Miracle-dz/UniFAR-FTUs huggingface dataset:
Design Space Exploration of Energy Efficient NoC-and Cache-Based Many-Core Architecture

Performance of parallel scientific applications on many-core processor architectures is a challenge that increases every day, especially when energy efficiency is concerned. To achieve this, it is necessary to explore architectures with high processing power composed by a network-on-chip to integrate many processing cores and other components. In this context, this paper presents a design space exploration over NoC-based manycore processor architectures with distributed and shared caches, using full-system simulations. We evaluate bottlenecks in such architectures with regard to energy efficiency, using different parallel scientific applications and considering aspects from caches and NoCs jointly. Five applications from NAS Parallel Benchmarks were executed over the proposed architectures, which vary in number of cores; in L2 cache size; and in 12 types of NoC topologies. A clustered topology was set up, in which we obtain performance gains up to 30.56% and reduction in energy consumption up to 38.53%, when compared to a traditional one.
Computational Thinking in the Danish High School: Learning Coding, Modeling, and Content Knowledge with NetLogo

Computational thinking (CT) is emerging as an important theme in computer science and high school education. However, research is needed to inform high-school teachers how to foster students' development of CT in computer science and other subjects. Evidence suggests that agent-based modeling is a valuable way for students to learn CT in different subjects. This paper reports a teaching experiment where researchers, developers, and high school teachers collaborated to develop six NetLogo models. The models were used in nine Danish High Schools in the following four subjects: Biotechnology, chemistry, biology, and social science. Teachers and students had no or very limited experience with programming. Students build CT and content knowledge by using, modifying, and creating code in the models. This paper provides details for others to adopt the models and the underlying CMC framework, which integrates: Coding, Modeling, and Content. The paper evaluates the results from an open-ended questionnaire with all participating students (n=210) and semi-structured interviews with all teachers (n=15). Thematic analysis was applied to categorize the qualitative data. Results showed that students were able to use, modify, and create code in NetLogo that enabled them to develop CT and content knowledge. The CMC framework represents a fruitful way for teachers to design and teach and for students to tinker with learning CT.
UIBK: user interactions for building knowledge

This half-day workshop seeks to bring together practitioners and academics interested in the challenges of structuring interactions for subject matter experts (SMEs) who are providing knowledge and/or feedback to an AI system, but are not well-versed in the underlying algorithms. Since the information provided by SMEs directly effects the efficacy of the final system, collecting the correct data is a problem that navigates issues ranging from curating data that may be tainted to structuring data collection tasks in such a way as to mitigate user boredom. The goal of this workshop is to discuss methods and new paradigms for productively interacting with users while collecting knowledge.
An Empirical Study of Using An Ensemble Model in e-Commerce Taxonomy Classification Challenge

In the Rakuten data challenge on taxonomy Classification for eCommerce scale Product Catalogs, we propose an approach based on deep convolutional neural networks to predict product taxonomies using their descriptions. The classification performance of the proposed system is further improved with oversampling, threshold moving and error correct output coding. The best classification accuracy is obtained through ensembling multiple networks trained differently with multiple inputs comprising of various extracted features.
Dynamic Query Substitution in Fast Evolving Fashion

In most e-commerce platforms, search system supports free text keyword based queries. These queries can be very open ended. In contrast with traditional web search engines, recall set for ecommerce platform is usually constrained by the underlying inventory of products that are available in stock. This demand and supply gap leads to null or sub-optimal query results, which in turn, leads to a bad user experience. In this paper, we propose a query substitution approach based on semantic comprehension of keywords in fashion domain which improves query results while preserving primary intent of the user. Our solution enriches query results by selectively replacing query terms with hypernyms and co-hyponyms using Entity Affinity Relationship Graph (EARG). We demonstrate significant improvement in recall and overall products sold through various experiments conducted on a leading fashion e-commerce portal.
A Parameter-Free Model Comparison Test Using Differential Algebra

We present a method for rejecting competing models from noisy time-course data that does not rely on parameter inference. First we characterize ordinary differential equation models in only measurable variables using differential-algebra elimination. This procedure gives input-output equations, which serve as invariants for time series data. We develop a model comparison test using linear algebra and statistics to reject incorrect models from their invariants. This algorithm exploits the dynamic properties that are encoded in the structure of the model equations without recourse to parameter values, and, in this sense, the approach is parameter-free. We demonstrate this method by discriminating between different models from mathematical biology.
Geospace Environment Modeling (GEM) magnetic reconnection challenge: MHD and Hall MHD—constant and current dependent resistivity models

