nils-herrmann/clean_scirepeval huggingface dataset:
Deep learning for topology optimization design

Generative modeling techniques are being rapidly developed in the field of deep learning, and they have been applied to topology optimization. The variational autoencoder (VAE) is a generative modeling technology that extends the autoencoder to generate new images with a limited latent space. We modified the basic VAE structure to encode optimization conditions and decode latent variables for topology optimization design. The modified VAE could efficiently predict the optimized structure after topology optimization. However, it was difficult to train the neural network to predict a very detailed structure. The generative adversarial network (GAN) is another generative modeling technique for generating images and is implemented by a system of two neural networks competing with each other to make realistic synthetic data. We applied GAN to obtain more detailed optimized results from the original design. The proposed methodology of using two generative modeling techniques for deep learning is expected to significantly increase the efficiency of topology optimization.
Methods of mapping from phase to sine amplitude in direct digital synthesis

There are many methods for performing functional mapping from phase to sine amplitude (e.g., ROM look-up, coarse/fine segmentation into multiple ROM's, Taylor series, CORDIC algorithm). The spectral purity of the conventional direct digital synthesizer (DDS) is also determined by the resolution of the values stored in the sine table ROM. Therefore, it is desirable to increase the resolution of the ROM. Unfortunately, larger ROM storage means higher power consumption, lower reliability, lower speed, and greatly increased costs. Different memory compression and algorithmic techniques and their effect on distortion and trade-offs are investigated in detail. A computer program has been created to simulate the effects of the memory compression and algorithmic techniques on the output spectrum of the DDS. For each memory compression and algorithmic technique, the worst case spurious response is calculated using the computer program.
Intrusion detection using autonomous agents

AAFID is a distributed intrusion detection architecture and system, developed in CERIAS at Purdue University. AAFID was the ®rst architecture that proposed the use of autonomous agents for doing intrusion detection. With its prototype implementation, it constitutes a useful framework for the research and testing of intrusion detection algorithms and mechanisms. We describe the AAFID architecture and the existing prototype, as well as some design and implementation experiences and future research issues. Ó 2000 Elsevier Science B.V. All rights reserved.
Indoor optical wireless communication: potential and state-of-the-art

In recent years, interest in optical wireless (OW) as a promising complementary technology for RF technology has gained new momentum fueled by significant deployments in solid state lighting technology. This article aims at reviewing and summarizing recent advancements in OW communication, with the main focus on indoor deployment scenarios. This includes a discussion of challenges, potential applications, state of the art, and prospects. Related issues covered in this article are duplex transmission, multiple access, MAC protocols, and link capacity improvements.
Characterization of RF Noise in UTBB FD-SOI MOSFET

In this paper, we report the noise measurements in the RF frequency range for ultrathin body and thin buried oxide fully depleted silicon on insulator (FD-SOI) transistors. We analyze the impact of back and front gate biases on the various noise parameters; along with discussions on the secondary effects in FD-SOI transistors which contribute to the thermal noise. Using calibrated TCAD simulations, we show that the noise figure changes with the substrate doping and buried oxide thickness.
A Survey on Dialogue Systems: Recent Advances and New Frontiers

Dialogue systems have attracted more and more attention. Recent advances on dialogue systems are overwhelmingly contributed by deep learning techniques, which have been employed to enhance a wide range of big data applications such as computer vision, natural language processing, and recommender systems. For dialogue systems, deep learning can leverage a massive amount of data to learn meaningful feature representations and response generation strategies, while requiring a minimum amount of hand-crafting. In this article, we give an overview to these recent advances on dialogue systems from various perspectives and discuss some possible research directions. In particular, we generally divide existing dialogue systems into task-oriented and nontask- oriented models, then detail how deep learning techniques help them with representative algorithms and finally discuss some appealing research directions that can bring the dialogue system research into a new frontier
Vertex Domination in t-Norm Fuzzy Graphs

For the first time, We do fuzzification the concept of domination in crisp graph on a generalization of fuzzy graph by using membership values of vertices, α-strong edges and edges. In this paper, we introduce the first variation on the domination theme which we call vertex domination. We determine the vertex domination number γv for several classes of t-norm fuzzy graphs which include complete t-norm fuzzy graph, complete bipartite t-norm fuzzy graph, star t-norm fuzzy graph and empty t-norm fuzzy graph. The relationship between effective edges and α-strong edges is obtained. Finally, we discuss about vertex dominating set of a fuzzy tree with respect to a t-norm ⊗ by using the bridges and α-strong edges equivalence.
ZenCrowd: leveraging probabilistic reasoning and crowdsourcing techniques for large-scale entity linking

