nils-herrmann/clean_scirepeval huggingface dataset:
Mental Modeler: A Fuzzy-Logic Cognitive Mapping Modeling Tool for Adaptive Environmental Management

Participatory modeling has grown in popularity in recent years with the acknowledgement that stakeholder knowledge is an essential component to informed environmental decision-making. Including stakeholders in model building and analysis allows decision-makers to understand important conceptual components in the environmental systems being managed, builds trust and common understanding between potentially diverse sets of competing groups, and reduces uncertainty by mining information that might not otherwise be a part of scientific assessment performed by experts alone. Software that facilitates the integration and analysis of stakeholder knowledge in modeling, however, is currently lacking. In this paper we report on the design and anticipated use of a participatory modeling tool based in fuzzy-logic cognitive mapping (FCM) called 'Mental Modeler' which makes the mental models of stakeholders explicit and provides an opportunity to incorporate different types of knowledge into environmental decision-making, define hypotheses to be tested, and run scenarios to determine perceived outcomes of proposed policies.
Development of open humanoid platform DARwIn-OP

This paper presents the design method for a humanoid which has a network based modular structure and a standard PC architecture. Based on the proposed method, we developed DARwIn-OP which meets the requirements for an open humanoid platform. DARwIn-OP has an expandable system structure, high performance, simple maintenance, familiar development environment, and affordable prices. All resources of DARwIn-OP including source codes, circuit diagrams, mechanical CAD files, and parts information will be opened to the public.
A mm-sized implantable device with ultrasonic energy transfer and RF data uplink for high-power applications

A first proof-of-concept mm-sized implant based on ultrasonic power transfer and RF uplink data transmission is presented. The prototype consists of a 1 mm × 1 mm piezoelectric receiver, a 1 mm × 2 mm chip designed in 65 nm CMOS and a 2.5 mm × 2.5 mm off-chip antenna, and operates through 3 cm of chicken meat which emulates human tissue. The implant supports a DC load power of 100 μW allowing for high-power applications. It also transmits consecutive UWB pulse sequences activated by the ultrasonic downlink data path, demonstrating sufficient power for an Mary PPM transmitter in uplink.
Combined Automatic Lane-Keeping and Driver's Steering Through a 2-DOF Control Strategy

In this paper, we address the problem of combining automatic lane-keeping and driver's steering for either obstacle avoidance or lane-change maneuvers for passing purposes or any other desired maneuvers, through a closed-loop control strategy. The automatic lane-keeping control loop is never opened, and no on/off switching strategy is used. During the driver's maneuver, the vehicle lateral dynamics are controlled by the driver himself through the vehicle steering system. When there is no driver's steering action, the vehicle center of gravity tracks the center of the traveling lane thanks to the automatic lane-keeping system. At the beginning (end) of the maneuver, the lane-keeping task is released (resumed) safely and smoothly. The performance of the proposed closed-loop structure is shown both by means of simulations and through experimental results obtained along Italian highways.
Circuit Implementation of a 300-MHz 64-bit Second-generation CMOS Alpha CPU

A 300-MHz, custom 64-bit VLSI, second-generation Alpha CPU chip has been developed. The chip was designed in a 0.5-um CMOS technology using four levels of metal. The die size is 16.5 mm by 18.1 mm, contains 9.3 million transistors, operates at 3.3 V, and supports 3.3-V/5.0-V interfaces. Power dissipation is 50 W. It contains an 8-KB instruction cache; an 8-KB data cache; and a 96-KB unified second-level cache. The chip can issue four instructions per cycle and delivers 1,200 mips/600 MFLOPS (peak). Several noteworthy circuit and implementation techniques were used to attain the target operating frequency.
Word Embeddings Go to Italy: A Comparison of Models and Training Datasets

In this paper we present some preliminary results on the generation of word embeddings for the Italian language. We compare two popular word representation models, word2vec and GloVe, and train them on two datasets with different stylistic properties. We test the generated word embeddings on a word analogy test derived from the one originally proposed for word2vec, adapted to capture some of the linguistic aspects that are specific of Italian. Results show that the tested models are able to create syntactically and semantically meaningful word embeddings despite the higher morphological complexity of Italian with respect to English. Moreover, we have found that the stylistic properties of the training dataset plays a relevant role in the type of information captured by the produced vectors.
Design of permanent magnet assisted synchronous reluctance motor with external rotor architecture

