Miracle-dz/UniFAR-FTUs huggingface dataset:
VISHNU: A Software Behavior Trustworthiness Control Method Based on DRTM

Based on secure execution technology provided by Late Launch and modern computer hardware, we propose a software dynamic trustworthy control method, which purpose is to solve trust control problem of key operations at software runtime through strong isolation. Key operations indicate operations that directly access to sensitive data. This method can prevent malware program accessing to sensitive data and key operations in protected processes for security, even that computer's application or operating system has been invaded or controlled. We developed a prototype system called VISHNU and take Online Payment as an example to validate the effectiveness of the method.
Towards a ranking approach for sensor services using primitive cognitive network process

Abstracting sensor capabilities as services is a promising approach to support interoperability and scalability in Internet of Things based applications; however, ranking and selecting these dynamic and large number of real-world services in real-time is a challenging problem, which involves decision making in the presence of multiple criteria. This paper demonstrates an application of the Primitive Cognitive Network Process (P-CNP) to the sensor service ranking with respect to multiple Quality-of-Service criteria.
Towards ant colony optimization of neuro-fuzzy interval rules

Neuro-fuzzy rules can be used in their fuzzy form and in an interval form that is a cut of the corresponding membership function. Such interval rules can be derived whenever a precise interval rule is useful in the application area. An example where interval rules can be applied is the area of virtual screening in chemistry. Current research focusses on finding novel drugs. Nowadays, preselection of potential molecules can be done by computational analysis of molecular properties. Usually, a high-dimensional descriptor vector represents the molecular properties for one molecule. With a well-established neuro-fuzzy system that is capable of selecting important features, we describe the process of interval rule generation within the application. Since the neuro-fuzzy interval rules need not to be optimal, the idea of ant colony optimization is adapted for solving the interval rule optimization problem. The results demonstrate the capability of interval rule optimization by an ant colony but also its dependency on the number of ants.
Hospitality management study programme and students’ perceptions: universities in South Africa

ABSTRACT This study using a questionnaire survey of 200 hospitality management students in two South African Universities assessed the perceptions of these students towards their study programme, the support they get while studying, and their intention to work in the hospitality sector over the long-term. Results from data analyses indicate that the students are generally satisfied with their study and would recommend hospitality management as a study programme to friends and relatives. Many of them also expressed the desire to work in the hospitality sector over the long-term. Their major concerns towards their study are in the areas of inadequacy of the hospitality management curriculum to address their study needs, the fear that fulfilling jobs will not be available for them after graduation, and their lack of knowledge regarding public and private funding for hospitality entrepreneurship activities. This study therefore calls on hospitality management study programme managers in South Africa to include entrepreneurship courses that address how graduates can source public or private funds to start up their own hospitality businesses, to decrease dependence on employers. Graduates with comprehensive and integrated knowledge of the hospitality sector operations and opportunities will not have fear of getting jobs and performing well in the sector.
An Experiment in Word Alignment with a Parallel Corpus

This report documents an experiment done on word alignment using a parallel , sentence aligned corpus. The languages are English and Japanese and the corpus is derived from the Asahi Shinbun daily newspaper editorials. The aims of the experiment are To nd out how accurate word alignment is with simple pattern matching. To nd out how useful a conventional English-Japanese lexicon is. To observe how well English and Japanese newspaper editorials correspond at the lexical level of transfer. The results show approximately 80% word alignment success using a small bilingual lexicon of 81 English words and a mean 17.4 translations per word. The reason for error is analysed and it is concluded that gaps in the senses of words in the lexicon and stylistic diierences in the English and Japanese are responsible. The shallow level of analysis is not a major problem although it is expected to become more important as coverage is increased.
Wild mouse RNA tumor viruses. A nongenetically transmitted virus group closely related to exogenous leukemia viruses of laboratory mouse strains

Type-C RNA viruses isolated from wild mice are causative of naturally occurring neoplasia and neurologic diseases. Biochemical and immunologic characterization of this virus group revealed that amphotropic viruses isolated from wild mice trapped in separate geographical areas are indistinguishable, whereas amphotropic and ecotropic viruses naturally infecting the same animal are env gene variants. Molecular hybridization studies established that neither host range variant is endogenous to the Mus musculus genome, although each demonstrates partial nucleotide sequence homology. Wild mouse type-C viruses exhibited much closer molecular and antigenic relatedness to the exogenous virus subgroup (Friend-, Moloney-, and Rauscher-MuLV) than to prototype endogenous viruses isolated from laboratory mouse strains. The evidence indicates that exogenous mouse type-C viruses have been maintained in nature over a long period of evolution as a separate virus group, causative of tumors in mice by a mechanism solely involving their transmission as infectious agents.
Aza-Henry Reactions on C-Alkyl Substituted Aldimines