We tackle the problem of entity linking for large collections of online pages; Our system, ZenCrowd, identifies entities from natural language text using state of the art techniques and automatically connects them to the Linked Open Data cloud. We show how one can take advantage of human intelligence to improve the quality of the links by dynamically generating micro-tasks on an online crowdsourcing platform. We develop a probabilistic framework to make sensible decisions about candidate links and to identify unreliable human workers. We evaluate ZenCrowd in a real deployment and show how a combination of both probabilistic reasoning and crowdsourcing techniques can significantly improve the quality of the links, while limiting the amount of work performed by the crowd.
Approximate queuing analysis for IEEE 802.15.4 sensor network

Wireless sensor networks (WSNs) have attracted much attention in recent years for their unique characteristics and wide use in many different applications. Especially, in military networks, all sensor motes are deployed randomly and densely. How can we optimize the number of deployed nodes (sensor node and sink) with a QoS guarantee (minimal end-to-end delay and packet drop)? In this paper, using the M/M/1 queuing model we propose a deployment optimization model for non-beacon-mode 802.15.4 sensor networks. The simulation results show that the proposed model is a promising approach for deploying the sensor network.
A Comprehensive Survey of Wireless Body Area Networks

Recent advances in microelectronics and integrated circuits, system-on-chip design, wireless communication and intelligent low-power sensors have allowed the realization of a Wireless Body Area Network (WBAN). A WBAN is a collection of low-power, miniaturized, invasive/non-invasive lightweight wireless sensor nodes that monitor the human body functions and the surrounding environment. In addition, it supports a number of innovative and interesting applications such as ubiquitous healthcare, entertainment, interactive gaming, and military applications. In this paper, the fundamental mechanisms of WBAN including architecture and topology, wireless implant communication, low-power Medium Access Control (MAC) and routing protocols are reviewed. A comprehensive study of the proposed technologies for WBAN at Physical (PHY), MAC, and Network layers is presented and many useful solutions are discussed for each layer. Finally, numerous WBAN applications are highlighted.
DESIGN OF RF ENERGY HARVESTING SYSTEM FOR ENERGIZING LOW POWER DEVICES

Electromagnetic energy harvesting holds a promising future for energizing low power electronic devices in wireless communication circuits. This article presents an RF energy harvesting system that can harvest energy from the ambient surroundings at the downlink radio frequency range of GSM-900 band. The harvesting system is aimed to provide an alternative source of energy for energizing low power devices. The system design consists of three modules: a single wideband 377 Ω E-shaped patch antenna, a pi matching network and a 7-stage voltage doubler circuit. These three modules were fabricated on a single printed circuit board. The antenna and Pi matching network have been optimized through electromagnetic simulation software, Agilent ADS 2009 environment. The uniqueness of the system lies in the partial ground plane and the alignment of induced electric field for maximum current flow in the antenna that maximizes the captured RF energy. The design and simulation of the voltage doubler circuit were performed using Multisim software. All the three modules were integrated and fabricated on a double sided FR 4 printed circuit board. The DC voltage obtained from the harvester system in the field test at an approximate distance of 50m from GSM cell tower was 2.9V. This voltage was enough to power the STLM20 temperature sensor.   Kavuri Kasi Annapurna Devi (kavurik.adevi@newinti.edu.my).
Differential Dataflow

Existing computational models for processing continuously changing input data are unable to efficiently support iterative queries except in limited special cases. This makes it difficult to perform complex tasks, such as social-graph analysis on changing data at interactive timescales, which would greatly benefit those analyzing the behavior of services like Twitter. In this paper we introduce a new model called differential computation, which extends traditional incremental computation to allow arbitrarily nested iteration, and explain—with reference to a publicly available prototype system called Naiad—how differential computation can be efficiently implemented in the context of a declarative dataparallel dataflow language. The resulting system makes it easy to program previously intractable algorithms such as incrementally updated strongly connected components, and integrate them with data transformation operations to obtain practically relevant insights from real data streams.
From Star Schemas to Big Data: 20+ Years of Data Warehouse Research

