Miracle-dz/UniFAR-FTUs huggingface dataset:
Energy-Driven Precision Scaling for Fixed-Point ConvNets

Data precision scaling is a well-known technique for power/energy minimization in error-resilient applications. It has proven particularly suited for embedded Convolutional Neural Networks (ConvNets) made run on fixed-point arithmetic coprocessors. The key observation is that methods that only account for accuracy during the precision assignment process may lead to sub-optimal energy minimization. This work introduces an energy-driven optimization that delivers per-layer quantization under a user-defined accuracy constraint. The tool is conceived for accelerators that dynamically adapt their energy and accuracy through software-programmable multiprecision Multiply&Accumulate (MAC) units. Simulation results collected on different ConvNets trained with public data-set show substantial energy savings and improved energy-accuracy tradeoffs w.r. t. conventional fixed-point methods.
Reconfigurable Streaming for the Mobile Edge

Deploying stream computing applications on edge networks brings a new set of challenges including frequent reconfigurations due to client mobility and topology changes, geographical constraints from application semantics, state management over wide-area networks, and more. Current stream processing frameworks do not adequately address these challenges since they are designed for use inside data centers and rely on global coordination between participating nodes. Merlin is a new stream processing framework designed from the ground up for stream processing on the edge. Merlin supports fast reconfiguration without disrupting applications by decoupling data delivery from data processing and removing the need for global coordination.
How perceptions of incivility and social endorsement in online comments (Dis) encourage engagements

ABSTRACT While the powerful role of partisan identity in provoking incivility has been pointed out, relatively little attention has been directed toward incivility that also occurs among individuals who share the same partisan identity. Two experiments examined how uncivil expressions made by in-group members with aligned partisanship as well as uncivil expressions made by out-group members with opposing partisanship led to perceptual, attitudinal, and behavioural intentions. Also examined are how the effects of uncivil comments are influenced by social endorsements, such as the ‘recommend’ feature. Results indicate that incivility, group identity, and the presence of social recommendations shape individuals’ perceived polarisation that, in turn, influences open-mindedness to (dis) encourage engagement during discussions. Overall, findings of this study provide a mechanism for explaining how incivility works with static factors, such as group identity, and situational factors, such as social recommendations that influence our perceptions, attitudes, and behavioural intentions to be engaged in online discussions.
Static Analyses in Python Programming Courses

Students learning to program often rely on feedback from the compiler and from instructor-provided test cases to help them identify errors in their code. This feedback focuses on functional correctness, and the output, which is often phrased in technical language, may be difficult to for novices to understand or effectively use. Static analyses may be effective as a complementary aid, as they can highlight common errors that may be potential sources of problems. In this paper, we introduce PyTA, a wrapper for pylint that provides custom checks for common novice errors as well as improved messages to help students fix the errors that are found. We report on our experience integrating PyTA into an existing online system used to deliver programming exercises to CS1 students and evaluate it by comparing exercise submissions collected from the integrated system to previously collected data. This analysis demonstrates that, for students who chose to read the PyTA output, we observed a decrease in time to solve errors, occurrences of repeated errors, and submissions to complete a programming problem. This suggests that PyTA, and static analyses in general, may help students identify functional issues in their code not highlighted by compiler feedback and that static analysis output may help students more quickly identify debug their code.
Teachers' Experiences of using PRIMM to Teach Programming in School

PRIMM is an approach to teaching programming at K-12 that facilitates the structure of lessons in a purposeful way. PRIMM stands for Predict-Run-Investigate-Modify-Make, and draws on recent research in programming education. In particular the PRIMM approach recognises that starting with existing code and being able to explain what it does gives novice programmers the confidence to write their own programs. Using the PRIMM approach, teachers can devise scaffolded and targeted tasks for students which helps engender understanding, particularly for those who may have previously struggled to understand programming concepts. In this techniques paper, we consider what PRIMM is, and the experiences that teachers have had of using the structure in the classroom. PRIMM materials have been trialled in schools in a study involving around 500 students aged 11-14. From interviews with nine participating teachers we have found that teachers particularly value the collaborative approach taken in PRIMM, the structure given to lessons, and the way that resources can be differentiated. We propose that PRIMM is an approach that could be adopted in all phases of programming education as well as in teacher training.
Large-Scale Taxonomy Problem: a Mixed Machine Learning Approach

Rakuten Data Challenge suggests tackling the Large-Scale Taxonomy Challenge. Given a large amount of product titles and category paths leading to these products, we would like to predict the category path of a given product, only based on its title. The provided paths are structured as a forest of 14 trees. The learning process is split into two steps: we first retrieve the tree the input belongs to and then handle the category path. We describe data embedding which represents an important task in this challenge and then introduced the so-called two step architecture. The original idea is based on deep neural network model. We also introduce an actual method as second step modification since the former second step is not efficient enough. This last technique makes usage of multiple sets of random forest classifiers to navigate inside each tree.
Wideband High Gain Printed Quasi-Yagi Diffraction Gratings-Based Antenna for 5G Applications

