Miracle-dz/UniFAR-FTUs huggingface dataset:
What Do Elderly Desire? A Case For Virtual Communities

Virtual communities have a potential to improve the community building process and care services through support for activities, participation and information needs. In this paper, we propose a mobile virtual community platform for elderly healthcare. The requirements for this platform are collected from interview sessions with clients and caregivers in a care institution, and a workshop with multiple stakeholders. We focus on the technical platform that fulfills the requirements gathered from this approach. The platform is an elaboration of our existing mobile virtual community platform and enables tailoring through an adaptable set of mobile and platform services. Reminder services, remote monitoring, video communication and (delegated) friend and service finding are discussed and exemplified through a case study.
OmniVista:an application for isovist field and path analysis

This paper briefly describes the software application OmniVista written for the Apple Macintosh Platform. OmniVista is essentially an isovist generating application, which uses the 2d plan of a building or urban environment as input data, and then can be used in one of three modal ways. Firstly, point isovists can be generated by 'clicking' onto any location in the environment. Secondly, all navigable space can be flood-filled with points, which may then beused to generate a field of isovists. Finally, a path of points can be used to examine how isovist properties vary along the path - the results of this can either be output as numerical data, or exported as a series of pictures, which may be combined to form an animation of the varying isovists along the route. This paper will examine all three modes of use in turn,starting from the simplest (point) to the more complex (the path). A description and equation for all isovist measures used in the application will also be given as an appendix to the paper.
Exploring the Relationships between Experiential Marketing , Customer Satisfaction and Customer Loyalty : An Empirical Examination in Konya

Experiential marketing is one of the marketing approaches that offer an exceptional framework to integrate elements of experience and entertainment in a product or service. Experiential marketing is defined as a memorable experience that goes deeply into the customer’s mind. Besides that, customer satisfaction is defined as an emotional response to the experiences provided by and associated with particular products or services purchased. Thus, experiential marketing activities can affect the level of customer satisfaction and loyalty. In this context, the research aims to explore the relationship among experiential marketing, customer satisfaction and customer loyalty among the cosmetic products customers in Konya. The partial least squares (PLS) method is used to analyze the survey data. Findings of the present study revealed that experiential marketing has been a significant predictor of customer satisfaction and customer loyalty, and also experiential marketing has a significantly positive effect on customer satisfaction and customer loyalty. Keywords—Customer satisfaction, customer loyalty, experiential marketing.
What is said or how it is said makes a difference: role of the right fronto‐parietal operculum in emotional prosody as revealed by repetitive TMS

Emotional signals in spoken language can be conveyed by semantic as well as prosodic cues. We investigated the role of the fronto‐parietal operculum, a somatosensory area where the lips, tongue and jaw are represented, in the right hemisphere to detection of emotion in prosody vs. semantics. A total of 14 healthy volunteers participated in the present experiment, which involved transcranial magnetic stimulation (TMS) in combination with frameless stereotaxy. As predicted, compared with sham stimulation, TMS over the right fronto‐parietal operculum differentially affected the reaction times for detection of emotional prosody vs. emotional semantics, showing that there is a dissociation at a neuroanatomical level. Detection of withdrawal emotions (fear and sadness) in prosody was delayed significantly by TMS. No effects of TMS were observed for approach emotions (happiness and anger). We propose that the right fronto‐parietal operculum is not globally involved in emotion evaluation, but sensitive to specific forms of emotional discrimination and emotion types.
Evaluation of sural nerve automated nerve conduction study in the diagnosis of peripheral neuropathy in patients with type 2 diabetes mellitus

Introduction New tests for improved diagnosis of diabetic peripheral neuropathy (DPN) are useful. Material and methods We evaluated the utility of automated nerve conduction study (NCS) of the sural nerve with a new portable device for the diagnosis of DPN in patients with type 2 diabetes mellitus (T2DM). This study included 114 T2DM patients (58 men) with mean age 64.60 ±8.61 years. Exclusion criteria were B12 depletion, alcohol abuse and other causes of peripheral neuropathy. The reference method was the Neuropathy Disability Score (NDS) with a threshold NDS ≥ 3. Sural nerve automated NCS was carried out with the portable NC-stat DPNCheck device. Sensory nerve conduction velocity and sensory nerve action potential amplitude were measured bilaterally. Automated NCS was considered abnormal when ≥ 1 of the two aforementioned neurophysiological parameters was abnormal in at least one leg. Results Examination with NC-stat DPNCheck exhibited 90.48% sensitivity, 86.11% specificity, 79.17% positive predictive value (PPV) and 93.94% negative predictive value (NPV). The positive likelihood ratio (LR+) was 6.51 and the negative likelihood ratio (LR–) was 0.11. Conclusions Sural nerve automated NCS with the NC-stat DPNCheck device exhibits high sensitivity and specificity for the diagnosis of DPN in T2DM.
Estimating the Return to College Selectivity Over the Career Using Administrative Earning Data

