nils-herrmann/clean_scirepeval huggingface dataset:
Path Planning Based on Bézier Curve for Autonomous Ground Vehicles

In this paper we present two path planning algorithms based on Bezier curves for autonomous vehicles with way points and corridor constraints. Bezier curves have useful properties for the path generation problem. The paper describes how the algorithms apply these properties to generate the reference trajectory for vehicles to satisfy the path constraints. Both algorithms join cubic Bezier curve segments smoothly to generate the path. Additionally, we discuss the constrained optimization problem that optimizes the resulting path for a user-defined cost function. The simulation shows the generation of successful routes for autonomous vehicles using these algorithms as well as control results for a simple kinematic vehicle. Extensions of these algorithms towards navigating through an unstructured environment with limited sensor range are discussed.
Evolutionary mining of relaxed dependencies from big data collections

Many modern application contexts, especially those related to the semantic Web, advocate for automatic techniques capable of extracting relationships between semi-structured data, for several purposes, such as the identification of inconsistencies or patterns of semantically related data, query rewriting, and so forth. One way to represent such relationships is to use relaxed functional dependencies (rfds), since they can embed approximate matching paradigms to compare unstructured data, and admit the possibility of exceptions for them. To this end, thresholds might need to be specified in order to limit the similarity degree in approximate comparisons or the occurrence of exceptions. Thanks to the availability of huge amount of data, including unstructured data available on the Web, nowadays it is possible to automatically discover rfds from data. However, due to the many different combinations of similarity and exception thresholds, the discovery process has an exponential complexity. Thus, it is vital devising proper optimization strategies, in order to make the discovery process feasible. To this end, in this paper, we propose a genetic algorithm to discover rfds from data, also providing an empirical evaluation demonstrating its effectiveness.
Topology Aware Fully Convolutional Networks for Histology Gland Segmentation

Sirinukunwattana et al. Gland Segmentation in colon histology images: the GlaS contest, arXiv 2016  Long et al. Fully convolutional networks for semantic segmentation, CVPR 2015  Chend et al. Semantic image segmentation with deep convolutional nets and fully connected CRFs,  2014  Zheng et al. Conditional random fields as recurrent neural networks, ICCV 2015 Alexnet-FCN (6 layers) FCN-8s (16 layers) UNet (23 layers) DN (26 layers) 0.93 0.79 0.86 0.83
A D-Q Frame Controller for a Full-Bridge Single Phase Inverter Used in Small Distributed Power Generation Systems

This paper presents a Direct-Quadrature (DQ) rotating frame control method for single phase full-bridge inverters used in small hybrid power systems. A secondary orthogonal imaginary circuit is created to provide the second phase required for the transformation; thus a DQ model of the inverter is obtained and its controller designed emulating the controls of three-phase power converters. The proposed controller attains infinite loop gain in the rotating coordinate, thus providing zero steady-state error at the fundamental frequency of the converter. The proposed controller is designed and validated through simulations using a DQ-frame average model in Matlab and a detailed switching model in Saber, as well as experimental results obtained with a 2.5 kW single phase full-bridge inverter prototype using a DSP/FPGA based digital control system where the proposed DQ-frame controller is fully implemented.
Implemented family presence protocols: an integrative review.

Family presence during resuscitation or invasive procedures is controversial, but research suggests multiple benefits. Professional organizations recommend developing family presence protocols; however, few hospitals have them. This integrative review examined research evidence regarding implemented family presence protocols for adults in the hospital setting-specifically, the use of protocols and providers' feedback. Four key findings include a positive trend in family presence practice, varying provider attitudes, problems with family facilitators, and factors inhibiting or facilitating protocol implementation.
Community-Based Conservation and Traditional Ecological Knowledge : Implications for Social-Ecological Resilience

Our review highlights how traditional ecological knowledge influences people's adaptive capacity to socialecological change and identifies a set of mechanisms that contribute to such capacity in the context of community-based biodiversity conservation initiatives. Twenty-three publications, including twenty-nine case studies, were reviewed with the aim of investigating how local knowledge, community-based conservation, and resilience interrelate in social-ecological systems. We highlight that such relationships have not been systematically addressed in regions where a great number of community conservation initiatives are found; and we identify a set of factors that foster people's adaptive capacity to social-ecological change and a number of social processes that, in contrast, undermine such capacity and the overall resilience of the socialecological system. We suggest that there is a need to further investigate how climate variability and other events affect the joint evolution of conservation outcomes and traditional ecological knowledge, and there is a need to expand the current focus on social factors to explain changes in traditional ecological knowledge and adaptive capacity towards a broader approach that pays attention to ecosystem dynamics and environmental change.
Positive emotions broaden the scope of attention and thought-action repertoires.

