nils-herrmann/clean_scirepeval huggingface dataset:
Measurement-Based Harmonic Modeling of an Electric Vehicle Charging Station Using a Three-Phase Uncontrolled Rectifier

The harmonic pollution of electric vehicle chargers is increasing rapidly as the scale of electric vehicle charging stations enlarges to meet the rising demand for electric vehicles. This paper investigates the operating characteristics of a three-phase uncontrolled rectification electric vehicle charger with passive power factor correction. A method for estimating the equivalent circuit parameters of chargers is proposed based on the measured feature data of voltage and current at the ac side and on the circuit constraint during the conduction charging process. A harmonic analytical model of the charging station is then established by dividing the same charger types into groups. The parameter estimation method and the harmonic model of the charging station are verified through simulation, experiment, and field test. The parameter sensitivities of the equivalent circuit to the charging current harmonic are also studied.
Can machine learning be secure?

Machine learning systems offer unparalled flexibility in dealing with evolving input in a variety of applications, such as intrusion detection systems and spam e-mail filtering. However, machine learning algorithms themselves can be a target of attack by a malicious adversary. This paper provides a framework for answering the question, "Can machine learning be secure?" Novel contributions of this paper include a taxonomy of different types of attacks on machine learning techniques and systems, a variety of defenses against those attacks, a discussion of ideas that are important to security for machine learning, an analytical model giving a lower bound on attacker's work function, and a list of open problems.
A 3-dimensional sift descriptor and its application to action recognition

In this paper we introduce a 3-dimensional (3D) SIFT descriptor for video or 3D imagery such as MRI data. We also show how this new descriptor is able to better represent the 3D nature of video data in the application of action recognition. This paper will show how 3D SIFT is able to outperform previously used description methods in an elegant and efficient manner. We use a bag of words approach to represent videos, and present a method to discover relationships between spatio-temporal words in order to better describe the video data.
Cell-Balancing Techniques: Theory and Implementation

In the safety chapter we briefly discussed the issue that when multiple cells are connected in series, the cell voltage is not always equal to the pack voltage divided by the number of cells. How does this happen? This chapter explores that question in detail, but the first question to answer is this: Why do we care? The first reason is safety. Remember, when lithium ion cell voltage exceeds 4.2V by a few hundred millivolts, it can undergo thermal runaway, melting the battery pack and device it is powering. It can even blow up as a big ball of fire. Although a well-designed pack has an overvoltage protection circuit that will prevent such an event (usually even two independent circuits!), it is better not to tempt fate by triggering this protection unnecessarily. The second reason is longevity. If the maximal recommended charging voltage is exceeded even a little, it will cause very accelerated degradation. Just increasing the charging voltage from 4.2 to 4.25V causes the degradation rate to increase by 30%. For this reason, the misbehaving cell that has higher than its due share of voltage will degrade faster. The third reason is incomplete charging of the pack. Let's assume the protector circuit does its job and that charging stops when just one cell gets close to unsafe conditions. Now we have successfully prevented thermal runaway, but all of the other cells now have lower voltages and are not fully charged. If we look at the pack voltage, it will be much less than 4.2V multiplied by the
Towards Good Practices for Image Retrieval Based on CNN Features

Recent works have demonstrated that Convolutional Neural Networks (CNNs) achieve state-of-the-art results in several computer vision tasks. CNNs have also shown their ability to provide effective descriptors for image retrieval. In this paper, we focus on CNN feature extraction for instance-level image search. We started by studying in depth several methods proposed to improve the Regional Maximal Activation (RMAC) approach. Then, we selected some of these advances and introduced a new approach that combines multi-scale and multi-layer feature extraction with feature selection. We also propose an approach for local RMAC descriptor extraction based on class activation maps. Our parameter-free approach provides short descriptors and achieves state-of-the-art performance without the need of CNN finetuning or additional data in any way. In order to demonstrate the effectiveness of our approach, we conducted extensive experiments on four well known instance-level image retrieval benchmarks (the INRIA Holidays dataset, the University of Kentucky Benchmark, Oxford5k and Paris6k).
Tuning metaheuristics: A data mining based approach for particle swarm optimization

