nils-herrmann/clean_scirepeval huggingface dataset:
A ROUTE IMPROVEMENT ALGORITHM FOR THE VEHICLE ROUTING PROBLEM WITH TIME DEPENDENT TRAVEL TIMES

In urban areas, congestion creates a substantial variation in travel speeds during peak morning and evening hours. This research presents a new solution approach, an iterative route construction and improvement algorithm (IRCI), for the time dependent vehicle routing problem (TDVRP) with hard or soft time windows. Improvements are obtained at a route level; hence the proposed approach does not rely on any type of local improvement procedure. Further, the solution algorithms can tackle constant speed or time-dependent speed problems without any alteration in their structure. A new formulation for the TDVRP with soft and hard time windows is presented. Leveraging on the well known Solomon instances, new test problems that capture the typical speed variations of congested urban settings are proposed. Results in terms of solution quality as well as computational time are presented and discussed. The computational complexity of the IRCI is analyzed and experimental results indicate that average computational time increases proportionally to the square of the number of customers.
The Michaelis-Menten-Stueckelberg Theorem

We study chemical reactions with complex mechanisms under t wo assumptions: (i) intermediates are present in small amounts (this is the q uasi-steady-state hypothesis or QSS) and (ii) they are in equilibrium relations with substra tes (this is the quasiequilibrium hypothesis or QE). Under these assumptions, we prove the gen ralized mass action law together with the basic relations between kinetic factors, which are sufficient for the positivity of the entropy production but hold even without m icroreversibility, when the detailed balance is not applicable. Even though QE and QSS pr oduce useful approximations by themselves, only the combination of these assumptions ca n render the possibility beyond the “rarefied gas” limit or the “molecular chaos” hypotheses . We do not use any a priori form of the kinetic law for the chemical reactions and describe th eir equilibria by thermodynamic relations. The transformations of the intermediate compou nds can be described by the Markov kinetics because of their low density ( low density of elementary events ). This combination of assumptions was introduced by Michaelis and Menten in 1913. In 1952, Stueckelberg used the same assumptions for the gas kinetics and produced the remarkable semi-detailed balance relations between collision rates i n the Boltzmann equation that are weaker than the detailed balance conditions but are stil l sufficient for the Boltzmann H-theorem to be valid. Our results are obtained within the Mic haelis-Menten-Stueckelbeg conceptual framework.
Channel estimation for wireless ofdm systems

Orthogonal frequency division multiplexing (OFDM) is a special case of multi-carrier transmission and it can accommodate high data rate requirement of multimedia based wireless systems. Since channel estimation is an integral part of OFDM systems, it is critical to understand the basis of channel estimation techniques for OFDM systems so that the most appropriate method can be applied. In this article, an extensive overview of channel estimation techniques employed in OFDM systems are presented. In addition, the advantages, drawbacks, and relationship of these estimation techniques with each other are analyzed and discussed. As the combination of multiple input multiple output (MIMO)-OFDM systems promises higher data rates, estimation techniques are further investigated for these systems. Although the existing proposed techniques differ in terms of computational complexity and their mean squared error (MSE) performance, it has been observed that many channel estimation techniques are indeed a subset of LMMSE channel estimation technique. Hence, based on a given system’s resources and specifications, a suitable method among the presented techniques can be applied.
Audiovisual Lombard speech: reconciling production and perception

An earlier study compared audiovisual perception of speech ’produced in environmental noise’ (Lombard speech) and speech ’produced in quiet’ with the same environmental noise added. The results and showed that listeners make differential use of the visual information depending on the recording condition, but gave no indication of how or why this might be so. A possible confound in that study was that high audio presentation levels might account for the small visual enhancements observed for Lombard speech. This paper reports results for a second perception study using much lower acoustic presentation levels, compares them with the results of the previous study, and integrates the perception results with analyses of the audiovisual production data: face and head motion, audio amplitude (RMS), and parameters of the spectral acoustics (line spectrum pairs).
Deep Unsupervised Convolutional Domain Adaptation

