Miracle-dz/UniFAR-FTUs huggingface dataset:
Interactive Mirror System based on Personal Purchase Information

Virtual fitting system is the system that coordinates the clothes that the customers want on their virtual avatars and it can be the representative interactive digital signage, and the virtual fitting systems that adopt the augmented reality method are researched currently. That the information on the products of the brands that adopt the system can be checked only, the coordination with other brands or other clothing products cannot be carried out and it has the problem that the personalized coordination information cannot be acquired because the coordination between the existing clothing products and newly-purchased clothing products cannot be performed. in order to coordinate the clothing product using the existing system, basic coordination knowledge or the information like recent fashion trends should be secured, however it is difficult to perform the proper coordination now that most of users do not possess appropriate coordination knowledge in general. Therefore In this paper, virtual realitybased interactive mirror system that provides the personalized coordination information was suggested to solve the problem of existing interactive mirror system that performs virtual coordination work.
Collision resolution in multiple access networks with physical-layer network coding and distributed fountain coding

We propose two new protocols based on physical layer network coding for collision resolution in multiple access networks.When a collision occurs the receiver decodes the sum of the collided packets and after a number of transmissions, equal to or slightly higher than the number of original packets, it can recover all of them. One of the proposed protocols based on fountain codes can resolve collisions by sending out just one acknowledgement (ACK), thus being particulary suited to networks with large round trip delays such as satellite networks. We carry out a comparison of the average delay achieved by the proposed schemes with other access techniques, and show how the performance can be improved with little coordination at the receiver.
Cosmological Parameters from Eigenmode Analysis of Sloan Digital Sky Survey Galaxy Redshifts

We present estimates of cosmological parameters from the application of the Karhunen-Loève transform to the analysis of the three-dimensional power spectrum of density fluctuations using Sloan Digital Sky Survey galaxy redshifts. We use Ωmh and fb = Ωb/Ωm to describe the shape of the power spectrum, σ for the (linearly extrapolated) normalization, and β to parameterize linear theory redshift-space distortions. On scales k ≲ 0.16 h Mpc-1, our maximum likelihood values are Ωmh = 0.264 ± 0.043, fb = 0.286 ± 0.065, σ = 0.966 ± 0.048, and β = 0.45 ± 0.12. When we take a prior on Ωb from the Wilkinson Microwave Anisotropy Probe (WMAP), we find Ωmh = 0.207 ± 0.030, which is in excellent agreement with WMAP and the Two-Degree Field. This indicates that we have reasonably measured the gross shape of the power spectrum, but we have difficulty breaking the degeneracy between Ωmh and fb, because the baryon oscillations are not resolved in the current spectroscopic survey window function.
Bestimmt die wirtschaftliche Dynamik der Städte die Intensität der Einkommenspolarisierung

