Miracle-dz/UniFAR-FTUs huggingface dataset:
An Extended Acceptance Model for Augmented Reality Educational Applications

Augmented Reality (AR) combines 3D visualization, multimodal interaction, animation and direct manipulation in a hands-on learning experience that is able to enhance the students’ motivation to learn. Designers and educators need a deeper understanding of the key factors that drive learners’ acceptance and continuing interaction with an AR-based educational system. A concern for educators is to understand how these factors interact and to what extent each factor contributes to the acceptance of the AR technology. This work further extends the technology acceptance model by included three additional constructs that point to the ease of learning how to use an AR-based application (learnability), perceived efficiency, and cognitive absorption. The results show that the perceived enjoyment is the main determinant of the intention to use having a greater positive influence than the perceived usefulness. The perceived efficiency and perceived cognitive absorption have only indirect effects on the intention to use, which are mediated by the perceived usefulness and perceived enjoyment.
ON INTERACTIONS OF INTENSE DEUTERIUM PLASMA STREAMS WITH SiC TARGETS IN PLASMA-FOCUS EXPERIMENTS

The paper presents results of experimental research on emission of the visible radiation (VR) from intense deuterium plasma streams propagating freely within a vacuum chamber or interacting with silicon-carbide (SiC) targets. The investigated pulsed plasma streams were generated by high-current discharges realized within two facilities of the Plasma-Focus (PF) type, i.e. within the PF-1000U facility operated at the IFPiLM and the PF-360U device operated at the NCBJ. Detailed measurements have been carried out using optical emission spectroscopy (OES) technique. Parameters of plasma were estimated from the D line only. Structural changes of the irradiated SiC targets were analyzed by means of a scanning electron microscope (SEM) and energy dispersive X-ray spectrometer (EDS). PACS: 52.59. Hq, 52.70. Kz, 52.30. -q, 52.40. Hf
Appearance-Based Dynamic Hand Gesture Recognition from Image Sequences with Complex Background

In this paper, the authors present an appearance based approach to dynamic hand gesture recognition. A motion based segmentation scheme for image motion estimation is proposed using variable order parameterized models of image motion and robust regression. Based on image motion parameters, two different appearance change models of hand gestures are created. Template Based classification technique is then employed to perform hand gesture recognition in which reference templates are created with a mini max type of optimization. A series of experiments on 120 image sequences show that high recognition rate, low computation load, and high stability can be achieved with the proposed methods.
Reliable Safety Broadcasting in Vehicular Ad hoc Networks using Network Coding

We study the application of network coding in periodic safety broadcasting in Vehicular Ad hoc Networks. We design a sub-layer in the application layer of the WAVE architecture. Our design uses rebroadcasting of network coded safety messages, which considerably improves the overall reliability. It also tackles the synchronized collision problem stated in the IEEE 1609.4 standard as well as congestion problem and vehicle-to-vehicle channel loss. We study how massage repetition can be used to optimize the reliability in combination with a simple congestion control algorithm. We analytically evaluate the application of network coding using a sequence of discrete phase-type distributions. Based on this model, a tight safety message loss probability upper bound is derived. Completion delay is defined as the delay that a node receives the messages of its neighbour nodes. We provide asymptotic delay analysis and prove a general and a restricted tighter asymptotic upper bound for the completion delay of random linear network coding. For some safety applications, average vehicle to vehicle reception delay is of interest. An instantly decodable network coding based on heuristics of index coding problem is proposed. Each node at each transmission opportunity tries to XOR some of its received original messages. The decision is made in a greedy manner and based on the side information provided by the feedback matrix. A distributed feedback mechanism is also introduced to piggyback the side information in the safety messages. We also construct a Tanner graph based on the feedback information and use the Belief Propagation algorithm as an efficient heuristic similar to LDPC decoding. Layered BP is shown to be an effective algorithm for our application. Lastly, we present a simple experimental framework to evaluate the performance of repetition based MAC protocols. We conduct an experiment to compare the POC-based MAC protocol with a random repetition-based MAC. %%%%PhD
Virtualization and Migration with GPGPUs Bachelorarbeit von cand