In multimedia analysis, the task of domain adaptation is to adapt the feature representation learned in the source domain with rich label information to the target domain with less or even no label information. Significant research endeavors have been devoted to aligning the feature distributions between the source and the target domains in the top fully connected layers based on unsupervised DNN-based models. However, the domain adaptation has been arbitrarily constrained near the output ends of the DNN models, which thus brings about inadequate knowledge transfer in DNN-based domain adaptation process, especially near the input end. We develop an attention transfer process for convolutional domain adaptation. The domain discrepancy, measured in correlation alignment loss, is minimized on the second-order correlation statistics of the attention maps for both source and target domains. Then we propose Deep Unsupervised Convolutional Domain Adaptation DUCDA method, which jointly minimizes the supervised classification loss of labeled source data and the unsupervised correlation alignment loss measured on both convolutional layers and fully connected layers. The multi-layer domain adaptation process collaborately reinforces each individual domain adaptation component, and significantly enhances the generalization ability of the CNN models. Extensive cross-domain object classification experiments show DUCDA outperforms other state-of-the-art approaches, and validate the promising power of DUCDA towards large scale real world application.
Machine-Learning Attacks on PolyPUFs, OB-PUFs, RPUFs, LHS-PUFs, and PUF–FSMs

A physically unclonable function (PUF) is a circuit of which the input– output behavior is designed to be sensitive to the random variations of its manufacturing process. This building block hence facilitates the authentication of any given device in a population of identically laid-out silicon chips, similar to the biometric authentication of a human. The focus and novelty of this work is the development of efficient impersonation attacks on the following five Arbiter PUF–based authentication protocols: (1) the so-called PolyPUF protocol of Konigsmark, Chen, and Wong, as published in the IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems in 2016, (2) the so-called OB-PUF protocol of Gao, Li, Ma, Al-Sarawi, Kavehei, Abbott, and Ranasinghe, as presented at the IEEE conference PerCom 2016, (3) the so-called RPUF protocol of Ye, Hu, and Li, as presented at the IEEE conference AsianHOST 2016, (4) the so-called LHS-PUF protocol of Idriss and Bayoumi, as presented at the IEEE conference RFID-TA 2017, and (5) the so-called PUF–FSM protocol of Gao, Ma, Al-Sarawi, Abbott, and Ranasinghe, as published in the IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems in 2018. The common flaw of all five designs is that the use of lightweight obfuscation logic provides insufficient protection against machine-learning attacks.
A Review : On Number Plate Recognition

As the population is increasing day by day daily need of people leads to increase in density of transportation system. Where the every vehicle contains the number plate which particularly distinguishes the vehicle and utilized for different reason. Number plate due to high Intensity can be easily read by humans but when the task comes to computer is getting complex. And problem getting increase in cases when images is captured from far distance, blurred type, defected and also depends on the weather condition. Now in today era the need of automation and fast technology has opened the door for the researcher in this field of Number Plate Recognition (NPR). The paper here categorized the used Number Plate Recognition techniques by many researcher at each step of System by comparing the pros/cons and accuracy.
Off-grid electricity generation with renewable energy technologies in India : An application of HOMER

Renewable energy-based off-grid or decentralised electricity supply has traditionally considered a single technology-based limited level of supply to meet the basic needs, without considering reliable energy provision to rural consumers. The purpose of this paper is to propose the best hybrid technology combination for electricity generation from a mix of renewable energy resources to satisfy the electrical needs in a reliable manner of an off-grid remote village, Palari in the state of Chhattisgarh, India. Four renewable resources, namely, small-scale hydropower, solar photovoltaic systems, wind turbines and bio-diesel generators are considered. The paper estimates the residential, institutional, commercial, agricultural and small-scale industrial demand in the pre-HOMER analysis. Using HOMER, the paper identifies the optimal off-grid option and compares this with conventional grid extension. The solution obtained shows that a hybrid combination of renewable energy generators at an off-grid location can be a cost-effective alternative to grid extension and it is sustainable, techno-economically viable and environmentally sound. The paper also presents a post-HOMER analysis and discusses issues that are likely to affect/influence the realisation of the optimal solution.
A tutorial on convex optimization

In recent years, convex optimization has become a computational tool of central importance in engineering, thanks to it's ability to solve very large, practical engineering problems reliably and efficiently. The goal of this tutorial is to give an overview of the basic concepts of convex sets, functions and convex optimization problems, so that the reader can more readily recognize and formulate engineering problems using modern convex optimization. This tutorial coincides with the publication of the new book on convex optimization, by Boyd and Vandenberghe, who have made available a large amount of free course material and links to freely available code. These can be downloaded and used immediately by the audience both for self-study and to solve real problems.
Action Recognition with Spatio-Temporal Visual Attention on Skeleton Image Sequences

