nils-herrmann/clean_scirepeval huggingface dataset:
iFeel: a system that compares and combines sentiment analysis methods

Sentiment analysis methods are used to detect polarity in thoughts and opinions of users in online social media. As businesses and companies are interested in knowing how social media users perceive their brands, sentiment analysis can help better evaluate their product and advertisement campaigns. In this paper, we present iFeel, a Web application that allows one to detect sentiments in any form of text including unstructured social media data. iFeel is free and gives access to seven existing sentiment analysis methods: SentiWordNet, Emoticons, PANAS-t, SASA, Happiness Index, SenticNet, and SentiStrength. With iFeel, users can also combine these methods and create a new Combined-Method that achieves high coverage and F-measure. iFeel provides a single platform to compare the strengths and weaknesses of various sentiment analysis methods with a user friendly interface such as file uploading, graphical visualizing, and weight tuning.
The Enemy in Your Own Camp: How Well Can We Detect Statistically-Generated Fake Reviews - An Adversarial Study

Online reviews are a growing market, but it is struggling with fake reviews. They undermine both the value of reviews to the user, and their trust in the review sites. However, fake positive reviews can boost a business, and so a small industry producing fake reviews has developed. The two sides are facing an arms race that involves more and more natural language processing (NLP). So far, NLP has been used mostly for detection, and works well on human-generated reviews. But what happens if NLP techniques are used to generate fake reviews as well? We investigate the question in an adversarial setup, by assessing the detectability of different fake-review generation strategies. We use generative models to produce reviews based on meta-information, and evaluate their effectiveness against deceptiondetection models and human judges. We find that meta-information helps detection, but that NLP-generated reviews conditioned on such information are also much harder to detect than conventional ones.
Active Convolution: Learning the Shape of Convolution for Image Classification

In recent years, deep learning has achieved great success in many computer vision applications. Convolutional neural networks (CNNs) have lately emerged as a major approach to image classification. Most research on CNNs thus far has focused on developing architectures such as the Inception and residual networks. The convolution layer is the core of the CNN, but few studies have addressed the convolution unit itself. In this paper, we introduce a convolution unit called the active convolution unit (ACU). A new convolution has no fixed shape, because of which we can define any form of convolution. Its shape can be learned through backpropagation during training. Our proposed unit has a few advantages. First, the ACU is a generalization of convolution, it can define not only all conventional convolutions, but also convolutions with fractional pixel coordinates. We can freely change the shape of the convolution, which provides greater freedom to form CNN structures. Second, the shape of the convolution is learned while training and there is no need to tune it by hand. Third, the ACU can learn better than a conventional unit, where we obtained the improvement simply by changing the conventional convolution to an ACU. We tested our proposed method on plain and residual networks, and the results showed significant improvement using our method on various datasets and architectures in comparison with the baseline. Code is available at https://github.com/jyh2986/Active-Convolution.
Design and Optimization of a 380–12 V High-Frequency, High-Current LLC Converter With GaN Devices and Planar Matrix Transformers

Data centers demand high-current, high-efficiency, and low-cost power solutions. The high-voltage dc distribution power architecture has been drawing attention due to its lower conduction loss on cables and harnesses. In this structure, the 380-12 V high output current isolated converter is the key stage. This paper presents a 1-MHz 1-kW LLC resonant converter using GaN devices and planar matrix transformers that are designed and optimized for this application. The transformer design and the optimization of the output capacitor termination are performed and verified. Finally, this cost-effective converter achieves above 97% peak efficiency and 700-W/in3 power density.
One-Shot Adaptation of Supervised Deep Convolutional Models

Dataset bias remains a significant barrier towards solving real world computer vision tasks. Though deep convolutional networks have proven to be a competitive approach for image classification, a question remains: have these models have solved the dataset bias problem? In general, training or fine-tuning a state-ofthe-art deep model on a new domain requires a significant amount of data, which for many applications is simply not available. Transfer of models directly to new domains without adaptation has historically led to poor recognition performance. In this paper, we pose the following question: is a single image dataset, much larger than previously explored for adaptation, comprehensive enough to learn general deep models that may be effectively applied to new image domains? In other words, are deep CNNs trained on large amounts of labeled data as susceptible to dataset bias as previous methods have been shown to be? We show that a generic supervised deep CNN model trained on a large dataset reduces, but does not remove, dataset bias. Furthermore, we propose several methods for adaptation with deep models that are able to operate with little (one example per category) or no labeled domain specific data. Our experiments show that adaptation of deep models on benchmark visual domain adaptation datasets can provide a significant performance boost.
Upper-Limb Robotic Exoskeletons for Neurorehabilitation: A Review on Control Strategies

