nils-herrmann/clean_scirepeval huggingface dataset:
Unsupervised Learning from Narrated Instruction Videos

We address the problem of automatically learning the main steps to complete a certain task, such as changing a car tire, from a set of narrated instruction videos. The contributions of this paper are three-fold. First, we develop a new unsupervised learning approach that takes advantage of the complementary nature of the input video and the associated narration. The method solves two clustering problems, one in text and one in video, applied one after each other and linked by joint constraints to obtain a single coherent sequence of steps in both modalities. Second, we collect and annotate a new challenging dataset of real-world instruction videos from the Internet. The dataset contains about 800,000 frames for five different tasks1 that include complex interactions between people and objects, and are captured in a variety of indoor and outdoor settings. Third, we experimentally demonstrate that the proposed method can automatically discover, in an unsupervised manner, the main steps to achieve the task and locate the steps in the input videos.
Content Aware Neural Style Transfer

In the emerging inter-disciplinary field of art and image processing, algorithms have been developed to assist the analysis of art work. In most applications, especially brush stroke analysis, high resolution digital images of paintings are required to capture subtle patterns and details in the high frequency range of the spectrum. Algorithms have been developed to learn styles of painters from their digitized paintings to help identify authenticity of controversial paintings. However, high quality testing datasets containing both original and forgery are limited to confidential image files provided by museums, which is not publicly available, and a small sets of original/copy paintings painted by the same artist, where copies were deferred to two weeks after the originals were finished. Up to date, no synthesized painting by computers from a real painting has been used as a negative test case, mainly due to the limitation of prevailing style transfer algorithms. There are two main types of style transfer algorithms, either transferring the tone (color, contrast, saturation, etc.) of an image, preserving its patterns and details, or distorting the texture uniformly of an image to create “style”. In this paper, we are interested in a higher level of style transfer, particularly, transferring a source natural image (e.g. a photo) to a high resolution painting given a reference painting of similar object. The transferred natural image would have a similar presentation of the original object to that of the reference painting. In general, an object is painted in a different style of brush strokes than that of the background, hence the desired style transferring algorithm should be able to recognize the object in the source natural image and transfer brush stroke styles in the reference painting in a content-aware way such that the styles of the foreground and the background, and moreover different parts of the foreground in the transferred image, are consistent to that in the reference painting. Recently, an algorithm based on deep convolutional neural network has been developed to transfer artistic style from an art painting to a photo . Successful as it is in transferring styles from impressionist paintings of artists such as Vincent van Gogh to photos of various scenes, the algorithm is prone to distorting the structure of the content in the source image and introducing artifacts/new
Challenges: an application model for pervasive computing

The way mobile computing devices and applications are developed, deployed and used today does not meet the expectations of the user community and falls far short of the potential for pervasive computing. This paper challenges the mobile computing community by questioning the roles of devices, applications, and a user's environment. A vision of pervasive computing is described, along with attributes of a new application model that supports this vision, and a set of challenges that must be met in order to bring the vision to reality.
Visualizing Large-scale and High-dimensional Data

We study the problem of visualizing large-scale and highdimensional data in a low-dimensional (typically 2D or 3D) space. Much success has been reported recently by techniques that first compute a similarity structure of the data points and then project them into a low-dimensional space with the structure preserved. These two steps suffer from considerable computational costs, preventing the state-ofthe-art methods such as the t-SNE from scaling to largescale and high-dimensional data (e.g., millions of data points and hundreds of dimensions). We propose the LargeVis, a technique that first constructs an accurately approximated K-nearest neighbor graph from the data and then layouts the graph in the low-dimensional space. Comparing to tSNE, LargeVis significantly reduces the computational cost of the graph construction step and employs a principled probabilistic model for the visualization step, the objective of which can be effectively optimized through asynchronous stochastic gradient descent with a linear time complexity. The whole procedure thus easily scales to millions of highdimensional data points. Experimental results on real-world data sets demonstrate that the LargeVis outperforms the state-of-the-art methods in both efficiency and effectiveness. The hyper-parameters of LargeVis are also much more stable over different data sets.
Comparison of nonlinear control design techniques on a model of the Caltech ducted fan

