Miracle-dz/UniFAR-FTUs huggingface dataset:
A Search Algorithm for Clusters in a Network or Graph

A novel breadth-first based structural clustering method for graphs is proposed. Clustering is an important task for analyzing complex networks such as biological networks, World Wide Web and social networks. The clusters are of various shapes such as cliques and stars, for example, in ProteinProtein Interactive(PPI) networks. Traditional algorithms may detect clique-shaped clusters, but they fail to identify star-shaped clusters that are common in scale free networks, including PPI networks. We propose a novel clustering algorithm to solve the problem. Experimental results demonstrate it outperforms other algorithms in one or several aspects: Detecting clusters of mixed shapes, including both cliques and stars; Faster. Its running time on a network with n nodes and m links is O(n), which is much faster than O(mdlogn) of the fastest modularity-based algorithm(where d is the depth of the dendrogram describing the hierarchical cluster structure); A non-parametric algorithm. It can accomplish all goals without requiring any input parameters.
YET ANOTHER ALGORITHM FOR PITCH TRACKING (YAAPT) by

This thesis presents a pitch detection algorithm that is extremely robust for both high quality and telephone speech. The kernel method for this algorithm is the Normalized Cross Correlation (NCCF) reported by David Talkin [16]. Major innovations include: processing of the original acoustic signal and a nonlinearly processed version of the signal to partially restore very weak F0 components; intelligent peak picking to select multiple F0 candidates and assign merit factors; and, incorporation of highly robust pitch contours obtained from smoothed versions of low frequency portions of spectrograms. Dynamic programming is used to find the " best " pitch track among all the candidates, using both local and transition costs. The algorithm has been evaluated using the Keele pitch extraction reference database as " ground truth " for both " high quality " and " telephone " speech. For both types of speech, the error rates obtained are lower than the lowest reported in the literature. iii be on my committee and for their valuable time and generous assistance. I would like to thank all my friends in the Speech Communication Lab for establishing a positive working environment. Finally, I wish to thank my family for their love and support in completion of my thesis.
Sleep scheduling for partial coverage in heterogeneous wireless sensor networks

An important issue of research in a dense and random deployment of a wireless sensor network (WSN) is to maintain the coverage of the field and yet minimise the energy consumed by the sensors. Some monitoring applications may require only partial coverage of the field in which case redundant sensors can be identified and scheduled to sleep to minimise the energy consumption. In this paper, we address the problem of determining if a sensor is redundant while the desired partial coverage requirement is met. We assume that the WSN is heterogeneous in which all the sensors may not have the same sensing and/or communication radii. In such a heterogeneous WSN, we use a probabilistic approach to determine the degree of redundancy in the coverage. Given a partial coverage requirement for the field, we propose a scheduling protocol to identify the redundant sensors and put them to sleep. The proposed protocol is fully distributed and does not use any geographical information. We demonstrate the utility of the proposed approach in determining the redundancy in coverage with numerical and simulation results. Our simulations show that the proposed protocol effectively maintains the desired coverage and prolongs the network lifetime.
NMFLUX: Improving Degradation Behavior of Server Applications through Dynamic Nursery Resizing

Currently, most generational collectors are tuned to either deliver peak performance when the heap is plentiful, but yield unacceptable performance when the heap is tight or maintain good degradation behavior when the heap is tight, but deliver sub-optimal performance when the heap is plentiful. In this paper, we present NMFLUX (continuously varying the Nursery/Mature ratio), a framework that switches between using a fixed-nursery generational collector and a variable-nursery collector to achieve the best of both worlds; i.e. our framework delivers optimal performance under normal workload, and graceful performance degradation under heavy workload. We use this framework to create two generational garbage collectors and evaluate their performances in both desktop and server settings. The experimental results show that our proposed collectors can significantly improve the throughput degradation behavior of large servers while maintaining similar peak performance to the optimally configured fixed-ratio collector.
Towards the Graceful Exit From the Anomaly-Induced Inflation

The method for the effective action for massive quantum fields in curved space-time is based on the conformal representation of the fields action and consequent integration of the conformal anomaly. As a relevant cosmological application, we find that if we take the masses of the fields into account, then the stable anomaly-induced inflation slows down due to the effect of massive fermions. In supersymmetric theories this mechanism can be specially efficient, for it may naturally provide the graceful exit from the inflationary to the FLRW phase. Taking the SUSY breaking into account, the anomaly-induced inflation could be free of the well-known difficulties with the initial data and also with the amplitude of the gravitational waves.
Forecasting Social Navigation in Crowded Complex Scenes

