Miracle-dz/UniFAR-FTUs huggingface dataset:
Revealing potential changes of significant terms in streams of textual data written in natural languages using windowing and text mining

The presented research deals with analyzing continuous streams of textual data written in natural languages. One of problems is revealing possible significant concept changes in Internet blogs, discussions, etc., together with discovering what represents such data, if it is more-or-less topically invariable or changing, and what kind of change occurred. A real-world textual dataset is analyzed using text-mining with automatically generated decision trees to find significant words that affect correct assignment of document labels (classes) and can be used for detecting noticeable changes. The changes and their detection are here modeled by assorted gradual mixture of two languages and the change degree is measured by cosine, Eucledian, and Jaccard distance (similarity), which provide qualitatively the same result. The monitoring procedure is based on analyzing successively adjacent couples of data-windows in the stream using the comparison of the current and its previous window, both represented by their lists of relevant features expressed in words. The presented results demonstrate that the suggested method provides reliable results.
Highly Flexible and Extensible Storage Scheme for Virtualization Platforms under Cloud Computing

This paper proposes a new brand of storage scheme named esStore for cloud computing platform, which provides a flexible and easily extensible image-based storage scheme for virtual machines to solve the issues in image expansion. esStore consists of two major components, esImg and esAgt. The esImg is a new disk image format, which can be easily expanded in storage capacity without complex operations. With esImg, the system is able to save time and resources on the virtual machine (VM) storage expansion. The esAgt, as an in-VM engine, is proposed to parse the esImg and provide file system functions for the VM. The prototype system is implemented and proved to achieve our design goals with satisfactory system performance.
Removing Requirement Defects and Automating Test

Organizations face many problems that impede rapid development of software systems critical to their operations and growth. This paper discusses model-based development and test automation methods that reduce the time and resources necessary to develop high quality systems. The focus is how organizations have implemented this approach of model-based verification to reduce requirements defects, manual test development effort, and development rework to achieve significant cost and schedule savings.
Analysis ofWiMedia-based UWB Mesh Networks

Regarding maximum transmission rates, Ultra Wideband (UWB) seems to be the wireless technology which could successfully replace most of the data-cables in office and home environments: With up to 480 Mb/s gross data rate, wireless high-definition video streaming and data synchronization become feasible. Of course, these advantages come at a price: UWB is designed for short-range communication, limited to 10 m. While this suffices for some application, it does not fulfill the vision of ubiquitous wireless access in the fully-connected home. A straightforward solution to increase the network coverage is given by Wireless Mesh Networks (WMNs). In this paper, we analyze if the combination of UWB and WMN is able to provide the required coverage and the expected data rates. Several different deployment concepts (including ad- hoc networking and dedicated mesh relays) are evaluated with a realistic system model, which is able to compute the resulting network capacity. The results show that under the assumptions of the model, i. e. a MAC which is able to exploit spatial divided frequency reuse, UWB mesh networks are able to provide a stable capacity of more than 100 Mb/s in a typical scenario of up to 250 m2. Hence, the combination of the two technologies is able to succeed in much more application scenarios in comparison to the current UWB standard.
Let's Play: The Use of Improv Games in Change Management Training

This paper is based on the concept of Improv games as a promising mechanism and design principle for enhancing an organization's capacity for learning and adaptability. The study explores how Improv games can be used to create a mindset conducive to change, facilitate ideation and guide discussions on bringing about systemic change. In the case study that forms the basis of the paper, Improv games were found to be particularly useful in helping participants to identify and confront their anxieties with respect to accepting and implementing change. Further, instead of being dismissed or criticized, their concerns could be creatively deployed in a realistic assessment of the action plans they had drawn up for implementing change. The paper demonstrates the technique of the games and how they can be integrated into change management programmes, and summarizes the benefits of the practice.
International Symposium on Multi-Agents and Mobile Agents in Virtual Organizations and E-Commerce ( MAMA ’ 2000 ) – – Final Version – –

Over the last few years, there has been a huge proliferation of mobile agent platforms, for the most different application domains. Although these platforms normally have a very interesting set of features, one common limitation found on most architectures is the lack of support for extensibility, i.e. the ability to add new features at runtime, after the platform has been written and deployed. This is an important question since it limits the usability of the platforms across application domains, and its future usefulness as software requirements change. In this paper, we present a general mechanism for platform extensibility based on binary software components. The mechanism was implemented using the JavaBeans component model and several services where implemented and deployed. The approach undertaken is generic and can be easily adapted into existing mobile agent platforms.
Synchronization of non-autonomous chaotic systems with time-varying delay via delayed feedback control

