nils-herrmann/clean_scirepeval huggingface dataset:
Locomotor activity in spinal man: significance of afferent input from joint and load receptors.

The aim of this study was to differentiate the effects of body load and joint movements on the leg muscle activation pattern during assisted locomotion in spinal man. Stepping movements were induced by a driven gait orthosis (DGO) on a treadmill in patients with complete para-/tetraplegia and, for comparison, in healthy subjects. All subjects were unloaded by 70% of their body weight. EMG of upper and lower leg muscles and joint movements of the DGO of both legs were recorded. In the patients, normal stepping movements and those mainly restricted to the hips (blocked knees) were associated with a pattern of leg muscle EMG activity that corresponded to that of the healthy subjects, but the amplitude was smaller. Locomotor movements restricted to imposed ankle joint movements were followed by no, or only focal EMG responses in the stretched muscles. Unilateral locomotion in the patients was associated with a normal pattern of leg muscle EMG activity restricted to the moving side, while in the healthy subjects a bilateral activation occurred. This indicates that interlimb coordination depends on a supraspinal input. During locomotion with 100% body unloading in healthy subjects and patients, no EMG activity was present. Thus, it can be concluded that afferent input from hip joints, in combination with that from load receptors, plays a crucial role in the generation of locomotor activity in the isolated human spinal cord. This is in line with observations from infant stepping experiments and experiments in cats. Afferent feedback from knee and ankle joints may be involved largely in the control of focal movements.
A Cloud-Based IoT Data Gathering and Processing Platform

The deployment of Internet of Things (IoT) smart devices, including sensors, is progressing at a rapid pace. However, the volume of data they generate is becoming difficult to store and process on local platforms. The scalability offered by Cloud computing provides a solution to this problem. Cloud computing provides resources at low cost for its users. However, platform-independent methods of gathering and transmitting sensor data to Clouds are not widely available. This paper presents a Cloud-based smart device data monitoring, gathering and processing platform. It first reviews the state-of-the-art embodied by the existing solutions and discusses their strengths and weaknesses. Informed by the survey analysis, a generic architecture is presented that addresses the identified challenges facing data gathering and processing in this area. Based on use case scenarios, we evaluate and demonstrate the novelty of our proposed platform.
Remote Real Time Automatic Meter Reading System Based on Wireless Sensor Networks

A remote real time AMR (automatic meter reading) system based on wireless sensor networks is presented in this paper. The useful remote AMR sensors were analyzed and efficient wireless network was suggested. The remote measurement system for water supply is taken as a typical example in experiments. The structure of system employs distributed structure based on wireless sensor networks, which consists of measure meters, sensor nodes, data collectors, server and wireless communication network. For a short distance transmission, the data collector collects data from the water meter sensors using the RF and ZigBee communication. For a long distance transmission, from the data collector to the server, system uses CDMA cellular network. The water meter data are received at the server through LAN using TCP/IP protocol. The proposed system have abroad application foreground in the real application field.
Alleviating the new user problem in collaborative filtering by exploiting personality information

The new user problem in recommender systems is still challenging, and there is not yet a unique solution that can be applied in any domain or situation. In this paper we analyze viable solutions to the new user problem in collaborative filtering (CF) that are based on the exploitation of user personality information: (a) personality-based CF, which directly improves the recommendation prediction model by incorporating user personality information, (b) personality-based active learning, which utilizes personality information for identifying additional useful preference data in the target recommendation domain to be elicited from the user, and (c) personality-based cross-domain recommendation, which exploits personality information to better use user preference data from auxiliary domains which can be used to compensate the lack of user preference data in the target domain. We benchmark the effectiveness of these methods on large datasets that span several domains, namely movies, music and books. Our results show that personality-aware methods achieve performance improvements that range from 6 to 94 % for users completely new to the system, while increasing the novelty of the recommended items by 3–40 % with respect to the non-personalized popularity baseline. We also discuss the limitations of our approach and the situations in which the proposed methods can be better applied, hence providing guidelines for researchers and practitioners in the field.
A DC to 22GHz, 2W high power distributed amplifier using stacked FET topology with gate periphery tapering

This paper describes a high power (2W) distributed amplifier (DA) MMIC. DA MMIC was fabricated using an Lg=0.25μm GaAs PHEMT process. DA MMIC contains an impedance transformer and heavily tapered gate periphery design for constant output power performance over 0.1 to 22GHz operational frequency. To obtain high voltage operation, the DA MMIC employed a three stacked FET topology. A 7-section DA demonstrated 2 W saturated output power and 12 dB small signal gain from 0.1 GHz to 22 GHz with peak output power of 3.5 W with power added efficiency (PAE) of 27%. Those test results exceeded recently reported GaN based power DA performance  with large margins.
A lightweight, procedural, vector watercolor painting engine

