nils-herrmann/clean_scirepeval huggingface dataset:
Ultra-Large Gain Step-Up Switched-Capacitor DC-DC Converter With Coupled Inductor for Alternative Sources of Energy

An ultra-large voltage conversion ratio converter is proposed by integrating a switched-capacitor circuit with a coupled inductor technology. The proposed converter can be seen as an equivalent parallel connection to the load of a basic boost converter and a number of forward converters, each one containing a switched-capacitor circuit. All the stages are activated by the boost switch. A single active switch is required, with no need of extreme duty-ratio values. The leakage energy of the coupled inductor is recycled to the load. The inrush current problem of switched capacitors is restrained by the leakage inductance of the coupled-inductor. The above features are the reason for the high efficiency performance. The operating principles and steady state analyses of continuous, discontinuous and boundary conduction modes are discussed in detail. To verify the performance of the proposed converter, a 200 W/20 V to 400 V prototype was implemented. The maximum measured efficiency is 96.4%. The full load efficiency is 95.1%.
Mixing Body-Part Sequences for Human Pose Estimation

In this paper, we present a method for estimating articulated human poses in videos. We cast this as an optimization problem defined on body parts with spatio-temporal links between them. The resulting formulation is unfortunately intractable and previous approaches only provide approximate solutions. Although such methods perform well on certain body parts, e.g., head, their performance on lower arms, i.e., elbows and wrists, remains poor. We present a new approximate scheme with two steps dedicated to pose estimation. First, our approach takes into account temporal links with subsequent frames for the less-certain parts, namely elbows and wrists. Second, our method decomposes poses into limbs, generates limb sequences across time, and recomposes poses by mixing these body part sequences. We introduce a new dataset "Poses in the Wild", which is more challenging than the existing ones, with sequences containing background clutter, occlusions, and severe camera motion. We experimentally compare our method with recent approaches on this new dataset as well as on two other benchmark datasets, and show significant improvement.
A Fully Integrated Ultra-Low-Power High-Voltage Driver for Bistable LCDs

A complete ultra-low-power high-voltage driver for 80 times 104 passive-matrix bistable LCD is integrated in a 0.7mum CMOS smart-power technology. It features 100V driving capability on all row and column outputs and comprises all necessary digitally programmable high-voltage generators and multiplexers to synthesize the required complex high-voltage waveforms from a 3V battery. An original level-shifter design for the high-voltage multiplexers yields extremely low internal power consumption below 1-10mW for the entire driver chip
LATENT SPACE MODELS FOR MULTIVIEW NETWORK DATA.

Social relationships consist of interactions along multiple dimensions. In social networks, this means that individuals form multiple types of relationships with the same person (e.g., an individual will not trust all of his/her acquaintances). Statistical models for these data require understanding two related types of dependence structure: (i) structure within each relationship type, or network view, and (ii) the association between views. In this paper, we propose a statistical framework that parsimoniously represents dependence between relationship types while also maintaining enough flexibility to allow individuals to serve different roles in different relationship types. Our approach builds on work on latent space models for networks [see, e.g.,  (2002) 1090-1098]. These models represent the propensity for two individuals to form edges as conditionally independent given the distance between the individuals in an unobserved social space. Our work departs from previous work in this area by representing dependence structure between network views through a multivariate Bernoulli likelihood, providing a representation of between-view association. This approach infers correlations between views not explained by the latent space model. Using our method, we explore 6 multiview network structures across 75 villages in rural southern Karnataka, India [Banerjee et al. (2013)].
A 77-GHz FMCW Radar System Using On-Chip Waveguide Feeders in 65-nm CMOS

This paper presents a 77-GHz radar system using a transmitter (Tx) and receiver (Rx) integrated with on-chip waveguide feeders in 65-nm CMOS. The newly proposed on-chip waveguide feeder shows the insertion loss of about 2 dB and more than 30% bandwidth. Additionally, both the Tx and Rx are integrated with internal ×10 frequency multipliers. Therefore, a radar system can be easily implemented without sensitive millimeter-wave packaging technology by mounting the Tx and Rx chips on the waveguide aperture. The interconnections for the low-frequency reference and baseband signals can be realized on the low-cost FR-4 printed circuit board. The radar system shows 9-dBm output power and 13-dB down-conversion gain from the waveguide port.
Improving Citation Polarity Classification with Product Reviews

Recent work classifying citations in scientific literature has shown that it is possible to improve classification results with extensive feature engineering. While this result confirms that citation classification is feasible, there are two drawbacks to this approach: (i) it requires a large annotated corpus for supervised classification, which in the case of scientific literature is quite expensive; and (ii) feature engineering that is too specific to one area of scientific literature may not be portable to other domains, even within scientific literature. In this paper we address these two drawbacks. First, we frame citation classification as a domain adaptation task and leverage the abundant labeled data available in other domains. Then, to avoid over-engineering specific citation features for a particular scientific domain, we explore a deep learning neural network approach that has shown to generalize well across domains using unigram and bigram features. We achieve better citation classification results with this cross-domain approach than using in-domain classification.
Botcoin: Monetizing Stolen Cycles

