Miracle-dz/UniFAR-FTUs huggingface dataset:
Ball Speed during the Tennis Serve in Relation to Skill Level and Body Height

The purpose of this study was to determine the possible relations between serve speed and tennis skill level, and body height. Participants were male (n= 16) and female (n= 17) junior (age= 13.58 ± 1.25) tennis players. Serve speed was evaluated through using a sports radar gun. Tennis skill level was assessed by means of International Tennis Number (ITN) on-court assessment protocol. Pearson’s correlation coefficient indicated significant and positive relationships between serve speed and ITN in both male (r= 0.678) and female (r= 0.490) participants. Results also revealed significant and positive correlation between serve speed and body height in male (r= 0.733) and female (r= 0.504) players. Thus, study highlighted the association between serve speed, skill level, and body height in junior male and female tennis players.
Stable Matching with Incomplete Information in Structured Networks

In this paper, we investigate stable matching in structured networks. Consider case of matching in social networks where candidates are not fully connected. A candidate on one side of the market gets acquaintance with which one on the heterogeneous side depends on the structured network. We explore four well-used structures of networks and define the social circle by the distance between each candidate. When matching within social circle, we have equilibrium distinguishes from each other since each social network's topology differs. Equilibrium changes with the change on topology of each network and it always converges to the same stable outcome as complete information algorithm if there is no block to reach anyone in agent's social circle.
Dynamic Credal Networks: introduction and use in robustness analysis

Dynamic Bayesian networks (DBN) are handy tools to model complex dynamical systems learned from collected data and expert knowledge. However, expert knowledge may be incomplete, and data may be scarce (this is typically the case in Life Sciences). In such cases, using precise parameters to describe the network does not faithfully account for our lack of information. This is why we propose, in this paper, to extend the notion of DBN to convex sets of probabilities, introducing the notion of dynamic credal networks (DCN). We propose different extensions relying on different independence concepts, briefly discussing the difficulty of extending classical algorithms for each concept. We then apply DCN to perform a robustness analysis of DBN in a real-case study concerning the microbial population growth during a French cheese ripening process.
An Integrated Model for Online Transactuions: Methodological Issues and Challenges

The purpose of this paper is to present a theoretical model that is based on the overall framework of the technology acceptance models and the concern for information privacy models, to better understand the linkages between the determinants of online transactions in information systems research. Emphasising on the methodological issues used in IS research, the proposed integrated model is focusing on the mediating variables, or the so-called ‘black box’, referring to the relationships between the primary independent and dependent variables, in online transactions. As such in this paper we attempt to fuse acknowledged technology acceptance models with information security and privacy models by proposing a general model which will enable the empirical validation and study of the factors that influence the users’ attitudes toward online transactions. The factors are represented in the model by variables (independent or mediating) and the analysis is proposed to be based on statistical techniques such as structural equation modelling. Since at this current stage the model is formed on a theoretical basis, we consider all mediating variables to be contained in a black box. Challenges such as the number of sub-boxes included in the linear causal process in a black box and what should be included in a sub-box are discussed.
Crosstalk behavior of cores in multi-core fiber under bent condition

Measurement results of bending diameter dependence of the crosstalk of each core in a multi-core fiber are presented. The crosstalk of cores shows different diameter dependence although the difference in core parameters is very small. The behavior is explained by taking into account the change of propagation constant caused by the fluctuation in the diameter and the refractive index of cores. The diameter dependence simulated with coupled-power theory shows the same tendency with the measured crosstalk.
Whole-Hand Interaction with 3D Environments

Graphical object manipulation in a computer-simulated environment holds much promise for increasing the productivity of users in a variety of occupations. Many of today's computer interfaces, such as mice and joysticks, are ill-suited for such a task and a better interaction paradigm is required. Once such approach is the use of a graphical hand which mimics the movement of the user's hand via an instrumented glove. While the concept of whole-hand interaction is intuitive and natural, its implementation is complex. This research note describe a VirtualHand® Toolkit being developed by researchers at Virtual Technologies, Inc. More specifically, it covers the ongoing development of a " Haptic Scene Graph, " which can work in conjunction with a simulation's master scene graph. A demonstration of a functional implementation of the toolkit will be given at the workshop.
Testing for anatomically specified regional effects