In this paper we compare di!erent nonlinear control design methods by applying them to the planar model of a ducted fan engine. The methods used range from Jacobian linearization of the nonlinear plant and designing an LQR controller, to using model predictive control and linear parameter varying methods. The controller design can be divided into two steps. The "rst step requires the derivation of a control Lyapunov function (CLF), while the second involves using an existing CLF to generate a controller. The main premise of this paper is that by combining the best of these two phases, it is possible to "nd controllers that achieve superior performance when compared to those that apply each phase independently. All of the results are compared to the optimal solution which is approximated by solving a trajectory optimization problem with a su$ciently large time horizon.
Ignition System Based on the Nanosecond Pulsed Discharge

A new design of the compact nanosecond pulse generator based of the drift step recovery diodes as an ignition system for internal combustion engines (ICEs) has been presented. Experimental results of the comparative analysis of the standard ignition system and an ignition system on the basis of the nanosecond pulsed discharge for the four-cylinder ICE have been presented. It was shown that a nonequilibrium plasma formed by the discharge was an effective way to reduce the specific fuel consumption as well as the concentration of nitrogen oxides in the engine exhaust gases. A numerical analysis of possible mechanisms of the nonequilibrium plasma influence on the suppression of the nitrogen oxides formation has been carried out.
Common phase error due to phase noise in OFDM-estimation and suppression

Orthogonal frequency division multiplexing (OFDM) has already become a very attractive modulation scheme for many applications. Unfortunately OFDM is very sensitive to synchronization errors, one of them being phase noise, which is of great importance in modern WLAN systems which target high data rates and tend to use higher frequency bands because of the spectrum availability. In this paper we propose a linear Kalman filter as a means for tracking phase noise and its suppression. The algorithm is pilot based. The performance of the proposed method is investigated and compared with the performance of other known algorithms.
Solving and Rating Sudoku Puzzles with Genetic Algorithms

This paper discusses solving and generating Sudoku puzzles with evolutionary algorithms. Sudoku is a Japanese number puzzle game that has become a worldwide phenomenon. As an optimization problem Sudoku belongs to the group of combinatorial problems, but it is also a constraint satisfaction problem. The objective of this paper is to test if genetic algorithm optimization is an efficient method for solving Sudoku puzzles and to generate new puzzles. Another goal is to find out if the puzzles, that are difficult for human solver, are also difficult for the genetic algorithms. In that case it would offer an opportunity to use genetic algorithm solver to test the difficulty levels of new Sudoku puzzles, i.e. to use it as a rating machine.
Desisting and persisting gender dysphoria after childhood: a qualitative follow-up study.

The aim of this qualitative study was to obtain a better understanding of the developmental trajectories of persistence and desistence of childhood gender dysphoria and the psychosexual outcome of gender dysphoric children. Twenty five adolescents (M age 15.88, range 14-18), diagnosed with a Gender Identity Disorder (DSM-IV or DSM-IV-TR) in childhood, participated in this study. Data were collected by means of biographical interviews. Adolescents with persisting gender dysphoria (persisters) and those in whom the gender dysphoria remitted (desisters) indicated that they considered the period between 10 and 13 years of age to be crucial. They reported that in this period they became increasingly aware of the persistence or desistence of their childhood gender dysphoria. Both persisters and desisters stated that the changes in their social environment, the anticipated and actual feminization or masculinization of their bodies, and the first experiences of falling in love and sexual attraction had influenced their gender related interests and behaviour, feelings of gender discomfort and gender identification. Although, both persisters and desisters reported a desire to be the other gender during childhood years, the underlying motives of their desire seemed to be different.
Pipelined Cyclic Redundancy Check (CRC) Calculation

Traditional methods to calculate CRC suffer from diminishing returns. Doubling the data width doesn't double the maximum data throughput, the worst case timing path becomes slower. Feedback in the traditional implementation makes pipelining problematic. However, the on chip data width used for high throughput protocols is constantly increasing. The battle of reducing static power consumption is one factor driving this trend towards wider data paths. This paper discusses a method for pipelining the calculation of CRC's, such as ISO-3309 CRC32. This method allows independent scaling of circuit frequency and data throughput by varying the data width and the number of pipeline stages. Pipeline latency can be traded for area while slightly affecting timing. Additionally it allows calculation over data that isn't the full width of the input. This often happens at the end of the packet, although it could happen in the middle of the packet if data arrival is bursty. Finally, a fortunate side effect is that it offers the ability to efficiently update a known good CRC value where a small subset of data in the packet has changed. This is a function often desired in routers, for example updating the TTL field in IPv4 packets.
Credit Assignment through Time: Alternatives to Backpropagation