The reactivity of C-CH3 substituted N-protected aldimines in aza-Henry addition reactions was compared with that of the analogous trifluoromethylated compounds. C-Alkyl aldimines easily reacted with nitro alkanes under solvent-free conditions and in the absence of catalyst, despite being worse electrophiles than C-CF3 aldimines, they gave the aza-Henry addition only when ZrCl4 was added. The presence of a bulky group on the imine carbon deeply influenced the reactivity.
Dancing with Turks

Dance is a dynamic art form that reflects a wide range of cultural diversity and individuality. With the advancement of motion-capture technology combined with crowd-sourcing and machine learning algorithms, we explore the complex relationship between perceived dance quality/dancer's gender and dance movements/music respectively. As a feasibility study, we construct a computational framework for an analysis-synthesis-feedback loop using a novel multimedia dance-music texture representation. Furthermore, we integrate crowd-sourcing, music and motion-capture data, and machine learning-based methods for dance segmentation, analysis and synthesis of new dancers. A quantitative validation of this framework on a motion-capture dataset of 172 dancers evaluated by more than 400 independent on-line raters demonstrates significant correlation between human perception and the algorithmically intended dance quality or gender of synthesized dancers. The technology illustrated in this work has a high potential to advance the multimedia entertainment industry via dancing with Turks.
Patterns in the Chaos—A Study of Performance Variation and Predictability in Public IaaS Clouds

Benchmarking the performance of public cloud providers is a common research topic. Previous work has already extensively evaluated the performance of different cloud platforms for different use cases, and under different constraints and experiment setups. In this article, we present a principled, large-scale literature review to collect and codify existing research regarding the predictability of performance in public Infrastructure-as-a-Service (IaaS) clouds. We formulate 15 hypotheses relating to the nature of performance variations in IaaS systems, to the factors of influence of performance variations, and how to compare different instance types. In a second step, we conduct extensive real-life experimentation on four cloud providers to empirically validate those hypotheses. We show that there are substantial differences between providers. Hardware heterogeneity is today less prevalent than reported in earlier research, while multitenancy has a dramatic impact on performance and predictability, but only for some cloud providers. We were unable to discover a clear impact of the time of the day or the day of the week on cloud performance.
Capacitive Boosting for FPGA Interconnection Networks

FPGA interconnection network is implemented using nMOS pass transistor multiplexers. Since the threshold voltage drop across an nMOS device degrades the high logic value, the pMOS transistors of the downstream buffers do not turn fully off, making this approach suffer from static power consumption due to the short-circuit currents and reduced noise margins. The standard pMOS transistor pull-up in the active feedback of an inverter reduces the static power consumption but degrades the switching time and/or dynamic power consumption. We propose to use capacitive boosting to increase the gate voltage of the pass transistors, thus driving the multiplexer output to the full high voltage level at a faster rate than the pMOS pull-up can alone. This way, the signal transitions are accelerated and the short-circuit current of downstream buffers are cut off. The simulations carried out with Cadence indicate a reduction of at least 10% in propagation delay for the proposed circuit versus the standard one across 180nm, 130nm, 90nm, and 65nm technologies.
Fabric Manipulation : An Eye Tracking Experiment

An eye tracking study of people engaged in a cloth sorting task is reported. Both eye fixations and hand movements were tracked during the experiments. The eye and hand movements were categorized and present alone a time line. The observations are consistence with a model of task driven covert global processing of the scene followed by overt sequential processing of fixated areas of interest for grasping. A computational model is presented.
The Impact of Perceived Mental Illness Stigma on Caregivers’ Desire to Relinquish Care

THE IMPACT OF PERCEIVED MENTAL ILLNESS STIGMA ON CAREGIVERS’ DESIRE TO RELINQUISH CARE By Tyler Rogers Corson, MS A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy at Virginia Commonwealth University. Virginia Commonwealth University, 2017. Major Director: Tracey Gendron, PhD, Associate Professor, Department of Gerontology Caregiving can be stressful, and older adults’ health and well-being may be impacted by the roles and responsibilities they assume as caregivers for persons with serious mental illness (SMI). This study is the first to apply the Stress Process Model of Caregiving (SPM) in an attempt to understand how mental illness stigma influences caregiver outcomes, specifically their desire to relinquish care. The intent of this study was to call attention to care relinquishment as an understudied stress process outcome and to explore stress factors, with a focus on mental illness stigma, that contribute to SMI caregivers’ desire to relinquish care. Using convenience sampling, members of the National Alliance on Mental Illness in the eastern U.S. were invited to participate in an online survey, resulting in a sample of n = 285. Regression analysis findings suggest that caregivers’ partnership status, exposure to problematic behaviors, and perceptions of courtesy stigma predicted desire to relinquish care. Neither age nor caregiver sense of mastery moderated the relationship between perceived courtesy stigma and relinquishment desire. Perceptions of stigma were negatively associated with caregiver health, sense of mastery, and social support levels, indicating stigma’s role in the erosion of caregiver resources. This study provides information that can inform the development of educational and supportive services that may help caregivers better cope with the stressors associated with SMI caregiving. With caregiving stressors diminished, older caregivers will be able to better apply their resources toward self-care and maintaining their quality of life.
Towards automated drive analysis: A multimodal synergistic approach