We present a simple method that allows statistical inferences to be made about the significance of regional effects in statistical parametric maps (SPMs) when the approximate location of the effect is specified in advance. The test can be thought of as analogous to assessing activations with uncorrected P values based on the height of SPMs but, in this instance, using the spatial extent or volume of the nearest activated region. The advantage of the current test is that it eschews a correction for multiple comparisons even though the exact location of the expected activation may not be known. Hum. Brain Mapping 5:133–136, 1997. © 1997 Wiley‐Liss, Inc.
Strategy hubs: Domain portals to help find comprehensive information

Recent studies suggest that the wide variability in type, detail, and reliability of online information motivate expert searchers to develop procedural search knowledge. In contrast to prior research that has focused on finding relevant sources, procedural search knowledge focuses on how to order multiple relevant sources with the goal of retrieving comprehensive information. Because such procedural search knowledge is neither spontaneously inferred from the results of search engines, nor from the categories provided by domainspecific portals, the lack of such knowledge leads most novice searchers to retrieve incomplete information. In domains like healthcare, such incomplete information can lead to dangerous consequences. To address the above problem, a new kind of domain portal called a Strategy Hub was developed and tested. Strategy Hubs provide critical search procedures and associated high-quality links to enable users to find comprehensive and accurate information. We begin by describing how we collaborated with physicians to systematically identify generalizable search procedures to find comprehensive information about a disease, and how these search procedures were made available through the Strategy Hub. A controlled experiment suggests that this approach can improve the ability of novice searchers in finding comprehensive and accurate information, when compared to general-purpose search engines and domain-specific portals. We conclude with insights on how to refine and automate the Strategy Hub design, with the ultimate goal of helping users find more comprehensive information when searching in unfamiliar domains. Strategy Hubs: Domain Portals to Help Find Comprehensive Information
Accurate Time Series Classification Using Shapelets

Time series data are sequences of values measured over time. One of the most recent approaches to classification of time series data is to find shapelets within a data set. Time series shapelets are time series subsequences which represent a class. In order to compare two time series sequences, existing work uses Euclidean distance measure. The problem with Euclidean distance is that it requires data to be standardized if scales differ. In this paper, we perform classification of time series data using time series shapelets and used Mahalanobis distance measure. The Mahalanobis distance is a descriptive statistic that provides a relative measure of a data point's distance (residual) from a common point. The Mahalanobis distance is used to identify and gauge similarity of an unknown sample set to a known one. It differs from Euclidean distance in that it takes into account the correlations of the data set and is scaleinvariant. We show that Mahalanobis distance results in more accuracy than Euclidean distance measure.
Turkish Large Vocabulary Continuous Speech Recognition by using limited audio corpus

In this paper, the recognition performances of several methodologies proposed in the context of Turkish Large Vocabulary Continuous Speech Recognition are retrieved by using a limited audio corpus. Word based, stem based, stem-ending based, and morph based language models are utilized with different n-gram orders. Word based and stem-ending based language models are extended by using several approaches. Also, a hybrid language model which is based on word based and stem-ending based language models is proposed. Word based language model is observed to outperform sub-word language models when limited audio corpus is used.
Person Search in a Scene by Jointly Modeling People Commonness and Person Uniqueness

This paper presents a novel framework for a multimedia search task: searching a person in a scene using human body appearance. Existing works mostly focus on two independent problems related to this task, i.e., people detection and person re-identification. However, a sequential combination of these two components does not solve the person search problem seamlessly for two reasons: 1) the errors in people detection are carried into person re-identification unavoidably; 2) the setting of person re-identification is different from that of person search which is essentially a verification problem. To bridge this gap, we propose a unified framework which jointly models the commonness of people (for detection) and the uniqueness of a person (for identification). We demonstrate superior performance of our approach on public benchmarks compared with the sequential combination of the state-of-the-art detection and identification algorithms.
Subspace Blind MIMO-OFDM Channel Estimation with Short Averaging Periods: Performance Analysis

Among all blind channel estimation problems, subspace-based algorithms are attractive due to its fast- converging nature. It primarily exploits the orthogonality structure of the noise and signal subspaces by applying a signal-noise space decomposition to the correlation matrix of the received signal. In practice, the correlation matrix is unknown and must be estimated through time averaging over multiple time samples. To this end, the wireless channel must be time-invariant over a sufficient time interval, which may pose a problem for wideband applications. We proposed a novel subspace-based blind channel estimation algorithm with short time averaging periods, as obtained by exploiting the frequency correlation among adjacent OFDM subcarriers. In this paper, asymptotic performance bounds of the proposed algorithm are investigated by using perturbation analysis. We also present numerical results of the proposed as well as referenced subspace-based methods, including cyclic prefix and virtual carriers approaches. Based on the asymptotic performance bounds, the proposed scheme is justified in obtaining a desired correlation matrix efficiently by reducing the number of the OFDM blocks for time averaging up to 85%.
Cooperative driving of automated vehicles with inter-vehicle communications

