Miracle-dz/UniFAR-FTUs huggingface dataset:
Benchmarking and Analysis of Variations of Work Stealing Scheduler on Clustered System

Classical work stealing is an efficient dynamic load-balancing technique in shared memory multiprocessor or multicore system. But the performance of the same classical work scheduling on cluster chip multicore is not appreciable. So modification to this is necessary to improve performance. In this paper, we have discussed many earlier proposed modifications, and also proposed some simplistic modifications to suite targeted clustered environment. We have described a methodology to evaluate all the variations of work stealing analytically and experimentally on multiprocessor simulator and on real platform. Our methodology of evaluation include designing of novel parametric synthetic benchmark, which can be used to mimic behavior (or profile) of many real life benchmarks. The designed synthetic benchmark caters a wide range of application profiles to evaluate the design space of both variations of work stealing algorithms and clustered chip multiprocessor. In this work, we found that if the number of available parallelism of the targeted application is higher and data sharing between tasks is high then one of the proposed modification of work stealing (probabilistic based victim search and threshold on size of migratable task) outperform the rest of the modifications.
Design of a 5-bit, 4.87GS/s, 240µW Flash ADC using a MUX-based decoder with Regenerative Buffer in 45-nm CMOS

This paper presents a 5 - bit 4.87GSps Flash ADC design using 45-nm GPDK CMOS technology library. The designed system consists of two main blocks as comparator array and digital decoder. The digital decoder contains 2:1 MUX based 1 - of - N decoder and Regenerative Buffer units. As a result, active die area and the power consumption are reduced in addition to increase in sampling frequency. The power supply voltage range for the overall system is ± 1.8V. The simulation results include maximum power consumption of 0.24 mW. The Conversion time 204p sec, the measured DNL and INL to less than 0.02LSB and 0.1LSB.
Context-awareness for adequate business process modelling

This paper investigates context awareness in business process modelling (BPM). It introduces a context model for BPM (CM4BPM) and a role-based business process model (RBPM), and presents an approach allowing enacting processes with respect to the context. Decisions driving business processes enactment are based on context related knowledge. The proposed approach consists on using contextual knowledge in order to enhance the adequacy of the assignments during the enactment of the business processes, it can be used to improve the design and implementation of business processes using existing process modeling languages in order to allow the support of contextaware process enactment.
Robust Image Denoising Using a Virtual Flash Image for Monte Carlo Ray Tracing

We propose an efficient and robust image‐space denoising method for noisy images generated by Monte Carlo ray tracing methods. Our method is based on two new concepts: virtual flash images and homogeneous pixels. Inspired by recent developments in flash photography, virtual flash images emulate photographs taken with a flash, to capture various features of rendered images without taking additional samples. Using a virtual flash image as an edge‐stopping function, our method can preserve image features that were not captured well only by existing edge‐stopping functions such as normals and depth values. While denoising each pixel, we consider only homogeneous pixels—pixels that are statistically equivalent to each other. This makes it possible to define a stochastic error bound of our method, and this bound goes to zero as the number of ray samples goes to infinity, irrespective of denoising parameters. To highlight the benefits of our method, we apply our method to two Monte Carlo ray tracing methods, photon mapping and path tracing, with various input scenes. We demonstrate that using virtual flash images and homogeneous pixels with a standard denoising method outperforms state‐of‐the‐art image‐space denoising methods.
Preferences for Rigid versus Individualized Wage Setting in Search Economies with Firing Frictions

Firing frictions affect worker and firm preferences for rigid wages versus individualized Nash bargaining in a standard model of equilibrium unemployment in which workers vary by skill. Benefits of rigid wages derive from savings on renegotiation costs; firing frictions further increase the attractiveness of wage rigidity. For standard calibrations , the model can account for political equilibria in favor of wage rigidity, as well as the existence of centralized bargaining, excess coverage and free-riding.
Advanced Partial Discharge Measuring System for Simultaneous Testing of Cable Accessories

In this paper measuring results of partial discharges in power cable accessories are described. For the purpose of measuring simultaneously partial discharge activity in three or more accessories during an acceptance test, a wireless partial discharge detection system was developed. Therefore, each power cable accessory is equipped with either an internal or external sensor to decouple the partial discharge signals. The system uses narrow-band detection and is described in this paper. The computers controlling the measuring units are connected via a wireless network which allows communication between the different units and the main computer situated at the voltage source site. Moreover, effective real time noise-suppression is possible by tuning the system to a noise-free frequency band. Practical examples of this system are shown based on several measurements performed in the field
Multi-level Control of an Intelligent Wheelchair in a Hospital Environment using a Cyber-Mouse Simulation System