At the current stratospheric value of Bitcoin, miners with access to significant computational horsepower are literally printing money. For example, the first operator of a USD $1,500 custom ASIC mining platform claims to have recouped his investment in less than three weeks in early February 2013, and the value of a bitcoin has more than tripled since then. Not surprisingly, cybercriminals have also been drawn to this potentially lucrative endeavor, but instead are leveraging the resources available to them: stolen CPU hours in the form of botnets. We conduct the first comprehensive study of Bitcoin mining malware, and describe the infrastructure and mechanism deployed by several major players. By carefully reconstructing the Bitcoin transaction records, we are able to deduce the amount of money a number of mining botnets have made.
Web Security: Detection of Cross Site Scripting in PHP Web Application using Genetic Algorithm

Cross site scripting (XSS) is one of the major threats to the web application security, where the research is still underway for an effective and useful way to analyse the source code of web application and removes this threat. XSS occurs by injecting the malicious scripts into web application and it can lead to significant violations at the site or for the user. Several solutions have been recommended for their detection. However, their results do not appear to be effective enough to resolve the issue. This paper recommended a methodology for the detection of XSS from the PHP web application using genetic algorithm (GA) and static analysis. The methodology enhances the earlier approaches of determining XSS vulnerability in the web application by eliminating the infeasible paths from the control flow graph (CFG). This aids in reducing the false positive rate in the outcomes. The results of the experiments indicated that our methodology is more effectual in detecting XSS vulnerability from the PHP web application compared to the earlier studies, in terms of the false positive rates and the concrete susceptible paths determined by GA Generator. Web Application Security; Security Vulnerability; Web Testing; Cross Site Scripting; Genetic Algorithm
Multidelay block frequency domain adaptive filter

A flexible multidelay block frequency domain (MDF) adaptive filter is presented. The distinct feature of the MDF adaptive filter is to allow one to choose the size of an FFT tailored to the efficient use of a hardware, rather than the requirement of a specific application. The MDF adaptive filter also requires less memory and so reduces the requirement and cost of a hardware. In performance, the MDF adaptive filter introduces smaller block delay and is faster,.ideal for a time-varying system such as modeling an acoustic path in a teleconference room. This is achieved by using smaller block size, updating the weight vectors more often, and reducing the total execution time of the adaptive process. The MDF adaptive filter compares favorably to other frequency domain adaptive filters when its adaptation speed and misadjustment are tested in computer simulations.
Large-scale distributed non-negative sparse coding and sparse dictionary learning

We consider the problem of building compact, unsupervised representations of large, high-dimensional, non-negative data using sparse coding and dictionary learning schemes, with an emphasis on executing the algorithm in a Map-Reduce environment. The proposed algorithms may be seen as parallel optimization procedures for constructing sparse non-negative factorizations of large, sparse matrices. Our approach alternates between a parallel sparse coding phase implemented using greedy or convex (l1) regularized risk minimization procedures, and a sequential dictionary learning phase where we solve a set of l0 optimization problems exactly. These two-fold sparsity constraints lead to better statistical performance on text analysis tasks and at the same time make it possible to implement each iteration in a single Map-Reduce job. We detail our implementations and optimizations that lead to the ability to factor matrices with more than 100 million rows and billions of non-zero entries in just a few hours on a small commodity cluster.
An Advanced Tabu Search Algorithm for the Job Shop Problem

The job shop scheduling problem with the makespan criterion is a certain NP-hard case from OR theory having excellent practical applications. This problem, having been examined for years, is also regarded as an indicator of the quality of advanced scheduling algorithms. In this paper we provide a new approximate algorithm that is based on the big valley phenomenon, and uses some elements of so-called path relinking technique as well as new theoretical properties of neighbourhoods. The proposed algorithm owns, unprecedented up to now, accuracy, obtainable in a quick time on a PC, which has been confirmed after wide computer tests.
Universal Sentence Encoder for English

We present easy-to-use TensorFlow Hub sentence embedding models having good task transfer performance. Model variants allow for trade-offs between accuracy and compute resources. We report the relationship between model complexity, resources, and transfer performance. Comparisons are made with baselines without transfer learning and to baselines that incorporate word-level transfer. Transfer learning using sentence-level embeddings is shown to outperform models without transfer learning and often those that use only word-level transfer. We show good transfer task performance with minimal training data and obtain encouraging results on word embedding association tests (WEAT) of model bias.
Probabilistic neural networks for classification, mapping, or associative memory

