nils-herrmann/clean_scirepeval huggingface dataset:
FreePDK15: An Open-Source Predictive Process Design Kit for 15nm FinFET Technology

This paper discusses design rules and layout guidelines for an open source predictive process design kit (PDK) for multi-gate 15nm FinFET devices. Additional design rules are introduced considering process variability, and challenges involved in fabrication beyond 20nm. Particularly, double patterning lithography is assumed and a unique set of design rules are developed for critical dimensions. In order to improve the FinFET layout density, Middle-of-line local interconnect layers are implemented for the FinFET layout. The rules are further validated by running Calibre design-rule checks on Virtuoso layout of an Inverter and NAND4 cells. As part of the validation process, the area of a FreePDK15 inverter was compared to the area of an inverter in 45nm bulk MOS process and the ratio was found to be 1:6. This kit primarily aims to support introduction of sub-20nm FinFET devices into research and universities.
Coupled-Resonator Filters With Frequency-Dependent Couplings: Coupling Matrix Synthesis

This letter presents a novel technique for synthesis of coupled-resonator filters with inter-resonator couplings varying linearly with frequency. The values of non-zero elements of the coupling matrix are found by solving a nonlinear least squares problem involving eigenvalues of matrix pencils derived from the coupling matrix and reference zeros and poles of scattering parameters. The proposed method was verified by numerical tests carried out for various coupling schemes including triplets and quadruplets for which the frequency-dependent coupling was found to produce an extra zero.
Student-t Processes as Alternatives to Gaussian Processes

We investigate the Student-t process as an alternative to the Gaussian process as a nonparametric prior over functions. We derive closed form expressions for the marginal likelihood and predictive distribution of a Student-t process, by integrating away an inverse Wishart process prior over the covariance kernel of a Gaussian process model. We show surprising equivalences between different hierarchical Gaussian process models leading to Student-t processes, and derive a new sampling scheme for the inverse Wishart process, which helps elucidate these equivalences. Overall, we show that a Studentt process can retain the attractive properties of a Gaussian process – a nonparametric representation, analytic marginal and predictive distributions, and easy model selection through covariance kernels – but has enhanced flexibility, and predictive covariances that, unlike a Gaussian process, explicitly depend on the values of training observations. We verify empirically that a Student-t process is especially useful in situations where there are changes in covariance structure, or in applications like Bayesian optimization, where accurate predictive covariances are critical for good performance. These advantages come at no additional computational cost over Gaussian processes.
A risk management framework for software engineering practice

Formal risk analysis and management in software engineering is still an emerging part of project management. We provide a brief introduction to the concepts of risk management for software development projects, and then an overview of a new risk management framework. Risk management for software projects is intended to minimize the chances of unexpected events, or more specifically to keep all possible outcomes under tight management control. Risk management is also concerned with making judgments about how risk events are to be treated, valued, compared and combined. The ProRisk management framework is intended to account for a number of the key risk management principles required for managing the process of software development. It also provides a support environment to operationalize these management tasks.
A Predictive Maintenance System for Epitaxy Processes Based on Filtering and Prediction Techniques

Silicon epitaxial deposition is a process strongly influenced by wafer temperature behavior, which has to be constantly monitored to avoid the production of defective wafers. However, temperature measurements are not reliable, and the sensors have to be appropriately calibrated with some dedicated procedure. A predictive maintenance (PdM) system is proposed with the aim of predicting process behavior and scheduling control actions on the sensors in advance. Two different prediction techniques have been employed and compared: the Kalman predictor and the particle filter with Gaussian kernel density estimator. The accuracy of the PdM module has been tested on real industrial production datasets.
A knowledge plane for the internet

We propose a new objective for network research: to build a fundamentally different sort of network that can assemble itself given high level instructions, reassemble itself as requirements change, automatically discover when something goes wrong, and automatically fix a detected problem or explain why it cannot do so.We further argue that to achieve this goal, it is not sufficient to improve incrementally on the techniques and algorithms we know today. Instead, we propose a new construct, the Knowledge Plane, a pervasive system within the network that builds and maintains high-level models of what the network is supposed to do, in order to provide services and advice to other elements of the network. The knowledge plane is novel in its reliance on the tools of AI and cognitive systems. We argue that cognitive techniques, rather than traditional algorithmic approaches, are best suited to meeting the uncertainties and complexity of our objective.
A generic neural acoustic beamforming architecture for robust multi-channel speech processing