A broadband high-gain printed Quasi-Yagi antenna with a perturbation-based planar dielectric lens is presented. The perturbation design parameters are based on the diffraction gratings theory for gain enhancement, radiation pattern improvement, and higher order modes suppression. The proposed antenna provides 94.5% aperture efficiency with a high gain of 15 dBi at 30 GHz, high radiation efficiency of ~90%, and (24–40) GHz ultra-wide matching (S11 < −10 dB) bandwidth. The measured cross-polarization is lower than −20 dB in both <inline-formula> <tex-math notation="LaTeX">${E}$ </tex-math></inline-formula>- and <inline-formula> <tex-math notation="LaTeX">${H}$ </tex-math></inline-formula>- planes. With these features in addition to being low-profile and lightweight, this antenna is suitable for various millimeter-wave applications.
Lightweight and High Performance SHA-256 using Architectural Folding and 4-2 Adder Compressor

The modern era of Internet-of-Things (IoT) is naturally imposing a tight area/runtime constraint on the computing kernels. Security kernels, as part of the standardized protocols as well as custom defense techniques, are among the most common tasks executed on every digital device. Therefore, low area cost and high performance implementation of security kernels is an important goal of current system designers. In this paper, we revisit the state-of-the-art implementations of SHA-256, a standardized security primitive for authentication and propose novel optimizations. Our optimizations, based on architectural folding and 4-2 adder compressor, are geared toward both lightweight and high performance implementations. Detailed experiments of our optimized architecture on different FPGA fabrics clearly demonstrate their benefits. Our presented design point successfully attained the highest hardware efficiency (throughput/area) figures among the published literature so far.
A Novel Method for Multilevel Color Image Segmentation Based on Dragonfly Algorithm and Differential Evolution

Multilevel thresholding is a simple and powerful image segmentation method that has received widespread attention in recent years. However, the accuracy and stability of thresholding techniques are still not ideal and cannot meet the needs of engineering problems. Therefore, this paper presents a memetic algorithm of dragonfly algorithm (DA) and differential evolution (DE) for color image segmentation, which is known as improved DA (IDA). On the one hand, DA algorithm has a satisfied capability of avoiding convergence to the local optimum, thus it is served as a global search technique. On the other hand, the DE algorithm is adopted as a local search technique, which can increase the precision of solutions. In this paper, two thresholding techniques, namely, Otsu and minimum cross entropy (MCE) methods are used to determine the optimal threshold values. In order to evaluate the performance of the proposed method, we conduct a series of experiments on color images from the Berkeley database and the results are compared with five state-of-the-art meta-heuristic algorithms. Besides, a non-parametric Wilcoxon’s rank sum test is also included for statistical analysis. From the experimental results, it is found that IDA-based method outperforms other compared methods in terms of average fitness values, standard deviation, peak signal to noise ratio, structural similarity index, and feature similarity index. The promising results indicate that the application of IDA-based thresholding technique is potential and meaningful.
A New Efficient Parallel Algorithm for Minimum Spanning Tree

In this paper, using the BSP/CGM model, we propose a parallel algorithm and implement it on a GPGPU to obtain a minimum spanning tree of a graph. Previous works for this problem are based on the solution of the list ranking problem which, though efficient in theory, did not produce good speedups in practice. In a later work, based on the idea of computing a structure called strut, we proposed a parallel algorithm under the BSP/CGP model to obtain a minimum spanning tree without using list ranking. It is based on the construction of an auxiliary bipartite graph and uses integer sorting. In this paper, we improve that work in some aspects. The proposed algorithm does not require the computation of the bipartite graph, and the strut construction does not require the sorting algorithm. The efficiency and scalability of the proposed algorithm are verified through experimental results obtained by an implementation on GPGPU.
Can You Teach Me To Machine Learn?