This paper proposes an external rotor Permanent Magnet assisted Synchronous Reluctance Motor (PMa-SynRM). Due to the high material cost of PM machine, there was a continuous effort to reduce the amount of permanent magnets (PM) in manufacturing an electric machine. PMa-SynRM has reduced great amount of PM usage by hybridizing the architectures between Synchronous Reluctance Motor and Permanent Magnet Motor. To further reduce the PM usage and to further increase of torque density, design of PMa-SynRM has been investigated through external rotor architecture. With best of author's knowledge, external rotor has not been properly researched even with great advantage and design flexibility with PMa-SynRM architecture. Analytical comparison between two different types of motor (internal and external) has been investigated to prove the superior design flexibility and performance of proposed method. Performance characteristics such as average torque developed and variation of torque with respect to variation in speed have been analyzed in finite element atmosphere (FEA) to validate the proposed design. A lower torque ripple model with distributed winding structure has been modeled and its FEA simulation results for developed torque have been compared with the concentrated type external rotor PMa-SynRM model.
Complex network measures of brain connectivity: Uses and interpretations

Brain connectivity datasets comprise networks of brain regions connected by anatomical tracts or by functional associations. Complex network analysis-a new multidisciplinary approach to the study of complex systems-aims to characterize these brain networks with a small number of neurobiologically meaningful and easily computable measures. In this article, we discuss construction of brain networks from connectivity data and describe the most commonly used network measures of structural and functional connectivity. We describe measures that variously detect functional integration and segregation, quantify centrality of individual brain regions or pathways, characterize patterns of local anatomical circuitry, and test resilience of networks to insult. We discuss the issues surrounding comparison of structural and functional network connectivity, as well as comparison of networks across subjects. Finally, we describe a Matlab toolbox (http://www.brain-connectivity-toolbox.net) accompanying this article and containing a collection of complex network measures and large-scale neuroanatomical connectivity datasets.
Residual Attention Network for Image Classification

In this work, we propose Residual Attention Network, a convolutional neural network using attention mechanism which can incorporate with state-of-art feed forward network architecture in an end-to-end training fashion. Our Residual Attention Network is built by stacking Attention Modules which generate attention-aware features. The attention-aware features from different modules change adaptively as layers going deeper. Inside each Attention Module, bottom-up top-down feedforward structure is used to unfold the feedforward and feedback attention process into a single feedforward process. Importantly, we propose attention residual learning to train very deep Residual Attention Networks which can be easily scaled up to hundreds of layers. Extensive analyses are conducted on CIFAR-10 and CIFAR-100 datasets to verify the effectiveness of every module mentioned above. Our Residual Attention Network achieves state-of-the-art object recognition performance on three benchmark datasets including CIFAR-10 (3.90% error), CIFAR-100 (20.45% error) and ImageNet (4.8% single model and single crop, top-5 error). Note that, our method achieves 0.6% top-1 accuracy improvement with 46% trunk depth and 69% forward FLOPs comparing to ResNet-200. The experiment also demonstrates that our network is robust against noisy labels.
Learning to Paraphrase for Question Answering

Question answering (QA) systems are sensitive to the many different ways natural language expresses the same information need. In this paper we turn to paraphrases as a means of capturing this knowledge and present a general framework which learns felicitous paraphrases for various QA tasks. Our method is trained end-toend using question-answer pairs as a supervision signal. A question and its paraphrases serve as input to a neural scoring model which assigns higher weights to linguistic expressions most likely to yield correct answers. We evaluate our approach on QA over Freebase and answer sentence selection. Experimental results on three datasets show that our framework consistently improves performance, achieving competitive results despite the use of sim-
Online Handwriting Data Acquisition using a Video Camera

A novel method for online data acquisition of cursive handwriting is described. A video camera is used to record the handwriting of a user. From the acquired sequence of images, the movement of the tip of the pen is reconstructed. A prototype of the system has been implemented and tested. In one series of tests, the performance of the system was visually assessed. In another series of experiments, the system was combined with an existing online handwriting recognizer. Good results have been obtained in both sets of experiments.
Vehicle detection based on LiDAR and camera fusion

Vehicle detection is important for advanced driver assistance systems (ADAS). Both LiDAR and cameras are often used. LiDAR provides excellent range information but with limits to object identification; on the other hand, the camera allows for better recognition but with limits to the high resolution range information. This paper presents a sensor fusion based vehicle detection approach by fusing information from both LiDAR and cameras. The proposed approach is based on two components: a hypothesis generation phase to generate positions that potential represent vehicles and a hypothesis verification phase to classify the corresponding objects. Hypothesis generation is achieved using the stereo camera while verification is achieved using the LiDAR. The main contribution is that the complementary advantages of two sensors are utilized, with the goal of vehicle detection. The proposed approach leads to an enhanced detection performance; in addition, maintains tolerable false alarm rates compared to vision based classifiers. Experimental results suggest a performance which is broadly comparable to the current state of the art, albeit with reduced false alarm rate.
An Iterative and Analytical Approach to Optimal Synthesis of a Multiplexer With a Star-Junction