Die großen europäischen Städte unterliegen seit den 1970er Jahren einem sozioökonomischen Strukturwandel, der sowohl das Städtesystem als auch die innere Struktur der Städte tangiert. Stadtentwicklung ist heute nicht mehr überall identisch mit Wachstum: Einigen weiterhin prosperierenden Städten steht eine wachsende Zahl schrumpfender Städte gegenüber. Ein einheitliches Modell von Stadtentwicklung, das auf Zunahme von Bevölkerung und Arbeitsplätzen sowie steigenden Realeinkommen beruhte, existiert zumindest in den OECD-Ländern nicht mehr. Die Pfade der Stadtentwicklung werden nicht nur zwischen den Städten unterschiedlicher, sondern auch innerhalb der Städte vergrößern sich die Differenzen zwischen den Lebenslagen und Lebenschancen ihrer Bewohner. Angesichts dieser Tendenzen muss man daher von einer doppelten Spaltung sprechen: einer Spaltung zwischen den Städten und einer Spaltung innerhalb der Städte. Mit einem wachsenden Anteil von Dienstleistungstätigkeiten auf dem Arbeitsmarkt – so die These – nimmt die Einkommensungleichheit zu. Entweder wird ein großer Teil der Arbeitskräfte, die früher Fabrikarbeiter waren, aus dem Arbeitsmarkt in die Dauerarbeitslosigkeit abgedrängt – oder aber es wird eine Vollbeschäftigung angestrebt, die nur dann annähernd erreicht werden kann, wenn die Regulierungen von Arbeitsmarkt und Gewerbe einen größeren Niedriglohnsektor zulassen und jene neue Schicht in der Erwerbstätigkeit entsteht, die bisher in deutschen Städten keine große Rolle gespielt hat: diejenigen, die voll arbeiten, aber von ihrer Arbeit nicht leben können, die »working poor«. Von Arbeitslosigkeit und Niedriglohn sind insbesondere die älteren Arbeitnehmer und die gering Qualifizierten betroffen, wozu viele Migranten zählen. Die zunehmende Tertiarisierung schafft Potentiale für eine höhere Lohndifferenzierung.1 Einfache Dienstleistungen mit geringen Möglichkeiten für Produktivitätssteigerungen nehmen nur dann zu, wenn ihre Entlohnung relativ zur Gesamteinkommensentwicklung zurückbleibt. Hochwertige Dienstleistungen können dagegen auch bei steigenden Löhnen expandieren, da Lohnkostenerhöhungen durch Produktivitätssteigerungen kompensiert werden können. Im Tertiarisierungsprozess entstehen dann parallel sowohl relativ schlecht entlohnte einfache Dienstleistungs1.
TDMA-based MAC protocols for scheduling channel allocation in multi-channel wireless mesh networks using cognitive radio

Maximizing spectrum usage and numerous applications of the wireless communication networks have forced to a high interest of vacant spectrum. Cognitive Radio influences its receiver and transmitter features accurately so that they can utilize the vacant approved spectrum without impacting the functionality of the principal licensed subscribers. The use of various channels assists to address interferences thereby improves the whole network efficiency. The MAC protocol in cognitive radio network explains the spectrum consumption by interacting the multiple channels among the subscribers. In this particular paper we studied traditional TDMA dependent MAC method with dynamically assigned slots. The majority of the MAC protocols suggested in the research operate Common-Control-Channel (CCC) to handle the services between Cognitive Radio end users. Traditional MAC protocol design and operations are implemented by using Multi-Channel-Collection method, a high rate multi-channel time schedule protocol for unbiased real-time data collection and their limitations are studied in Wireless mesh Networks. In this paper, an extensive study of Multi-Channel-Collection with sophisticated techniques for multiple band or frequency range channel allotment and continually synchronized TDMA scheduling are shown in summarized way.
Classification of electroencephalography (EEG) signals for different mental activities using Kullback Leibler (KL) divergence

Automatic classification of electroencephalography (EEG) signals, for different type of mental activities, is an active area of research and has many applications such as brain computer interface (BCI) and medical diagnoses. We introduce a simple yet effective way to use Kullback-Leibler (KL) divergence in the classification of raw EEG signals. We show that k-nearest neighbor (k-NN) algorithm with KL divergence as the distance measure, when used using our feature vectors, gives competitive classification accuracy and consistently outperforms the more commonly used Euclidean k-NN. We also develop and demonstrate the use of a KL-based kernel to classify EEG data using support vector machines (SVMs). Our KL-distance based kernel compares favorably to other well established kernels such as linear and radial basis function (RBF) kernel. The EEG data, used in our experiments for classification, was recorded while the subject performed 5 different mental activities such as math problem solving, letter composing, 3-D block rotation, counting and resting (baseline). We present classification results for this data set that are obtained by using raw EEG data with no explicit artifact removal in the pre-processing steps.
Multi-Ring Cyclic Scheduling for Spatial-TDMA Energy-Saving MAC Protocol in MANET