It can be shown that by replacing the sigmoid activation function often used in neural networks with an exponential function, a neural network can be formed which computes nonlinear decision boundaries. This technique yields decision surfaces which approach the Bayes optimal under certain conditions. There is a continuous control of the linearity of the decision boundaries, from linear for small training sets to any degree of nonlinearity justified by larger training sets. A four-layer neural network of the type proposed can map any input pattern to any number of classifications. The input variables can be either continuous or binary. Modification of the decision boundaries based on new data can be accomplished in real time simply by defining a set of weights equal to the new training vector. The decision boundaries can be implemented using analog 'neurons', which operate entirely in parallel. The organization proposed takes into account the projected pin limitations of neural-net chips of the near future. By a change in architecture, these same components could be used as associative memories, to compute nonlinear multivariate regression surfaces, or to compute a posteriori probabilities of an event.<<ETX>>
Virtual Adversarial Training: a Regularization Method for Supervised and Semi-supervised Learning

We propose a new regularization method based on virtual adversarial loss: a new measure of local smoothness of the conditional label distribution given input. Virtual adversarial loss is defined as the robustness of the conditional label distribution around each input data point against local perturbation. Unlike adversarial training, our method defines the adversarial direction without label information and is hence applicable to semi-supervised learning. Because the directions in which we smooth the model are only "virtually" adversarial, we call our method virtual adversarial training (VAT). The computational cost of VAT is relatively low. For neural networks, the approximated gradient of virtual adversarial loss can be computed with no more than two pairs of forward- and back-propagations. In our experiments, we applied VAT to supervised and semi-supervised learning tasks on multiple benchmark datasets. With a simple enhancement of the algorithm based on the entropy minimization principle, our VAT achieves state-of-the-art performance for semi-supervised learning tasks on SVHN and CIFAR-10.
Text Understanding with the Attention Sum Reader Network

Several large cloze-style context-questionanswer datasets have been introduced recently: the CNN and Daily Mail news data and the Children’s Book Test. Thanks to the size of these datasets, the associated text comprehension task is well suited for deep-learning techniques that currently seem to outperform all alternative approaches. We present a new, simple model that uses attention to directly pick the answer from the context as opposed to computing the answer using a blended representation of words in the document as is usual in similar models. This makes the model particularly suitable for question-answering problems where the answer is a single word from the document. Our model outperforms models previously proposed for these tasks by a large margin.
A Coherence Model Based on Syntactic Patterns

We introduce a model of coherence which captures the intentional discourse structure in text. Our work is based on the hypothesis that syntax provides a proxy for the communicative goal of a sentence and therefore the sequence of sentences in a coherent discourse should exhibit detectable structural patterns. Results show that our method has high discriminating power for separating out coherent and incoherent news articles reaching accuracies of up to 90%. We also show that our syntactic patterns are correlated with manual annotations of intentional structure for academic conference articles and can successfully predict the coherence of abstract, introduction and related work sections of these articles.
A vision-aided inertial navigation system for agile high-speed flight in unmapped environments: Distribution statement A: Approved for public release, distribution unlimited

Small, lightweight flight vehicles, such as consumer-grade quadrotors, are becoming increasingly common. These vehicles' on-board state estimators are typically reliant upon frequent and accurate updates from external systems such as the Global Positioning System (GPS) to provide state estimates required for stable flight. However, in many cases GPS signals may be unavailable or unreliable, and loss of GPS can cause these vehicles to go unstable or crash, potentially putting operators, bystanders, and property in danger. Thus reliance on GPS severely limits the robustness and operational capabilities of lightweight flight vehicles. This paper introduces the Smoothing And Mapping With In-ertial State Estimation (SAMWISE) navigation system. SAM-WISE is a vision-aided inertial navigation system capable of providing high-rate, low-latency state estimates to enable high-dynamic flight through obstacle-laden unmapped indoor and outdoor environments. SAMWISE offers a flexible framework for inertial navigation with nonlinear measurements, such as those produced by visual feature trackers, by utilizing an incremental smoother to efficiently optimize a set of nonlinear measurement constraints, estimating the vehicle trajectory in a sliding window in real-time with a slight processing delay. To overcome this delay and consistently produce state estimates at the high rates necessary for agile flight, we propose a novel formulation in which the smoother runs in a background thread while a low-latency inertial strapdown propagator outputs position, attitude, and velocity estimates at high-rate. We additionally propose a novel measurement buffering approach to seamlessly handle delayed measurements, measurements produced at inconsistent rates, and sensor data requiring significant processing time, such as camera imagery. We present experimental results high-speed flight with a fully autonomous quadrotor using SAMWISE for closed-loop state estimation from flight demonstrations during the DARPA Fast Lightweight Autonomy (FLA) program in April and November of 2016. SAMWISE achieved less than 1% position error and up to 5.5 m/s (12 mph) flight in a simulated indoor warehouse environment using a scanning-lidar, inertial measurement unit, and laser altimeter during the first FLA milestone event in April 2016. In November 2016, SAMWISE achieved approximately 3% error and up to 20 m/s (45 mph) flight in an open outdoor environment with large obstacles during the second FLA milestone event. The results of these flight tests demonstrate that our navigation system works robustly at high speed across multiple distinct environments.
SCADA-specific Intrusion Detection / Prevention Systems : A Survey and Taxonomy