When humans navigate a crowed space such as a university campus or the sidewalks of a busy street, they follow common sense rules based on social etiquette. In this paper, we argue that in order to enable the design of new algorithms that can take fully advantage of these rules to better solve tasks such as target tracking or trajectory forecasting, we need to have access to better data in the first place. To that end, we contribute the very first large scale dataset (to the best of our knowledge) that collects images and videos of various types of targets (not just pedestrians, but also bikers, skateboarders, cars, buses, golf carts) that navigate in a real-world outdoor environment such as a university campus. We present an extensive evaluation where different methods for trajectory forecasting are evaluated and compared. Moreover, we present a new algorithm for trajectory prediction that exploits the complexity of our new dataset and allows to: i) incorporate inter-class interactions into trajectory prediction models (e.g, pedestrian vs bike) as opposed to just intra-class interactions (e.g., pedestrian vs pedestrian); ii) model the degree to which the social forces are regulating an interaction. We call the latter "social sensitivity"and it captures the sensitivity to which a target is responding to a certain interaction. An extensive experimental evaluation demonstrates the effectiveness of our novel approach.
An Efficient Algorithm for Intrusion Source Identification

There has been a growing interest in the design and development of intrusion detection systems for the Internet. One of the goals of these systems is to detect the source(s) of attack. Based on the position of the victim in the network, our scheme selects only a small fraction of routers to monitor the traffic and identify packets that bear signatures of the attack packets. From the information provided by these chosen routers, the network is pruned and another set of routers is chosen to identify the source of attack, until the source router is detected. By using the properties of the centroid tree, we present an efficient algorithm for identifying the source of the intrusion.
HIGHER-ORDER INTEGRATED WAVETABLE SYNTHESIS

Wavetable synthesis is a popular sound synthesis method enabling the efficient creation of musical sounds. Using sample rate conversion techniques, arbitrary musical pitches can be generated from one wavetable or from a small set of wavetables: downsampling is used for raising the pitch and upsampling for lowering it. A challenge when changing the pitch of a sampled waveform is to avoid disturbing aliasing artifacts. Besides bandlimited resampling algorithms, the use of an integrated wavetable and a differentiation of the output signal has been proposed previously by Geiger. This paper extends Geiger’s method by using several integrator and differentiator stages to improve alias-reduction. The waveform is integrated multiple times before it is stored in a wavetable. During playback, a sample rate conversion method is first applied and the output signal is then differentiated as many times as the wavetable has been integrated. The computational cost of the proposed technique is independent of the pitch-shift ratio. It is shown that the higher-order integrated wavetable technique reduces aliasing more than the first-order technique with a minor increase in computational cost. Quantization effects are analyzed and are shown to become notable at high frequencies, when several integration and differentiation stages are used.
A Novel Role of E-Cadherin-Based Adherens Junctions in Neoplastic Cell Dissemination

Using confocal microscopy, we analyzed the behavior of IAR-6-1, IAR1170, and IAR1162 transformed epithelial cells seeded onto the confluent monolayer of normal IAR-2 epithelial cells. Live-cell imaging of neoplastic cells stably expressing EGFP and of normal epithelial cells stably expressing mKate2 showed that transformed cells retaining expression of E-cadherin were able to migrate over the IAR-2 epithelial monolayer and invade the monolayer. Transformed IAR cells invaded the IAR-2 monolayer at the boundaries between normal cells. Studying interactions of IAR-6-1 transformed cells stably expressing GFP-E-cadherin with the IAR-2 epithelial monolayer, we found that IAR-6-1 cells established E-cadherin-based adhesions with normal epithelial cells: dot-like dynamic E-cadherin-based adhesions in protrusions and large adherens junctions at the cell sides and rear. A comparative study of a panel of transformed IAR cells that differ by their ability to form E-cadherin-based AJs, either through loss of E-cadherin expression or through expression of a dominant negative E-cadherin mutant, demonstrated that E-cadherin-based AJs are key mediators of the interactions between neoplastic and normal epithelial cells. IAR-6-1DNE cells expressing a dominant-negative mutant form of E-cadherin with the mutation in the first extracellular domain practically lost the ability to adhere to IAR-2 cells and invade the IAR-2 epithelial monolayer. The ability of cancer cells to form E-cadherin-based AJs with the surrounding normal epithelial cells may play an important role in driving cancer cell dissemination in the body.
Performance Evaluation of a Multi-Stage Network Event Detection Scheme for Decreasing the False-Positive Rate for a Large Number of Simultaneous, Unknown Events

Change-point detection schemes are a promising approach for detecting network anomalies, such as attacks and epidemics by unknown viruses and worms. They detect those events as change-points. However, they generally also detect false-positive change-points, those caused by other events such as hardware trouble. A scheme is needed that only detects true- positive change-points, caused by attacks and epidemics. True- positive change-points tend to occur simultaneously in very large numbers, while false-positive change-points tend to occur sporadically. We can exclude false-positive change-points by excluding change-points that occur sporadically, based on information gathered from the entire network. In this paper, we propose a multi-stage network event detection scheme that aggregates change-point information from distributed IDSs (intrusion detection systems) and detects the true-positive change-points. Simulation results show that, compared to a scheme using only one IDS, our method always yields a smaller false-positive rate under the constraint that the detection rate of the true-positive change-points must exceed 0.99.
Biochemical properties of a high fidelity DNA ligase from Thermus species AK16D.