This paper presents an energy-saving multi-ring cyclic scheduling for spatial-TDMA (MRSTDMA). We design a cyclic scheduling of STDMA to realize the energy-saving property of STDMA and overcome the drawbacks of STDMA - reducing TDMA overhead, increasing TDMA throughput, and decreasing end-to-end delay. Besides energy-saving mechanisms, there are initial slot assignment, joining of node, exiting of node, changing slots, and synchronization. Simulation results show that the proposed protocol outperforms the IEEE 802.11 in terms of the amount of energy consumed. The throughput is close to 802.11's, and the packet delivery ratio is near the ratio of 802.11
Wearable Assistance Device for Visually Disabled

There are 37 million visually disable people in this world [1]. Advances in computer human interface and 3D world perception permit production of affordable and user friendly devices to assist the visually disabled [3]. This research paper presents a different approach in wearable computing using open source software and inexpensive hardware to assist the visually disabled section of population. General Terms Wearable Computing
HSMM-Based Model Adaptation Algorithms for Average-Voice-Based Speech Synthesis

In HMM-based speech synthesis, we have to choose the modeling strategy for speech synthesis units depending on the amount of available speech data to generate synthetic speech of better quality. In general, speaker-dependent modeling is an ideal choice for a large speech data, whereas speaker adaptation with average voice model becomes promising when available speech data of a target speaker is limited. This paper describes several speaker adaptation algorithms and MAP modification to develop consistent method for synthesizing speech in a unified way for arbitrary amount of the speech data. We incorporate these adaptation algorithms into our HSMM-based speech synthesis system and show its effectiveness from results of several evaluation tests
An algorithm for choosing Japanese acknowledgments using prosodic cues and context

In human dialog a wide variety of acknowledgments are used. One function of this seems to be indicating attention, interest, and involvement to the other speaker. Based on study of Japanese memory game dialogs, we propose an algorithm for choosing among acknowledgment responses, including hai (yes), so (right), and un (mm). The primary factors involved are aspects of the user’s internal state, including con(cid:12)dence and liveliness, as inferred from the context and the user’s prosody. We found that judges preferred algorithm-chosen responses to randomly varied responses, con(cid:12)rming our hypothesis that ‘sensitive’ and subtle choice of response may improve helpfulness and naturalness of man-machine spoken language interaction.
Writer-Dependent Recognition of Handwritten Whiteboard Notes in Smart Meeting Room Environments

In this paper we present a writer-dependent handwriting recognition system based on hidden Markov models (HMMs). This system, which has been developed in the context of research on smart meeting rooms, operates in two stages. First, a Gaussian mixture model (GMM)-based writer identification system developed for smart meeting rooms identifies the person writing on the whiteboard. Then a recognition system adapted to the individual writer is applied. Two different methods for obtaining writer-dependent recognizers are proposed. The first method uses the available writer-specific data to train an individual recognition system for each writer from scratch, while the second method takes a writer-independent recognizer and adapts it with the data from the considered writer. The experiments have been performed on the IAM-OnDB. In the first stage,the writer identification system produces a perfect identification rate. In the second stage, the writer-specific recognition system gets significantly better recognition results, compared to the writer-independent recognizer. The final word recognition rate on the IAM-OnDB-t1 benchmark task is close to 80 %.
Surface material recognition through haptic exploration using an intelligent contact sensing finger

Object surface properties are among the most important information which a robot requires in order to effectively interact with an unknown environment. This paper presents a novel haptic exploration strategy for recognizing the physical properties of unknown object surfaces using an intelligent finger. This developed intelligent finger is capable of identifying the contact location, normal and tangential force, and the vibrations generated from the contact in real time. In the proposed strategy, this finger gently slides along the surface with a short stroke while increasing and decreasing the sliding velocity. By applying a dynamic friction model to describe this contact, rich and accurate surface physical properties can be identified within this stroke. This allows different surface materials to be easily distinguished even if when they have very similar texture. Several supervised learning algorithms have been applied and compared for surface recognition based on the obtained surface properties. It has been found that the naïve Bayes classifier is superior to radial basis function network and k-NN method, achieving an overall classification accuracy of 88.5% for distinguishing twelve different surface materials.
Dissertation Proposal : Social Capital in Online Communities