Acoustic beamforming can greatly improve the performance of Automatic Speech Recognition (ASR) and speech enhancement systems when multiple channels are available. We recently proposed a way to support the model-based Generalized Eigenvalue beamforming operation with a powerful neural network for spectral mask estimation. The enhancement system has a number of desirable properties. In particular, neither assumptions need to be made about the nature of the acoustic transfer function (e.g., being anechonic), nor does the array configuration need to be known. While the system has been originally developed to enhance speech in noisy environments, we show in this article that it is also effective in suppressing reverberation, thus leading to a generic trainable multichannel speech enhancement system for robust speech processing. To support this claim, we consider two distinct datasets: The CHiME 3 challenge, which features challenging real-world noise distortions, and the Reverb challenge, which focuses on distortions caused by reverberation. We evaluate the system both with respect to a speech enhancement and a recognition task. For the first task we propose a new way to cope with the distortions introduced by the Generalized Eigenvalue beamformer by renormalizing the target energy for each frequency bin, and measure its effectiveness in terms of the PESQ score. For the latter we feed the enhanced signal to a strong DNN back-end and achieve stateof-the-art ASR results on both datasets. We further experiment with different network architectures for spectral mask estimation: One small feed-forward network with only one hidden layer, one Convolutional Neural Network and one bi-directional Long Short-Term Memory network, showing that even a small network is capable of delivering significant performance improvements.
A novel orthogonal NMF-based belief compression for POMDPs

High dimensionality of POMDP's belief state space is one major cause that makes the underlying optimal policy computation intractable. Belief compression refers to the methodology that projects the belief state space to a low-dimensional one to alleviate the problem. In this paper, we propose a novel orthogonal non-negative matrix factorization (O-NMF) for the projection. The proposed O-NMF not only factors the belief state space by minimizing the reconstruction error, but also allows the compressed POMDP formulation to be efficiently computed (due to its orthogonality) in a value-directed manner so that the value function will take same values for corresponding belief states in the original and compressed state spaces. We have tested the proposed approach using a number of benchmark problems and the empirical results confirms its effectiveness in achieving substantial computational cost saving in policy computation.
Emotions in Students ’ Self-Regulated Learning and Achievement : A Program of Qualitative and Quantitative Research

Academic emotions have largely been neglected by educational psychology, with the exception of test anxiety. In 5 qualitative studies, it was found that students experience a rich diversity of emotions in academic settings. Anxiety was reported most often, but overall, positive emotions were described no less frequently than negative emotions. Based on the studies in this article, taxonomies of different academic emotions and a self-report instrument measuring students’ enjoyment, hope, pride, relief, anger, anxiety, shame, hopelessness, and boredom (Academic Emotions Questionnaire [AEQ]) were developed. Using the AEQ, assumptions of a cognitive-motivational model of the achievement effects of emotions, and of a control/value theory of their antecedents (Pekrun, 1992b, 2000), were tested in 7 cross-sectional, 3 longitudinal, and 1 diary study using samples of university and school students. Results showed that academic emotions are significantly related to students’ motivation, learning strategies, cognitive resources, self-regulation, and academic achievement, as well as to personality and classroom antecedents. The findings indicate that affective research in educational psychology should acknowledge emotional diversity in academic settings by addressing the full range of emotions experienced by students at school and university.
Secure Firmware Updates over the Air in Intelligent Vehicles

Modern intelligent vehicles have electronic control units containing firmware that enables various functions in the vehicle. New firmware versions are constantly developed to remove bugs and improve functionality. Automobile manufacturers have traditionally performed firmware updates over cables but in the near future they are aiming at conducting firmware updates over the air, which would allow faster updates and improved safety for the driver. In this paper, we present a protocol for secure firmware updates over the air. The protocol provides data integrity, data authentication, data confidentiality, and freshness. In our protocol, a hash chain is created of the firmware, and the first packet is signed by a trusted source, thus authenticating the whole chain. Moreover, the packets are encrypted using symmetric keys. We discuss the practical considerations that exist for implementing our protocol and show that the protocol is computationally efficient, has low memory overhead, and is suitable for wireless communication. Therefore, it is well suited to the limited hardware resources in the wireless vehicle environment.
Data and Information Visualization Methods, and Interactive Mechanisms: A Survey