The development of intelligent wheelchairs is a very good solution to assist severely handicapped people who are unable to operate classical electrical wheelchair by themselves in their daily activities. This paper describes the integration of a robotic simulator with our intelligent wheelchair shared control and planning modules. An adapted version of the free Cyber-Mouse robotic simulator was used to simulate the movement of the intelligent wheelchair in a hospital environment. Adaptations of the subsumption architecture, Strips Planning and A* Algorithms were employed and integrated to allow wheelchair intelligent behavior. The experimental results have demonstrated the success of the integration of these algorithms in our simulator allowing very safe motion of the intelligent wheelchair in the simulated hospital environment. Also, the adapted Cyber-Mouse simulator proved its capability and robustness in simulating the hospital environment and wheelchair physic characteristics.
Generation of caption selection for news images using stemming algorithm

Image based search engine is the process of searching information by using related images. The huge resources of images are available on the web, In that many of the images are contain as with labeled and without labeled caption. The users are required to search the images depending on their needs. In that many of the users are unable to retrieve the relevant images because of their unpredicted suitable caption on their images. Our task is to generate an automatic caption for the images based on the related information. Earlier, the manual captioning model is available to generate a description for the pictures. It is difficult and expensive task. The proposed model learns to create a caption for the images that are embedded with the related news articles. Which is most related to the image based applications. This process makes the easy understanding of news by seeing the images with caption and without reading the whole content of the news in the news articles.
Visualizing the 3D structure of medical objects based on 2D data

3D imaging allows the detailed visualization of medical structures that can be very important in the process of treating patients. In this paper we describe a method for generating 3D models representing medical structures, using data derived from a single image showing a single cross-section of an object. The proposed method utilizes a 3D deformable model that is allowed to deform in order to approximate the shape of a medical structure as seen in a single cross-section, allowing in this way the estimation of the complete 3D structure of the object. Experimental results using synthetic data prove the potential of this approach in generating 3D pelvis models.
Caching in Named Data Networking for the wireless Internet of Things

Named Data Networking (NDN) is a promising Information-Centric future Internet architecture. Besides its recognized potentialities as a content retrieval solution in wired domains, NDN has been also recently considered as an enabling technology for the Internet of Things (IoT), thanks to its innovative features like named-based routing and in-network caching. In particular, the possibility of caching at intermediate nodes can be especially useful to reduce the retrieval delay, and limit the network traffic and the load on data producer. However, unlike traditional Internet contents, IoT data are typically transient and periodically refreshed by the producer. At the same time, unlike Internet routers, IoT devices can be resource-constrained, with limitations in terms of energy, storage and processing capabilities. Therefore, caching algorithms designed for (intransient) Internet traffic and Internet routers do not well suit IoT domains. In this paper, we consider a wireless NDN-IoT network and propose a novel distributed probabilistic caching strategy that relies on the freshness of data and on potentially constrained capabilities of devices (energy level and storage capacity). The proposed solution has been evaluated through simulations with ndnSIM and results show that it outperforms traditional NDN caching mechanisms in terms of data retrieval and network energy efficiency.
An Essential Quality Control Mechanism at the Eukaryotic Basal Body Prior to Intraflagellar Transport

Constructing a eukaryotic cilium/flagellum is a demanding task requiring the transport of proteins from their cytoplasmic synthesis site into a spatially and environmentally distinct cellular compartment. The clear potential hazard is that import of aberrant proteins could seriously disable cilia/flagella assembly or turnover processes. Here, we reveal that tubulin protein destined for incorporation into axonemal microtubules interacts with a tubulin cofactor C (TBCC) domain‐containing protein that is specifically located at the mature basal body transitional fibres. RNA interference‐mediated ablation of this protein results in axonemal microtubule defects but no effect on other microtubule populations within the cell. Bioinformatics analysis indicates that this protein belongs to a clade of flagellum‐specific TBCC‐like proteins that includes the human protein, XRP2, mutations which lead to certain forms of the hereditary eye disease retinitis pigmentosa. Taken with other observations regarding the role of transitional fibres in cilium/flagellum assembly, we suggest that a localized protein processing capacity embedded at transitional fibres ensures the ‘quality’ of tubulin imported into the cilium/flagellum, and further, that loss of a ciliary/flagellar quality control capability may underpin a number of human genetic disorders.
Tuning Recurrent Neural Networks with Reinforcement Learning