NAD+-dependent DNA ligases from thermophilic bacteria Thermus species are highly homologous with amino acid sequence identities ranging from 85 to 98%. Thermus species AK16D ligase, the most divergent of the seven Thermus isolates collected worldwide, was cloned, expressed in Escherichia coli and purified to homogeneity. This Thermus ligase is similar to Thermus thermophilus HB8 ligase with respect to pH, salt, NAD+, divalent cation profiles and steady-state kinetics.However, the former is more discriminative toward T/G mismatches at the 3'-side of the ligation junction, as judged by the ratios of initial ligation rates of matched and mismatched substrates. The two wild-type Thermus ligases and a Tth ligase mutant (K294R) demonstrate 1-2 orders of magnitude higher fidelity than viral T4 DNA ligase. Both Thermus ligases are active with either the metal cofactor Mg2+, Mn2+or Ca2+but not with Co2+, Ni2+, Cu2+or Zn2+. While the nick closure step with Ca2+becomes rate-limiting which results in the accumulation of DNA-adenylate intermediate, Ni2+only supports intermediate formation to a limited extent. Both Thermus ligases exhibit enhanced mismatch ligation when Mn2+is substituted for Mg2+, but the Tsp. AK16D ligase remains more specific toward perfectly matched substrate.
Optimized sampling patterns for practical compressed MRI

The performance of compressed sensing (CS) algorithms is dependent on the sparsity level of the underlying signal, the type of sampling pattern used and the reconstruction method applied. The higher the incoherence of the sampling pattern used for undersampling, less aliasing will be noticeable in the aliased signal space, resulting in better CS reconstruction. In this work, based on point spread function (PSF) properties, we compare random, Poisson disc and constrained random sampling patterns and show their usefulness in practical compressed sensing applied to dynamic cardiac magnetic resonance imaging (MRI).
Towards a Research Agenda for SCM systems

In recent years Supply Chain Management (SCM) in general and SCM information systems in particular have raised growing interest among researchers from several knowledge areas, such as Information Systems, Artificial Intelligence, Simulation, Telecommunications, Economics and Business Management, among others. Based upon a recent survey that we have undertaken, and which updates a prior one, in this paper we provide a focused analysis of previous research in SCM systems, before providing a tentative list of potential research issues that we consider worth studying. To our knowledge, there is no published research agenda on SCM systems, which contrasts with the mentioned research interest in the area and with the big amounts of projects and money that companies around the world are dedicating to SCM projects. For a better understanding, we have classified the emerging research topics along the lines of a proposed life-cycle framework.
Making a Journey in Knowledge Management Strategy

This paper reports results from an ongoing project examining what managers think about knowledge management in the context of their organisation. This was done in a facilitated computer-assisted group workshop environment. Here we compare the outcomes of workshops held for two relatively large UK organisations, one public sector and the other private. Our conclusions are that there are relatively few differences between the perceptions of these two groups of managers, and that these differences stem more from the stage of the knowledge management life cycle that the two organisations have reached, rather than from the difference in context between public and private sector.
3D Segmentation of the Lung Based on the Neighbor Information and Curvature

A novel method for the automatic segmentation of the lung in X-ray computed tomography (CT) images is presented. In this paper, a maximum a posteriori (MAP) estimation framework, combining neighbor prior information and image gray level information, is used to extract the boundary of lung. The relationship of the left lung and the right lung is represented as a joint density function. We use the principal component analysis (PCA) to build the neighbor prior model in a set of training images. A double dimension reduction algorithm is developed to improve the efficiency. The model is formulated in terms of level set functions, and the surfaces evolve according to the associated Euler-Lagrange equations. Then we propose a new algorithm to refine the rough boundary generated by the MAP framework. This algorithm consists of two stages: 1. automatically detecting and rough fitting the region of lung hilum, 2. refining the fitting curve based on the curvature information.
Age-Related Deficits in the Processing of Fundamental Frequency Differences for the Intelligibility of Competing Voices