Learning to recognize or predict sequences using long-term context has many applications. However, practical and theoretical problems are found in training recurrent neural networks to perform tasks in which input/output dependencies span long intervals. Starting from a mathematical analysis of the problem, we consider and compare alternative algorithms and architectures on tasks for which the span of the input/output dependencies can be controlled. Results on the new algorithms show performance qualitatively superior to that obtained with backpropagation.
An Optimised Density Based Clustering Algorithm

The DBSCAN  algorithm is a popular algorithm in Data Mining field as it has the ability to mine the noiseless arbitrary shape Clusters in an elegant way. As the original DBSCAN algorithm uses the distance measures to compute the distance between objects, it consumes so much processing time and its computation complexity comes as O (N2). In this paper we have proposed a new algorithm to improve the performance of DBSCAN algorithm. The existing algorithms A Fast DBSCAN Algorithm[6] and Memory effect in DBSCAN algorithm[7] has been combined in the new solution to speed up the performance as well as improve the quality of the output. As the RegionQuery operation takes long time to process the objects, only few objects are considered for the expansion and the remaining missed border objects are handled differently during the cluster expansion. Eventually the performance analysis and the cluster output show that the proposed solution is better to the existing algorithms.
A Critical Evaluation of Website Fingerprinting Attacks

Recent studies on Website Fingerprinting (WF) claim to have found highly effective attacks on Tor. However, these studies make assumptions about user settings, adversary capabilities, and the nature of the Web that do not necessarily hold in practical scenarios. The following study critically evaluates these assumptions by conducting the attack where the assumptions do not hold. We show that certain variables, for example, user's browsing habits, differences in location and version of Tor Browser Bundle, that are usually omitted from the current WF model have a significant impact on the efficacy of the attack. We also empirically show how prior work succumbs to the base rate fallacy in the open-world scenario. We address this problem by augmenting our classification method with a verification step. We conclude that even though this approach reduces the number of false positives over 63\%, it does not completely solve the problem, which remains an open issue for WF attacks.
A Wireless Embedded Device for Personalized Ultraviolet Monitoring

The skin care product market is growing due to the threat of ultraviolet (UV) radiation caused by the destruction of the ozone layer, increasing demand for tanning, and the tendency to wear less clothing. Accordingly, there is a potential demand for a personalized UV monitoring device, which can play a fundamental role in skin cancer prevention by providing measurements of UV radiation intensities and corresponding recommendations. This paper highlights the development and initial validation of a wireless and portable embedded device for personalized UV monitoring which is based on a novel software architecture, a high-end UV sensor, and conventional PDA (or a cell phone). In terms of short-term applications, by calculating the UV index, it informs the users about their maximum recommended sun exposure time by taking their skin type and sun protection factor (SPF) of the applied sunscreen into consideration. As for long-term applications, given that the damage caused by UV light is accumulated over days, it displays the amount of UV received over a certain course of time, from a single day to a month.
Experimental quantum teleportation

Quantum teleportation — the transmission and reconstruction over arbitrary distances of the state of a quantum system — is demonstrated experimentally. During teleportation, an initial photon which carries the polarization that is to be transferred and one of a pair of entangled photons are subjected to a measurement such that the second photon of the entangled pair acquires the polarization of the initial photon. This latter photon can be arbitrarily far away from the initial one. Quantum teleportation will be a critical ingredient for quantum computation networks.
Hardware Implementation of LZMA Data Compression Algorithm

Data transmission, storage and processing are the integral parts of today’s information systems. Transmission and storage of huge volume of data is a critical task in spite of the advancements in the integrated circuit technology and communication. In order to store and transmit such a data as it is, requires larger memory and increased bandwidth utilization. This in turn increases the hardware and transmission cost. Hence, before storage or transmission the size of data has to be reduced without affecting the information content of the data. Among the various encoding algorithms, the Lempel Ziv Marcov chain Algorithm (LZMA) algorithm which is used in 7zip was proved to be effective in unknown byte stream compression for reliable lossless data compression. However the encoding speed of software based coder is slow compared to the arrival time of real time data. Hence hardware implementation is needed since number of instructions processed per unit time depends directly on system clock. The aim of this work is to implement the LZMA algorithm on SPARTAN 3E FPGA to design hardware encoder/decoder with reduces circuit size and cost of storage. General Terms Data Compression, VLSI
Face Recognition for Smart Environments

