Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimal quorum for the model of computational grid with redundancy

We consider a model of grid computing system solving numerous similar problems. Computers produce correct answers with some known probability. Each problem is solved up to the fixed number (quorum) of identical answers; this redundancy allows reducing the risk of accepting a wrong answer. The cost function is the sum of computational time and penalty in case of wrong answer. We reveal the conditions when such redundancy is advantageous and how the optimal quorum can be determined. We compare results for different numbers of possible answers and show that behavior of the model is similar.
Strategies for dealing with standard-setting resistance

Purpose - The purpose of this paper is to contribute to an understanding of how auditing standard setting is adapted to changing patterns of resistance at various stages of its development. Design/methodology/approach - The research is conducted as a longitudinal single case study of the auditing standard-setting activities of the Danish professional body 1970-1978. The case is based on extensive interviews with members of the standard-setting board at that time, supplemented with documentary studies. Findings - The paper identifies and discusses five general measures to deal with the immanent resistance to auditing standard setting: alignment of interests, representation, due process, soft texts, and reference to ideology. It concludes by analyzing how these strategies are adapted to changing patterns of standard-setting resistance at various stages. Research limitations/implications - The paper discusses auditing standard setting only. Although resistance to accounting standard setting may be dealt with in the same way, the patterns of resistance are most likely different and the adaptation strategies may therefore also be different. Practical implications - Presenting insight into the processes of auditing standard setting, the paper is of interest to anyone involved in the process, either as developers or as users. Originality/value - The paper supplements previous studies of standard setting by applying the Actor-Network Theory approach to auditing standard setting, thereby outlining a new general framework for the study of standard setting.
Conslide: A Thai soft keyboard for mobile text input

We propose Conslide, a new Thai input method for compact mobile devices. To address the complexity of Thai language, our design adopts drawing approach for text input. This results in a smaller keyboard (more area for applications). With appropriate layout, users can input words without lifting finger from the screen, hence the better efficiency. Empirical result shows that the average movement distance and minimal movement time required for each Thai word are comparable to those of the quasi-Kedmanee keyboards such as Droidsans Thai keyboard.
Disease Prediction Based on Symptoms Using Ensemble and Hybrid Machine Learning Models

The health sector is one of the prominent sectors in which continuous advances are happening to meet present population requirements as well as to withstand new diseases. As the population increases analyzing patients' data becomes a huge task as big data analytics helps full to manage and track those data efficiently lack of infrastructure in many areas cause providing health care a challenging task due to the constant change in the environment leads to many health crises even predicting disease based on symptoms at early stages by the doctors are less accurate because continuous mutations are also happening in disease-causing agents which makes most similar symptoms and difficult to predict. As the health sector consists of a huge amount of data, prediction of disease building a Machine learning (ML) model will give more accurate results. In this research, hybrid ml model (Naïve bayes + Ada boost) and Ensemble ml model(Random forest + Gradient boosting) and the final prediction is done by Voting classifier this model is also termed as voting Ensemble, showing huge promises and potential for accurate prediction than ML models. Performed 10 fold Cross Validation and can attain an accuracy of 85 % for Naive Bayes + Ada Boost and 92 % for Random forest + Gradient boosting,
Implementation of data affinity-based distributed parallel processing on a distributed key value store

The spread of cloud computing has increased the necessity of accumulating large amounts of data and performing high-speed data processing. Because strict consistency is not necessarily required for such large amount of data that cloud computing stores, a distributed Key Value Stores (KVS) is considered suitable for their data storage, based on an eventual consistency paradigm. In order to provide services such as SNS, mining and statistical processing of these data is indispensable. However because general distributed KVS systems are not designed for processing, these data must be transferred to distributed file systems such as HDFS, which enables data processing. The transfer cost issue has occurred in this case. To find a solution for this issue, we propose a method that performs high-speed data processing directly on a distributed KVS. In this paper, we extend the Apache Cassandra database, a distributed KVS that handles large amounts of data, to enable data affinity-based parallel processing. The parallel data processing mechanism runs the local processing on the stored values at each data node that stores the values, and it then returns only the results of the processing as an answer to a request. From the evaluation experiments, the proposed method is shown to be faster than the typically used Cassandra approach. In addition, even if the writing process is performed in the background while processing the data, the processing efficiency is appropriate for specific loads. The experimental results show that the data processing can be performed during the process of writing at approximately 10 Mbyte/sec if there are eight data nodes in the experiment environment.
Transcribing broadcast news shows