Existing natural media painting simulations have produced high quality results, but have required powerful compute hardware and have been limited to screen resolutions. Digital artists would like to be able to use watercolor-like painting tools, but at print resolutions and on lower end hardware such as laptops or even slates. We present a procedural algorithm for generating watercolor-like dynamic paint behaviors in a lightweight manner. Our goal is not to exactly duplicate watercolor painting, but to create a range of dynamic behaviors that allow users to achieve a similar style of process and result, while at the same time having a unique character of its own. Our stroke representation is vector-based, allowing for rendering at arbitrary resolutions, and our procedural pigment advection algorithm is fast enough to support painting on slate devices. We demonstrate our technique in a commercially available slate application used by professional artists.
Concurrency and Automata on Infinite Sequences

The paper is concerned with ways in which fair concurrency can be modelled using notations for omega-regular languages languages containing infinite sequences, whose recognizers are modified forms of B~chi or Muller-McNaughton automata. There are charecterization of these languages in terms of recursion equation sets which involve both minimal and maximal fixpoint operators. The class of e-regular languages is closed under a fair concurrency operator. A general method for proving/deciding equivalences between such languages is obtained, derived from Milner's notion of "simulation". i. IntIDduction If a construct (C 1 P~ C 2) is understood to call for concurrent interpretation of cormmands, 'fairness' is the constraint On the language involved which guarantees that x:=true; y:=l; ((while x do y:=y+i) par x:=false) always terminates, though without guaranteeing any bound on the value of y when it does so. The constraint appears to present a number of fundamental problems one reason being its association with'unbounded nondetemminism', as exhibited by the value of y in the example, which comes out with a defined, but unbounded value. Apparently this variety of nondeterminism can he given an accurate semantics only by resorting to features such as the use of monotone, non-continuous functions which do not otherwise occur in computation theory. Thus the rich variety of models due to Scott, Plotkin and others is not apparently available, so that satisfactory answers seem to call for a return to more primitive notions. In a previous paper [ii] a semantics was obtained for a primitive programming language with fair concurrency. This involved the use of what are here called m-regular expressions. This paper will he concerned primarily with the theory sumrounding such notations. We hope to discuss practical examples in a later paper, but briefly point out here the sort of r~asoning we would expect to he useful in practice. One would expect that aconstruc t such as While E do C should be associated with an ~-regular expression of the form A~'(: A* + A a~) where A is determined by E and C. And concurrency (the fair merging of command
Synaptic plasticity, memory and the hippocampus: a neural network approach to causality

On page 66 of this article, there were several errors in figure 1. The tract labelled ‘Temporoammonic path’ should have been labelled ‘Perforant path to CA1’, and the tract labelled ‘Perforant path’ should have been labelled ‘Perforant path to dentate gyrus’. The fibres from layer III cells in the lateral entorhinal cortex that were shown projecting to proximal CA1 cells should have been depicted projecting to the distal CA1 cells, and the fibres from layer III cells in the medial entorhinal cortex that were shown projecting to distal CA1 cells should have been depicted projecting to proximal CA1 cells. The layer II cells in the medial and lateral entorhinal cortex that project to the granule cells should have been depicted as continuing to the stratum lacunosum moleculare, where they contact CA3 pyramidal cells. These errors have been corrected in the online version. The authors thank J. Z. Young for pointing out the errors and M. Witter for advice in making the alterations to the figure.
Performance Prediction and Optimization of Solar Water Heater via a Knowledge-Based Machine Learning Method

Measuring the performance of solar energy and heat transfer systems requires a lot of time, economic cost and manpower. Meanwhile, directly predicting their performance is challenging due to the complicated internal structures. Fortunately, a knowledge-based machine learning method can provide a promising prediction and optimization strategy for the performance of energy systems. In this Chapter, the authors will show how they utilize the machine learning models trained from a large experimental database to perform precise prediction and optimization on a solar water heater (SWH) system. A new energy system optimization strategy based on a high-throughput screening (HTS) process is proposed. This Chapter consists of: i) Comparative studies on varieties of machine learning models (artificial neural networks (ANNs), support vector machine (SVM) and extreme learning machine (ELM)) to predict the performances of SWHs; ii) Development of an ANN-based software to assist the quick prediction and iii) Introduction of a computational HTS method to design a high-performance SWH system.
Analysis and modeling of YouTube traffic