This paper deals with cooperative driving of automated vehicles on highways with data transmission based on inter-vehicle communications. It presents a data transmission algorithm in a platoon for a unidirectional medium and an omni-directional one under both a steady state and a transient state. The algorithm features a larger throughput rate than the conventional system, when the number of vehicles in a platoon is less than 15. This paper also focuses on the merging issue, which is a typical case of cooperative driving, and presents a merging control algorithm that makes the best use of inter-vehicle communications. Results of the simulation study and indoor experiment show the feasibility of the algorithms of data transmission and merging control.
Automatic locomotion design and experiments for a Modular robotic system

This paper presents a design method and experiments for whole-body locomotion by a modular robot. There are two types of locomotion for modular robots: a repeating self-reconfiguration and whole-body motion such as walking or crawling. For whole-body locomotion, designing a control method is more difficult than for ordinary robots because a modular robotic system can form various configurations, each of which has many degrees of freedom. This study proposes a unified framework for automatically designing an efficient locomotion controller suitable for any module configuration. The method utilizes neural oscillators (central pattern generators, CPGs), each of which works as a distributed joint controller of each module, and a genetic algorithm to optimize the CPG network. We verified the method by software simulations and hardware experiments, in which our modular robotic system, named M-TRAN II, performed stable and effective locomotion in various configurations.
3D sound image control by individualized parametric head-related transfer functions

It is well known that the listener’s own head-related transfer functions (HRTFs) provide accurate 3D sound image localization. However, the HRTFs of other listeners often cause degradation of localization accuracy. Though the 3D auditory display with a head-motion trucker, which provides a dynamic spatial cue to a listener, improves the rate of front-back confusion, this dynamic cue is not enough to accomplish accurate 3D sound image control. In other words, the individualization of HRTFs is necessary for accurate and realistic 3D sound image control. The authors have shown that the frequency of the first and the second spectral notches (N1 and N2) above 4 kHz in HRTFs act an important role as spectral cues for vertical localization. Furthermore, it is indicated that a sound image in any direction in the upper hemisphere can be localized with parametric median-plane HRTFs composed of N1 and N2 combined with frequency-independent interaural time difference. This means that the individualization of HRTFs of all the directions in the upper hemisphere can be replaced by that of N1 and N2 frequency of the HRTFs on the median plane. Thus, this paper describes a 3D sound image control method using individualized parametric HRTFs. In addition, our 3D auditory display system named SIRIUS (Sound Image Reproduction system with Individualized-HRTF, graphical User-interface, and Successive head-movement tracking) is introduced.
Building Scalable Distributed Intrusion Detection Systems Based on the MapReduce Framework

Intrusion detection systems (IDS) are essential components in a secure network environment, allowing for early detection of malicious activities and attacks. By employing information provided by an IDS it is possible to apply appropriate countermeasures and mitigate attacks that would otherwise seriously undermine network security. However, current high volumes of network traffic overwhelm most IDS techniques, requiring new approaches that are able to handle huge quantities of traffic during analysis while still maintaining high throughput. We propose an architecture for distributed Network Intrusion Detection Systems where comprehensive data analysis is executed in a cloud computing environment. Network traffic, operating system logs and general application data are collected from various sensors in different places in the network, comprising networking equipment, servers and user workstations. The data collected from different sources is aggregated, processed and compared using the Map-Reduce framework, analysing event correlations which may indicate intrusion attempts and malicious activities. The proposed architecture is able to efficiently handle large volumes of collected data and consequent high processing loads, seamlessly scaling to enterprise network environments. Also, differently from previous IDS models, it capable of detecting complex attacks through the correlation of information obtained from different sources, identifying patterns which may not be apparent in centralized traffic captures or single host log analysis. Besides an architecture description, we present feasibility results based on experiments performed on real cluster and cloud infrastructure.
A Bayesian approach to reliability prediction and assessment of component based systems