While significant improvements have been made in large vocabulary continuous speech recognition of large read-speech corpora such as the ARPA Wall Street Journal-based CSR corpus (WSJ) for American English and the BREF corpus for French, these tasks remain relatively artificial. In this paper we report on our development work in moving from laboratory read speech data to real-world speech data in order to build a system for the new ARPA broadcast news transcription task. The LIMSI Nov96 speech recognizer makes use of continuous density HMMs with Gaussian mixtures for acoustic modeling and n-gram statistics estimated on newspaper texts. The acoustic models are trained on the WSJO/WSJ1, and adapted using MAP estimation with task-specific training data. The overall word error on the Nov96 partitioned evaluation test was 27.1%.
Capacitive Coupled Elliptical and Differential Dual Polarized Magnetic Dipole Antennas for SATCOM

In this paper, two K band antenna elements which consist of proposed semi-elliptical capacitive coupled and magnetic dipole patch antennas are designed and investigated for beam steering applications in 5G/6G communication systems. The elements have been simulated with CST Microwave Studio and its Floquet port option. These proposed antenna elements prepare high gain (more than 5dB), frequency bandwidth (about 50%) and isolation between ports and wide scanning angle (φ = 360°, θ = ±60°). The designed dual polarized antennas can prepare any polarization and differential versions are suitable for differential power amplifiers (PAs) and low noise amplifiers (LNAs) for the transmitters and receivers, respectively.
A practical transmission scheme for half-duplex decode-and-forward MIMO relay channels

We propose a novel and practical transmission strategy for decode-and-forward MIMO relay channels operating in half-duplex mode that is based on precoding with time, power, and spatial stream allocation. In the proposed scheme, the message from the source is divided into two parts and sent to the destination, one part directly and the other with the help of the relay. We formulate the problem of finding the rate-maximizing precoding matrices and resource allocation and then transform it into a convex form by employing the proposed suboptimal but simplifying precoding structure and performing other manipulations. The numerical results show that the achievable rate of the proposed scheme is close to a well-known theoretical lower bound and even outperforms it in certain scenarios, despite our transmission strategy being based on a much simpler coding scheme. Additionally, the proposed scheme is able to outperform another scheme based on repetition coding with fixed time slot duration.
OH-initiated oxidation of benzene Part I . Phenol formation under atmospheric conditions

a Universität Heidelberg, Institut für Umweltphysik, Im Neuenheimer Feld 229, D-69120 Heidelberg, Germany. E-mail: rainer.volkamer@iup.uni-heidelberg.de; Fax: +49 6221 54 6405; Tel: +49 6221 54 6527 b Fundación CEAM, Parque Tecnológico, Calle Charles R. Darwin 14, E-46980 Paterna (Valencia), Spain c National Institute for Environmental Studies, Atmospheric Environment Division, 16-2 Onogawa, Tsukuba-shi, Ibaraki-ken 305-8506, Japan d Bergische Universität Wuppertal, Physikalische Chemie-FB 9, Gaußstraße 20, D-42097 Wuppertal, Germany e Kyoto University, Department of Chemistry, Graduate School of Science, Sakyo-ku, Kyoto 606-8502, Japan
Fast Pixel/Part Selection with Sparse Eigenvectors