Machine learning (ML) has become an important topic for students across disciplines to understand because of its useful applications and its societal impacts. At the same time, there is little existing work on ML education, particularly about teaching ML to non-majors. This paper presents an exploration of the pedagogical content knowledge (PCK) for teaching ML to non-majors. Through ten interviews with instructors of ML courses for non-majors, we inquired about student preconceptions as well as what students find easy or difficult about learning ML. We identified PCK in the form of three preconceptions and five barriers faced by students, and six pedagogical tactics adopted by instructors. The preconceptions were found to concern themselves more with ML's reputation rather than its inner workings. Student barriers included underestimating human decision in ML and conflating human thinking with computer processing. Pedagogical tactics for teaching ML included strategically choosing datasets, walking through problems by hand, and customizing to the domain(s) of students. As we consider the lessons from these findings, we hope that this will serve as a first step toward improving the teaching of ML to non-majors.
A Survey on Emerging SDN and NFV Security Mechanisms for IoT Systems

The explosive rise of Internet of Things (IoT) systems have notably increased the potential attack surfaces for cybercriminals. Accounting for the features and constraints of IoT devices, traditional security countermeasures can be inefficient in dynamic IoT environments. In this vein, the advantages introduced by software defined networking (SDN) and network function virtualization (NFV) have the potential to reshape the landscape of cybersecurity for IoT systems. To this aim, we provide a comprehensive analysis of security features introduced by NFV and SDN, describing the manifold strategies able to monitor, protect, and react to IoT security threats. We also present lessons learned in the adoption of SDN/NFV-based protection approaches in IoT environments, comparing them with conventional security countermeasures. Finally, we deeply discuss the open challenges related to emerging SDN- and NFV-based security mechanisms, aiming to provide promising directives to conduct future research in this fervent area.
Infusing Cooperative Learning into AP Computer Science Principles Courses to Promote Engagement and Diversity

The Advanced Placement Computer Science Principles (AP CSP) course was the culmination of an eight-year NSF/College Board pilot project that exceeded all expectations in terms of enrollment in its first two official years. Four NSF-sponsored projects and six other projects have endorsed AP CSP curricula and professional development (PD), a first for an AP course. In this paper, we report on an NSF-sponsored multi-year effort to infuse cooperative learning (CL) structures into AP CSP classrooms to improve class participation and student learning. As we report, CL structures have been beneficial for both new and experienced teachers and across the curricula of the endorsed providers. Since AP CSP was designed to engage all learners, the CL structures used in our PD workshops and the CL resources designed by participating teachers in our project have the potential to positively impact all AP CSP classrooms. Research was conducted on the extent to which use of CL structures impacted student efficacy and student achievement. Three cohorts of AP CSP teachers participated in PD that focused on AP CSP pedagogical content knowledge using CL structures. A cumulative 143 teachers attended one-week PD workshops spanning July 2015, 2016, and 2017. We studied the effect of CL structures on student learning using AP scores as an outcomes measure. Use of CL structures was a statistically significant and positive predictor of student AP scores for participating classes in cohorts 2 and 3. Additionally, the use of pair programming was a significant and positive predictor of AP scores.
Intelligent Classification of Supernovae Using Artificial Neural Networks

The classification of supernovae (explosions of certain stars) divides them into two main types, those of type I do not present Hydrogen in the spectrum while those of type II present. In addition to the division into these two types, there is still a subdivision that establishes types Ia, Ib and Ic. In practice, the classification of supernovae requires specialized knowledge of astronomers and data (light spectra) of good quality. Some automatic/intelligent classifiers have been developed and are reported in the literature, one of them is CIntIa, which uses 4 Artificial Neural Networks to classify supernovae types Ia, Ib, Ic and II. The objective of this work is to improve CIntIa, so that it has more diversity in its learning, proposing CIntIa 2.0. In this way, this work is a hierarchical learning structure that connects Artificial Neural Networks in an integrated system that allows a more secure and unambiguous classification. The computational improvement of this new version included the increased amount of data used at all stages of development of intelligent classifier and a new approach to filtering and processing of spectral data, ensuring better quality of information that are to be trained networks. The results achieved were good, especially in the classification of types Ia and II. A comparison with the works found in theliterature shows that CIntIa 2.0 is superior in quantity and diversity of data and achieves higher classification indices than the other classifiers.
Neuromorphic Computing - From Robust Hardware Architectures to Testing Strategies

This paper provides an overview of the challenges faced by hardware implemented Spiking Neural Networks, from device to circuit design, reliability and test. We present a comprehensive description of the state-of-the-art neuromorphic architectures inspired by brain computation, with special emphasis on Spiking Neural Networks (SNNs), together with emerging technologies that have enabled such systems, namely Phase Change and Metal Oxide Resistive Memories. Finally, we discuss the main challenges faced by hardware implementations of SNNs, their reliability and post-fabrication test issues.
IoT Maps: Charting the Internet of Things

Internet of Things (IoT) devices are becoming increasingly ubiquitous in our everyday environments. While the number of devices and the degree of connectivity is growing, it is striking that as a society we are increasingly unaware of the locations and purposes of such devices. Indeed, much of the IoT technology being deployed is invisible and does not communicate its presence or purpose to the inhabitants of the spaces within which it is deployed. In this paper, we explore the potential benefits and challenges of constructing IoT maps that record the location of IoT devices. To illustrate the need for such maps, we draw on our experiences from multiple deployments of IoT systems.
Sparse Detection Algorithms Based on Two-Dimensional Compressive Sensing for Sub-Nyquist Pulse Doppler Radar Systems