Recently, cloud computing providers have started to offer virtual machines specifically for high performance computing as a service (HPCaaS). The cloud computing providers usually employ virtualization as an abstraction layer between the application software and the underlying hardware. Virtualization allows flexible migration between physical systems, which is a requirement for many load balancing techniques. In the area of high performance computing, graphics processing units (GPUs) play a large role due to their high degree of parallelism and their high performance. If GPUs are used in a HPCaaS environment, they need to be integrated into the virtualization system. This GPU virtualization solution needs to provide near-native performance and needs to support migration. However, previous approaches to GPU virtualization either do not support migration or suffer from significant virtualization overhead. In this work, we analyze existing GPU virtualization solutions. Based on this analysis, we present a GPU virtualization solution which exploits the features of the underlying GPU in order to significantly reduce virtualization overhead. In the proposed design, the virtual machine is given direct access to selected parts of the GPU so that it can directly execute programs on the GPU, without intervention of the hypervisor. We show that in the proposed design, the hypervisor is still able to transparently suspend and resume the GPU and can temporarily revoke direct access to it. This functionality can be used in order to implement transparent migration of the virtual machine as well as the GPU resources allocated to it. We propose a migration strategy which is designed to provide live migration for virtual machines with GPUs. We evaluate the proposed GPU virtualization solution using a prototype built on top of Linux, the kernel-based virtual machine (KVM) and a NVidia GPU. Our experiments show that the proposed design results in very low virtualization overhead. Also, our experiments show that the proposed migration strategy provides low virtual machine downtime and keeps the overall impact of migration on performance low.
AN EFFECTIVE ALGORITHM FOR SOLVING A SINGLE-SUPPLIER MULTIPLE- HETEROGENEOUS CUSTOMERS NETWORK PROBLEM

This paper focuses on a single-supplier multiple-heterogamous customer network problem with the consideration of dynamic demand. We analyze a two-period special case, and then develop an effective algorithm for solving this problem. The performance of the proposed algorithm is evaluated through simulation of 100 consultative planning cycles with 10 time periods in each cycle.
Objective cognitive functioning three months after stroke : a comparison of young , middle-aged and elderly patients

3 Introduction. 4 Age differences in stroke. 4 Cognitive impairment after stroke. 5 Cognitive impairments by age. 6 Cognitive impairments after stroke by age. 7 Method. 8 Participants. 8 Measures. 9 Design and statistical analyses. 10 Procedure. 11 Results. 11 RBMT. 11 Zoo-map test. 15 DCST. 18 Covariates.20 Discussion. 21 Conclusion.25 References. 26 Appendix. 31 3 Objective cognitive functioning three months after stroke: a comparison of young, middle-aged and elderly patients.
Effects of Animals on Performance of UHF RFID Tags in Animal Traceability Application