With availability of enough visualization techniques it can be very confusing to know what and when should be appropriate technique to use in order to convey maximum possible understanding. The basic purpose of visual representation is to efficiently interpret what is insight, as easy as possible. Different available visualization techniques are use for different situation which convey different level of understanding. This document is guide for the young researchers who wants to start work in visualization. The purpose of this piece of document is to collect all visualization techniques with their brief introduction. This paper deals with many definitions and aspects of visualization, how visualization take place i.e. different steps of visualization process, problems that are confront in visualization, categorization of visualization techniques on the bases of distinct perspective, typically known common data and information visualization techniques, basic interactive methods for visualization their advantages and disadvantages, interactivity process, and the scope of visualization up to some extent in different field of research. General Terms Visualization, Visualization techniques, Challenges, Interactive techniques.
Pseudo-Multiple-Exposure-Based Tone Fusion With Local Region Adjustment

New generations of display technologies provide a significantly improved dynamic range compared to conventional display devices. Inverse tone mapping methods have been proposed to convert low dynamic range (LDR) images to HDR ones, and several of them require multiple exposure LDR images of the same scene as inputs. However, the vast majority of LDR images and videos available have only one single exposure. In this paper, we propose a region-based enhancement of the pseudo-exposures to generate an HDR image. First, we present an exposure dependent curve to convert one LDR image to the pseudo-multiple-exposures. Only certain regions of the pseudo-exposures contain noticeable detail information. We propose a region-based enhancement on the pseudo-exposures to boost details in the most distinct region. Thereby the region-enhanced pseudo-exposures are fused into an HDR image. The fused image thus enhances details in the bright region of the dark image and the dark region of the bright image. Compared with other inverse tone mapped methods, our method generates lower total contrast error measured under the dynamic range independent image quality assessment method in .
Harnessing linked knowledge sources for topic classification in social media

Topic classification (TC) of short text messages offers an effective and fast way to reveal events happening around the world ranging from those related to Disaster (e.g. Sandy hurricane) to those related to Violence (e.g. Egypt revolution). Previous approaches to TC have mostly focused on exploiting individual knowledge sources (KS) (e.g. DBpedia or Freebase) without considering the graph structures that surround concepts present in KSs when detecting the topics of Tweets. In this paper we introduce a novel approach for harnessing such graph structures from multiple linked KSs, by: (i) building a conceptual representation of the KSs, (ii) leveraging contextual information about concepts by exploiting semantic concept graphs, and (iii) providing a principled way for the combination of KSs. Experiments evaluating our TC classifier in the context of Violence detection (VD) and Emergency Responses (ER) show promising results that significantly outperform various baseline models including an approach using a single KS without linked data and an approach using only Tweets.
A multiclass classification method based on deep learning for named entity recognition in electronic medical records

Research of named entity recognition (NER) on electrical medical records (EMRs) focuses on verifying whether methods to NER in traditional texts are effective for that in EMRs, and there is no model proposed for enhancing performance of NER via deep learning from the perspective of multiclass classification. In this paper, we annotate a real EMR corpus to accomplish the model training and evaluation. And, then, we present a Convolutional Neural Network (CNN) based multiclass classification method for mining named entities from EMRs. The method consists of two phases. In the phase 1, EMRs are pre-processed for representing samples with word embedding. In the phase 2, the method is built by segmenting training data into many subsets and training a CNN binary classification model on each of subset. Experimental results showed the effectiveness of our method.
Tohme: detecting curb ramps in google street view using crowdsourcing, computer vision, and machine learning

Building on recent prior work that combines Google Street View (GSV) and crowdsourcing to remotely collect information on physical world accessibility, we present the first 'smart' system, Tohme, that combines machine learning, computer vision (CV), and custom crowd interfaces to find curb ramps remotely in GSV scenes. Tohme consists of two workflows, a human labeling pipeline and a CV pipeline with human verification, which are scheduled dynamically based on predicted performance. Using 1,086 GSV scenes (street intersections) from four North American cities and data from 403 crowd workers, we show that Tohme performs similarly in detecting curb ramps compared to a manual labeling approach alone (F- measure: 84% vs. 86% baseline) but at a 13% reduction in time cost. Our work contributes the first CV-based curb ramp detection system, a custom machine-learning based workflow controller, a validation of GSV as a viable curb ramp data source, and a detailed examination of why curb ramp detection is a hard problem along with steps forward.
Recent Advance in Content-based Image Retrieval: A Literature Survey

The explosive increase and ubiquitous accessibility of visual data on the Web have led to the prosperity of research activity in image search or retrieval. With the ignorance of visual content as a ranking clue, methods with text search techniques for visual retrieval may suffer inconsistency between the text words and visual content. Content-based image retrieval (CBIR), which makes use of the representation of visual content to identify relevant images, has attracted sustained attention in recent two decades. Such a problem is challenging due to the intention gap and the semantic gap problems. Numerous techniques have been developed for content-based image retrieval in the last decade. The purpose of this paper is to categorize and evaluate those algorithms proposed during the period of 2003 to 2016. We conclude with several promising directions for future research.
A consolidated approach for estimation of data security breach costs