Sub-Nyquist pulse Doppler radar system has received wide attention recently because it can make the sampling rate lower than the Nyquist sampling rate. However, there are still two problems that must be addressed for sub-Nyquist pulse Doppler radar. One is that the large memory is required for sparse signal recovery. Another is that the performance of the sparse signal recovery will be distorted in the non-Gaussian impulse noise environment. For the first problem, this paper proposes four 2-D compressive sensing (CS) algorithms which are extended from the traditional 1-D CS algorithms (the ZAP, IHT, ISTA, and FISTA algorithms). The proposed 2-D-CS algorithms recover the signal in the delay-Doppler domain, which is a matrix domain. For the second problem, robust 2-D-CS algorithms are proposed for the non-Gaussian impulse noise environment. The proposed 2-D-CS algorithms can achieve comparable detection performance with lower memory requirement. The proposed robust 2-D-CS algorithms can eliminate the interference of impulsive noise in the non-Gaussian impulse noise environment. Simulation results are given to verify the effectiveness of the proposed algorithms.
Review of the Complexity of Managing Big Data of the Internet of Things

There is a growing awareness that the complexity of managing Big Data is one of the main challenges in the developing field of the Internet of Things (IoT). Complexity arises from several aspects of the Big Data life cycle, such as gathering data, storing them onto cloud servers, cleaning and integrating the data, a process involving the last advances in ontologies, such as Extensible Markup Language (XML) and Resource Description Framework (RDF), and the application of machine learning methods to carry out classifications, predictions, and visualizations. In this review, the state of the art of all the aforementioned aspects of Big Data in the context of the Internet of Things is exposed. The most novel technologies in machine learning, deep learning, and data mining on Big Data are discussed as well. Finally, we also point the reader to the state-of-the-art literature for further in-depth studies, and we present the major trends for the future.
Human Sensitivity to Community Structure Is Robust to Topological Variation

Despite mounting evidence that human learners are sensitive to community structure underpinning temporal sequences, this phenomenon has been studied using an extremely narrow set of network ensembles. The extent to which behavioral signatures of learning are robust to changes in community size and number is the focus of the present work. Here we present adult participants with a continuous stream of novel objects generated by a random walk along graphs of 1, 2, 3, 4, or 6 communities comprised of N = 24, 12, 8, 6, and 4 nodes, respectively. Nodes of the graph correspond to a unique object and edges correspond to their immediate succession in the stream. In short, we find that previously observed processing costs associated with community boundaries persist across an array of graph architectures. These results indicate that statistical learning mechanisms can flexibly accommodate variation in community structure during visual event segmentation.
A Systematic Review of Modifications and Validation Methods for the Extension of the Keystroke-Level Model

The keystroke-level model (KLM) is the simplest model of the goals, operators, methods, and selection rules (GOMS) family. The KLM computes formative quantitative predictions of task execution time. This paper provides a systematic literature review of KLM extensions across various applications and setups. The objective of this review is to address research questions concerning the development and validation of extensions. A total of 54 KLM extensions have been exhaustively reviewed. The results show that the original keystroke and mental act operators were continuously preserved or adapted and that the drawing operator was used the least. Excluding the original operators, almost 45 operators were collated from the primary studies. Only half of the studies validated their model’s efficiency through experiments. The results also identify several research gaps, such as the shortage of KLM extensions for post-GUI/WIMP interfaces. Based on the results obtained in this work, this review finally provides guidelines for researchers and practitioners.
Asymptotic Consistency of α-Rényi-Approximate Posteriors

We study the asymptotic consistency properties of $\alpha$-R\'enyi approximate posteriors, a class of variational Bayesian methods that approximate an intractable Bayesian posterior with a member of a tractable family of distributions, the member chosen to minimize the $\alpha$-R\'enyi divergence from the true posterior. Unique to our work is that we consider settings with $\alpha > 1$, resulting in approximations that upperbound the log-likelihood, and consequently have wider spread than traditional variational approaches that minimize the Kullback-Liebler (KL) divergence from the posterior. Our primary result identifies sufficient conditions under which consistency holds, centering around the existence of a 'good' sequence of distributions in the approximating family that possesses, among other properties, the right rate of convergence to a limit distribution. We further characterize the good sequence by demonstrating that a sequence of distributions that converges too quickly cannot be a good sequence. We also extend our analysis to the setting where $\alpha$ equals one, corresponding to the minimizer of the reverse KL divergence, and to models with local latent variables. We also illustrate the existence of good sequence with a number of examples. Our results complement a growing body of work focused on the frequentist properties of variational Bayesian methods.