epidemiologic evidence of an association between childhood leukemia and exposure to extremely low frequency (ELF) magnetic fields has led to their classification by the International Agency for Research on Cancer as a “possible human carcinogen. ” Concerns about the potential vulnerability of children to radio frequency (RF) fields have been raised because of the potentially greater susceptibility of their developing nervous systems; in addition, their brain tissue is more conductive, RF penetration is greater relative to head size, and they will have a longer lifetime of exposure than adults. To evaluate information relevant to children's sensitivity to both ELF and RF EMFs and to identify research needs, the World Health Organization held an expert workshop in Istanbul, Turkey, in June 2004. This article is based on discussions from the workshop and provides background information on the development of the embryo, fetus, and child, with particular attention to the developing brain; an outline of childhood susceptibility to environmental toxicants and childhood diseases implicated in EMF studies; and a review of childhood exposure to EMFs. It also includes an assessment of the potential susceptibility of children to EMFs and concludes with a recommendation for additional research and the development of precautionary policies in the face of scientific uncertainty.
Touchbox: intriguing touch between strangers

The Touchbox is about facilitating intriguing touch interaction between strangers. The Participants each wear a pair of headphones, and when they touch each others bare skin, they both hear a complex sound pattern. Previous (successful) work involved a skilled Performer and one Participant; the Touchbox was designed to be played by pairs of pristine Participants exploring the interaction situation on their own. It turned out that their interaction experiences were quite engaging albeit more varied in mood and character. The Touchbox illustrates a novel approach to embodied interaction design where social norms are transcended by means of daring and captivating interactions.
Measuring Cortical Connectivity in Alzheimer’s Disease as a Brain Neural Network Pathology: Toward Clinical Applications

Abstract Objectives: The objective was to review the literature on diffusion tensor imaging as well as resting-state functional magnetic resonance imaging and electroencephalography (EEG) to unveil neuroanatomical and neurophysiological substrates of Alzheimer’s disease (AD) as a brain neural network pathology affecting structural and functional cortical connectivity underlying human cognition. Methods: We reviewed papers registered in PubMed and other scientific repositories on the use of these techniques in amnesic mild cognitive impairment (MCI) and clinically mild AD dementia patients compared to cognitively intact elderly individuals (Controls). Results: Hundreds of peer-reviewed (cross-sectional and longitudinal) papers have shown in patients with MCI and mild AD compared to Controls (1) impairment of callosal (splenium), thalamic, and anterior–posterior white matter bundles; (2) reduced correlation of resting state blood oxygen level-dependent activity across several intrinsic brain circuits including default mode and attention-related networks; and (3) abnormal power and functional coupling of resting state cortical EEG rhythms. Clinical applications of these measures are still limited. Conclusions: Structural and functional (in vivo) cortical connectivity measures represent a reliable marker of cerebral reserve capacity and should be used to predict and monitor the evolution of AD and its relative impact on cognitive domains in pre-clinical, prodromal, and dementia stages of AD. (JINS, 2016, 22, 138–163)
DGA Botnet Detection Utilizing Social Network Analysis

Botnets are one of the major threats to network security. A botnet can launch attacks by stealing information, phishing sites, sending spam mail and setting up distributed denial of service (DDoS). Some botnets called Domain Generation Algorithm (DGA) Botnets apply a domain generation algorithm to avoid being detected by the traditional blacklist detection scheme. Using a domain generation algorithm, a DGA bot periodically generates a huge list of candidate Command and Control server (C&C) domains. The bot then attempts to connect to the C&C server by querying DNS servers for the domains on the list one by one until it connects to an existing C&C server. By doing this, DGA botnets become very elusive and difficult to detect by traditional defending systems and thus have high survivability. To resolve this issue, this study proposes a DGA botnet detection mechanism utilizing the feature-based characteristics of social networks. The effectiveness of this mechanism was measured by implementing it in a campus network environment and observing it over eighteen months. The most interesting finding of this experiment is a new class of DGA botnet with a query pattern that has not been detected before. The results show that the proposed mechanism has the ability to accurately and effectively detect both well-known and new malicious DGA botnets in real-world networks.
Predication in the Logic of Terms

The paper contrasts modern predicate logic (MPL) and term/functor logic (TFL) on predication. A predication in TFL consists of two terms and a «logical copula» that has formal properties such as symmetry or transitivity. The predicative functors are shown to give TFL a slight advantage over MPL in expressive and inference power when dealing with singular statements
Ultrastructural analysis of central serotoninergic neurons immunolabeled by silver-gold-intensified diaminobenzidine chromogen. Completion of immunocytochemistry with X-ray microanalysis.

Serotonin immunoreactive structures of the rat central nervous system (CNS) were detected by the recently developed silver-intensified peroxidase-antiperoxidase complex (SI-PAP) method at both the light and electron microscopic levels. The silver postintensification of the diaminobenzidine (DAB) chromogen increased the sensitivity of the original PAP method, resulting in a very Golgi-like appearance of serotonin-immunopositive neuronal elements. The metallic silver and gold deposited onto DAB-labeled organelles, filling out the whole immunoreactive neuron, assures the easy tracing of thin neuronal processes far from the cell body. At the ultrastructural level, metallic grains were seen over immunolabeled structures only, proving the specificity of the silver method. In neurons of the dorsal raphe nucleus, free ribosomes, endoplasmic reticulum, and granules (80-100 nm in diameter) were labeled. Immunoreactive, e.g., serotoninergic, dendrites were seen to receive afferent terminals. The increased electron density of the intensified immunolabel facilitates the ultrastructural recognition of even weakly labeled profiles, while its metallic components (Ag and Au) provide a base for X-ray analysis of the immunolabeled biological specimen.