Many security breaches have been reported in the past few years impacting both large and small organizations. There has often been considerable disagreement about the overall cost of such breaches. No significant formal studies have yet addressed this issue, though some proprietary approaches exist. A few computational models for evaluating partial data breach costs have been implemented, but these approaches have not been formally compared and have not been systematically optimized. There is a need to develop a more complete and formal model that will minimize redundancy among the factors considered and will confirm with the available data regarding the costs of data breaches. Existing approaches also need to be validated using the data for some of the well documented breaches. It is noted that the existing models yield widely different estimates. The reasons for this variation are examined, and the need for better models is identified.
Wireless sensors networks for Internet of Things

The Internet is smoothly migrating from an Internet of people towards an Internet of Things (IoT). By 2020, it is expected to have 50 billion things connected to the Internet. However, such a migration induces a strong level of complexity when handling interoperability between the heterogeneous Internet things, e.g., RFIDs (Radio Frequency Identification), mobile handheld devices, and wireless sensors. In this context, a couple of standards have been already set, e.g., IPv6, 6LoWPAN (IPv6 over Low power Wireless Personal Area Networks), and M2M (Machine to Machine communications). In this paper, we focus on the integration of wireless sensor networks into IoT, and shed further light on the subtleties of such integration. We present a real-world test bed deployment where wireless sensors are used to control electrical appliances in a smart building. Encountered problems are highlighted and suitable solutions are presented.
Exploration by Random Network Distillation

We introduce an exploration bonus for deep reinforcement learning methods that is easy to implement and adds minimal overhead to the computation performed. The bonus is the error of a neural network predicting features of the observations given by a fixed randomly initialized neural network. We also introduce a method to flexibly combine intrinsic and extrinsic rewards. We find that the random network distillation (RND) bonus combined with this increased flexibility enables significant progress on several hard exploration Atari games. In particular we establish state of the art performance on Montezuma’s Revenge, a game famously difficult for deep reinforcement learning methods. To the best of our knowledge, this is the first method that achieves better than average human performance on this game without using demonstrations or having access to the underlying state of the game, and occasionally completes the first level.
Design of Koch Fractal Circularly Polarized Antenna for Handheld UHF RFID Reader Applications

In this study, a novel design of circularly polarized antenna based on Koch fractal geometry is presented and experimentally investigated. Circular polarization and size reduction are achieved by placing two asymmetric Koch fractal geometries on x-and y-planes of the single-probe feed square radiator. Further, to tune resonant frequency around the 911MHz, four arrow-shaped slots are inserted in diagonal axes of the square radiator. It is perceived that compact size of the antenna can be achieved by increasing the overall size of the arrow-shaped slots and indentation angles of Koch fractal geometry. The antenna is fabricated on FR4 substrate with a size of 54 × 54 × 1.6 mm3 (0.162λ0 × 0.162λ0 × 0.0048λ0 at 900 MAbstract-In this study, a novel design of circularly polarized antenna based on Koch fractal geometry is presented and experimentally investigated. Circular polarization and size reduction are achieved by placing two asymmetric Koch fractal geometries on xand y-planes of the single-probefeed square radiator. Further, to tune resonant frequency around the 911MHz, four arrow-shaped slots are inserted in diagonal axes of the square radiator. It is perceived that compact size of the antenna can be achieved by increasing the overall size of the arrow-shaped slots and indentation angles of Koch fractal geometry. The antenna is fabricated on FR4 substrate with a size of 54 × 54 × 1.6 mm3 (0.162λ0 × 0.162λ0 × 0.0048λ0 at 900 MHz) and tested to validate the proposed design. The 3-dB axial-ratio bandwidth and impedance bandwidth of the proposed antenna design are found to be 8 MHz (907.0-915 MHz) and 37.0 MHz (891.0-928.0 MHz), respectively. The measured results confirm that the proposed design covers US radio-frequency identification (RFID) band (902-928 MHz) and can be used for RFID short-range reading applications.Hz) and tested to validate the proposed design. The 3-dB axial-ratio bandwidth and impedance bandwidth of the proposed antenna design are found to be 8 MHz (907.0-915 MHz) and 37.0 MHz (891.0-928.0 MHz), respectively. The measured results confirm that the proposed design covers US radio-frequency identification (RFID) band (902-928 MHz) and can be used for RFID short-range reading applications.