Since the late 1990s, there has been a burst of research on robotic devices for poststroke rehabilitation. Robot-mediated therapy produced improvements on recovery of motor capacity; however, so far, the use of robots has not shown qualitative benefit over classical therapist-led training sessions, performed on the same quantity of movements. Multidegree-of-freedom robots, like the modern upper-limb exoskeletons, enable a distributed interaction on the whole assisted limb and can exploit a large amount of sensory feedback data, potentially providing new capabilities within standard rehabilitation sessions. Surprisingly, most publications in the field of exoskeletons focused only on mechatronic design of the devices, while little details were given to the control aspects. On the contrary, we believe a paramount aspect for robots potentiality lies on the control side. Therefore, the aim of this review is to provide a taxonomy of currently available control strategies for exoskeletons for neurorehabilitation, in order to formulate appropriate questions toward the development of innovative and improved control strategies.
The Impact of Diversity on Online Ensemble Learning in the Presence of Concept Drift

Online learning algorithms often have to operate in the presence of concept drift (i.e., the concepts to be learned can change with time). This paper presents a new categorization for concept drift, separating drifts according to different criteria into mutually exclusive and nonheterogeneous categories. Moreover, although ensembles of learning machines have been used to learn in the presence of concept drift, there has been no deep study of why they can be helpful for that and which of their features can contribute or not for that. As diversity is one of these features, we present a diversity analysis in the presence of different types of drifts. We show that, before the drift, ensembles with less diversity obtain lower test errors. On the other hand, it is a good strategy to maintain highly diverse ensembles to obtain lower test errors shortly after the drift independent on the type of drift, even though high diversity is more important for more severe drifts. Longer after the drift, high diversity becomes less important. Diversity by itself can help to reduce the initial increase in error caused by a drift, but does not provide the faster recovery from drifts in long-term.
Linear Algebraic Structure of Word Senses, with Applications to Polysemy

Word embeddings are ubiquitous in NLP and information retrieval, but it is unclear what they represent when the word is polysemous. Here it is shown that multiple word senses reside in linear superposition within the word embedding and simple sparse coding can recover vectors that approximately capture the senses. The success of our approach, which applies to several embedding methods, is mathematically explained using a variant of the random walk on discourses model (Arora et al., 2016). A novel aspect of our technique is that each extracted word sense is accompanied by one of about 2000 “discourse atoms” that gives a succinct description of which other words co-occur with that word sense. Discourse atoms can be of independent interest, and make the method potentially more useful. Empirical tests are used to verify and support the theory.
Comparison of Control Strategies for Shunt Active Power Filters in Three-Phase Four-Wire Systems

Strategies for extracting the three-phase reference currents for shunt active power filters are compared, evaluating their performance under different source and load conditions with the new IEEE Standard 1459 power definitions. The study was applied to a three-phase four-wire system in order to include imbalance. Under balanced and sinusoidal voltages, harmonic cancellation and reactive power compensation can be attained in all the methods. However, when the voltages are distorted and/or unbalanced, the compensation capabilities are not equivalent, with some strategies unable to yield an adequate solution when the mains voltages are not ideal. Simulation and experimental results are included
Predicting the NFL using Twitter

We study the relationship between social media output and National Football League (NFL) games, using a dataset containing messages from Twitter and NFL game statistics. Specifically, we consider tweets pertaining to specific teams and games in the NFL season and use them alongside statistical game data to build predictive models for future game outcomes (which team will win?) and sports betting outcomes (which team will win with the point spread? will the total points be over/under the line?). We experiment with several feature sets and find that simple features using large volumes of tweets can match or exceed the performance of more traditional features that use game statistics.
Using Ultrasonic and Infrared Sensors for Distance Measurement

The amplitude response of infrared (IR) sensors depends on the reflectance properties of the target. Therefore, in order to use IR sensor for measuring distances accurately, prior knowledge of the surface must be known. This paper describes the Phong Illumination Model for determining the properties of a surface and subsequently calculating the distance to the surface. The angular position of the IR sensor is computed as normal to the surface for simplifying the calculation. Ultrasonic (US) sensor can provide the initial information on distance to obtain the parameters for this method. In addition, the experimental results obtained by using LabView are discussed. More care should be taken when placing the objects from the sensors during acquiring data since the small change in angle could show very different distance than the actual one. Since stereo camera vision systems do not perform well under some environmental conditions such as plain wall, glass surfaces, or poor lighting conditions, the IR and US sensors can be used additionally to improve the overall vision systems of mobile robots. Distance Measurement, Infrared sensor, Surface properties, Ultrasonic sensor.
Octree-based Point-Cloud Compression