The broaden-and-build theory (Fredrickson, 1998, 2001) hypothesises that positive emotions broaden the scope of attention and thought-action repertoires. Two experiments with 104 college students tested these hypotheses. In each, participants viewed a film that elicited (a) amusement, (b) contentment, (c) neutrality, (d) anger, or (e) anxiety. Scope of attention was assessed using a global-local visual processing task (Experiment 1) and thought-action repertoires were assessed using a Twenty Statements Test (Experiment 2). Compared to a neutral state, positive emotions broadened the scope of attention in Experiment 1 and thought-action repertoires in Experiment 2. In Experiment 2, negative emotions, relative to a neutral state, narrowed thought-action repertoires. Implications for promoting emotional well-being and physical health are discussed.
Noisy and Missing Data Regression: Distribution-Oblivious Support Recovery

Many models for sparse regression typically assume that the covariates are known completely, and without noise. Particularly in high-dimensional applications, this is often not the case. Worse yet, even estimating statistics of the noise (the noise covariance) can be a central challenge. In this paper we develop a simple variant of orthogonal matching pursuit (OMP) for precisely this setting. We show that without knowledge of the noise covariance, our algorithm recovers the support, and we provide matching lower bounds that show that our algorithm performs at the minimax optimal rate. While simple, this is the first algorithm that (provably) recovers support in a noise-distribution-oblivious manner. When knowledge of the noise-covariance is available, our algorithm matches the best-known `-recovery bounds available. We show that these too are min-max optimal. Along the way, we also obtain improved performance guarantees for OMP for the standard sparse regression problem with Gaussian noise.
Consciousness: a unique way of processing information

In this article, I argue that consciousness is a unique way of processing information, in that: it produces information, rather than purely transmitting it; the information it produces is meaningful for us; the meaning it has is always individuated. This uniqueness allows us to process information on the basis of our personal needs and ever-changing interactions with the environment, and consequently to act autonomously. Three main basic cognitive processes contribute to realize this unique way of information processing: the self, attention and working memory. The self, which is primarily expressed via the central and peripheral nervous systems, maps our body, the environment, and our relations with the environment. It is the primary means by which the complexity inherent to our composite structure is reduced into the “single voice” of a unique individual. It provides a reference system that (albeit evolving) is sufficiently stable to define the variations that will be used as the raw material for the construction of conscious information. Attention allows for the selection of those variations in the state of the self that are most relevant in the given situation. Attention originates and is deployed from a single locus inside our body, which represents the center of the self, around which all our conscious experiences are organized. Whatever is focused by attention appears in our consciousness as possessing a spatial quality defined by this center and the direction toward which attention is focused. In addition, attention determines two other features of conscious experience: periodicity and phenomenal quality. Self and attention are necessary but not sufficient for conscious information to be produced. Complex forms of conscious experiences, such as the various modes of givenness of conscious experience and the stream of consciousness, need a working memory mechanism to assemble the basic pieces of information selected by attention.
Automatic Rule Induction for Unknown-Word Guessing

Words unknown to the lexicon present a substantial problem to NLP modules that rely on morphosyntactic information, such as part-of-speech taggers or syntactic parsers. In this paper we present a technique for fully automatic acquisition of rules that guess possible part-of-speech tags for unknown words using their starting and ending segments. The learning is performed from a general-purpose lexicon and word frequencies collected from a raw corpus. Three complimentary sets of word-guessing rules are statistically induced: prefix morphological rules, suffix morphological rules and ending-guessing rules. Using the proposed technique, unknown-word-guessing rule sets were induced and integrated into a stochastic tagger and a rule-based tagger, which were then applied to texts with unknown words.
Exploiting Hardware Vulnerabilities to Attack Embedded System Devices : a Survey of Potent Microarchitectural Attacks

Cyber-Physical system devices nowadays constitute a mixture of Information Technology (IT) and Operational Technology (OT) systems that are meant to operate harmonically under a security critical framework. As security IT countermeasures are gradually been installed in many embedded system nodes, thus securing them from many well-know cyber attacks there is a lurking danger that is still overlooked. Apart from the software vulnerabilities that typical malicious programs use, there are some very interesting hardware vulnerabilities that can be exploited in order to mount devastating software or hardware attacks (typically undetected by software countermeasures) capable of fully compromising any embedded system device. Real-time microarchitecture attacks such as the cache side-channel attacks are such case but also the newly discovered Rowhammer fault injection attack that can be mounted even remotely to gain full access to a device DRAM (Dynamic Random Access Memory). Under the light of the above dangers that are focused on the device hardware structure, in this paper, an overview of this attack field is provided including attacks, threat directives and countermeasures. The goal of this paper is not to exhaustively overview attacks and countermeasures but rather to survey the various, possible, existing attack directions and highlight the security risks that they can pose to security critical embedded systems as well as indicate their strength on compromising the Quality of Service (QoS) such systems are designed to provide.
Prediction of patient-reported physical activity scores from wearable accelerometer data: a feasibility study