The paper is concerned with practices for tuning the parameters of metaheuristics. Settings such as, e.g., the cooling factor in simulated annealing, may greatly affect a metaheuristic’s efficiency as well as effectiveness in solving a given decision problem. However, procedures for organizing parameter calibration are scarce and commonly limited to particular metaheuristics. We argue that the parameter selection task can appropriately be addressed by means of a data mining based approach. In particular, a hybrid system is devised, which employs regression models to learn suitable parameter values from past moves of a metaheuristic in an online fashion. In order to identify a suitable regression method and, more generally, to demonstrate the feasibility of the proposed approach, a case study of particle swarm optimization is conducted. Empirical results suggest that characteristics of the decision problem as well as search history data indeed embody information that allows suitable parameter values to be determined, and that this type of information can successfully be extracted by means of nonlinear regression models.
Collaborative Recurrent Autoencoder: Recommend while Learning to Fill in the Blanks

Hybrid methods that utilize both content and rating information are commonly used in many recommender systems. However, most of them use either handcrafted features or the bag-of-words representation as a surrogate for the content information but they are neither effective nor natural enough. To address this problem, we develop a collaborative recurrent autoencoder (CRAE) which is a denoising recurrent autoencoder (DRAE) that models the generation of content sequences in the collaborative filtering (CF) setting. The model generalizes recent advances in recurrent deep learning from i.i.d. input to non-i.i.d. (CF-based) input and provides a new denoising scheme along with a novel learnable pooling scheme for the recurrent autoencoder. To do this, we first develop a hierarchical Bayesian model for the DRAE and then generalize it to the CF setting. The synergy between denoising and CF enables CRAE to make accurate recommendations while learning to fill in the blanks in sequences. Experiments on real-world datasets from different domains (CiteULike and Netflix) show that, by jointly modeling the order-aware generation of sequences for the content information and performing CF for the ratings, CRAE is able to significantly outperform the state of the art on both the recommendation task based on ratings and the sequence generation task based on content information.
Agile Amulet: Real-Time Salient Object Detection with Contextual Attention

This paper proposes an Agile Aggregating Multi-Level feaTure framework (Agile Amulet) for salient object detection. The Agile Amulet builds on previous works to predict saliency maps using multi-level convolutional features. Compared to previous works, Agile Amulet employs some key innovations to improve training and testing speed while also increase prediction accuracy. More specifically, we first introduce a contextual attention module that can rapidly highlight most salient objects or regions with contextual pyramids. Thus, it effectively guides the low-layer convolutional feature learning and tells the backbone network where to look. The contextual attention module is a fully convolutional mechanism that simultaneously learns complementary features and predicts saliency scores at each pixel. In addition, we propose a novel method to aggregate multi-level deep convolutional features. As a result, we are able to use the integrated side-output features of pre-trained convolutional networks alone, which significantly reduces the model parameters leading to a model size of 67 MB, about half of Amulet. Compared to other deep learning based saliency methods, Agile Amulet is of much lighter-weight, runs faster (30 fps in real-time) and achieves higher performance on seven public benchmarks in terms of both quantitative and qualitative evaluation.
1 FEEDFORWARD NEURAL NETWORKS : AN INTRODUCTION

1 A neural network is a massively parallel distributed processor that has a natural propensity for storing experiential knowledge and making it available for use. It resembles the brain in two respects (Haykin 1998): 1. Knowledge is acquired by the network through a learning process. 2. Interconnection strengths known as synaptic weights are used to store the knowledge. Basically, learning is a process by which the free parameters (i.e., synaptic weights and bias levels) of a neural network are adapted through a continuing process of stimulation by the environment in which the network is embedded. The type of learning is determined by the manner in which the parameter changes take place. In a general sense, the learning process may be classified as follows:
Sentiment Analysis on Tweets about Diabetes: An Aspect-Level Approach

In recent years, some methods of sentiment analysis have been developed for the health domain; however, the diabetes domain has not been explored yet. In addition, there is a lack of approaches that analyze the positive or negative orientation of each aspect contained in a document (a review, a piece of news, and a tweet, among others). Based on this understanding, we propose an aspect-level sentiment analysis method based on ontologies in the diabetes domain. The sentiment of the aspects is calculated by considering the words around the aspect which are obtained through N-gram methods (N-gram after, N-gram before, and N-gram around). To evaluate the effectiveness of our method, we obtained a corpus from Twitter, which has been manually labelled at aspect level as positive, negative, or neutral. The experimental results show that the best result was obtained through the N-gram around method with a precision of 81.93%, a recall of 81.13%, and an F-measure of 81.24%.
Training Deep and Recurrent Networks with Hessian-Free Optimization