In this paper, we investigate the synchronization of non-autonomous chaotic systems with time-varying delay via delayed feedback control. Using a combination of Riccati differential equation approach, Lyapunov-Krasovskii functional, inequality techniques, some new sufficient conditions for exponentially stability of the error system are formulated in form of a solution to the standard Riccati differential equation. The designed controller ensures that the synchronization of non-autonomous chaotic systems are proposed via delayed feedback control. Numerical simulations are presented to illustrate the effectiveness of these synchronization criteria.
AN IMPROVED CONSTRAINT BASED RESOURCE SCHEDULING APPROACH USING JOB GROUPING STRATEGY IN GRID COMPUTING

Grid computing is a collection of distributed resources interconnected by networks to provide a unified virtual computing resource view to the user. Grid computing has one important responsibility of resource management and techniques to allow the user to make optimal use of the job completion time and achieving good throughput. It is a big deal to design the efficient scheduler and is implementation. In this paper, the constraint based job and resource scheduling algorithm has been proposed. The four constraints are taken into account for grouping the jobs, i.e. Resource memory, Job memory, Job MI and the fourth constraint L2 cache are considered. Our implementation is to reduce the processing time efficiently by adding the fourth constraint L2 cache of the resource and is allocated to the resource for parallel computing. The L2 cache is a part of computer’s processor; it increases the performance of computer. It is smaller and extremely fast computer memory. The use of more constraint of the resource and job can increase the efficiency more. The work has been done in MATLAB using the parallel computing toolbox. All the constraints are calculated using different functions in MATLAB and are allocated to the resource based on it. The resource memory, Cache, job memory size and job MI are the key factors to group the jobs according to the available capability of the selected resource. The processing time is taken into account to analyze the feasibility of the algorithms.
Identification of Synchronous Generator Parameters Using Continuous ARX Model and Least Square Estimation

In this paper, least square estimation (LSE)-based synchronous generator model identification is carried out. The synchronous generator model including electromechanical dynamics and primary frequency control is first expressed by continuous-time AutoRegressive with eXogenous (ARX) models. The high-order derivatives in a continuous ARX model are approximated by expressions in terms of discrete-time data. The proposed approximation method (central difference approximation) is applied to second and third order derivatives. The continuous time model is now converted to a discrete time model. Generator parameters are then identified by LSE. We proposed two estimation structures to apply LSE. Benchmark model is simulated with and without input noise. The simulation data is used for estimation. It is found that one estimation structure is better than the other in terms of noise handling.
Hyper-local, directions-based ranking of places

Studies find that at least 20% of web queries have local intent; and the fraction of queries with local intent that originate from mobile properties may be twice as high. The emergence of standardized support for location providers in web browsers, as well as of providers of accurate locations, enables so-called hyper-local web querying where the location of a user is accurate at a much finer granularity than with IP-based positioning. This paper addresses the problem of determining the importance of points of interest, or places, in local-search results. In doing so, the paper proposes techniques that exploit logged directions queries. A query that asks for directions from a location a to a location b is taken to suggest that a user is interested in traveling to b and thus is a vote that location b is interesting. Such user-generated directions queries are particularly interesting because they are numerous and contain precise locations. Specifically, the paper proposes a framework that takes a user location and a collection of near-by places as arguments, producing a ranking of the places. The framework enables a range of aspects of directions queries to be exploited for the ranking of places, including the frequency with which places have been referred to in directions queries. Next, the paper proposes an algorithm and accompanying data structures capable of ranking places in response to hyper-local web queries. Finally, an empirical study with very large directions query logs offers insight into the potential of directions queries for the ranking of places and suggests that the proposed algorithm is suitable for use in real web search engines.
An Energy-Based Recurrent Neural Network for Multiple Fundamental Frequency Estimation

Many naturally occurring phenomena such as music, speech, or human motion are inherently sequential. Complex sequences are often non-local (longterm temporal dependencies) and high-dimensional (multi-modal conditional distribution). For the example of polyphonic music, these properties represent the basic components of Western music, namely rhythm and harmony. Here we wish to exploit the recurrent neural network (RNN) internal memory that can in principle represent long-term dependencies, and energy-based models, such as the Restricted Boltzmann Machine (RBM), that allow us to express complex distributions by the means of an energy function. This combination was first put forward with the so-called Temporal RBM (TRBM) [3], the first such probabilistic model which however uses a heuristic training procedure. The Recurrent TRBM (RTRBM) [4] is a slight modification of the TRBM that allows for exact inference and efficient training by contrastive divergence (CD). The RTRBM can be understood as a sequence of RBMs whose parameters
A Research Agenda toward Assessing Perceived Affective Quality of IT