Online communities are connecting hordes of individuals and generating rich social network data. The social capital that resides within these networks is largely unknown. We propose to create a general framework for measuring and leveraging social capital based upon explicit social networks, implicit affinities, and the mobilization of social resources. The resulting quantitative models are used to characterize social capital in several online communities.
Adaptive and Self Healing Routing for Mobile AdHoc Networks Using Cross Layer Design

The construction of reliable and stable routes in a mobile ad hoc network is one of the primary research issues in equipping each device to continuously maintain the information required to properly route traffic. Mobility of nodes often leads to link failures and hence requires route reconstruction to resume the communication between the nodes. The stability factor of a route can reduce the number of times the route is changed or reconstructed. This paper presents a novel idea for discovering a stable set of routes using the metrics from multiple layers rather than depending on network layer along with a finite set of parameters to qualify a link or connecting to a node. The link stability factor and link received signal strength are considered as the main metrics to qualify the stability of a route, derived from the physical and data link layer based on bit or packet error rate, retrieved from the soft output decoder. The simulation results based on the analysis of the proposed algorithm prove to be more efficient in terms of discovering stable routes, reducing frequent reconstruction of routes and hence improving the overall performance of the network.
Dead reckoning of biped robots with estimated contact points based on the minimum velocity criterion

A novel technique of dead reckoning for biped robots, which could be utilized for agile motion controls, is proposed. A complementary filter combines the estimations of the position of robot body from both the kinematic computation and the acceleration information, where the former is relied on in the lower frequency domain and the latter is done on in the higher domain. Even though the supporting foot of the robot happens to roll and rotate on the terrain, the estimation accuracy of the kinematics is improved by taking such movements into consideration. We suppose that the contact point moves with respect to the ground at the instantaneously minimum velocity, and thus name it the instantaneous minimum velocity point (IMVP), which is estimated by an optimization. IMVP can be computed for each foot, so that the weighted sum of them by the magnitude of reaction forces on each foot is adopted as a candidate of the contact point under an assumption that the contact condition is more steady when a larger reaction force is applied. Finally, it is merged with the twice-integrated acceleration through the complementary filter, where the crossover frequency is also determined by the reaction forces. Hence, it is robust against the change of contact conditions. Results of computer simulations will show that the proposed method reduces the estimation error comparing with the conventional methods.
Recent sojourn time results for multilevel processor‐sharing scheduling disciplines

Multilevel Processor‐Sharing (MLPS) disciplines refer to a family of age‐based scheduling disciplines introduced decades ago. A time‐discretized version of an MLPS discipline is applied in the scheduler of the traditional UNIX operating system. In recent years, MLPS disciplines have been used to study the way that packet level scheduling mechanisms impact the performance perceived at the flow level in the Internet. Inspired by this latter application, many new sojourn time results have been discovered for these disciplines in the context of the M/G/1 queue. The aim of this paper was to give a consistent overview of these new results. In addition, it points out some intriguing open problems for further research.
Automatic Music Tag Classification Based On Block-Level Features

In this paper we propose to use a set of block-level audio features for automatic tag prediction. As the proposed feature set is extremely high-dimensional we will investigate the Principal Component Analysis (PCA) as compression method to make the tag classification computationally tractable. We will then compare this block-level feature set to a standard feature set that is used in a state-of-theart tag prediction approach. To compare the two feature sets we report on the tag classification results obtained for two publicly available tag classification datasets using the same classification approach for both feature sets. We will show that the proposed features set outperform the standard feature set, thus contributing to the state-of-the-art in automatic tag prediction.
The Effect of Random Relationship Lengths on Trust