The work is part of several papers investigating two-dimensional magnetic reconnection in the same initial configuration with different plasma approximations. The present work compares the results of magnetic reconnection in this configuration using constant and current dependent resistivity models in the framework of traditional MHD and Hall MHD. The results can be summarized as follows. For constant resistivity the effects of diffusion or the formation of a long thin current layer limit the reconnection rate. Current dependent resistivity models result in enhanced reconnection rates in all considered cases. In these cases, reconnection is faster by a factor of 2 in the Hall MHD approximation compared to the traditional MHD. The maximum reconnection rate for the Hall MHD for all current dependent resistivities is ∼0.2 and appears largely independent of the model specifics and the initial perturbation.
A Line Integral Convolution Method With Dynamically Determining Step Size and Interpolation Mode for Vector Field Visualization

The line integral convolution method can obtain a continuous and dense vector streamline texture. The traditional fixed step size gets larger texture noise, while the exact calculation step method (such as the Runge–Kutta method) is time-consuming. In the process of generating streamline texture, if using bilinear interpolation calculation, we can get the method of calculating the point vector value, and it is also time-consuming. To address the above-mentioned problems, the line integral convolution method for dynamically determining integral step size and interpolation mode is proposed. The effect of streamline drawing mainly depends on the size of the integration step and the interpolation mode. The method proposed in this paper gives the rules for determining the integration step size and interpolation mode according to the vector speed size and the angle between two vectors. The experimental results show that compared with the previous fixed step method and the bilinear interpolation method, the proposed method not only has faster computation speed but also clearer texture and stronger contrast.
Parameter Self-Adaptation in an Ant Colony Algorithm for Continuous Optimization

ACO<inline-formula> <tex-math notation="LaTeX">$_{\mathbb {R}}$ </tex-math></inline-formula> is a well-established ant colony optimization algorithm for continuous-domain optimization. We present an approach for the dynamic adaptation of the ACO<inline-formula> <tex-math notation="LaTeX">$_{\mathbb {R}}$ </tex-math></inline-formula> algorithm’s controlling parameters, focusing on the search width parameter, based on using several pre-specified parameter configurations, which we call personalities. Before an ant starts to generate a candidate solution, it stochastically adopts a personality based on the relative past success of different personalities. The success of a personality is measured, in turn, by the survival rate of the previous solutions generated by the ants adopting that personality. The premise of our approach is that some personalities will be more appropriate than others for different phases of the search. In addition, our adaptive approach can accommodate solution recombination, the use of which within ACO<inline-formula> <tex-math notation="LaTeX">$_{\mathbb {R}}$ </tex-math></inline-formula> was recently explored in the previous work. It allows the frequency of applying recombination and the type of recombination operator to be dynamically adapted, by having one or more recombination personalities among the competing personalities. We evaluate these proposals experimentally on two applications: 1) training feedforward neural networks for classification using 65 benchmark datasets from the University of California Irvine repository and 2) optimizing several popular synthetic benchmark continuous-domain functions. Our experimental results indicate that our proposals perform better than the standard ACO<inline-formula> <tex-math notation="LaTeX">$_{\mathbb {R}}$ </tex-math></inline-formula> on both applications, to a statistically significant extent.
Multicore Performance Engineering of Sparse Triangular Solves Using a Modified Roofline Model

The Roofline model is widely used to visualize the performance of executed code together with the upper performance bounds given by the memory bandwidth and the processor peak performance. The model can thus provide an insightful visualization of bottlenecks. In this paper, we try to establish realistic bandwidth ceilings for the sparse triangular solve step of PARDISO, a leading sparse direct solver package, which is also part of the Intel MKL library. The performance of the forward and backward substitution process is analyzed and benchmarked for a representative set of sparse matrices on seven modern x86-type multicore architectures and the Knights Landing manycore architecture. It is shown how to accurately measure the necessary quantities also for threaded code, and the measurement approach, its validation, as well as limitations are discussed. Our modeling approach covers the serial and parallel execution phases, allowing for in-socket performance predictions.
A New Mathematical Modeling Method for Four-Stage Helicopter Main Gearbox and Dynamic Response Optimization