Data Warehouses are the core of the modern systems for decision making. They store integrated information extracted from various and heterogeneous data sources, making it available in multidimensional form for analyses aimed at improving the users’ knowledge of their business. Though the first use of the term dates back to the 80s, only during the late 90s data warehousing has emerged as a research area on its own, though in strict correlation with several other research topics as database integration, view materialization, data visualization, etc. This paper surveys more than 20 years of research on data warehouse systems, from their early relational implementations (still widely adopted in corporate environments), to the new architectures solicited by Business Intelligence 2.0 scenarios during the last decade, and up to the exciting challenges now posed by the integration with big data settings. The timeline of research is organized into three interrelated tracks: techniques, architectures, and methodologies.
Conceptualizing and testing formative constructs: tutorial and annotated example

Although abundant advice is available for how to develop and validate multi-item scales based on reflective constructs, scant attention has been directed to how to construct and validate formative constructs. Such advice is important because (1) theory suggests many constructs are formative and (2) recent advances in software render testing models with formative constructs more tractable. In this tutorial, our goal is to enhance understanding of formative constructs at the conceptual, statistical and methodological levels. Specifically, we (1) provide general principles for specifying whether a construct should be conceptually modeled as reflective or formative, (2) discuss the statistical logic behind formative constructs, and (3) illustrate how to model and evaluate formative constructs. In particular, we provide a tutorial in which we test and validate professional reward structure, a formative construct, in two popular structural equation modeling programs: EQS and PLS. We conclude with a summary of guidelines for how to conduct and evaluate research using formative constructs.
Adaptive Road Crack Detection System by Pavement Classification

This paper presents a road distress detection system involving the phases needed to properly deal with fully automatic road distress assessment. A vehicle equipped with line scan cameras, laser illumination and acquisition HW-SW is used to storage the digital images that will be further processed to identify road cracks. Pre-processing is firstly carried out to both smooth the texture and enhance the linear features. Non-crack features detection is then applied to mask areas of the images with joints, sealed cracks and white painting, that usually generate false positive cracking. A seed-based approach is proposed to deal with road crack detection, combining Multiple Directional Non-Minimum Suppression (MDNMS) with a symmetry check. Seeds are linked by computing the paths with the lowest cost that meet the symmetry restrictions. The whole detection process involves the use of several parameters. A correct setting becomes essential to get optimal results without manual intervention. A fully automatic approach by means of a linear SVM-based classifier ensemble able to distinguish between up to 10 different types of pavement that appear in the Spanish roads is proposed. The optimal feature vector includes different texture-based features. The parameters are then tuned depending on the output provided by the classifier. Regarding non-crack features detection, results show that the introduction of such module reduces the impact of false positives due to non-crack features up to a factor of 2. In addition, the observed performance of the crack detection system is significantly boosted by adapting the parameters to the type of pavement.
Accelerometer Based Joint Step Detection and Adaptive Step Length Estimation Algorithm Using Handheld Devices

—The pedestrian inertial navigation systems are generally based on Pedestrian Dead Reckoning (PDR) algorithm. Considering the physiological characteristics of pedestrian movement, we use the cyclical characteristics and statistics of acceleration waveform and features which are associated with the walking speed to estimate the stride length. Due to the randomness of the pedestrian hand-held habit, the step events cannot always be detected by using the periods of zero velocity updates (ZUPTs). Furthermore, the signal patterns of the sensor could differ significantly depending on the carrying modes and the user’s hand motion. Hence, the step detection and the associated adaptive step length model using a handheld device equipped with accelerometer is required to obtain high-accurate measurements. To achieve this goal, a compositional algorithm of empirical formula and Back-propagation neural network by using handheld devices is proposed to estimate the step length, as well as achieve the accuracy of step detection higher than 98%. Furthermore, the proposed joint step detection and adaptive step length estimation algorithm can help much in the development of Pedestrian Navigation Devices (PNDs) based on the handheld inertial sensors.
Morphological Embeddings for Named Entity Recognition in Morphologically Rich Languages

In this work, we present new state-ofthe-art results of 93.59% and 79.59% for Turkish and Czech named entity recognition based on the model of (Lample et al., 2016). We contribute by proposing several schemes for representing the morphological analysis of a word in the context of named entity recognition. We show that a concatenation of this representation with the word and character embeddings improves the performance. The effect of these representation schemes on the tagging performance is also investigated.
IMAGE TO LATEX VIA NEURAL NETWORKS A Project