Due to standardization and connectivity to the Internet, Supervisory Control and Data Acquisition (SCADA) systems now face the threat of cyber attacks. SCADA systems were designed without cyber security in mind and hence the problem of how to modify conventional Information Technology (IT) intrusion detection techniques to suit the needs of SCADA is a big challenge. We explain the nuance associated with the task of SCADA-specific intrusion detection and frame it in the domain interest of control engineers and researchers to illuminate the problem space. We present a taxonomy and a set of metrics for SCADA-specific intrusion detection techniques by heightening their possible use in SCADA systems. In particular, we enumerate Intrusion Detection Systems (IDS) that have been proposed to undertake this endeavor. We draw upon the discussion to identify the deficits and voids in current research. Finally, we offer recommendations and future research venues based upon our taxonomy and analysis on which SCADAspecific IDS strategies are most likely to succeed, in part through presenting a prototype of our efforts towards this goal.
Big Data Analytics in the Cloud: Spark on Hadoop vs MPI/OpenMP on Beowulf

One of the biggest challenges of the current big data landscape is our inability to process vast amounts of information in a reasonable time. In this work, we explore and compare two distributed computing frameworks implemented on commodity cluster architectures: MPI/OpenMP on Beowulf that is high-performance oriented and exploits multi-machine/multicore infrastructures, and Apache Spark on Hadoop which targets iterative algorithms through in-memory computing. We use the Google Cloud Platform service to create virtual machine clusters, run the frameworks, and evaluate two supervised machine learning algorithms: KNN and Pegasos SVM. Results obtained from experiments with a particle physics data set show MPI/OpenMP outperforms Spark by more than one order of magnitude in terms of processing speed and provides more consistent performance. However, Spark shows better data management infrastructure and the possibility of dealing with other aspects such as node failure and data replication.
ELASTIC: A Client-Side Controller for Dynamic Adaptive Streaming over HTTP (DASH)

Today, video distribution platforms use adaptive video streaming to deliver the maximum Quality of Experience to a wide range of devices connected to the Internet through different access networks. Among the techniques employed to implement video adaptivity, the stream-switching over HTTP is getting a wide acceptance due to its deployment and implementation simplicity. Recently it has been shown that the client-side algorithms proposed so far generate an on-off traffic pattern that may lead to unfairness and underutilization when many video flows share a bottleneck. In this paper we propose ELASTIC (fEedback Linearization Adaptive STreamIng Controller), a client-side controller designed using feedback control theory that does not generate an on-off traffic pattern. By employing a controlled testbed, allowing bandwidth capacity and delays to be set, we compare ELASTIC with other client-side controllers proposed in the literature. In particular, we have checked to what extent the considered algorithms are able to: 1) fully utilize the bottleneck, 2) fairly share the bottleneck, 3) obtain a fair share when TCP greedy flows share the bottleneck with video flows. The obtained results show that ELASTIC achieves a very high fairness and is able to get the fair share when coexisting with TCP greedy flows.
A High Step-Up Converter With a Voltage Multiplier Module for a Photovoltaic System

A novel high step-up converter is proposed for a front-end photovoltaic system. Through a voltage multiplier module, an asymmetrical interleaved high step-up converter obtains high step-up gain without operating at an extreme duty ratio. The voltage multiplier module is composed of a conventional boost converter and coupled inductors. An extra conventional boost converter is integrated into the first phase to achieve a considerably higher voltage conversion ratio. The two-phase configuration not only reduces the current stress through each power switch, but also constrains the input current ripple, which decreases the conduction losses of metal-oxide-semiconductor field-effect transistors (MOSFETs). In addition, the proposed converter functions as an active clamp circuit, which alleviates large voltage spikes across the power switches. Thus, the low-voltage-rated MOSFETs can be adopted for reductions of conduction losses and cost. Efficiency improves because the energy stored in leakage inductances is recycled to the output terminal. Finally, the prototype circuit with a 40-V input voltage, 380-V output, and 1000- W output power is operated to verify its performance. The highest efficiency is 96.8%.