A new mathematical modeling method, namely, the finite element method and the lumped mass method (LMM-FEM) mixed modeling, is applied to establish the overall multinode dynamic model of a four-stage helicopter main gearbox. The design of structural parameters of the shaft is the critical link in the four-stage gearbox; it affects the response of multiple input and output branches; however, only the meshing pairs were frequently shown in the dynamic model in previous research. Therefore, each shaft is also treated as a single node and the shaft parameters are coupled into the dynamic equations in this method, which is more accurate for the transmission chain. The differential equations of the system are solved by the Fourier series method, and the dynamic response of each meshing element is calculated. The sensitivity analysis method and parameter optimization method are applied to obtain the key shaft parameters corresponding to each meshing element. The results show that the magnitude of dynamic response in converging meshing pair and tail output pair is higher than that of other meshing pairs, and the wall thickness has great sensitivity to a rotor shaft. In addition, the sensitivity analysis method can be used to select the corresponding shaft node efficiently and choose parameters appropriately for reducing the system response.
Novel Index Modulation Techniques: A Survey

In fifth generation wireless networks, the escalating teletraffic and energy consumption has necessitated the development of green communication techniques in order to further enhance both the system’s spectral efficiency and energy efficiency. In the past few years, the novel index modulation (IM) has emerged as a promising technology that is widely employed in wireless communications. In this paper, we present a survey on IM in order to provide the readers with a better understanding of its principles, advantages, and potential applications. We start with a comprehensive literature review, where the concept of IM is introduced, and various existing IM schemes are classified according to their signal domains, including the frequency domain, spatial domain, time domain and channel domain. Then the principles of different IM-aided systems are detailed, where the transceiver design is illustrated, followed by descriptions of typical systems and corresponding performance evaluation. A range of challenges and open issues on IM are discussed before we conclude this survey.
Understanding the Design Space of Wavelength-Routed Optical NoC Topologies for Power-Performance Optimization

Silicon photonics is the most promising emerging technology to deliver on- and off-chip communication performance and power that vastly exceed the capabilities of electronics. However, a significant abstraction gap does exist between novel devices and circuits and the higher-order switching structures that system designers need to instantiate. Currently, designers mostly rely on their intuition to bridge this abstraction gap. This paper lays the groundwork for a more rigorous and effective approach, by vertically-integrating the most advanced design methods and tools for topology synthesis and refinement in the context of a novel performance analysis framework. As a result, we can extract the highest aggregate bandwidth out of an optical network-on-chip topology, and provide an early-stage analysis of its static power, thus unveiling unexplored portions of the design space and interpreting its characteristics.
Semantic analysis on faces using deep neural networks

In this paper we address the problem of automatic emotion recognition and classification through video. Nowadays there are excellent results focused on lab-made datasets, with posed facial expressions. On the other hand there is room for a lot of improvement in the case of ‘in the wild’ datasets, where light, face angle to the camera, etc. are taken into account. In these cases it could be very harmful to work with a small dataset. Currently, there are not big enough datasets of adequately labeled faces for the task. We use Generative Adversarial Networks in order to train models in a semi-supervised fashion, generating realistic face images in the process, allowing the exploitation of a big cumulus of unlabeled face images. Resumen En este trabajo se aborda el problema de reconocimiento y clasificación de Expresiones Faciales a partir de video. Actualmente existen excelentes resultados enfocados en entornos controlados, donde se encuentran expresiones faciales artificiales. En cambio, queda mucho por mejorar cuando se trata de entornos no controlados, en los cuales las variaciones de iluminación, ángulo a la cámara, encuadre del rostro, hacen que la poca cantidad de datos etiquetados disponibles sea un impedimento a la hora de entrenar modelos de aprendizaje automatizado. Para atacar esta dificultad se utilizó de forma innovadora la técnica Generative Adversarial Networks, que permite utilizar un gran cúmulo de imágenes no etiquetadas con un estilo de entrenamiento semi supervisado.
Multi-bit nonvolatile flip-flop based on NAND-like spin transfer torque MRAM

Nonvolatile flip-flops (NVFFs) integrating emerging spintronics devices such as magnetic tunnel junction (MTJ) are under intensive investigation. They allow computing systems to be powered-off during the standby state, hence high static power issue of conventional CMOS technology can be addressed. MTJ based on spin transfer torque (STT) effect provide non-volatility, good endurance and 3D integration with CMOS based circuits. However, it suffers from relative long switching delay, high switching power and asymmetric switching issues. In this work, we first present a multi-bit NVFF using NAND-like spintronics (NANS-SPIN) devices which are written by STT and spin orbit torque (SOT) currents. It shows advantages in terms of power consumption, area overhead and write voltage. Then, functionality and performance of the proposed NVFF will be simulated and validated.
Speech Recognition Using Deep Neural Networks: A Systematic Review