A common complaint of older listeners is that they can hear speech, yet cannot understand it, especially when listening to speech in a background noise. When target and competing speech signals are concurrently presented, a difference in the fundamental frequency (ΔF0) between competing speech signals, which determines the pitch of voice, can be an important and commonly occurring cue to facilitate the separation of the target message from the interfering message, consequently improving intelligibility of the target message. To address the question of whether the older listeners have reduced ability to use ΔF0 and how the age-related deficits in the processing of ΔF0 are theoretically explained, this paper is divided into three parts. The first part of this article summarizes how the speech-communication difficulties that older listeners have are theoretically explained. In the second part, literatures on the perceptual benefits from ΔF0 and the age-related deficits on the use of ΔF0 are reviewed. As a final part, three theoretical models explaining the general processing of ΔF0 are compared to discuss which better explains the age-related deficits in the processing of ΔF0.
Classroom environment required for meeting the information needs of students with hearing impairment in Nigerian universities

ABSTRACT This study examined classroom environment required for meeting the information needs of students with hearing impairment. It employed descriptive design. A total of 165 respondents participated in the study. Three objectives guided the study. Questionnaires and focus group discussion were used for data gathering. Findings identified sign language interpreters, note takers, overhead projectors, telecommunication device for the deaf and hearing aid-compatible sound systems as very highly needed resources in an inclusive university classroom. It was recommended amongst others that lecturers and librarians should endeavour to acquire sign language skill. The study concluded that efficient teaching strategies and resources are important in meeting the information needs of students with hearing impairment.
On the Tweety Penguin Triangle Problem

In this paper, one studies the famous well-known and challenging Tweety Penguin Triangle Problem (TPTP or TP2) pointed out by Judea Pearl in one of his books. We first present the solution of the TP2 based on the fallacious Bayesian reasoning and prove that reasoning cannot be used to conclude on the ability of the penguin-bird Tweety to fly or not to fly. Then we present in details the counter-intuitive solution obtained from the Dempster-Shafer Theory (DST). Finally, we show how the solution can be obtained with our new theory of plausible and paradoxical reasoning (DSmT).
Robust Virtual Inertia Control of a Low Inertia Microgrid Considering Frequency Measurement Effects

Virtual inertia emulation could be regarded as an inevitable component of microgrids with renewable energy, enhancing microgrid inertia and damping properties. In applying this control technique, a phase-locked loop (PLL) is necessary to obtain the estimation of the system frequency data. However, the employment of PLL could cause larger frequency oscillation to the microgrid due to its dynamics. This issue would be exacerbated in a low-inertia microgrid driven by high renewable penetration, severely deteriorating the frequency stability. Thus, the effect of PLL with measurement delay is a critical issue in utilizing the virtual inertia control. To overcome such problem, this paper proposes a robust virtual inertia control for a low-inertia microgrid to minimize the undesirable frequency measurement effects, improving the microgrid frequency stability. The robust <inline-formula> <tex-math notation="LaTeX">$H_{{\infty }}$ </tex-math></inline-formula> control design using a linear fractional transformation (LFT) technique is used to develop the virtual inertia control loop, considering the dynamics of PLL with measurement delay and the uncertainties of system inertia and damping. The efficacy of the proposed <inline-formula> <tex-math notation="LaTeX">$H_{{\infty }}$ </tex-math></inline-formula> control method is compared to the conventional and optimum proportional-integral (PI)-based inertia control. The results show that the <inline-formula> <tex-math notation="LaTeX">$H_{\mathrm {{\infty }}}$ </tex-math></inline-formula>-based robust virtual inertia control is superior to both conventional virtual inertia control and optimum PI-based virtual inertia control against a wide range of microgrid operating conditions, disturbances, and parametric uncertainties.
Software shaders in interactive environments using relief impostors

This work presents an architecture for real-time visualization, which is able to render a set of 3D objects by using the CPU idle time. The objects being rendered are sent to the graphics pipeline as relief impostors. Their depth maps are used to apply 3D image-warping operations in order to prolong the life cycle of the synthesized images. While an image is within the graphics pipeline the CPU idle time is used to generate additional necessary images. With this method, it is possible to render images with special illumination models and effects that are impossible or unsuitable to be implemented with the shaders of the available graphics cards.
Security Threats in Cloud Computing Models: Domains and Proposals

This work displays metrics about publications available in the literature which deal with some of the seven security threats in Cloud Computing based in the guide entitled "Top Threats to Cloud Computing" from the Cloud Security Alliance (CSA). Through this research, it is identified the most explored threats, distributed the results between fifteen Security Domains and identified between eight types of solutions proposed for the threats. In face of those results, it was observed that publications in the literature mostly show a certain trend as the proposals presented for the involved Threats. However, in some cases some variations occur, what motivated us to fulfill an analysis of standard deviation in the results obtained in our research. Based on these data, we present our conception regarding this behavior.