Naturalistic driving studies (NDS) capture huge amounts of drive data, that is analyzed for critical information about driver behavior, driving characteristics etc. Moreover, NDS involve data collected from a wide range of sensing technologies in cars and this makes the analysis of this data a challenging task. In this paper, we propose a multimodal synergistic approach for automated drive analysis process that can be employed in analyzing large amounts of drive data. The visual information from cameras, vehicle dynamics from CAN bus, vehicle global positioning coordinates from GPS and digital road map data, that are collected during the drive, are analyzed in a collaborative and complementary manner in the approach presented in this paper. It will be shown that the proposed synergistic drive analysis approach automatically determines a wide range of critical information about the drive in varying road conditions.
POINTING IN VIRTUAL REALITY : EXPERIMENTAL STUDY

Developing an industry-complaint Virtual Reality (VR) CAD application is a difficult task mainly because computer human interaction using 3D input is still an open issue. Six degrees of freedom (6DOF) devices and stereo vision are often reported in literature for general tasks such as navigation and selection, but not for the specific modelling activity. For this reason a VR experimental test bed called SpaceXperiment has been developed and a statistical study has been carried out. Speed, limbs position and direction have proven to affect the user’s interaction performances during pointing, picking and lines sketching. Average values of precision, reliability and speeds have been extracted in order to serve as reference for future interface design. The presented results offer a significant contribution for developers of modelling applications in Virtual Reality.
HMG-CoA reductase inhibitors and the risk of fractures.

CONTEXT Recent animal studies have suggested that 3-hydroxy-3-methylglutaryl coenzyme A (HMG-CoA) reductase inhibitors (statins) increase bone formation, volume, and density. It is unknown whether use of statins is associated with a decreased risk of fractures in humans. OBJECTIVE To determine whether exposure to statins, fibrates, or other lipid-lowering drugs is associated with reduced bone fracture risk. DESIGN Population-based, nested case-control analysis. SETTING The UK-based General Practice Research Database (GPRD), comprising some 300 practices, with data collection from the late 1980s until September 1998. SUBJECTS Within a base population of 91,611 individuals aged at least 50 years (28,340 individuals taking lipid-lowering drugs, 13,271 untreated individuals with a diagnosis of hyperlipidemia, and 50,000 randomly selected individuals without diagnosis of hyperlipidemia), we identified 3940 case patients who had a bone fracture and 23,379 control patients matched for age (+/-5 years), sex, general practice attended, calendar year, and years since enrollment in the GPRD. MAIN OUTCOME MEASURES Use of statins, fibrates, or other lipid-lowering drugs in case patients vs control patients. RESULTS After controlling for body mass index, smoking, number of physician visits, and corticosteroid and estrogen use, current use of statins was associated with a significantly reduced fracture risk (adjusted odds ratio [OR], 0.55; 95% confidence interval [CI], 0.44-0.69) compared with nonuse of lipid-lowering drugs. Current use of fibrates or other lipid-lowering drugs was not related to a significantly decreased bone fracture risk (adjusted OR, 0.87; 95% CI, 0.70-1.08 and adjusted OR, 0.76; 95% CI, 0.41-1.39, respectively). CONCLUSIONS This study suggests that current exposure to statins is associated with a decreased risk of bone fractures in individuals age 50 years and older. This finding has a potentially important public health impact and should be confirmed further in controlled prospective trials. JAMA. 2000;283:3205-3210
Prediction of Service Reliability Based on Grouping