Recently, the radio frequency identification (RFID ) technology is popular for automatically identifying the objects such as pallets, books, ani m ls, or human beings. In the RFID system, the data is transferred between a transponder (tag) and an interrogator (reader) using magnetic or electromagnetic fields [1]. The magnetic coupling t echnique is used in the low frequency (LF) and high frequency (HF) bands, where the read range is lim ted. On the other hand, the electromagnetic backscattering technique is used in the ultra-high frequency (UHF) band where a longer read range can be achieved. The UHF RFID is widely used in man y pplications where tags cannot be moved close to the reader, such as logistics or airline b aggage system. However, the performance of the UHF RFID system is strongly affected by the dielect ric bodies and the metals nearby. Various studies have been proposed for improving the read r ange and performance of the UHF RFID system [2-5] Due to the animal disease outbreak in recent years such as the avian influenza, the system that can immediately identify the origin of animals is urgently required. The RFID technology is one of the suitable solutions for a quick track and trace [6]. The RFID Program at National Electronics and Computer Technology Center (NECTEC) implemented a pilot project of poultry traceability system [7]. In this system, the UHF R ID is used for tracing the cage containing about 6-8 chickens instead of using LF RFID for tracing t he individual chicken. However, the RFID tag cannot be directly attached to the cage. The packag e is needed for protecting the RFID tag from water, sterilizing agent, and bumping. The dielectr ic bodies of package, cage, and poultry have a significant effect on the UHF RFID system performan ce. In order to study the performance of the RFID tag placed inside a protective package, we have examined the effects of the air gap size betwe en the RFID tag and the package by neglecting the poultry inside the cage [8]. The results in [8] indicated that high performance RFID tags can be achieved by using the FR4 substrate and increasing the air gap size to at least 2 mm, both on top and bottom of the RFID tag. In this paper, we study the effects of the animal i nside the cage on the performance of the RFID tag. The poultry is modeled as a simple cube a nd sphere with the dielectric property of a muscle. We vary the size of the poultry model and t he air space between the model and the cage to examine the UHF RFID tag performance, the resonant frequency, and the read range.
Exploring Latent Structure in Data: Algorithms and Implementations

Feature representations for raw data is one of the most important component in a machine learning system. Traditionally, features are hand crafted by domain experts which can often be a time consuming process. Furthermore, they do not generalize well to unseen data and novel tasks. Recently, there have been many efforts to generate data-driven representations using clustering and sparse models. This dissertation focuses on building data-driven unsupervised models for analyzing raw data and developing efficient feature representations. Simultaneous segmentation and feature extraction approaches for silicon-pores sensor data are considered. Aggregating data into a matrix and performing low rank and sparse matrix decompositions with additional smoothness constraints are proposed to solve this problem. Comparison of several variants of the approaches and results for signal de-noising and translocation/trapping event extraction are presented. Algorithms to improve transform-domain features for ion-channel time-series signals based on matrix completion are presented. The improved features achieve better performance in classification tasks and in reducing the false alarm rates when applied to analyte detection. Developing representations for multimedia is an important and challenging problem with applications ranging from scene recognition, multi-media retrieval and personal life-logging systems to field robot navigation. In this dissertation, we present a new framework for feature extraction for challenging natural environment sounds. Proposed features outperform traditional spectral features on challenging environmental sound datasets. Several algorithms are proposed that perform supervised tasks such as recognition and tag annotation. Ensemble methods are proposed to improve the tag annotation process. To facilitate the use of large datasets, fast implementations are developed for
Small Molecule Inhibitors of Wnt/ β -Catenin / Lef-1 Signaling Induces Apoptosis in Chronic Lymphocytic Leukemia Cells In Vitro and In Vivo 1,2

BACKGROUND: Lymphoid enhancer factor-1 ( lef-1 ) is overexpressed in B-cell chronic lymphocytic leukemia (CLL) when compared with normal B cells and transcribes several genes implicated in the pathogenesis of CLL. We there-fore hypothesize that antagonism of lef-1 might lead to killing of CLL cells. We used two small molecule inhibitors of Wnt/ β -catenin/lef-1 signaling (CGP049090 and PKF115-584) to test our hypothesis. DESIGN AND METHODS: Enriched CLL cells and healthy B cells were used in this study. Small interfering RNA (siRNA) – mediated knockdown of lef-1 in primary CLL cells was done using nucleofection, and 50% lethal concentration (LC 50 ) of two small molecules was assessed using ATP-based cell viability assay. Apoptotic response was investigated in time course experiments with different apoptotic markers. Specificity of the small molecules was demonstrated by coimmunoprecipitation experiments for the lef-1 / β -catenin interaction. In vivo studies were done in JVM-3 subcutaneous xenograft model. RESULTS: Inhibition of lef-1 by siRNA leads to increased apoptosis of CLL cells and inhibited proliferation of JVM-3 cell lines. The two small molecule inhibitors (CGP049090 and PKF115-584) efficiently kill CLL cells (LC 50 < 1 μ M), whereas normal B cells were not significantly affected. Coimmunoprecipitation showed a selective disruption of β -catenin / lef-1 interaction. In vivo studies exhibited tumor inhibition of 69% with CGP049090 and 57% with PKF115-584 when compared with vehicle-treated controls, and the intervention was well tolerated. CONCLUSIONS: We have demonstrated that targeting lef-1 is a new and selective therapeutic approach in CLL. CGP049090 or PKF115-584 may be attractive compounds for CLL and other malignancies that deserve further (pre)clinical evaluation. JVM-3 cell lines. Forty-eight hours after nucleofection, protein extracts were analyzed for lef-1 , c-myc , cyclin D1 , β -catenin , PCNA , and cleaved PARP by immunoblot analysis, with β -actin as the loading control. There was down-regulation of lef-1 target genes along with PCNA and PARP, indicating growth inhibition of the cell line. These observations were confirmed in two independent experiments.
▬ Self-Sacrifice for Unrelated Individuals : Further Considerations