Action recognition with 3D skeleton sequences became popular due to its speed and robustness. The recently proposed Convolutional Neural Networks (CNN) based methods shown good performance in learning spatio-temporal representations for skeleton sequences. Despite the good recognition accuracy achieved by previous CNN based methods, there existed two problems that potentially limit the performance. First, previous skeleton representations were generated by chaining joints with a fixed order. The corresponding semantic meaning was unclear and the structural information among the joints was lost. Second, previous models did not have an ability to focus on informative joints. The attention mechanism was important for skeleton based action recognition because different joints contributed unequally towards the correct recognition. To solve these two problems, we proposed a novel CNN based method for skeleton based action recognition. We first redesigned the skeleton representations with a depth-first tree traversal order, which enhanced the semantic meaning of skeleton images and better preserved the associated structural information. We then proposed the general two-branch attention architecture that automatically focused on spatio-temporal key stages and filtered out unreliable joint predictions. Based on the proposed general architecture, we designed a Global Long-sequence Attention Network (GLAN) with refined branch structures. Furthermore, in order to adjust the kernel’s spatio-temporal aspect ratios and better capture long term dependencies, we proposed a Sub-Sequence Attention Network (SSAN) that took sub-image sequences as inputs. We showed that the two-branch attention architecture could be combined with the SSAN to further improve the performance. Our experiment results on the NTU RGB+D dataset and the SBU Kinetic Interaction dataset outperformed the state-of-the-art. The model was further validated on noisy estimated poses from the subsets of the UCF101 dataset and the Kinetics dataset.
Lexical pragmatics , ad hoc concepts and metaphor : A Relevance Theory perspective

Ostensive communication, the paradigm case of which is verbal communication, is the domain of a dedicated cognitive system, according to Relevance Theory (RT). This ‘pragmatics’ module is responsible for inferring the content or meaning that the communicator intends by his/her ostensive stimulus. An important sub-process within the system is the adjustment or modulation of lexically-encoded meaning, which makes it possible for speakers to communicate a vastly greater range of concepts than those that are stably encoded in their linguistic system. This includes the meaning communicated by at least some cases of metaphorically-used language. Taking a broadly Fodorian view that lexical concepts are atomic (unstructured), this paper looks at some issues raised by the idea that addressees infer ad hoc concepts as part of the on-line comprehension process. As a cognitive-scientific theory, RT is open to evidence from a range of sources, including native speaker-hearer intuitions, recorded instances of linguistic communication (corpus data) from both communicatively typical and atypical populations, results from relevant psychological and psycholinguistic experiments, and findings in cognitive neuroscience on brain activation during both utterance production and comprehension. At the end of the paper, some tentative suggestions are made of questions arising from work in theoretical lexical pragmatics which might be amenable to investigation at the neuropragmatic level1*.
Weakly-Supervised 3D Hand Pose Estimation from Monocular RGB Images

Compared with depth-based 3D hand pose estimation, it is more challenging to infer 3D hand pose from monocular RGB images, due to substantial depth ambiguity and the difficulty of obtaining fullyannotated training data. Different from existing learning-based monocular RGB-input approaches that require accurate 3D annotations for training, we propose to leverage the depth images that can be easily obtained from commodity RGB-D cameras during training, while during testing we take only RGB inputs for 3D joint predictions. In this way, we alleviate the burden of the costly 3D annotations in real-world dataset. Particularly, we propose a weakly-supervised method, adaptating from fully-annotated synthetic dataset to weakly-labeled real-world dataset with the aid of a depth regularizer, which generates depth maps from predicted 3D pose and serves as weak supervision for 3D pose regression. Extensive experiments on benchmark datasets validate the effectiveness of the proposed depth regularizer in both weakly-supervised and fullysupervised settings.
Onix: A Distributed Control Platform for Large-scale Production Networks

Computer networks lack a general control paradigm, as traditional networks do not provide any networkwide management abstractions. As a result, each new function (such as routing) must provide its own state distribution, element discovery, and failure recovery mechanisms. We believe this lack of a common control platform has significantly hindered the development of flexible, reliable and feature-rich network control planes. To address this, we present Onix, a platform on top of which a network control plane can be implemented as a distributed system. Control planes written within Onix operate on a global view of the network, and use basic state distribution primitives provided by the platform. Thus Onix provides a general API for control plane implementations, while allowing them to make their own trade-offs among consistency, durability, and scalability.
Speeding up Convolutional Neural Networks with Low Rank Expansions