This paper presents an iterative and analytical approach to optimal synthesis of a multiplexer with a star-junction. Two types of commonly used lumped-element junction models, namely, nonresonant node (NRN) type and resonant type, are considered and treated in a uniform way. A new circuit equivalence called phased-inverter to frequency-invariant reactance inverter transformation is introduced. It allows direct adoption of the optimal synthesis theory of a bandpass filter for synthesizing channel filters connected to a star-junction by converting the synthesized phase shift to the susceptance compensation at the junction. Since each channel filter is dealt with individually and alternately, when synthesizing a multiplexer with a high number of channels, good accuracy can still be maintained. Therefore, the approach can be used to synthesize a wide range of multiplexers. Illustrative examples of synthesizing a diplexer with a common resonant type of junction and a triplexer with an NRN type of junction are given to demonstrate the effectiveness of the proposed approach. A prototype of a coaxial resonator diplexer according to the synthesized circuit model is fabricated to validate the synthesized result. Excellent agreement is obtained.
NIST Cloud Computing Reference Architecture

This paper presents the first version of the NIST Cloud Computing Reference Architecture (RA). This is a vendor neutral conceptual model that concentrates on the role and interactions of the identified actors in the cloud computing sphere. Five primary actors were identified Cloud Service Consumer, Cloud Service Provider, Cloud Broker, Cloud Auditor and Cloud Carrier. Their roles and activities are discussed in this report. A primary goal for generating this model was to give the United States Government (USG) a method for understanding and communicating the components of a cloud computing system for Federal IT executives, Program Managers and IT procurement officials. Keywords-component; cloud computing, reference architecture, Federal Government
A Data Mining Approach to Consumer Behavior

Various studies on consumer purchasing behaviors have been presented and used in real problems. Data mining techniques are expected to be a more effective tool for analyzing consumer behaviors. However, the data mining method has disadvantages as well as advantages. Therefore, it is important to select appropriate techniques to mine databases. The objective of this paper is to improve conventional data mining analysis by applying several methods including fuzzy clustering, principal component analysis, and discriminate analysis. Many defects included in the conventional methods are improved in the paper. Moreover, in an experiment, association rule is employed to mine rules for trusted customers using sales data in a fiber industry
Face Recognition via Local Directional Pattern

In this paper, we propose an illumination-robust face recognition system via local directional pattern images. Usually, local pattern descriptors including local binary pattern and local directional pattern have been used in the field of the face recognition and facial expression recognition, since local pattern descriptors have important properties to be robust against the illumination changes and computational simplicity. Thus, this paper represents the face recognition approach that employs the local directional pattern descriptor and twodimensional principal analysis algorithms to achieve enhanced recognition accuracy. In particular, we propose a novel methodology that utilizes the transformed image obtained from local directional pattern descriptor as the direct input image of two-dimensional principal analysis algorithms, unlike that most of previous works employed the local pattern descriptors to acquire the histogram features. The performance evaluation of proposed system was performed using well-known approaches such as principal component analysis and Gabor-wavelets based on local binary pattern, and publicly available databases including the Yale B database and the CMU-PIE database were employed. Through experimental results, the proposed system showed the best recognition accuracy compared to different approaches, and we confirmed the effectiveness of the proposed method under varying lighting conditions.
Shadow Remover: Image Shadow Removal Based on Illumination Recovering Optimization

In this paper, we present a novel shadow removal system for single natural images as well as color aerial images using an illumination recovering optimization method. We first adaptively decompose the input image into overlapped patches according to the shadow distribution. Then, by building the correspondence between the shadow patch and the lit patch based on texture similarity, we construct an optimized illumination recovering operator, which effectively removes the shadows and recovers the texture detail under the shadow patches. Based on coherent optimization processing among the neighboring patches, we finally produce high-quality shadow-free results with consistent illumination. Our shadow removal system is simple and effective, and can process shadow images with rich texture types and nonuniform shadows. The illumination of shadow-free results is consistent with that of surrounding environment. We further present several shadow editing applications to illustrate the versatility of the proposed method.
Learning Complexity-Aware Cascades for Deep Pedestrian Detection

The design of complexity-aware cascaded detectors, combining features of very different complexities, is considered. A new cascade design procedure is introduced, by formulating cascade learning as the Lagrangian optimization of a risk that accounts for both accuracy and complexity. A boosting algorithm, denoted as complexity aware cascade training (CompACT), is then derived to solve this optimization. CompACT cascades are shown to seek an optimal trade-off between accuracy and complexity by pushing features of higher complexity to the later cascade stages, where only a few difficult candidate patches remain to be classified. This enables the use of features of vastly different complexities in a single detector. In result, the feature pool can be expanded to features previously impractical for cascade design, such as the responses of a deep convolutional neural network (CNN). This is demonstrated through the design of a pedestrian detector with a pool of features whose complexities span orders of magnitude. The resulting cascade generalizes the combination of a CNN with an object proposal mechanism: rather than a pre-processing stage, CompACT cascades seamlessly integrate CNNs in their stages. This enables state of the art performance on the Caltech and KITTI datasets, at fairly fast speeds.
Dynamic Access Control Policy based on Blockchain and Machine Learning for the Internet of Things