In this paper we present a progressive compression method for point sampled models that is specifically apt at dealing with densely sampled surface geometry. The compression is lossless and therefore is also suitable for storing the unfiltered, raw scan data. Our method is based on an octree decomposition of space. The point-cloud is encoded in terms of occupied octree-cells. To compress the octree we employ novel prediction techniques that were specifically designed for point sampled geometry and are based on local surface approximations to achieve high compression rates that outperform previous progressive coders for point-sampled geometry. Moreover we demonstrate that additional point attributes, such as color, which are of great importance for point-sampled geometry, can be well integrated and efficiently encoded in this framework.
The Role of Leaders in Influencing Unethical Behavior in the Workplace

A fter years of focusing on explaining and predicting positive employee attitudes (e.g., job satisfaction, employee commitment) and behaviors (e.g., employee citizenship, work performance), organizational behavior researchers have increasingly turned their attention to understanding what drives Although researchers have used a variety of terms to describe such employee behavior (e.g., deviance, antisocial behavior, misbehavior, counterproductive behavior, unethical behavior), all of them share a concern with counternormative behavior intended to harm the organization or its stakeholders (O'Leary-Kelly, Duffy, & Griffin, 2000). Unethical behavior in organizations has been widely reported in the wake of many recent high-profile corporate scandals. As researchers and practitioners consider what may be driving such behavior, leaders are coming under increasing scrutiny not only because many senior executives are accused of having committed unethical acts but also because of the role that leaders at all levels are thought to play in managing the ethical (and unethical) conduct of organization members. For example, Bernie Ebbers, the former chief executive officer of WorldCom, was hailed as a great leader for growing the company into a telecommunications superpower.
Sentiment analysis in twitter using machine learning techniques

Sentiment analysis deals with identifying and classifying opinions or sentiments expressed in source text. Social media is generating a vast amount of sentiment rich data in the form of tweets, status updates, blog posts etc. Sentiment analysis of this user generated data is very useful in knowing the opinion of the crowd. Twitter sentiment analysis is difficult compared to general sentiment analysis due to the presence of slang words and misspellings. The maximum limit of characters that are allowed in Twitter is 140. Knowledge base approach and Machine learning approach are the two strategies used for analyzing sentiments from the text. In this paper, we try to analyze the twitter posts about electronic products like mobiles, laptops etc using Machine Learning approach. By doing sentiment analysis in a specific domain, it is possible to identify the effect of domain information in sentiment classification. We present a new feature vector for classifying the tweets as positive, negative and extract peoples' opinion about products.
Training Skinny Deep Neural Networks with Iterative Hard Thresholding Methods

Deep neural networks have achieved remarkable success in a wide range of practical problems. However, due to the inherent large parameter space, deep models are notoriously prone to overfitting and difficult to be deployed in portable devices with limited memory. In this paper, we propose an iterative hard thresholding (IHT) approach to train Skinny Deep Neural Networks (SDNNs). An SDNN has much fewer parameters yet can achieve competitive or even better performance than its full CNN counterpart. More concretely, the IHT approach trains an SDNN through following two alternative phases: (I) perform hard thresholding to drop connections with small activations and fine-tune the other significant filters; (II) re-activate the frozen connections and train the entire network to improve its overall discriminative capability. We verify the superiority of SDNNs in terms of efficiency and classification performance on four benchmark object recognition datasets, including CIFAR-10, CIFAR-100, MNIST and ImageNet. Experimental results clearly demonstrate that IHT can be applied for training SDNN based on various CNN architectures such as NIN and AlexNet.
Adaptive Key Frame Extraction using Unsupervised Clustering

Key frame extraction has been recognized as one of the important research issues in video information retrieval. Although progress has been made in key frame extraction, the existing approaches are either computationally expensive or ine ective in capturing salient visual content. In this paper, we rst discuss the importance of key frame selection; and then brie y review and evaluate the existing approaches. To overcome the shortcomings of the existing approaches, we introduce a new algorithm for key frame extraction based on unsupervised clustering. The proposed algorithm is both computationally simple and able to adapt to the visual content. The e ciency and e ectiveness are validated by large amount of real-world videos.
Design of automatic target-scoring system of shooting game based on computer vision

This paper describes a machine vision system with back lighting illumination and friendly man-machine interface. Subtraction is used to segment target holes quickly and accurately. The oval obtained after tracing boundary is processed by Generalized Hough Transform to acquire the target's center. Marked-hole's area, perimeter and moment invariants are extracted as cluster features. The auto-scoring software, programmed by Visual C++, has successfully solved the recognition of off-target and overlapped holes through alarming surveillance and bullet tacking programs. The experimental results show that, when the target is distorted obviously, the system can recognize the overlapped holes on real time and also clusters random shape holes on the target correctly. The high accuracy, fast computing speed, easy debugging and low cost make the system can be widely used.
Adiabatic theorem and continuous coupled-mode theory for efficient taper transitions in photonic crystals.