We extend the "Sparse LDA" algorithm of [7] with new sparsity bounds on 2-class separability and efficient partitioned matrix inverse techniques leading to 1000-fold speed-ups. This mitigates the 0(n4) scaling that has limited this algorithm's applicability to vision problems and also prioritizes the less-myopic backward elimination stage by making it faster than forward selection. Experiments include "sparse eigenfaces" and gender classification on FERET data as well as pixel/part selection for OCR on MNIST data using Bayesian (GP) classification. Sparse- LDA is an attractive alternative to the more demanding Automatic Relevance Determination. State-of-the-art recognition is obtained while discarding the majority of pixels in all experiments. Our sparse models also show a better fit to data in terms of the "evidence" or marginal likelihood.
Professionalization in Youth Work? Opening and Deepening Circles of Inquiry

In this article, the authors examine the claims of professionalization in youth work as both a process and outcome that aims to ultimately improve the lives of young people. The topic of professionalization is then approached through three circles of inquiry that reframe the issue(s) through the historical and sociocultural situatedness of professional movements; the contemporary sociopolitical and sociolegal context; and those self-referential questions that deal with the practice of youth work itself.
Parental Perceptions of the Use of Internal Sources of Information About Children's Development

This exploratory study examined parental perceptions of the information sources parents use when wanting or needing information about their three-year-old child's motor, social, and cognitive development. Specifically, this study compared parental perceptions of the use of internal information sources (i.e., parents' own intuitions about development, religious beliefs/teachings, and childhood experiences) to perceptions of the use of external information sources (i.e., books, magazines, counselors, etc.). Further, this study examined differences in parental perceptions of use of internal information sources by parent and child gender, and by developmental domain (i.e., information about a child's motor, social, and cognitive development). Sixty mothers and 60 fathers of a three-year-old child completed an information use structured interview. Findings revealed that almost half of the parents reported referring to their own intuitions, religious beliefs/teachings, and/or childhood experiences as sources of.
Everyday Digital Entrepreneurship: The Inception, Shifts, and Scaling of Future Shaping Practices

Digitization has created extremely favourable conditions for digital innovation, initiating shifts in the organizing logic of products, services, and various aspects of organizational practices. Current literature has eagerly explored this digitally enabled diversification. However, it has paid little attention to the digitiz ation of everyday practices and how forms of entrepreneurship emerge from such practices. In this paper we propose that digital technology instigates a new type of digital innovation – future-shaping practices, and will attempt to exemplify and explore the forces behind such newly emerging everyday practices. Our research draws on a multimethod research design based on four case studies that represent future shaping practices in transportation, healthcare, education, and sustainability. Drawing on document analysis and semi-structured interviews, we report on our research-in-progress that seeks to determine the generative mechanisms that enable the inception, shift, and scaling of future shaping practices.
Genome‐wide and locus‐specific DNA hypomethylation in G9a deficient mouse embryonic stem cells

In the mammalian genome, numerous CpG‐rich loci define tissue‐dependent and differentially methylated regions (T‐DMRs). Euchromatin from different cell types differs in terms of its tissue‐specific DNA methylation profile as defined by these T‐DMRs. G9a is a euchromatin‐localized histone methyltransferase (HMT) and catalyzes methylation of histone H3 at lysines 9 and 27 (H3‐K9 and ‐K27). To test whether HMT activity influences euchromatic cytosine methylation, we analyzed the DNA methylation status of approximately 2000 CpG‐rich loci, which are predicted in silico, in G9a−/− embryonic stem cells by restriction landmark genomic scanning (RLGS). While the RLGS profile of wild‐type cells contained about 1300 spots, 32 new spots indicating DNA demethylation were seen in the profile of G9a−/− cells. Virtual‐image RLGS (Vi‐RLGS) allowed us to identify the genomic source of ten of these spots. These were confirmed to be cytosine demethylated, not just at the Not I site detected by the RLGS but extending over several kilobase pairs in cis. Chromatin immunoprecipitation (ChIP) confirmed these loci to be targets of G9a, with decreased H3‐K9 and/or ‐K27 dimethylation in the G9a−/− cells. These data indicate that G9a site‐selectively contributes to DNA methylation.
On the stability of end-to-end congestion control for the internet