Many diseases are characterized by limitations in mobility, including a wide range of musculoskeletal and neurological conditions. Reduced mobility impacts a patients ability to perform activities of daily living, which in turn reduces health-related quality of life. Mobility can be assessed by collecting patient-reported outcome scores from standardized questionnaires and by directly measuring physical activity parameters from wearable accelerometer data. In this work, we explored the relationship between subjectively and objectively measured mobility by training machine learning models to predict patient responses based on features derived from realworld acceleration data. Our method achieved up to 82% accuracy using a random forest classifier and set the basis to develop novel data-driven digital biomarkers for objective, quantitative and more frequent evaluation of patients’ mobility.
QR code image detection using run-length coding

In order to improve the practical application property of the two-dimensional barcode Quick Response (QR) code, we investigate the coding and decoding process of the QR code image. Run-length coding is applied to binary QR code image so as to accelerate the identification of QR code image. The QR code is transformed into many runs of data in alternate pixels of black and white. The related runs of data among adjacent rows are formed a unit module. After the whole image has been scanned, all of such modules in binary QR code image can be generated accordingly. With a noisy QR image captured by an industrial camera as an example, the experiments of image binarization, image seeking and localization adjustment are accomplished in sequence. Also the error correction algorithm is discussed in detail. A decoding system of QR code is designed and the online detection experiments are carried out. The satisfied results are achieved.
A Four-Plate Compact Capacitive Coupler Design and LCL-Compensated Topology for Capacitive Power Transfer in Electric Vehicle Charging Application

This paper proposes a four-plate compact capacitive coupler and its circuit model for large air-gap distance capacitive power transfer (CPT). The four plates are arranged vertically, instead of horizontally, to save space in the electric vehicle charging application. The two plates that are on the same side are placed close to each other to maintain a large coupling capacitance, and they are of different sizes to maintain the coupling between the primary and secondary sides. The circuit model of the coupler is presented, considering all six coupling capacitors. The LCL compensation topology is used to resonate with the coupler and provide high voltage on the plates to transfer high power. The circuit model of the coupler is simplified to design the parameters of the compensation circuit. Finite-element analysis is employed to simulate the coupling capacitance and design the dimensions of the coupler. The circuit performance is simulated in LTspice to design the specific parameter values. A prototype of the CPT system was designed and constructed with the proposed vertical plate structure. The prototype achieved an efficiency of 85.87% at 1.88-kW output power with a 150-mm air-gap distance.
Analysis of Key-Exchange Protocols and Their Use for Building Secure Channels

We present a formalism for the analysis of key-exchange protocols that combines previous definitional approaches and results in a definition of security that enjoys some important analytical benefits: (i) any key-exchange protocol that satisfies the security definition can be composed with symmetric encryption and authentication functions to provide provably secure communication channels (as defined here); and (ii) the definition allows for simple modular proofs of security: one can design and prove security of key-exchange protocols in an idealized model where the communication links are perfectly authenticated, and then translate them using general tools to obtain security in the realistic setting of adversary-controlled links. We exemplify the usability of our results by applying them to obtain the proof of two classes of key-exchange protocols, Diffie-Hellman and key-transport, authenticated via symmetric or asymmetric techniques.
Virtual machine placement strategies in cloud computing

The prominent technology that drives the industry now-a-days is cloud computing. The growth of cloud computing has resulted in the setup of large number of data centers around the world. The data centers consume more power making it source for the carbon dioxide emission and a major contributor to greenhouse effect. This led to the deployment of virtualization. Infrastructure as a Service is one of the important services offered by cloud computing that allows virtualization and hardware to get virtualized by creating many instances of Virtual Machine (VM) on a single Physical Machine (PM) and helps in improving utilization of resources. VM consolidation includes method of choosing the more appropriate algorithm for migration of VM's and placement of VMs to the most suitable host. VM placement is a part of VM migration. The effective placement of VM is aimed to improve performance, resource utilization and reduce the energy consumption in data centers without SLA violation. This paper aims to focus on various VM placement schemes.
Device fingerprinting for augmenting web authentication: classification and analysis of methods