Hessian-Free optimization (HF) is an approach for unconstrained minimization of real-valued smooth objective functions. Like standard Newton’s method, it uses local quadratic approximations to generate update proposals. It belongs to the broad class of approximate Newton methods that are practical for problems of very high dimensionality, such as the training objectives of large neural networks. Different algorithms that use many of the same key principles have appeared in the literatures of various communities under different names such as Newton-CG, CG-Steihaug, Newton-Lanczos, and Truncated Newton [27, 28], but applications to machine learning and especially neural networks, have been limited or non-existent until recently. With the work of Martens [22] and later Martens and Sutskever [23] it has been demonstrated that such an approach, if carefully designed and implemented, can work very well for optimizing nonconvex functions such as the training objective for deep neural networks and recurrent neural networks (RNNs), given sensible random initializations. This was significant because gradient descent methods have been observed to be very slow and sometimes completely ineffective [16, 4, 17] on these problems, unless special non-random initializations schemes like layer-wise pre-training [16, 15, 3] are used. HF, which is a general optimization-based approach, can be used in conjunction with or as an alternative to existing pre-training methods and is more widely applicable, since it relies on fewer assumptions about the specific structure of the network. In this report we will first describe the basic HF approach, and then examine well-known general purpose performance-improving techniques as well as others that are specific to HF (versus other Truncated-Newton type approaches) or to neural networks. We will also provide practical tips for creating efficient and correct implementations, and discuss the pitfalls which may arise when designing and using an HF-based approach in a particular application.
NFC Antenna Design for Low-Permeability Ferromagnetic Material

A novel structure of a near-field communication (NFC) antenna for a mobile handset is proposed by changing the sequence of loop winding and modifying the inner loop structure. In general, the sintered ferrite sheets with higher relative permeability (μr ≈ 200) have been used to reduce the performance deterioration of NFC antennas due to the eddy current in the battery pack of a mobile handset. However, their costs are high, and they are considerably breakable. In this letter, we effectively enhance the performance of an NFC antenna by employing the ferrite-polymer composite with lower relative permeability (μr ≈ 55). The proposed antenna improves up to 23% more reading range and up to 65% better load modulation performance.
SLIQ: A Fast Scalable Classifier for Data Mining

Classi cation is an important problem in the emerging eld of data mining. Although classi cation has been studied extensively in the past, most of the classi cation algorithms are designed only for memory-resident data, thus limiting their suitability for data mining large data sets. This paper discusses issues in building a scalable classier and presents the design of SLIQ, a new classi er. SLIQ is a decision tree classi er that can handle both numeric and categorical attributes. It uses a novel pre-sorting technique in the tree-growth phase. This sorting procedure is integrated with a breadthrst tree growing strategy to enable classi cation of disk-resident datasets. SLIQ also uses a new tree-pruning algorithm that is inexpensive, and results in compact and accurate trees. The combination of these techniques enables SLIQ to scale for large data sets and classify data sets irrespective of the number of classes, attributes, and examples (records), thus making it an attractive tool for data mining.
Data-Efficient Hierarchical Reinforcement Learning

Hierarchical reinforcement learning (HRL) is a promising approach to extend traditional reinforcement learning (RL) methods to solve more complex tasks. Yet, the majority of current HRL methods require careful task-specific design and on-policy training, making them difficult to apply in real-world scenarios. In this paper, we study how we can develop HRL algorithms that are general, in that they do not make onerous additional assumptions beyond standard RL algorithms, and efficient, in the sense that they can be used with modest numbers of interaction samples, making them suitable for real-world problems such as robotic control. For generality, we develop a scheme where lower-level controllers are supervised with goals that are learned and proposed automatically by the higher-level controllers. To address efficiency, we propose to use off-policy experience for both higherand lower-level training. This poses a considerable challenge, since changes to the lower-level behaviors change the action space for the higher-level policy, and we introduce an off-policy correction to remedy this challenge. This allows us to take advantage of recent advances in off-policy model-free RL to learn both higherand lower-level policies using substantially fewer environment interactions than on-policy algorithms. We term the resulting HRL agent HIRO and find that it is generally applicable and highly sample-efficient. Our experiments show that HIRO can be used to learn highly complex behaviors for simulated robots, such as pushing objects and utilizing them to reach target locations,1 learning from only a few million samples, equivalent to a few days of real-time interaction. In comparisons with a number of prior HRL methods, we find that our approach substantially outperforms previous state-of-the-art
Joint Probabilistic Data Association Revisited

In this paper, we revisit the joint probabilistic data association (JPDA) technique and propose a novel solution based on recent developments in finding the m-best solutions to an integer linear program. The key advantage of this approach is that it makes JPDA computationally tractable in applications with high target and/or clutter density, such as spot tracking in fluorescence microscopy sequences and pedestrian tracking in surveillance footage. We also show that our JPDA algorithm embedded in a simple tracking framework is surprisingly competitive with state-of-the-art global tracking methods in these two applications, while needing considerably less processing time.
Implementation and verification of a generic universal memory controller based on UVM