YouTube currently accounts for a significant percentage of the Internet’s global traffic. Hence, understanding the characteristics of the YouTube traffic generation pattern can provide a significant advantage in predicting user video quality and in enhancing network design. In this paper we present a characterization of the traffic generated by YouTube when accessed from a regular PC. Based on this characterization, a YouTube server traffic generation model is proposed, which, for example, can be easily implemented in simulation tools. The derived characterization and model are based on experimental evaluations of traffic generated by the application layer of YouTube servers. A YouTube server commences the download with an initial burst and later throttles down the generation rate. If the available bandwidth is reduced (e.g., in the presence of network congestion), the server behaves as if the data excess that cannot be transmitted due to the reduced bandwidth were accumulated at a server’s buffer, which is later drained if the bandwidth availability is recovered. As we will show, the video clip encoding rate plays a relevant role in determining the traffic generation rate, and therefore, a cumulative density function for the most viewed video clips will be presented. The proposed traffic generation model was implemented in a YouTube emulation server, and the generated synthetic traffic traces were compared to downloads from the original YouTube server. The results show that the relative error between downloads from the emulation server and the original server does not exceed 6% for the 90% of the considered videos.
Neural Lattice-to-Sequence Models for Uncertain Inputs

The input to a neural sequence-tosequence model is often determined by an up-stream system, e.g. a word segmenter, part of speech tagger, or speech recognizer. These up-stream models are potentially error-prone. Representing inputs through word lattices allows making this uncertainty explicit by capturing alternative sequences and their posterior probabilities in a compact form. In this work, we extend the TreeLSTM (Tai et al., 2015) into a LatticeLSTM that is able to consume word lattices, and can be used as encoder in an attentional encoderdecoder model. We integrate lattice posterior scores into this architecture by extending the TreeLSTM’s child-sum and forget gates and introducing a bias term into the attention mechanism. We experiment with speech translation lattices and report consistent improvements over baselines that translate either the 1-best hypothesis or the lattice without posterior scores.
Unsupervised Visual Attribute Transfer with Reconfigurable Generative Adversarial Networks

Learning to transfer visual attributes requires supervision dataset. Corresponding images with varying attribute values with the same identity are required for learning the transfer function. This largely limits their applications, because capturing them is often a difficult task. To address the issue, we propose an unsupervised method to learn to transfer visual attribute. The proposed method can learn the transfer function without any corresponding images. Inspecting visualization results from various unsupervised attribute transfer tasks, we verify the effectiveness of the proposed method.
Enabling search over encrypted multimedia databases

Performing information retrieval tasks while preserving data confidentiality is a desirable capability when a database is stored on a server maintained by a third-party service provider. This paper addresses the problem of enabling content-based retrieval over encrypted multimedia databases. Search indexes, along with multimedia documents, are first encrypted by the content owner and then stored onto the server. Through jointly applying cryptographic techniques, such as order preserving encryption and randomized hash functions, with image processing and information retrieval techniques, secure indexing schemes are designed to provide both privacy protection and rank-ordered search capability. Retrieval results on an encrypted color image database and security analysis of the secure indexing schemes under different attack models show that data confidentiality can be preserved while retaining very good retrieval performance. This work has promising applications in secure multimedia management.
LOKOIRAN- A novel robot for rehabilitation of spinal cord injury and stroke patients

This paper will focus on the design and development of a new gait training robotic device (LOKOIRAN) for patients with balance and locomotion disorders. The process of rehabilitation in this system is based on partial weight bearing system and programmable foot-plate training. The system consisted of several subsystems following: Driving and transmission system, back supporting system, leg exoskeleton, body weight support system and control system. A virtual reality environment was also designed and integrated to the system in order to motivate patients during rehabilitation protocol. The experimental tests on healthy subjects were performed to evaluate the performances of mechanical and electrical subsystems of LOKOIRAN robotic system. The newly developed device has several advantages in comparison with similar commercial systems. Firstly, the physical improvements of patients are expected to increase due to synchronized and functional movements of upper and lower limbs. Secondly, special leg exoskeleton and back supporting system were designed to increase the stability and balance of subjects. Although the aforementioned robotic gait device is well suitable for patients with SCI and stroke, it could be used for MS patients, sport injury cases, aging and people with stability and balance disorders.
Feasibility study of micro hydro power plant for rural electrification in Thailand by using axial flux permanent magnet