Although it is well known that trust and trustworthiness (i.e., the fulfillment of trust) are important behaviors for the fulfillment of incomplete contracts, less is known about how the economic environment influences them. In this paper we design an experiment to examine how exogenously determined (stochastic) past relationship lengths affect trust and trustworthiness in new relationships. We find that shorter-lasting relationships have an immediate negative impact on both behaviors in the relationships that immediately follow, while longer-lasting relationships have the opposite effect. The effect of stochastic endpoints declines for trustworthiness but not for trust as subjects gain experience, indicating that trust is able to rebound when longer-lasting relationships follow shorter-lasting ones. keywords: Trust, Repeated Games, Incomplete Contracts, Experimental Economics. JEL classification nos. C72, C91. Contact Information: Corresponding Author: Engle-Warnick: Department of Economics, 855 Sherbrooke St. W. , McGill University, Montreal, QC, H3A 2T7, Canada, tel. : (514) 398-1559; fax: (514) 398-4938, e-mail: jim.engle-warnick@mcgill.ca; Slonim: Department of Economics, Weatherhead School of Management, Case Western Reserve University, Cleveland, OH, 44106, USA, tel. : (216) 368-5811, fax: (216) 368-0703, e-mail: rls18@po.cwru. edu. Acknowledgements: We thank Colin Camerer, David J. Cooper, John Duffy, Ellen Garbarino, Susan Helper, Margaret Meyer, John Miller, Mari Rege, Jim Rebitzer, Alvin E. Roth, Bradley Ruffle for valuable comments. The paper benefited enormously from comments from an associate editor and a referee. We also received valuable suggestions from participants at the 2000 Economic Science Association Meetings and the University of Oxford Department of Economics Experimental Economics Workshop. The authors gratefully acknowledge financial support from the Economic Departments at the University of Pittsburgh, the Weatherhead School of Management, Case Western Reserve University, and the University of Oxford George Webb Medley Endowment Fund. Much of this work was completed with Engle-Warnick at Nuffield College, University of Oxford, UK.
A Survey on Masquerader Detection Approaches

This paper presents a survey on the area of masquerader detection. The three most popular publicly available UNIX commandline datasets are showed and their features are compared. Several different masquerader detection approaches are reviewed and their results are compared applying the most popular measures of detection effectiveness in this area, introducing the most extensive quantitative comparison of results in literature. Possible ways for future work in this area are proposed as well.
Nonlinear Dimensionality Reduction Applied to the Binary Classification of Images

In this project we are interested in the reduction of high-dimensional data points x from a space R to a lower dimensional space R (where d << D) in a way that preserves certain important characteristics. In particular, we are interested in reducing the size of high-dimensional points for their application in the binary classification of signals. We first examine a MatLab implementation of the Locally Linear Embeddings (LLE) algorithm, and it to a specific image database. We then use the output of the LLE and the original dataset to test and compare the performance ability of a MatLab implementation of the support vector machine.
TS4J: a fluent interface for defining and computing typestate analyses

Typestate analyses determine whether a program's use of a given API obeys this API's usage constraints in the sense that the right methods are called on the right objects in the right order. Previously, we and others have described approaches that generate typestate analyses from textual finite-state property definitions written in specialized domain-specific languages. While such an approach is feasible, it requires a heavyweight compiler, hindering an effective integration into the programmer's development environment and thus often also into her software-development practice. Here we explain the design of a pure-Java interface facilitating both the definition and evaluation of typestate analyses. The interface is fluent, a term coined by Eric Evans and Martin Fowler. Fluent interfaces provide the user with the possibility to write method-invocation chains that almost read like natural-language text, in our case allowing for a seemingly declarative style of typestate definitions. In all previously described approaches, however, fluent APIs are used to build configuration objects. In this work, for the first time we show how to design a fluent API in such a way that it also encapsulates actual computation, not just configuration. We describe an implementation on top of Soot, Heros and Eclipse, which we are currently evaluating together with pilot customers in an industrial context at Fraunhofer SIT.