We estimate the monetary return to attending a highly selective college using the College and Beyond (C&B) Survey linked to Detailed Earnings Records from the Social Security Administration (SSA). This paper extends earlier work by Dale and Krueger (2002) that examined the relationship between the college that students attended in 1976 and the earnings they self-reported reported in 1995 on the C&B follow-up survey. In this analysis, we use administrative earnings data to estimate the return to various measures of college selectivity for a more recent cohort of students: those who entered college in 1989. We also estimate the return to college selectivity for the 1976 cohort of students, but over a longer time horizon (from 1983 through 2007) using administrative data. We find that the return to college selectivity is sizeable for both cohorts in regression models that control for variables commonly observed by researchers, such as student high school GPA and SAT scores. However, when we adjust for unobserved student ability by controlling for the average SAT score of the colleges that students applied to, our estimates of the return to college selectivity fall substantially and are generally indistinguishable from zero. There were notable exceptions for certain subgroups. For black and Hispanic students and for students who come from less-educated families (in terms of their parents' education), the estimates of the return to college selectivity remain large, even in models that adjust for unobserved student characteristics.
Galactic Indigestion: Numerical Simulations of the Milky Way's Closest Neighbor

Are dwarf spheroidal galaxies dark matter dominated? We present N-body simulations of the interaction between the Milky Way and its closest companion, the Sagittarius dwarf spheroidal galaxy, constrained by new kinematic, distance, and surface density observations (detailed in a companion paper). It is shown that there is no possible self-consistent solution to the present existence of the Sagittarius dwarf if its distribution of luminous matter traces the underlying distribution of mass. The luminous component of the dwarf galaxy must therefore be shielded within a small dark matter halo. Although at present we are unable to construct a fully self-consistent model that includes both the stellar and dark matter components, it is shown numerically that it is possible that a pure dark matter model, approximating the dark matter halo deduced for the Sagittarius dwarf from analytical arguments, may indeed survive the Galactic tides. The orbit of the Sagittarius dwarf around the Milky Way is considered, taking into account the perturbative effects of the Magellanic Clouds. It is shown that at the present time, the orbital period must be short, ~0.7 Gyr; the initial orbital period for a 109 M☉ model will have been ~1 Gyr. It is found that a close encounter with the Magellanic Clouds may have occurred, although the chances of such an interaction affecting the orbit of the Sagittarius dwarf are negligible.
Rening Pid Controllers Using Neural Networks

The Kbann approach uses neural networks to re ne knowledge that can be written in the form of simple propositional rules. We extend this idea further by presenting theManncon algorithm by which the mathematical equations governing a PID controller determine the topology and initial weights of a network, which is further trained using backpropagation. We apply this method to the task of controlling the out ow and temperature of a water tank, producing statistically-signi cant gains in accuracy over both a standard neural network approach and a non-learning PID controller. Furthermore, using the PID knowledge to initialize the weights of the network produces statistically less variation in testset accuracy when compared to networks initialized with small random numbers.
Application of Fuzzy Ontology Framework to Information Retrieval for SCM

Information retrieval is the important work of Supply Chain Management (SCM) on the Semantic Web. Ontology-based semantic retrieval is a hotspot of current research. In order to achieve fuzzy semantic retrieval, this paper applies a fuzzy ontology framework to information retrieval system in SCM. The framework includes three parts: concepts, properties of concepts and values of properties, in which propertypsilas value can be either standard data type or linguistic values of fuzzy concepts. The semantic query expansion is constructed by order relation, equivalence relation and inclusion relation between fuzzy concepts defined in fuzzy linguistic variable ontologies. The application to retrieve customer and product information in supply chain shows that the framework can overcome the localization of other fuzzy ontology models, and this research facilitates the semantic retrieval of information through fuzzy concepts on the Semantic Web.
A Failure Adapted, Load-Balanced Distributed Routing for Wireless Ad Hoc Sensor Networks

this paper proposes a distributed routing protocol for ad hoc sensor networks which uses Fuzzy Logic. Each sensor uses a Fuzzy decision making process to find the best Cluster Head. Simulation shows that this protocol is able to dynamically adapt to sensors’ mobility and failure. By a new load balancing method, it provides also stable clusters and so a cluster head have greater lifetime, which results minimum message exchange and so minimum energy consumption.
Automated Formal Verification and Testing of C Programs for Embedded Systems