This paper presents the study to investigate the possibility of the stand-alone micro hydro for low-cost electricity production which can satisfy the energy load requirements of a typical remote and isolated rural area. In this framework, the feasibility study in term of the technical and economical performances of the micro hydro system are determined according to the rural electrification concept. The proposed axial flux permanent magnet (AFPM) generator will be designed for micro hydro under sustainable development to optimize between cost and efficiency by using the local materials and basic engineering knowledge. First of all, the simple simulation of micro hydro model for lighting system is developed by considering the optimal size of AFPM generator. The simulation results show that the optimal micro hydro power plant with 70 W can supply the 9 W compact fluorescent up to 20 set for 8 hours by using pressure of water with 6 meters and 0.141 m3/min of flow rate. Lastly, a proposed micro hydro power plant can supply lighting system for rural electrification up to 525.6 kWh/year or 1,839.60 Baht/year and reduce 0.33 ton/year of CO2 emission.
Topic-Informed Neural Machine Translation

In recent years, neural machine translation (NMT) has demonstrated state-of-the-art machine translation (MT) performance. It is a new approach to MT, which tries to learn a set of parameters to maximize the conditional probability of target sentences given source sentences. In this paper, we present a novel approach to improve the translation performance in NMT by conveying topic knowledge during translation. The proposed topic-informed NMT can increase the likelihood of selecting words from the same topic and domain for translation. Experimentally, we demonstrate that topic-informed NMT can achieve a 1.15 (3.3% relative) and 1.67 (5.4% relative) absolute improvement in BLEU score on the Chinese-to-English language pair using NIST 2004 and 2005 test sets, respectively, compared to NMT without topic information.
The viewpoint consistency constraint

The viewpoint consistency constraint requires that the locations of all object features in an image must be consistent with projection from a single viewpoint. The application of this constraint is central to the problem of achieving robust recognition, since it allows the spatial information in an image to be compared with prior knowledge of an object's shape to the full degree of available image resolution. In addition, the constraint greatly reduces the size of the search space during model-based matching by allowing a few initial matches to provide tight constraints for the locations of other model features. Unfortunately, while simple to state, this constraint has seldom been effectively applied in model-based computer vision systems. This paper reviews the history of attempts to make use of the viewpoint consistency constraint and then describes a number of new techniques for applying it to the process of model-based recognition. A method is presented for probabilistically evaluating new potential matches to extend and refine an initial viewpoint estimate. This evaluation allows the model-based verification process to proceed without the expense of backtracking or search. It will be shown that the effective application of the viewpoint consistency constraint, in conjunction with bottom-up image description based upon principles of perceptual organization, can lead to robust three-dimensional object recognition from single gray-scale images.
Accelerating Somewhat Homomorphic Evaluation using FPGAs

After being introduced in 2009, the first fully homomorphic encryption (FHE) scheme has created significant excitement in academia and industry. Despite rapid advances in the last 6 years, FHE schemes are still not ready for deployment due to an efficiency bottleneck. Here we introduce a custom hardware accelerator optimized for a class of reconfigurable logic to bring LTV based somewhat homomorphic encryption (SWHE) schemes one step closer to deployment in real-life applications. The accelerator we present is connected via a fast PCIe interface to a CPU platform to provide homomorphic evaluation services to any application that needs to support blinded computations. Specifically we introduce a number theoretical transform based multiplier architecture capable of efficiently handling very large polynomials. When synthesized for the Xilinx Virtex 7 family the presented architecture can compute the product of large polynomials in under 6.25 msec making it the fastest multiplier design of its kind currently available in the literature and is more than 102 times faster than a software implementation. Using this multiplier we can compute a relinearization operation in 526 msec. When used as an accelerator, for instance, to evaluate the AES block cipher, we estimate a per block homomorphic evaluation performance of 442 msec yielding performance gains of 28.5 and 17 times over similar CPU and GPU implementations, respectively.
Exclusive Lasso for Multi-task Feature Selection

We propose a novel group regularization which we call exclusive lasso. Unlike the group lasso regularizer that assumes covarying variables in groups, the proposed exclusive lasso regularizer models the scenario when variables in the same group compete with each other. Analysis is presented to illustrate the properties of the proposed regularizer. We present a framework of kernel based multi-task feature selection algorithm based on the proposed exclusive lasso regularizer. An efficient algorithm is derived to solve the related optimization problem. Experiments with document categorization show that our approach outperforms state-of-theart algorithms for multi-task feature selection.
Dropout Inference in Bayesian Neural Networks with Alpha-divergences

def bbalpha_softmax_cross_entropy_with_mc_logits(alpha): def loss(y_true, mc_logits):  mc_ll = K.sum(y_true * mc_log_softmax, -1) # M x K return 1. / alpha * (logsumexp(alpha * mc_ll, 1) + K.log(1.0 / K_mc)) return loss