52 Montenegrin society described by Boehm (1984) and to Evans-Pritchard’s (1940) original account of segmentary opposition among the Nuer. In these societies, segments shift back and forth between competition and cooperation depending upon the scale of the common threat. Conventions and norms are required for segments of the society to resolve their conflicts other than by endless feuding. The leopard skin chief performed this function in traditional Nuer society (Evans-Pritchard 1940; discussed from a multi-level cultural evolutionary perspective in Sober and Wilson 1998 p. 186-191). Chosen from an unimportant lineage, he was given sacred status to arbitrate homicide disputes. Without this convention enforced by norms, it is almost certain that Neur society would have disintegrated into smaller social units that would be unable to unite in the face of common threats. In other words, between-group selection is a strong and observable force capable of explaining the cultural evolution of the social convention. I can well imagine that besa performed a similar function in Albanian Society. Currently opposed segments would need to mend their differences to unite in the face of a common threat. This would require meeting in each other’s homes and protecting visitors from one’s own kinsmen bent on revenge. In this fashion, the norm of besa can be explained at face value as a group-level adaptation without turning it into a puzzle. References
Stimulus Word into Brain Activity using an N-gram Corpus with NLP Methods

Corpus-based approaches are widely used in modern neuroscience. In the field, it is important to build a model of the mental context of a subject during functional magnetic resonance imaging (fMRI) experiments. This paper compares the different methods frequently used in natural language processing (NLP). For a better translation between language and brain activation, additional techniques related to NLP, such as pointwise mutual information (PMI) and Normalized Google Distance (NGD), are also introduced. These methods are compared so as to fit brain activations when shown stimulus nouns. The experimental results indicate that NGD was more effective among these methods when applying NLP techniques.
Data Analytics to Dissect Healthcare Delivery Patterns

We systematically dissect and explain variation in Medicare costs by exploring the correlation structure in a recently released set of Medicare data. A portion of regional variation in Medicare cost occurs due to geographic differences in payment rates due to standard-of-living adjustments for individual services. However, we are concerned with explaining the variance that remains after standardized cost adjustment. By relating the demographic, service utilization, and prevention quality indicator data to the standardized per capita Medicare costs (SPCC), we highlight key service usage patterns which explain the variance in cost. By taking this information into account, it should be possible to develop policy-driving recommendations that encourage more costefficient, high-quality medical care. We present a method to identify minimal multiple linear regression models which account for the most variance in Medicare cost. We then focus our attention on a model trained on data from 2007 which explains 95% of this variance with only five predictor variables. Finally, we discuss the results of this method across multiple years of data where we observe a shift of the predictors of SPCC from quantity of services to quality of services. Our contribution is a principled approach to deconstruct a vital real-world data set that can better inform policy decisions about regional differences in Medicare costs and benefits.
Mechanical properties of blended cements at elevated temperatures predicted using a fuzzy logic model