Many research papers in mathematics, computer science, and physics are written in LaTeX. Technical papers and articles in these areas often involve mathematical equations. Writing such equations in LaTeX takes longer than handwriting the same equations on paper. In this report, we want to show that the time-consuming process of typesetting LaTeX equations from images of these equations can be automated and optimized. Neural networks are good at solving related problems such as handwritten digit recognition, so we adapted these well-studied approaches to the LaTeX problem. Neural network model training involves large amounts of good quality data. So, for our project, we propose a convolutional neural network architecture to recognize LaTeX equations along with a way to generate labeled datasets of mathematical equation images and their corresponding LaTeX expressions. Our neural network model predicts from mathematical equations involving numbers, letters, mathematical symbols, and matrix images, the corresponding LaTeX for these equations. We have achieved an accuracy of more than 90% in predicting LaTeX for these complex equations involving up to 35 characters.
Urban Physics : Effect of the micro-climate on comfort , health and energy demand

Y-NC-ND license.  The global trend towards urbanisation explains the growing interest in the study of the modification of the urban climate due to the heat island effect and global warming, and its impact on energy use of buildings. Also urban comfort, health and durability, referring respectively to pedestrian wind/ thermal comfort, pollutant dispersion and wind-driven rain are of interest. Urban Physics is a wellestablished discipline, incorporating relevant branches of physics, environmental chemistry, aerodynamics, meteorology and statistics. Therefore, Urban Physics is well positioned to provide keycontributions to the current urban problems and challenges. The present paper addresses the role of Urban Physics in the study of wind comfort, thermal comfort, energy demand, pollutant dispersion and wind-driven rain. Furthermore, the three major research methods applied in Urban Physics, namely field experiments, wind tunnel experiments and numerical simulations are discussed. Case studies illustrate the current challenges and the relevant contributions of Urban Physics. & 2012.  Production and hosting by Elsevier B.V. Open access under CC BY-NC-ND license.
Value creation process in the fast fashion industry . Towards a networking approach < Value co-creation and the changing role of suppliers and customers >

Quick response, short product life cycles, customer-centric businesses, agile supply chains and reduction of lead times are considered to be the core business strategies in the sector of fast fashion. The work is an attempt to identify the most revealing management and organizational tools that support the final value creation and service delivery processes in the international industry of fast fashion Design/Methodology/approach – In order to fulfill such a purpose, the paper detects the sector through the recent developments from Service-Dominant (S-D) Logic and Network Theory scientific proposals Findings – Value is a co-created performance, requiring several actors participation. In the fast fashion, such actors are represented by textile businesses, providers, retailers, stores, customers. They all interact within a supply chain becoming a value chain in which stakeholders interact with each other contributing to the final value generation. At the light of this research, it seems to be restrictive to identify in the lead time reduction the success factor of the fast fashion businesses, but it should be pursued in politics of integration, interaction, co-creation and sharing throughout the chain. Fast fashion is an example of totally integrated global chain working and performing as a whole business in which every single component co-creates the final mutual benefit Practical implications – Textile organizations taking part in the network represented by fast fashion are called to re-organize their activities in function of a highly developed integration level and so to move towards a networking approach, fostering the mutual exchange of resources in order to co-create a common value  The paper adds value to the study of the fast fashion industry, valorizing the importance of the co-creating perspective within the supply chain management
Character-level Chinese Writer Identification using Path Signature Feature, DropStroke and Deep CNN

Most existing online writer-identification systems require that the text content is supplied in advance and rely on separately designed features and classifiers. The identifications are based on lines of text, entire paragraphs, or entire documents; however, these materials are not always available. In this paper, we introduce a path-signature feature to an end-to-end text-independent writer-identification system with a deep convolutional neural network (DCNN). Because deep models require a considerable amount of data to achieve good performance, we propose a data-augmentation method named DropStroke to enrich personal handwriting. Experiments were conducted on online handwritten Chinese characters from the CASIA-OLHWDB1.0 dataset, which consists of 3,866 classes from 420 writers. For each writer, we only used 200 samples for training and the remaining 3,666. The results reveal that the path-signature feature is useful for writer identification, and the proposed DropStroke technique enhances the generalization and significantly improves performance. Deep convolutional neural network; online text-independent writer identification; data augmentation; path-signature feature; Chinese character