Device fingerprinting is commonly used for tracking users. We explore device fingerprinting but in the specific context of use for augmenting authentication, providing a state-of-the-art view and analysis. We summarize and classify 29 available methods and their properties; define attack models relevant to augmenting passwords for user authentication; and qualitatively compare them based on stability, repeatability, resource use, client passiveness, difficulty of spoofing, and distinguishability offered.
A Survey on Applications of Augmented Reality

The term Augmented Reality (AR) refers to a set of technologies and devices able to enhance and improve human perception, thus bridging the gap between real and virtual space. Physical and artificial objects are mixed together in a hybrid space where the user can move without constraints. This mediated reality is spread in our everyday life: work, study, training, relaxation, time spent traveling are just some of the moments in which you can use AR applications. This paper aims to provide an overview of current technologies and future trends of augmented reality as well as to describe the main application domains, outlining benefits and open issues.
RQUERY: Rewriting Natural Language Queries on Knowledge Graphs to Alleviate the Vocabulary Mismatch Problem

For non-expert users, a textual query is the most popular and simple means for communicating with a retrieval or question answering system. However, there is a risk of receiving queries which do not match with the background knowledge. Query expansion and query rewriting are solutions for this problem but they are in danger of potentially yielding a large number of irrelevant words, which in turn negatively influences runtime as well as accuracy. In this paper, we propose a new method for automatic rewriting input queries on graph-structured RDF knowledge bases. We employ a Hidden Markov Model to determine the most suitable derived words from linguistic resources. We introduce the concept of triplebased co-occurrence for recognizing co-occurred words in RDF data. This model was bootstrapped with three statistical distributions. Our experimental study demonstrates the superiority of the proposed approach to the traditional n-gram model.
Towards Zero Copy Dataflows using RDMA

Remote Direct Memory Access (RDMA) offers ultra-low latency and CPU bypass networking to application programmers. Existing applications are often designed around socket based software stack that manages application buffers separately from networking buffers and do memory copies between them when sending/receiving data. With large sized (up to hundreds MB) application buffers, the cost of such copies adds non trivial overhead to the end-to-end communication pipeline. In this work, we made an attempt to design a zero copy transport for distribute dataflow frameworks that unifies application and networking buffer management and completely eliminates unnecessary memory copies. Our prototype on top of TensorFlow shows 2.43x performance improvement over gRPC based transport and 1.21x performance improvement over an alternative RDMA transport with private buffers and memory copies.
Public Goods and Ethnic Divisions

We present a model that links heterogeneity of preferences across ethnic groups in a city to the amount and type of public good the city supplies. We test the implications of the model with three related data sets: U. S. cities, U. S. metropolitan areas, and U. S. urban counties. Results show that the shares of spending on productive public goods -education, roads, sewers and trash pickup -in U. S. cities (metro areas/urban counties) are inversely related to the city’s (metro area’s/county’s) ethnic fragmentation, even after controlling for other socioeconomic and demographic determinants. We conclude that ethnic conflict is an important determinant of local public finances.
Multiuser MIMO-OFDM for Next-Generation Wireless Systems

This overview portrays the evolution of orthogonal frequency division multiplexing (OFDM) research. The amelioration of powerful multicarrier OFDM arrangements with multiple-input multiple-output (MIMO) systems has numerous benefits, which are detailed in this treatise. We continue by highlighting the limitations of conventional detection and channel estimation techniques designed for multiuser MIMO OFDM systems in the so-called rank-deficient scenarios, where the number of users supported or the number of transmit antennas employed exceeds the number of receiver antennas. This is often encountered in practice, unless we limit the number of users granted access in the base station's or radio port's coverage area. Following a historical perspective on the associated design problems and their state-of-the-art solutions, the second half of this treatise details a range of classic multiuser detectors (MUDs) designed for MIMO-OFDM systems and characterizes their achievable performance. A further section aims for identifying novel cutting-edge genetic algorithm (GA)-aided detector solutions, which have found numerous applications in wireless communications in recent years. In an effort to stimulate the cross pollination of ideas across the machine learning, optimization, signal processing, and wireless communications research communities, we will review the broadly applicable principles of various GA-assisted optimization techniques, which were recently proposed also for employment in multiuser MIMO OFDM. In order to stimulate new research, we demonstrate that the family of GA-aided MUDs is capable of achieving a near-optimum performance at the cost of a significantly lower computational complexity than that imposed by their optimum maximum-likelihood (ML) MUD aided counterparts. The paper is concluded by outlining a range of future research options that may find their way into next-generation wireless systems.