This study aimed to develop a Rule Based Mamdani Type Fuzzy Logic (RBMFL) model to predict the flexural strengths and compressive strengths of blended cements under elevated temperatures. Clinoptilolite was used as cement substitution material in the experimental stage. Substitution ratios in the cement mortar mix designs were selected as 0% (reference), 5%, 10%, 15% and 20%. The data used in the modeling process were obtained experimentally, after mortar specimens having reached the age of 90 days and exposed to 300°C, 400°C, 500°C temperatures for 3 hours. In the RBMFL model, temperature (C°) and substitution ratio of clinoptilolite (%) were inputs while the compressive strengths and flexural strengths of mortars were outputs. Results were compared by using some statistical methods. Statistical comparison results showed that rule based Mamdani type fuzzy logic can be an alternative approach for the evaluation of the mechanical properties of concrete under elevated temperature.
DESIGNING OF DRIVER BEHAVIOR MODEL AND ITS DATA ANALYSIS FOR VEHICULAR APPLICATIONS

Design and Implementation of Evidence Collection System from Event Data Recorder”, deals with collection of Evidence for accident analysis. These evidences are in form of graphs. It includes graph of Speed Vs Time, Engine Temperature Vs Time, Distance Vs Time, Brake Pressure Vs Time, Engine Speed (RPM) Vs Time, Impact Vs Time and Tire Pressure Vs Time. Also it provides single graph merged with all these parameters at the time of accident. One more advantage of system is that, it can also provide Driver Behavior Model. By using this, driver’s driving performance could be found out. This system is nothing but the GUI constructed using Microsoft Visual Studio. All graphs are plotted using this Graphical User Interface. For Data Analysis purpose, B-tree algorithm is used here. Its performance for secondary storage devices and huge data handling capacity makes it more efficient. Data recorded by Event Data Recorder is used here as input. Event Data Recorder is also called as Black box of vehicles. It records vehicular data like speed, date, time, location (latitude, longitude), alcohol content of driver, airbag deployment etc. This data plays an important role detection and analysis of accident if any persists. These EDR are controlled by processors for very accurate operations.
Full-Text Manipulation in Databases

IntroductIon The textual data format is one of the most important data types in database management. Databases support a wide range of special tex-tual types that can be used to store string data. In the case of textual data, information retrieval mostly concerns the selection and the ranking of documents. In the traditional database management systems (DBMS), text manipulation is related to the usual string manipulation facilities, i.e. the exact matching of substrings. The main disadvantages of the traditional string-level operations are the high cost as they work without task-oriented index structures and the restriction to the syntax level. The required appropriate full-text management operations belong to text mining, an interdisci-plinary field of natural language processing and data mining. As the traditional DBMS engine is inefficient for these operations, database management systems are usually extended with a special full-text search (FTS) engine module. Full-text search engines provide a set of full text manipulation primitives that are based on the semantic aspects of the words and sentences. In Full-Text Manipulation in Databases the recent years, the area of semantic query on full-text is treated as a special area of universal ontology. The main goal of ontology is to define a common set of concepts and relationships for knowledge representation. The market of FTS engines is very promising because the amount of textual information stored in databases rises steadily. According to the study of Meryll Lynch (Blumberg & Arte, 2003), 85% of business information are text documents – e-mails, business and research reports, memos, presentations, advertisements, news, etc. – and their proportion still increases. In 2006, there were more than 20 billion documents available on the Internet (Chang, 2006). The estimated size of the pool reaches 550 billion documents when the documents of the hidden (or deep) web are also considered. In a broader sense, the area of full-text manipulation is not restricted only to the management of database content. The query interface may be extended with a natural language interface for databases (NLIDB). The NLIDB means that a user can use natural languages to create query expressions, and also the answer can be presented in the same languages. The processing of the incoming queries includes a full-text analysis both at syntactic and semantic levels.