Most IT evaluation and usage studies emphasize cognition or high-order affective reactions that come from more deliberate cognitive processing. A primitive concept, perceived affective quality (PAQ) of IT, has not yet attracted much attention. PAQ refers to an individual’s perception of IT’s ability to change his/her affective state. IS and psychology literature shows that PAQ has significant impacts on higher-order affect, cognition, and behaviors. Thus understanding the nature and measurement of PAQ is of great importance. However, there is no PAQ instrument specifically designed for IT. This study intends to develop and validate such an instrument based on literature review and surveys. This scale will provide researchers with a useful tool to identify favorable affective quality of IT. It will also help IT managers and designers to provide better products and enhance profits.
I show you how I like you - can you read it in my face? [robotics]

We report work on a LEGO robot that displays different emotional expressions in response to physical stimulation, for the purpose of social interaction with humans. This is a first step toward our longer-term goal of exploring believable emotional exchanges to achieve plausible interaction with a simple robot. Drawing inspiration from theories of human basic emotions, we implemented several prototypical expressions in the robot's caricatured face and conducted experiments to assess the recognizability of these expressions.
Local stereo matching based on the improved matching cost function and the adaptive window

This paper presents a stereo matching algorithm based on the improved matching cost function and the adaptive window. First of all, AD, Census and gradient gap of left and right images are calculated. Joint bilateral filter is used for calculating weights of pixels, thereby constructing a matching cost function. Then, different aggregation window construction strategies are used for aggregating matching costs through pixel classification. One judgment of disparity computation validness is increased on the basis of using Winner-Takes-All strategy. Finally, disparity map is refined through regional voting, joint image inpainting and other optimization steps. Middlebury data sets are adopted for the experiment. The result shows that algorithm proposed in the paper has good processing result.
Joint estimation of offset parameters and high-resolution images via l1-norm minimization principle

We propose a joint estimation algorithm of offset parameters and a high resolution image from a set of multiple low resolution images based on the l1-norm minimization principle. Advantages of the joint approach include that, since it uses low-resolution images in a batch manner, we are less suffered from aliasing effects. The l1-norm minimization principle is effective because we assume sparsity on underlying high-resolution images. The proposed algorithm first minimizes the l1-norm of a vector that satisfies data constraint with the offset parameters fixed. Then, the minimum value is further minimized with respect to the parameters. Even though this is a heuristic approach, the computer simulations show that the proposed algorithm perfectly reconstructs sparse images with a probability more than or equal to 99% for large dimensional images. The proposed approach is attractive because of its computational efficiency.
A Shift-Invariant Latent Variable Model for Automatic Music Transcription

Abstract In this work, a probabilistic model for multiple-instrument automatic music transcription is proposed. The model extends the shift-invariant probabilistic latent component analysis method, which is used for spectrogram factorization. Proposed extensions support the use of multiple spectral templates per pitch and per instrument source, as well as a time-varying pitch contribution for each source. Thus, this method can effectively be used for multiple-instrument automatic transcription. In addition, the shift-invariant aspect of the method can be exploited for detecting tuning changes and frequency modulations, as well as for visualizing pitch content. For note tracking and smoothing, pitch-wise hidden Markov models are used. For training, pitch templates from eight orchestral instruments were extracted, covering their complete note range. The transcription system was tested on multiple-instrument polyphonic recordings from the RWC database, a Disklavier data set, and the MIREX 2007 multi-F0 data set. Results demonstrate that the proposed method outperforms leading approaches from the transcription literature, using several error metrics.
Maintenance of Fast Updated Frequent Pattern Trees for Record Modification

In this paper, we attempt to extend the FP-tree construction algorithm for efficiently handling record modification. A fast updated FP-tree (FUFP-tree) structure is used, which makes the tree update process become easier. An FUFP-tree maintenance algorithm is also proposed for reducing the execution time in reconstructing the tree when records are modified. Experimental results show that the proposed FUFP-tree maintenance algorithm for record modification runs faster than the batch FP-tree construction algorithm for handling updated records and generates nearly the same tree structure as the FP-tree algorithm
Narrow heavy-hole cyclotron resonances split by the cubic Rashba spin-orbit interaction in strained germanium quantum wells