Over the past decades, a tremendous amount of research has been done on the use of machine learning for speech processing applications, especially speech recognition. However, in the past few years, research has focused on utilizing deep learning for speech-related applications. This new area of machine learning has yielded far better results when compared to others in a variety of applications including speech, and thus became a very attractive area of research. This paper provides a thorough examination of the different studies that have been conducted since 2006, when deep learning first arose as a new area of machine learning, for speech applications. A thorough statistical analysis is provided in this review which was conducted by extracting specific information from 174 papers published between the years 2006 and 2018. The results provided in this paper shed light on the trends of research in this area as well as bring focus to new research topics.
Constructive Interference in 802.15.4: A Tutorial

Constructive interference (CI) can happen when multiple wireless devices send the same frame at the same time. If the time offset between the transmissions is less than 500 ns, a receiver will successfully decode the frame with high probability. CI can be useful for achieving low-latency communication or low-overhead flooding in a multi-hop low-power wireless network. The contribution of this paper is threefold. First, we present the current state-of-the-art CI-based protocols. Second, we provide a detailed hands-on tutorial on how to implement CI-based protocols on TelosB motes, with well documented open-source code. Third, we discuss the issues and challenges of CI-based protocols, and list open issues and research directions. This paper is targeted at the level of practicing engineers and advanced researchers and can serve both as a primer on CI technology and a reference to its implementation.
Inferential Logic: a Machine Learning Inspired Paradigm for Combinational Circuits

Machine learning (ML) theories and tools suggest alternative forms to conceive and represent relationships among data. The same theories find their application in the Boolean domain, where logic functions can be described as inference rules. This paper introduces Inferential Logic, a novel paradigm that leverages the ML concept of statistical inference for the design of combinational logic circuits, the Inferential Logic Circuits (ILCs). This new design concept is conceived for low-power circuits that run quasi-exact computation in error-resilient applications, but it also provides an exact run-mode that can be dynamically enabled when accuracy scaling is not an option.
Segmentation of design protocol using EEG

Abstract Design protocol data analysis methods form a well-known set of techniques used by design researchers to further understand the conceptual design process. Verbal protocols are a popular technique used to analyze design activities. However, verbal protocols are known to have some limitations. A recurring problem in design protocol analysis is to segment and code protocol data into logical and semantic units. This is usually a manual step and little work has been done on fully automated segmentation techniques. Physiological signals such as electroencephalograms (EEG) can provide assistance in solving this problem. Such problems are typical inverse problems that occur in the line of research. A thought process needs to be reconstructed from its output, an EEG signal. We propose an EEG-based method for design protocol coding and segmentation. We provide experimental validation of our methods and compare manual segmentation by domain experts to algorithmic segmentation using EEG. The best performing automated segmentation method (when manual segmentation is the baseline) is found to have an average deviation from manual segmentations of 2 s. Furthermore, EEG-based segmentation can identify cognitive structures that simple observation of design protocols cannot. EEG-based segmentation does not replace complex domain expert segmentation but rather complements it. Techniques such as verbal protocols are known to fail in some circumstances. EEG-based segmentation has the added feature that it is fully automated and can be readily integrated in engineering systems and subsystems. It is effectively a window into the mind.
Automotive Antenna Roof for Cooperative Connected Driving

An automotive concept for vehicular communications is proposed that utilizes the potential of the roof area for antennas. Antennas are distributed in cavities and on shelves in the center and on the front and rear roof ends. The arrangement of antennas on the roof allows better radiation to the front and back of automobiles than shark-fins and single cavities. Combining several modules provides space for further antennas, sensors, and integrated front-ends, as well as better spatial separation for multiple-input multiple-output (MIMO) arrays beyond 5G, and cooperative connected and automated driving. A prototype was developed and built into a car chassis. The measured data were analyzed and evaluated in the view of coverage for vulnerable road users and on correlation for the MIMO.
Sentiment polarity classification of tweets using a extended dictionary

With the purpose of classifying text based on its sentiment polarity (positive or negative), we proposed an extension of a 68,000 tweets corpus through the inclusion of word definitions from a dictionary of the Real Academia Espa\~{n}ola de la Lengua (RAE). A set of 28,000 combinations of 6 Word2Vec and support vector machine parameters were considered in order to evaluate how positively would affect the inclusion of a RAE's dictionary definitions classification performance. We found that such a corpus extension significantly improve the classification accuracy. Therefore, we conclude that the inclusion of a RAE's dictionary increases the semantic relations learned by Word2Vec allowing a better classification accuracy.