This paper presents a coverage driven constraint random based functional verification method based on the Universal Verification Methodology (UVM) using System Verilog for generic universal memory controller architecture. This universal memory controller is looking forward to improving the performance of the existing memory controllers through a complete integration of the existing memory controllers features in addition of providing novel features. It also reduces the consumed power through providing high power consumption control due to its proposed different power levels supported to fit all power scenarios. While implementing a worthy architecture like the proposed generic universal memory controller, UVM is the best choice to build well-constructed, high controlled and reusable verification environment to efficiently verify it. More than 200 coverage points have been covered to verify the validation of the integrated features which makes the proposed universal memory controller replaces the existing controllers on the scene as it provides all of their powerful features in addition of novel features to control two of the most dominated types of memory; FLASH and DRAM through one memory controller.
Joint Inference for Event Timeline Construction

This paper addresses the task of constructing a timeline of events mentioned in a given text. To accomplish that, we present a novel representation of the temporal structure of a news article based on time intervals. We then present an algorithmic approach that jointly optimizes the temporal structure by coupling local classifiers that predict associations and temporal relations between pairs of temporal entities with global constraints. Moreover, we present ways to leverage knowledge provided by event coreference to further improve the system performance. Overall, our experiments show that the joint inference model significantly outperformed the local classifiers by 9.2% of relative improvement in F1. The experiments also suggest that good event coreference could make remarkable contribution to a robust event timeline construction system.
Semantic Parsing of Pre-university Math Problems

We have been developing an end-to-end math problem solving system that accepts natural language input. The current paper focuses on how we analyze the problem sentences to produce logical forms. We chose a hybrid approach combining a shallow syntactic analyzer and a manuallydeveloped lexicalized grammar. A feature of the grammar is that it is extensively typed on the basis of a formal ontology for pre-university math. These types are helpful in semantic disambiguation inside and across sentences. Experimental results show that the hybrid system produces a well-formed logical form with 88% precision and 56% recall.
Cross-Entropy Clustering

We build a general and highly applicable clustering theory, which we call cross-entropy clustering (shortly CEC) which joins advantages of classical kmeans (easy implementation and speed) with those of EM (affine invariance and ability to adapt to clusters of desired shapes). Moreover, contrary to k-means and EM, CEC finds the optimal number of clusters by automatically removing groups which carry no information. Although CEC, similarly like EM, can be build on an arbitrary family of densities, in the most important case of Gaussian CEC the division into clusters is affine invariant, while the numerical complexity is comparable to that of k-means.
An Empirical Study of a Highly Available File System

In this paper we present results from a six-month empirical study of the high availability aspects of the Coda File System. We report on the service failures experienced by Coda clients, and show that such failures are masked successfully. We also explore the effectiveness and resource costs of key aspects of server replication and disconnected operation, the two high availability mechanisms of Coda. Wherever possible, we compare our measurements to simulation-based predictions from earlier papers and to anecdotal evidence from users. Finally, we explore how users take advantage of the support provided by Coda for mobile computing.
Sleep viewed as a state of adaptive inactivity

Sleep is often viewed as a vulnerable state that is incompatible with behaviours that nourish and propagate species. This has led to the hypothesis that sleep has survived because it fulfills some universal, but as yet unknown, vital function. I propose that sleep is best understood as a variant of dormant states seen throughout the plant and animal kingdoms and that it is itself highly adaptive because it optimizes the timing and duration of behaviour. Current evidence indicates that ecological variables are the main determinants of sleep duration and intensity across species.
A CSRR Loaded MIMO Antenna System for ISM Band Operation

A 2 × 2 (four-element) multiple-input multiple-output (MIMO) patch antenna system is designed and fabricated for a 2.45-GHz ISM band operation. It uses complementary split-ring resonator (CSRR) loading on its ground plane for antenna miniaturization. This reduces the single-element antenna size by 76%. The total board size of the proposed MIMO antenna system, including the GND plane is 100 × 50 × 0.8 mm3, while the single-patch antenna element has a size of 14 × 18 mm2. The antenna is fabricated and tested. Measured results are in good agreement with simulations. A minimum measured isolation of 10 dB is obtained given the close interelement spacing of 0.17λ. The maximum measured gain for a single operating element is -0.8 dBi.