The approach of training sequence models using supervised learning and next-step prediction suffers from known failure modes. For example, it is notoriously difficult to ensure multi-step generated sequences have coherent global structure. We propose a novel sequence-learning approach in which we use a pre-trained Recurrent Neural Network (RNN) to supply part of the reward value in a Reinforcement Learning (RL) model. Thus, we can refine a sequence predictor by optimizing for some imposed reward functions, while maintaining good predictive properties learned from data. We propose efficient ways to solve this by augmenting deep Q-learning with a cross-entropy reward and deriving novel off-policy methods for RNNs from KL control. We explore the usefulness of our approach in the context of music generation. An LSTM is trained on a large corpus of songs to predict the next note in a musical sequence. This Note-RNN is then refined using our method and rules of music theory. We show that by combining maximum likelihood (ML) and RL in this way, we can not only produce more pleasing melodies, but significantly reduce unwanted behaviors and failure modes of the RNN, while maintaining information learned from data.
Boosting facial expression recognition using LDGP ¿ Local Distinctive Gradient Pattern

Appearance based local feature methods are widely used for facial expression recognition because of their simplicity and high accuracy rates of recognition. However, the achieved accuracy rates and running time yet need to be improved. A new appearance based local feature method, called Local Distinctive Gradient Pattern (LDGP) is proposed in this paper. It derives two 4-bit local binary patterns from two different layers for a pixel by comparing the gray color intensity value of the pixel with its neighboring pixels in four distinct directions. Since each face image is divided into equal sized blocks, two histograms for the two 4-bit LDGP patterns of all pixels in each block can be constructed. The histograms of all blocks are then concatenated to build the feature vector for the given image. To evaluate the effectiveness of the proposed descriptor, experiments were conducted on the popular JAFFE dataset using Support Vector Machine (SVM) as the classifier. Extensive experimental results with seven prototype expressions show that proposed LDGP descriptor is superior to other appearance-based feature descriptors in terms of accuracy rates of recognition.
The entity-relationship model: a basis for the enterprise view of data

The concept of the enterprise view of data is very useful in the database design process and in the construction of conceptual schema. This paper discusses the use of the entity-relationship approach in describing and maintaining the enterprise view of data. Fundamental operations for changing the enterprise schema are presented. Finally, an example is given to show the differences between the entity-relationship approach and the data-structure approach in modeling the enterprise view of data.
Dynamic voltage and frequency scaling control for crossbars in input-queued switches

The power consumption in chips, in general, and in crossbars switching fabrics, in particular, grows with the maximum sustainable throughput. Due to the fast increasing traffic demands, the performance scalability of crossbars is severely limited by the capability of cooling the hardware devices. Hence, reducing the power consumption is an important design question to improve the crossbar switching performance. We propose to leverage Dynamic Voltage and Frequency Scaling (DVFS) hardware technique for the switching fabric. The main idea is to exploit temporary underloaded conditions to decrease the crossbar transmission rate while preserving maximum throughput. Differently from previous works, we consider a scenario in which the arrival rates are unknown in advance. Our proposed architecture is based on a power controller which runs periodically and independently of the packet scheduler, and whose decisions are based on the real time estimation of the arrival rates. We discuss the performance tradeoff in terms of throughput, delays and power, and show the relevant performance gain due to the use of DVFS in controlling the crossbar.
Test Anxiety Inventory in Iranian Culture

The study explored the Test Anxiety Inventory in Iranian Culture. Participants were 402 Data were analyzed using Principal component analysis and Confirmatory Factor Analysis. Principal component analysis resulted in four components with Eigen values >1, which together explain 70% of the variance. Confirmatory factor analysis of indicators of structural equation model testing demonstrated that the postulated model was a fit to the data.
Unconscious Thought Theory and Marketing: A novel perspective for new insights

The Unconscious Thought Theory (UTT), a theory about human thought, is able to bring a new perspective for the marketing field. Marketing researches appropriate from the conscious thought, which represents only 5% of our brain capacity. By the time UTT starts to be incorporated as a new lens for a wide range of marketing topics, the outcomes may bring important contributions and advances for the field. This article aims to better understand how marketing is using UTT in order to extent knowledge. The desk research procedure resulted in 6 categories relating UTT and marketing and show that there is an important gap related to the use of this theory as a new perspective for marketing well-explored issues.
Automated Quantification of Mitral Regurgitation by Three Dimensional Real Time Full Volume Color Doppler Transthoracic Echocardiography: A Validation with Cardiac Magnetic Resonance Imaging and Comparison with Two Dimensional Quantitative Methods