It is generally believed that component-based software development leads to improved application quality, maintainability and reliability. However most software reliability techniques model integrated systems. These models disregard system's internal structure, taking into account only the failure data and interactions with the environment. We propose a novel approach to reliability analysis of component-based systems. Reliability prediction algorithm allows system architects to analyze reliability of the system before it is built, taking into account component reliability estimates and their anticipated usage. Fully integrated with the UML, this step can guide the process of identifying critical components and analyze the effect of replacing them with the more/less reliable ones. Reliability assessment algorithm, applicable in the system test phase, utilizes these reliability predictions as prior probabilities. In the Bayesian estimation. framework, posterior probability of failure is calculated from the priors and test failure data.
A fully associative, tagless DRAM cache

This paper introduces a tagless cache architecture for large in-package DRAM caches. The conventional die-stacked DRAM cache has both a TLB and a cache tag array, which are responsible for virtual-to-physical and physical-to-cache address translation, respectively. We propose to align the granularity of caching with OS page size and take a unified approach to address translation and cache tag management. To this end, we introduce cache-map TLB (cTLB), which stores virtual-to-cache, instead of virtual-to-physical, address mappings. At a TLB miss, the TLB miss handler allocates the requested block into the cache if it is not cached yet, and updates both the page table and cTLB with the virtual-to-cache address mapping. Assuming the availability of large in-package DRAM caches, this ensures that an access to the memory region within the TLB reach always hits in the cache with low hit latency since a TLB access immediately returns the exact location of the requested block in the cache, hence saving a tag-checking operation. The remaining cache space is used as victim cache for memory pages that are recently evicted from cTLB. By completely eliminating data structures for cache tag management, from either on-die SRAM or inpackage DRAM, the proposed DRAM cache achieves best scalability and hit latency, while maintaining high hit rate of a fully associative cache. Our evaluation with 3D Through-Silicon Via (TSV)-based in-package DRAM demonstrates that the proposed cache improves the IPC and energy efficiency by 30.9% and 39.5%, respectively, compared to the baseline with no DRAM cache. These numbers translate to 4.3% and 23.8% improvements over an impractical SRAM-tag cache requiring megabytes of on-die SRAM storage, due to low hit latency and zero energy waste for cache tags.
Limits on the Masses of Supersymmetric Particles at √ s = 189 GeV

Searches for charginos, neutralinos and sleptons at LEP2 centre-of-mass energies from 130 GeV to 189 GeV have been used to set lower limits on the mass of the Lightest Supersymmetric Particle and other supersymmetric particles within the MSSM framework. R-parity conservation has been assumed. The lightest neutralino was found to be heavier than 32.3 GeV/c independent of the m0 value. The lightest chargino, the second-to-lightest neutralino, the next-toheaviest neutralino, the heaviest neutralino, the sneutrino and the right-handed selectron were found to be heavier than 62.4 GeV/c, 62.4 GeV/c, 99.9 GeV/c, 116.0 GeV/c, 61.0 GeV/c, and 87.0 GeV/c, respectively. These limits do not depend on m0 or M2 and are valid for 1 ≤ tanβ ≤ 40, in the μ region where the lightest neutralino is the LSP. If the sneutrino Is heavier than the chargino the lightest neutralino has to be heavier than 32.4 GeV/c. The effects of mixings in the third family of sfermions on these limits are discussed. The confidence level of all limits given is 95%. (Accepted by Physics Letters B)
Chemoenzymatic Assembly of Bacterial Glycoconjugates for Site-Specific Orthogonal Labeling

The cell surfaces of bacteria are replete with diverse glycoconjugates that play pivotal roles in determining how bacteria interact with the environment and the hosts that they colonize. Studies to advance our understanding of these interactions rely on the availability of chemically defined glycoconjugates that can be selectively modified under orthogonal reaction conditions to serve as discrete ligands to probe biological interactions, in displayed arrays and as imaging agents. Herein, enzymes in the N-linked protein glycosylation (Pgl) pathway of Campylobacter jejuni are evaluated for their tolerance for azide-modified UDP-sugar substrates, including derivatives of 2,4-diacetamidobacillosamine and N-acetylgalactosamine. In vitro analyses reveal that chemoenzymatic approaches are useful for the preparation of undecaprenol diphosphate-linked glycans and glycopeptides with site-specific introduction of azide functionality for orthogonal labeling at three specific sites in the heptasaccharide glycan. The uniquely modified glycoconjugates represent valuable tools for investigating the roles of C. jejuni cell surface glycoconjugates in host pathogen interactions.