We prove that an adiabatic theorem generally holds for slow tapers in photonic crystals and other strongly grated waveguides with arbitrary index modulation, exactly as in conventional waveguides. This provides a guaranteed pathway to efficient and broad-bandwidth couplers with, e.g., uniform waveguides. We show that adiabatic transmission can only occur, however, if the operating mode is propagating (nonevanescent) and guided at every point in the taper. Moreover, we demonstrate how straightforward taper designs in photonic crystals can violate these conditions, but that adiabaticity is restored by simple design principles involving only the independent band structures of the intermediate gratings. For these and other analyses, we develop a generalization of the standard coupled-mode theory to handle arbitrary nonuniform gratings via an instantaneous Bloch-mode basis, yielding a continuous set of differential equations for the basis coefficients. We show how one can thereby compute semianalytical reflection and transmission through crystal tapers of almost any length, using only a single pair of modes in the unit cells of uniform gratings. Unlike other numerical methods, our technique becomes more accurate as the taper becomes more gradual, with no significant increase in the computation time or memory. We also include numerical examples comparing to a well-established scattering-matrix method in two dimensions.
Software effort estimation as a multiobjective learning problem

Ensembles of learning machines are promising for software effort estimation (SEE), but need to be tailored for this task to have their potential exploited. A key issue when creating ensembles is to produce diverse and accurate base models. Depending on how differently different performance measures behave for SEE, they could be used as a natural way of creating SEE ensembles. We propose to view SEE model creation as a multiobjective learning problem. A multiobjective evolutionary algorithm (MOEA) is used to better understand the tradeoff among different performance measures by creating SEE models through the simultaneous optimisation of these measures. We show that the performance measures behave very differently, presenting sometimes even opposite trends. They are then used as a source of diversity for creating SEE ensembles. A good tradeoff among different measures can be obtained by using an ensemble of MOEA solutions. This ensemble performs similarly or better than a model that does not consider these measures explicitly. Besides, MOEA is also flexible, allowing emphasis of a particular measure if desired. In conclusion, MOEA can be used to better understand the relationship among performance measures and has shown to be very effective in creating SEE models.
A Two-Step Classification Approach to Unsupervised Record Linkage

Linking or matching databases is becoming increasingly important in many data mining projects, as linked data can contain information that is not available otherwise, or that would be too expensive to collect manually. A main challenge when linking large databases is the classification of the compared record pairs into matches and non-matches. In traditional record linkage, classification thresholds have to be set either manually or using an EM-based approach. More recently developed classification methods are mainly based on supervised machine learning techniques and thus require training data, which is often not available in real world situations or has to be prepared manually. In this paper, a novel two-step approach to record pair classification is presented. In a first step, example training data of high quality is generated automatically, and then used in a second step to train a supervised classifier. Initial experimental results on both real and synthetic data show that this approach can outperform traditional unsupervised clustering, and even achieve linkage quality almost as good as fully supervised techniques.
Query-by-example keyword spotting using long short-term memory networks

We present a novel approach to query-by-example keyword spotting (KWS) using a long short-term memory (LSTM) recurrent neural network-based feature extractor. In our approach, we represent each keyword using a fixed-length feature vector obtained by running the keyword audio through a word-based LSTM acoustic model. We use the activations prior to the softmax layer of the LSTM as our keyword-vector. At runtime, we detect the keyword by extracting the same feature vector from a sliding window and computing a simple similarity score between this test vector and the keyword vector. With clean speech, we achieve 86% relative false rejection rate reduction at 0.5% false alarm rate when compared to a competitive phoneme posteriorgram with dynamic time warping KWS system, while the reduction in the presence of babble noise is 67%. Our system has a small memory footprint, low computational cost, and high precision, making it suitable for on-device applications.
Automatic Grid Segmentation of Populated Chessboard Taken at a Lower Angle View

Segmentation of the chessboard grid is a crucial step in coordinate extraction for chess video annotation, content-based indexing of chess videos and for content analysis in prototyping vision systems such as a chess robot. This paper deals with the segmentation of grid elements from a populated chessboard taken at a lower angle view. The proposed approach couples the line-based grid detection method with domain knowledge of the chessboard to achieve improved accuracy and reliability.