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PWM control and experiment of modular multilevel converters

A modular multilevel converter (MMC) is one of the next-generation multilevel PWM converters intended for high- or medium-voltage power conversion without transformers. The MMC consists of cascade connection of multiple bidirectional PWM chopper-cells and floating dc capacitors per leg, thus requiring voltage-balancing control of their chopper-cells. However, no paper has been discussed explicitly on voltage-balancing control with theoretical and experimental verifications. This paper deals with two types of modular multilevel PWM converters with focus on their circuit configurations and voltage-balancing control. Combination of averaging and balancing controls enables the MMCs to achieve voltage balancing without any external circuit. The viability of the MMCs as well as the effectiveness of the PWM control method is confirmed by simulation and experiment.
MCS Selection for Throughput Improvement in Downlink LTE Systems

In this paper, we investigate resource block (RB) assignment and modulation-and-coding scheme (MCS) selection to maximize downlink throughput of long-term evolution (LTE) systems, where all RB's assigned to the same user in any given transmission time interval (TTI) must use the same MCS. We develop several effective MCS selection schemes by using the effective packet-level SINR based on exponential effective SINR mapping (EESM), arithmetic mean, geometric mean, and harmonic mean. From both analysis and simulation results, we show that the system throughput of all the proposed schemes are better than that of the scheme in . Furthermore, the MCS selection scheme using harmonic mean based effective packet-level SINR almost reaches the optimal performance and significantly outperforms the other proposed schemes.
Solving Large Imperfect Information Games Using CFR+

Counterfactual Regret Minimization and variants (e.g. Public Chance Sampling CFR and Pure CFR) have been known as the best approaches for creating approximate Nash equilibrium solutions for imperfect information games such as poker. This paper introduces CFR, a new algorithm that typically outperforms the previously known algorithms by an order of magnitude or more in terms of computation time while also potentially requiring less memory.
A hierarchical genetic fuzzy system based on genetic programming for addressing classification with highly imbalanced and borderline data-sets

Lots of real world applications appear to be a matter of classification with imbalanced data-sets. This problem arises when the number of instances from one class is quite different to the number of instances from the other class. Traditionally, classification algorithms are unable to correctly deal with this issue as they are biased towards the majority class. Therefore, algorithms tend to misclassify the minority class which usually is the most interesting one for the application that is being sorted out. Among the available learning approaches, fuzzy rule-based classification systems have obtained a good behavior in the scenario of imbalanced data-sets. In this work, we focus on some modifications to further improve the performance of these systems considering the usage of information granulation. Specifically, a positive synergy between data sampling methods and algorithmic modifications is proposed, creating a genetic programming approach that uses linguistic variables in a hierarchical way. These linguistic variables are adapted to the context of the problem with a genetic process that combines rule selection with the adjustment of the lateral position of the labels based on the 2-tuples linguistic model. An experimental study is carried out over highly imbalanced and borderline imbalanced data-sets which is completed by a statistical comparative analysis. The results obtained show that the proposed model outperforms several fuzzy rule based classification systems, including a hierarchical approach and presents a better behavior than the C4.5 decision tree.
Neural networks for deceptive opinion spam detection: An empirical study

The products reviews are increasingly used by individuals and organizations for purchase and business decisions. Driven by the desire of profit, spammers produce synthesized reviews to promote some products or demote competitors products. So deceptive opinion spam detection has attracted significant attention from both business and research communities in recent years. Existing approaches mainly focus on traditional discrete features, which are based on linguistic and psychological cues. However, these methods fail to encode the semantic meaning of a document from the discourse perspective, which limits the performance. In this work, we empirically explore a neural network model to learn document-level representation for detecting deceptive opinion spam. First, the model learns sentence representation with convolutional neural network. Then, sentence representations are combined using a gated recurrent neural network, which can model discourse information and yield a document vector. Finally, the document representations are directly used as features to identify deceptive opinion spam. Based on three domains datasets, the results on in-domain and cross-domain experiments show that our proposed method outperforms state-of-the-art methods.