The spin-orbit interaction was found to split the cyclotron resonance of heavy holes confined in high-mobility, compressively strained germanium quantum wells. The interference between coherent spin-split cyclotron resonances was tracked on picosecond time scales using terahertz time-domain spectroscopy. Analysis in the time domain, or using a time-frequency decomposition based on the Gabor-Morlet wavelet, was necessary when the difference between cyclotron frequencies was comparable to the linewidth. The cubic Rashba spin-orbit coefficient β was determined via two methods: (i) the magnetic-field dependence of the cyclotron frequencies, and (ii) the spin-resolved subband densities. An enhanced β and spin polarization was created by tailoring the strain to enhance the spin-orbit interaction. The amplitude modulation of the narrow, interfering cyclotron resonances is a signature of spin coherences persisting for more than 10 ps.
Metatranscriptional Response of Chemoautotrophic Ifremeria nautilei Endosymbionts to Differing Sulfur Regimes

Endosymbioses between animals and chemoautotrophic bacteria are ubiquitous at hydrothermal vents. These environments are distinguished by high physico-chemical variability, yet we know little about how these symbioses respond to environmental fluctuations. We therefore examined how the γ-proteobacterial symbionts of the vent snail Ifremeria nautilei respond to changes in sulfur geochemistry. Via shipboard high-pressure incubations, we subjected snails to 105 μM hydrogen sulfide (LS), 350 μM hydrogen sulfide (HS), 300 μM thiosulfate (TS) and seawater without any added inorganic electron donor (ND). While transcript levels of sulfur oxidation genes were largely consistent across treatments, HS and TS treatments stimulated genes for denitrification, nitrogen assimilation, and CO2 fixation, coincident with previously reported enhanced rates of inorganic carbon incorporation and sulfur oxidation in these treatments. Transcripts for genes mediating oxidative damage were enriched in the ND and LS treatments, potentially due to a reduction in O2 scavenging when electron donors were scarce. Oxidative TCA cycle gene transcripts were also more abundant in ND and LS treatments, suggesting that I. nautilei symbionts may be mixotrophic when inorganic electron donors are limiting. These data reveal the extent to which I. nautilei symbionts respond to changes in sulfur concentration and species, and, interpreted alongside coupled biochemical metabolic rates, identify gene targets whose expression patterns may be predictive of holobiont physiology in environmental samples.
Convergence of a stochastic approximation version of the EM algorithm

The expectation-maximization (EM) algorithm is a powerful computational technique for locating maxima of functions. It is widely used in statistics for maximum likelihood or maximum a posteriori estimation in incomplete data models. In certain situations, however, this method is not applicable because the expectation step cannot be performed in closed form. To deal with these problems, a novel method is introduced, the stochastic approximation EM (SAEM), which replaces the expectation step of the EM algorithm by one iteration of a stochastic approximation procedure. The convergence of the SAEM algorithm is established under conditions that are applicable to many practical situations. Moreover, it is proved that, under mild additional conditions, the attractive stationary points of the SAEM algorithm correspond to the local maxima of the function. presented to support our findings.
Nye's Trie and Floret Estimators: Techniques for Detecting and Repairing Divergence in the SCADS Distributed Storage Toolkit

We present two novel data structures developed in the SCADS distributed storage toolkit [4] for synchronizing replicated datasets with predictable performance: Nye’s trie is a lightweight index for ordered key-value sets that supports synchronization with time and bandwidth utilization proportional to the number of diverging entries. While efficient, this process is only predictable if the number of divergent entries can be measured. For this, we introduce the floret estimator, a novel sublinear-space set summarization structure used to estimate the cardinalities of set difference, union, and intersection operations. We describe how these structures satisfy the design requirements of the SCADS system, detail their design and implementation, and present a set of microbenchmarks demonstrating their functionality.
Exploring the Use of Coupled Oscillators for Humanoid Gait Control

The dream of a world where robots assist and cooperate with humans has long inhabits the imagination of our society. In books and movies, the relationship between people and artificial beings has assumed tragic proportions, for example when the machines rebelled against humanity. In other works, however, robots with high intelligence and skill gently help people in many tasks. Currently, industrial robots are employed in the areas of handling and transport, involving manipulator arms and automatic transport vehicles. Seeking go a step further, this project aims to develop techniques using coupled oscillators that enable robots to move in ambients made for humans while interacting with them.
Statistical performance analysis of super-resolution

Recently, there has been a great deal of work developing super-resolution algorithms for combining a set of low-quality images to produce a set of higher quality images. Either explicitly or implicitly, such algorithms must perform the joint task of registering and fusing the low-quality image data. While many such algorithms have been proposed, very little work has addressed the performance bounds for such problems. In this paper, we analyze the performance limits from statistical first principles using Crame/spl acute/r-Rao inequalities. Such analysis offers insight into the fundamental super-resolution performance bottlenecks as they relate to the subproblems of image registration, reconstruction, and image restoration.