In [2] Johari and Tan consider an Internet type network with distributed congestion control of the form proposed by Kelly et al in [3], and determine a sufficient condition for local stability of the network under the condition that all round trip times are equal. They conjecture that the same condition will also guarantee local stability when the round trip times are disparate. The continuous time version of this conjecture is true.
Within-clock power gating architecture implimentation to reduce leakage

With the technology scaling leakage power has become comparable to dynamic power. Power gating is a technique which is used to reduce standby leakage by shutting down the power supply of the inactive block of the circuit. There is also scope of using power gating in active block to reduce run time leakage. Within clock period there is certain portion which is idle and in this period power gating may be used. In this paper we present this within-clock power gating for minimizing leakage and total power of the sequential circuits during active mode of operation. The technique is used to implement the architecture of ISCAS89 benchmark circuit. Power results have been reported for different frequency. Simulation of the implemented architecture in CADENCE VLSI tool at 45nm technology shows leakage saving of 73% and 54.78% saving in switching compared to the designs without within-clock power gating at 1.25 MHZ.
SUBSPACE IDENTIFICATION OF MULTIVARIABLE HAMMERSTEIN AND WIENER MODELS

Abstract In this paper, subspace-based algorithms for the simultaneous identification of the linear and nonlinear parts of multivariable Hammerstein and Wiener models are presented. The proposed algorithms consist basically of two steps. The first one is a standard (linear) subspace algorithm applied to an equivalent linear system whose inputs (respectively outputs) are filtered (by the nonlinear functions describing the static nonlinearities) versions of the original inputs (respectively outputs). The second step consists in a 2-norm minimization problem which is solved via a Singular Value Decomposition.
Automatic Grading and Feedback using Program Repair for Introductory Programming Courses

We present GradeIT, a system that combines the dual objectives of automated grading and program repairing for introductory programming courses (CS1). Syntax errors pose a significant challenge for testcase-based grading as it is difficult to differentiate between a submission that is almost correct and has some minor syntax errors and another submission that is completely off-the-mark. GradeIT also uses program repair to help in grading submissions that do not compile. This enables running testcases on submissions containing minor syntax errors, thereby awarding partial marks for these submissions (which, without repair, do not compile successfully and, hence, do not pass any testcase). Our experiments on 15613 submissions show that GradeIT results are comparable to manual grading by teaching assistants (TAs), and do not suffer from unintentional variability that happens when multiple TAs grade the same assignment. The repairs performed by GradeIT enabled successful compilation of 56% of the submissions having compilation errors, and resulted in an improvement in marks for 11% of these submissions.
Compressed sensing based range detection and Doppler estimation for portable surveillance radar

Compressed sensing is a novel technique of under sampling sparse or compressible signals using random linear non adaptive measurements. Under the sparsity constraints it allows for reconstruction of the data with small or zero error using minimum l1 norm reconstruction methods. Since most of the real world data are either sparse or compressible in some suitable basis, the proposed method has immediately found application in varied fields like imaging systems, MRI, Radar systems etc. But most of the applications considered in literature deal with real data. For radar applications the data is complex and suitable amendments for processing are considered. In this paper we proposed an application of compressed sensing towards portable radar signal processor. We have used the method to accurately detect target's range and doppler with highly reduced quantum of input data. Since the doppler information is corrupted during the signal transformation to lower dimension we have used a parallel processing channel for estimating the doppler information.
Improve Channel Uniformity of an Si-Nanowire AWG Demultiplexer by Using Dual-Tapered Auxiliary Waveguides