In this paper, we introduce an approach for automated verification and testing of ANSI C programs for embedded systems. We automatically extract an automaton model from the C code of the SUT (system under test). This automaton model is on the one hand used for formal verification of the requirements defined in the system specification, on the other hand, we can derive test cases from this model, for both methods we use a model checker. We describe our techniques for test case generation, based on producing counterexamples with a model checker by formulating trap properties. The resulting test cases can then be applied to the SUT on different test levels. An important issue for model checking C-source code, is the correct modeling of the semantics of a C program for an embedded system. We focus on challenges and possible restrictions that appear, when model checking is used for the verification of C-source code. We specifically show how to deal with arithmetic expressions in the model checker NuSMV and how to preserve the numerical results in case of modeling the platform-specific semantics of C
Efficiency of new method of removing the noisy background from the sequence of MRI scans depending on structuring elements used to morphological processing

The paper presents a new method of removing the noisy background from the sequence of magnetic resonance imaging (MRI) scans. The scans contain the noisy head-cross data and also the noisy background data. The latter have to be removed and excluded from a further analysis. It is achieved by applying some basic morphological operations of dilation, erosion, opening and closing to the previously binarized MRI scans. The results of removing the background from the sequence of scans with the use of some different shapes and sizes of structuring elements are presented in the paper. The aim of this paper is to choose the best structuring elements to make the new method the most immune to various qualities and appearances of the processed MRI images.
A novel approach to remote access picture archiving and communication system on mobile devices over wireless networks

As the growing of computing capability, handy mobile devices are used to explore and deal with medical images. More and more attention was paid on remote accessing to picture archiving and communication system (PACS). In this paper, the iPACS, a web-based application running on iPad, is provided to explore medical information on PACS server over a wireless local area network (WLAN), as well as to apply a series of interactive operations adapting with the multi-touch features of iPad, including 2D image processing, 3D visualization and patient information query. So the medical staff could explore medical images in the remote PACS sever smoothly with iPACS. Finally, the performance of iPACS is tested to demonstrate that the inter-device delay is small enough to provide a smooth operation.
Virtual MIMO communication based on D2D link

Applying MIMO technology into handheld equipments has always been a widely argued problem due to their body and power limit. The appearance of device-to-device (D2D) communication provides one way for solving this problem. In this paper, we developed the way to construct virtual antenna array by D2D links and find the method to get diversity gains using space time block coding between inter cluster communication. We focused on researching Alamouti coding which is used in virtual MIMO communication in two different ways, one is decoding and the other is amplifying. Signal combination in receiver cluster is also analyzed for increasing the possibility of decoding.
Blocking of SQL Injection Attacks by Comparing Static and Dynamic Queries

Due to internet expansion web applications have now become a part of everyday life. As a result a number of incidents which exploit web application vulnerabilities are increasing. A large number of these incidents are SQL Injection attacks which are a serious security threat to databases which contain sensitive information, the leakage of which cause a large amount of loss. SQL Injection Attacks occur when an intruder changes the query structure by inserting any malicious input. There are a number of methods available to detect and prevent SQL Injection Attacks. But these are too complex to use. This paper proposes a very simple, effective and time saving technique to detect SQLIAs which uses combined static and dynamic analysis and also defines an attack other than existing classification of SQLIAs.
Variable throughput LDPC decoders using SIMD-based adaptive quantization

In this paper, we present an LDPC decoder design equipped with an adaptive throughput mechanism achievable using a multiple quantization scheme. Three representations are supported by the proposed architecture: 1-bit (hard decision), 2-bit, and 4-bit messages. A throughput increase by of factor of 4, 2 and 1 can be achieved with respect to the 4-bit message representation version, by simultaneously decoding 4, 2, or 1 codewords. This is done by employing a single instruction multiple data (SIMD) approach at processing unit level which is able to process 4, 2 and 1 operands corresponding to the distinct codewords. We provide implementation results for a partial parallel flooding architecture, with serial processing at processing node level. FPGA implementation results indicate that the proposed SIMD approach has an overhead of about 60% in logic with respect to the fixed 4-bit LDPC decoder, with no memory increase, while having a throughput increase of 4× when the hard-decision decoding is used.
Incorporating facial features into a multi-channel gesture recognition system for the interpretation of Irish Sign Language sequences

In this paper we present a novel gesture recognition system for the interpretation of Irish Sign Language sequences which incorporates manual and non-manual information. We implement a set of independent Hidden Markov Model networks to recognize hand gestures, head movements and facial features into a single framework for interpreting Irish Sign Language. This framework is not specific to any particular type of gesture and we demonstrate this by showing that manual and non manual signals can be robustly spotted and classified from with continuous sign sequences.
Towards a principled approach to tutoring mathematical proofs