Background Accurate assessment of mitral regurgitation (MR) severity is crucial for clinical decision-making and optimizing patient outcomes. Recent advances in real-time three dimensional (3D) echocardiography provide the option of real-time full volume color Doppler echocardiography (FVCD) measurements. This makes it practical to quantify MR by subtracting aortic stroke volume from the volume of mitral inflow in an automated manner. Methods Thirty-two patients with more than a moderate degree of MR assessed by transthoracic echocardiography (TTE) were consecutively enrolled during this study. MR volume was measured by 1) two dimensional (2D) Doppler TTE, using the proximal isovelocity surface area (PISA) and the volumetric quantification methods (VM). Then, 2) real time 3D-FVCD was subsequently obtained, and dedicated software was used to quantify the MR volume. MR volume was also measured using 3) phase contrast cardiac magnetic resonance imaging (PC-CMR). In each patient, all these measurements were obtained within the same day. Automated MR quantification was feasible in 30 of 32 patients. Results The mean regurgitant volume quantified by 2D-PISA, 2D-VM, 3D-FVCD, and PC-CMR was 72.1 ± 27.7, 79.9 ± 36.9, 69.9 ± 31.5, and 64.2 ± 30.7 mL, respectively (p = 0.304). There was an excellent correlation between the MR volume measured by PC-CMR and 3D-FVCD (r = 0.85, 95% CI 0.70-0.93, p < 0.001). Compared with PC-CMR, Bland-Altman analysis for 3D-FVCD showed a good agreement (2 standard deviations: 34.3 mL) than did 2D-PISA or 2D-VM (60.0 and 62.8 mL, respectively). Conclusion Automated quantification of MR with 3D-FVCD is feasible and accurate. It is a promising tool for the real-time 3D echocardiographic assessment of patients with MR.
OSIRIS: Efficient and Consistent Recovery of Compartmentalized Operating Systems

Much research has gone into making operating systems more amenable to recovery and more resilient to crashes. Traditional solutions rely on partitioning the operating system (OS) to contain the effects of crashes within compartments and facilitate modular recovery. However, state dependencies among the compartments hinder recovery that is globally consistent. Such recovery typically requires expensive runtime dependency tracking which results in high performance overhead, highcomplexity and a large Reliable Computing Base (RCB). We propose a lightweight strategy that limits recovery to cases where we can statically and conservatively prove that compartment recovery leads to a globally consistent state - trading recoverable surface for a simpler and smaller RCB with lower performance overhead and maintenance cost. We present OSIRIS, a research OS design prototype that demonstrates efficient and consistent crash recovery. Our evaluation shows that OSIRIS effectively recovers from important classes of real-world software bugs with a modest RCB and low overheads.
Automatically generating custom instruction set extensions

General-purpose processors that are utilized as cores are often incapable of achieving the challenging cost, performance, and power demands of high-performance audio, video, and networking applications. To meet these demands, most systems employ a number of hardware accelerators to off-load the computationally demanding portions of the application. As an alternative to this strategy, we examine customizing the computation capabilities of a core processor for a particular application. Our goal is to enable some or all of the computation that is off-loaded to the accelerators to be taken over by the customized core. The computation capabilities of the core processor are extended with hardware in the form of a set custom function units and new instructions. The compiler is responsible for analyzing the target application and identifying a set of cost-effective custom function units. In this paper, we provide an overview of the system that we are developing to automatically identify instruction set extensions and report some preliminary analysis of four media benchmarks.
Duplication processes in Saccharomyces cerevisiae haploid strains

Duplication is thought to be one of the main processes providing a substrate on which the effects of evolution are visible. The mechanisms underlying this chromosomal rearrangement were investigated here in the yeast Saccharomyces cerevisiae. Spontaneous revertants containing a duplication event were selected and analyzed. In addition to the single gene duplication described in a previous study, we demonstrated here that direct tandem duplicated regions ranging from 5 to 90 kb in size can also occur spontaneously. To further investigate the mechanisms in the duplication events, we examined whether homologous recombination contributes to these processes. The results obtained show that the mechanisms involved in segmental duplication are RAD52-independent, contrary to those involved in single gene duplication. Moreover, this study shows that the duplication of a given gene can occur in S.cerevisiae haploid strains via at least two ways: single gene or segmental duplication.
Ranking Techniques for Cluster Based Search Results in a Textual Knowledge-base

This paper presents a framework and methodology to improve the search experience in digital library systems. The approach taken is to cluster a textual knowledgebase along multiple relations and return search results in the form of small, focused clusters. Specifically, we generate multiple relationship networks, one per relationship type, and then cluster these networks. At search time, we present a ranked set of clusters—one ranking per relationship type. The intuition for this approach is that returning clusters of contextually related information provides users with a situational and contextual awareness of the search results rather than returning a ranked list of only those documents that match the query. We address the use of both implicit (such as textual content) and explicit (such as citations, authors etc.) relations between documents. The primary question we focus on is how to rank the clusters, given a search query. We explore two approaches: a text-based rank (using the text‘s similarity to the user‘s query) and a social network-based rank (using information centrality). A comparison of these two ranking methods suggest that using information centrality for ranking is very useful for ranking clusters and its documents because the documents that characterize that cluster get the highest rank.