Dual-tapered auxiliary waveguides at the exit of the waveguide array are introduced to improve the channel uniformity of an Si-nanowire-based arrayed waveguide grating (AWG) demultiplexer. By using a hybrid simulation method, the dual-tapered auxiliary waveguides of the AWG demultiplexer are optimized reliably and efficiently. A 12-channel AWG demultiplexer is designed as an example, and a small nonuniformity (< 0.5 dB) is achieved.
Research on Low SNR Target Detection Based on Improved YOLOv5 in PD Radar Application

In order to solve the worsen of radar traditional signal processing constant false alarm rate detector (CFAR) in low signal-to-ratio (SNR) target detection condition, this paper proposes an improved target detection algorithm based on YOLOv5. In this paper, a large number of radar simulated target echoes with different SNR are produced based on Linear Frequency Modulated (LFM) signals. A portion of real echoes is added as a training set, and finally the actual echo data is used for verification. The detection probability is improved by applying the proposed algorithm in the case of low SNR. Experimental results show that compared with CFAR, the proposed algorithm in this paper can improve the detection performance of low SNR targets by 16.3%.
Turbulent Reynolds Stresses Prediction using Stochastic Gradient Boosting Regression

Predicting turbulent Reynolds stresses (TRS) accurately is crucial for the advancement of fluid dynamics and engineering applications. This study presents an application of stochastic gradient boosting regression (GBR) to predict TRS within a turbulent vertical axisymmetric jet. Leveraging a comprehensive dataset encompassing flow rate, spatial development ratios, velocity profiles, and root mean square of velocity fluctuations, we engaged in a thorough analysis to capture the intricate dynamics governing TRS. Our approach involved the application of GBR, a machine learning algorithm renowned for its precision and flexibility. GBR’s capability to optimize any differentiable loss function was utilized to minimize predictive errors iteratively. The model’s performance was scrutinized using metrics such as mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), and the coefficient of determination. Intriguingly, our findings revealed that feature scaling, a common preprocessing technique, did not enhance the model’s performance. The unscaled model exhibited superior accuracy, challenging the notion that feature scaling is universally beneficial. The study’s findings underscore the importance of empirical validation of preprocessing techniques and spotlight the effectiveness of GBR in modeling TRS. The insights from this research could have significant implications for turbulence modeling practices and machine learning methodologies within fluid dynamics and other related fields.
A Microstrip Filtering Antenna With Controllable Radiation Nulls

This article proposes a microstrip patch antenna with electromagnetic coupling and band-stop structure. The microstrip filtering antenna is driven by a slotted feedline with trapezoidal gradient structures, in turn, the driven patch excites the parasitic patches through electromagnetic coupling. The slotted feeding line generates a radiation null at 3 GHz, the electromagnetic coupling between the driving patch and the parasitic patch generates a radiation null at 3.875 GHz. The antenna achieves |S11|< -10 dB within the frequency range of 3.24 GHz - 3.72 GHz (13.7%) in simulation. It achieves a peak gain of 7.35 dBi within the passband.
The acquisition of qualitative physics knowledge during textbook-based physics training

Several earlier investigations found that teaching standard textbook physics causes only moderate change in qualitative understanding. Many investigations have tried to explain why teaching textbook physics results in so little learning of qualitative physics. In contrast, we examined cases where learning did occur and tried to understand them, hoping that this might help us to understand how to support such learning. We developed computerized simulation models of both qualitative, conceptual problem solving and quantitative problem solving and used them to assess changes in students' qualitative knowledge as they learned textbook physics. In many cases, qualitative knowledge has been acquired on the basis of information explicitly presented in the textbook. We also found cases, however, where learning of qualitative physics took place on the basis of information only implicitly addressed in the instruction. Even more important, in various cases, this newly acquired qualitative knowledge led to a less fre.
The Semantic Conference Organizer

34.1 Background . 571 34.2 Latent Semantic Indexing . 572 34.3 Software Issues . 573 34.4 Creating a Conference . 575 34.5 Future Extensions . 579 Acknowledgements . 580 References . 580