The focus of this paper is speeding up the application of convolutional neural networks. While delivering impressive results across a range of computer vision and machine learning tasks, these networks are computationally demanding, limiting their deployability. Convolutional layers generally consume the bulk of the processing time, and so in this work we present two simple schemes for drastically speeding up these layers. This is achieved by exploiting cross-channel or filter redundancy to construct a low rank basis of filters that are rank-1 in the spatial domain. Our methods are architecture agnostic, and can be easily applied to existing CPU and GPU convolutional frameworks for tuneable speedup performance. We demonstrate this with a real world network designed for scene text character recognition , showing a possible 2.5⇥ speedup with no loss in accuracy, and 4.5⇥ speedup with less than 1% drop in accuracy, still achieving state-of-the-art on standard benchmarks.
Rehabilitation after total hip arthroplasty: a systematic review of controlled trials on physical exercise programs.

Total hip arthroplasty (THA) has revolutionized the care of patients with end-stage joint disease, leading to pain relief, functional recovery, and substantial improvement in quality of life. However, long-term studies indicate persistence of impairment and functional limitation after THA, and the optimal rehabilitation protocols are largely unknown. The aim of this paper was to systematically review the controlled trials published on the effectiveness of physical exercise programs after THA. Nine studies were retrieved from MEDLINE and reviewed. Results show that the physical exercise protocols most frequently used after THA in the early postoperative phase are neither supported nor denied by clinical controlled trials. Convincing evidence for the effectiveness of single interventions in addition to usual exercise programs exists for each of the three following options: treadmill training with partial body-weight support, unilateral resistance training of the quadriceps muscle (operated side), and arm-interval exercises with an arm ergometer. In the late postoperative phase (operation interval > 8 weeks) exercise programs consistently improve both impairment and ability to function. Weight-bearing exercises with hip-abductor eccentric strengthening may be the crucial component of the late-phase protocols. Substantial limitations were found in the nine studies, including small sample size, patient selection, heterogeneity of outcome assessments, and potential sources of variability not investigated. Despite limitations, we conclude that three main suggestions emerge from controlled trials on physical exercise after THA: early postoperative protocols should include additive interventions whose effectiveness has been shown. Late postoperative programs are useful and should comprise weight-bearing exercises with hip-abductor eccentric strengthening.
ASIC implementation of image enhancement technique for Fingerprint recognition process

The performance of Fingerprint recognition system depends on minutiae which are extracted from raw fingerprint image. Often the raw fingerprint image captured from a scanner may not be of good quality, which leads to inaccurate extraction of minutiae. Hence it is essential to preprocess the fingerprint image before extracting the reliable minutiae for matching of two fingerprint images. Image enhancement technique followed by minutiae extraction completes the fingerprint recognition process. Fingerprint recognition process with a matcher constitutes Fingerprint recognition system ASIC implementation of image enhancement technique for fingerprint recognition process using Cadence tool is proposed. Further, the result obtained from hardware design is compared with that of software using MatLab tool.
A Machine Learning Framework for Programming by Example

Learning programs is a timely and interesting challenge. In Programming by Example (PBE), a system attempts to infer a program from input and output examples alone, by searching for a composition of some set of base functions. We show how machine learning can be used to speed up this seemingly hopeless search problem, by learning weights that relate textual features describing the provided input-output examples to plausible sub-components of a program. This generic learning framework lets us address problems beyond the scope of earlier PBE systems. Experiments on a prototype implementation show that learning improves search and ranking on a variety of text processing tasks found on help forums.
End-to-end LSTM-based dialog control optimized with supervised and reinforcement learning

This paper presents a model for end-toend learning of task-oriented dialog systems. The main component of the model is a recurrent neural network (an LSTM), which maps from raw dialog history directly to a distribution over system actions. The LSTM automatically infers a representation of dialog history, which relieves the system developer of much of the manual feature engineering of dialog state. In addition, the developer can provide software that expresses business rules and provides access to programmatic APIs, enabling the LSTM to take actions in the real world on behalf of the user. The LSTM can be optimized using supervised learning (SL), where a domain expert provides example dialogs which the LSTM should imitate; or using reinforcement learning (RL), where the system improves by interacting directly with end users. Experiments show that SL and RL are complementary: SL alone can derive a reasonable initial policy from a small number of training dialogs; and starting RL optimization with a policy trained with SL substantially accelerates the learning rate of RL.