With the wide applications of Internet-based services, service reliability has received more and more attention in the research field of Services Computing. Reliability prediction approaches basically compute reliability according to historical records. However, in most current prediction methods, each user is supposed to return a Boolean feedback representing his/her subjective evaluation on service reliability. This binary feedback cannot express the user's evaluation of reliability precisely. Even though some researchers propose to compute reliability according to feedback from similar users or similar services, accuracy of reliability prediction cannot be guaranteed successfully. To address the problems above, this paper presents a fine-grained reliability prediction method based on grouping (RPMBG). In RPMBG, feedback is divided into several dimensions and each user can choose a service degree for each dimension to represent his/her evaluation on consumed service. A quantifying approach of users' feedback on each dimension will be given with the employment of entropy in information theory. At the same time, a grouping methodology including grouping of users and grouping of services is described with the employments of fuzzy c-means theory, PCC, and Levenshtein Distance. Based on grouping, filtered feedback from similar user group and similar service group will be engaged into reliability prediction. Experimental results demonstrate that our proposed RPMBG has significantly increased the accuracy of prediction result compared with other prediction methods.
On graph-based feature selection for multi-hop performance characterization in industrial smart water networks

Recent deployments of Smart Water Networks in urban environments are causing a paradigm shift towards sustainable water resources management. Nevertheless, there exists a substantial gap on respective solutions for industrial water treatment. In such deployments the wireless network backbone would have to overcome limiting factors that span across different layers of a protocol stack. Incorporating data analytics for capturing multi-dimensional correlations could be extremely beneficial to the design of reconfigurable network protocols for industrial Smart Water Networks. In this work, we exploit recent findings in the arena of network measurements and we propose a graph-based unsupervised feature selection approach for extracting the dominant network conditions that affect the performance of user-defined links. We employ a real- life industrial Smart Water Network deployed in a desalination plant to evaluate the efficacy of our approach. Finally, we provide useful insights on how different locations in a desalination plant affect the performance of the network backbone.
GC1 deletion prevents light-dependent arrestin translocation in mouse cone photoreceptor cells.

PURPOSE Light-driven translocation of phototransduction regulatory proteins between the inner and outer segments of photoreceptor cells plays a role in the adaptation of these cells to light. The purpose of this study was to examine the effects of the absence of guanylate cyclase 1 (GC1) on light-driven protein translocation in rod and cone cells. Both cell types express GC1, but differ in sensitivity, saturation, and response times to light. METHODS Immunohistochemical techniques employing antibodies specific for cone and rod transducin alpha (Talpha) subunits and arrestins were used to examine light-driven translocation of these proteins in the retinas of wild-type and GC1 knockout (KO) mice. RESULTS Translocation of cone arrestin from cone outer segments to the inner cell regions was disrupted in the absence of GC1, whereas translocation of arrestin and Talpha in rods was not affected. Cone Talpha did not translocate in wild-type and GC1 KO mice, but differed in its subcellular distribution in GC1 KO retina, remaining in the cone outer segment in light and in dark. CONCLUSIONS These results suggest that multiple, independent pathways regulate the translocation of phototransduction proteins and that GC1, and presumably cGMP, are of key importance in signaling the translocation of cone arrestin.
Advances in Time-Domain Modeling of Periodic Structures and Related Metamaterial Applications

The periodic analysis of candidate plasmonic topologies for the implementation of left-handed media at optical frequencies is pursued through a triangular mesh based finite-difference time-domain (FDTD), equipped with Floquet boundary conditions. The technique is shown to possess excellent convergence and accuracy properties, as opposed to the conventional rectangular cell based FDTD.
ADAPTIVE FUZZY WAVELET NETWORK CONTROL DESIGN FOR NONLINEAR SYSTEMS

This paper presents a new adaptive fuzzy wavelet network controller (A-FWNC) for control of nonlinear affine systems, inspired by the theory of multiresolution analysis (MRA) of wavelet transforms and fuzzy concepts. The proposed adaptive gain controller, which results from the direct adaptive approach, has the ability to tune the adaptation parameter in the THEN-part of each fuzzy rule during real-time operation. Each fuzzy rule corresponds to a sub-wavelet neural network (subWNN) and one adaptation parameter. Each sub-WNN consists of wavelets with a specified dilation value. The degree of contribution of each sub-WNN can be controlled flexibly. Orthogonal least square (OLS) method is used to determine the number of fuzzy rules and to purify the wavelets for each subWNN. Since the efficient procedure of selecting wavelets used in the OLS method is not very sensitive to the input dimension, the dimension of the approximated function does not cause the bottleneck for constructing FWN. FWN is constructed based on the training data set of the nominal system and the constructed fuzzy rules can be adjusted by learning the translation parameters of the selected wavelets and also determining the shape of membership functions. Then, the constructed adaptive FWN controller is employed, such that the feedback linearization control input can be best approximated and the closed-loop stability is guaranteed. The performance of the proposed A-FWNC is illustrated by applying a second-order nonlinear inverted pendulum system and compared with previously published methods. Simulation results indicate the remarkable capabilities of the proposed control algorithm. It is worth noting that the proposed controller significantly improves the transient response characteristics and the number of fuzzy rules and on-line adjustable parameters are reduced.