Studies comparing human and computer-based tutoring have identified natural language communication about the tutorial goal as a major source for the increased learning success in human tutoring. However, due to the inherent difficulty of natural language processing in non-tightly restricted tasks, which tutoring is even in simple domains, only few state-of-the-art systems use natural-language style interaction. Addressing tutoring in a principled way, we are developing a system that teaches proving skills in mathematics, by incrementally enhancing a testable Wizard-of Oz environment with tutoring components, existing domain reasoning systems and natural language processing components. The first component integrated is an elaborate hinting algorithm. Experiments carried out recently provided valuable insights in relevant phenomena and demands for tutorial and task-specific enhancements of available mathematical reasoning and natural language processing components.
A Technique for Predicting the Effect of Data Cache Associativity

Prior knowledge of the target application leads to new optimization and customization opportunities in embedded system design. Such techniques often lead to design solutions that are better in terms of performance, area, or power. We present a technique that analyzes a given application and statically estimates the number of data cache misses for different associativity values, which is then used in performance and energy estimates. The technique consists of an initial analysis of array reference pairs and determining cache conflict frequency, followed by combining the conflict estimate for all references in a loop nest taken together, incorporating the given associativity value. This analytical estimation is orders of magnitude faster than simulation based techniques and is independent of the data size, leading to significant savings in time in the early stages of embedded system design where decisions on hardware/software trade-offs and architectural customization are taken.
Expected utility estimation via cross-validation

SUMMARY We discuss practical methods for the assessment, comparison and selection of complex hierarchical Bayesian models. A natural way to assess the goodness of the model is to estimate its future predictive capability by estimating expected utilities. Instead of just making a point estimate, it is important to obtain the distribution of the expected utility estimate in order to describe the associated uncertainty. We synthesize and extend the previous work in several ways. We give a unified presentation from the Bayesian viewpoint emphasizing the assumptions made and propose practical methods to obtain the distributions of the expected utility estimates. We discuss the properties of two practical methods, the importance sampling leave-one-out and the k-fold cross-validation. We propose a quick and generic approach based on the Bayesian bootstrap for obtaining samples from the distributions of the expected utility estimates. These distributions can also be used for model comparison, for example, by computing the probability of one model having a better expected utility than some other model. We discuss how the crossvalidation approach differs from other predictive density approaches, and the relationship of cross-validation to information criteria approaches, which can also be used to estimate the expected utilities. We illustrate the discussion with one toy and two real world examples.
A modification of the in vitro canine Purkinje fiber repolarization assay using variable frequency pacing

The in vitro canine cardiac Purkinje fiber (CCPF) assay used to predict drug-induced QT prolongation typically employs trains of constant frequency stimulation. We describe a modification of the model using a variable pacing protocol that mimics the sinus arrhythmia of living dogs. Using this model, Sotalol (10 and 50 /spl mu/M) elicited a dose-dependent shift of the variable frequency pacing return curve in a dose dependent manner to lower diastolic intervals. The shift and increased variability of the return curve with the high Sotalol concentration represents greater electrophysiologic instability. Further investigations are needed to assess the utility of the variable pacing protocol to evaluate electrophysiologic instability as a surrogate marker for proarrhythmia in the CCPF model.
Correlating Intravital Multi-Photon Microscopy to 3D Electron Microscopy of Invading Tumor Cells Using Anatomical Reference Points

Correlative microscopy combines the advantages of both light and electron microscopy to enable imaging of rare and transient events at high resolution. Performing correlative microscopy in complex and bulky samples such as an entire living organism is a time-consuming and error-prone task. Here, we investigate correlative methods that rely on the use of artificial and endogenous structural features of the sample as reference points for correlating intravital fluorescence microscopy and electron microscopy. To investigate tumor cell behavior in vivo with ultrastructural accuracy, a reliable approach is needed to retrieve single tumor cells imaged deep within the tissue. For this purpose, fluorescently labeled tumor cells were subcutaneously injected into a mouse ear and imaged using two-photon-excitation microscopy. Using near-infrared branding, the position of the imaged area within the sample was labeled at the skin level, allowing for its precise recollection. Following sample preparation for electron microscopy, concerted usage of the artificial branding and anatomical landmarks enables targeting and approaching the cells of interest while serial sectioning through the specimen. We describe here three procedures showing how three-dimensional (3D) mapping of structural features in the tissue can be exploited to accurately correlate between the two imaging modalities, without having to rely on the use of artificially introduced markers of the region of interest. The methods employed here facilitate the link between intravital and nanoscale imaging of invasive tumor cells, enabling correlating function to structure in the study of tumor invasion and metastasis.