The Internet of Things (IoT) is now destroying the barriers between the real and digital worlds. However, one of the huge problems that can slow down the development of this global wave, or even stop it, concerns security and privacy requirements. The criticality of these latter comes especially from the fact that the smart objects may contain very intimate information or even may be responsible for protecting people’s lives. In this paper, the focus is on access control in the IoT context by proposing a dynamic and fully distributed security policy. Our proposal will be based, on one hand, on the concept of the blockchain to ensure the distributed aspect strongly recommended in the IoT; and on the other hand on machine learning algorithms, particularly on reinforcement learning category, in order to provide a dynamic, optimized and selfadjusted security policy. Internet of Things; security; access control; dynamic policy; security policy; blockchain; machine learning; reinforcement learning
A Multidimensional Critical State Analysis for Detecting Intrusions in SCADA Systems

A relatively new trend in Critical Infrastructures (e.g., power plants, nuclear plants, energy grids, etc.) is the massive migration from the classic model of isolated systems, to a system-of-systems model, where these infrastructures are intensifying their interconnections through Information and Communications Technology (ICT) means. The ICT core of these industrial installations is known as Supervisory Control And Data Acquisition Systems (SCADA). Traditional ICT security countermeasures (e.g., classic firewalls, anti-viruses and IDSs) fail in providing a complete protection to these systems since their needs are different from those of traditional ICT. This paper presents an innovative approach to Intrusion Detection in SCADA systems based on the concept of Critical State Analysis and State Proximity. The theoretical framework is supported by tests conducted with an Intrusion Detection System prototype implementing the proposed detection approach.
Dynamic Manga: Animating Still Manga via Camera Movement

We propose a method for animating still manga imagery through camera movements. Given a series of existing manga pages, we start by automatically extracting panels, comic characters, and balloons from the manga pages. Then, we use a data-driven graphical model to infer per-panel motion and emotion states from low-level visual patterns. Finally, by combining domain knowledge of film production and characteristics of manga, we simulate camera movements over the manga pages, yielding an animation. The results augment the still manga contents with animated motion that reveals the mood and tension of the story, while maintaining the original narrative. We have tested our method on manga series of different genres, and demonstrated that our method can generate animations that are more effective in storytelling and pacing, with less human efforts, as compared with prior works. We also show two applications of our method, mobile comic reading, and comic trailer generation.
Global Software Development Challenges: A Case Study on Temporal, Geographical and Socio-Cultural Distance

Global software development (GSD) is a phenomenon that is receiving considerable interest from companies all over the world. In GSD, stakeholders from different national and organizational cultures are involved in developing software and the many benefits include access to a large labour pool, cost advantage and round-the-clock development. However, GSD is technologically and organizationally complex and presents a variety of challenges to be managed by the software development team. In particular, temporal, geographical and socio-cultural distances impose problems not experienced in traditional systems development. In this paper, we present findings from a case study in which we explore the particular challenges associated with managing GSD. Our study also reveals some of the solutions that are used to deal with these challenges. We do so by empirical investigation at three US based GSD companies operating in Ireland. Based on qualitative interviews we present challenges related to temporal, geographical and socio-cultural distance
STT-RAM Energy Reduction Using Self-Referenced Differential Write Termination Technique

Spin-transfer torque random access memory (STT-RAM) has emerged as an attractive candidate for future nonvolatile memories. It advantages the benefits of current state-of-the-art memories including high-speed read operation (of static RAM), high density (of dynamic RAM), and nonvolatility (of flash memories). However, the write operation in the 1T-1MTJ STT-RAM bitcell is asymmetric and stochastic, which leads to high energy consumption and long latency. In this paper, a new write assist technique is proposed to terminate the write operation immediately after switching takes place in the magnetic tunneling junction (MTJ). As a result, both the write time and write energy consumption of 1T-1MTJ bitcells improves. Moreover, the proposed write assist technique leads to an error-free write operation. The simulation results using a 65-nm CMOS access transistor and a 40-nm MTJ technology confirm that the proposed write assist technique results in three orders of magnitude improvement in bit error rate compared with the best existing techniques. Moreover, the proposed write assist technique leads to 81% energy saving compared with a cell without write assist and adds only 9.6% area overhead to a 16-kbit STT-RAM array.