nils-herrmann/clean_scirepeval huggingface dataset:
Alexa, My Love: Analyzing Reviews of Amazon Echo

The phenomenal success of Amazon Echo as a voice-activated wireless speaker has intrigued us to understand how consumers use the device with an embodied conversational agent in real-world situations. In addition, we are interested in learning what kind of relationship people develop with the device, which has a humanized name. Thus, we performed a large-scale analysis of the user reviews of Amazon Echo, with the dataset consisting of 55,502 reviews spanning over 2 years from May 2015 to May 2017. We first conducted a qualitative study using content analysis of 144 representative reviews to discover what features people like and dislike about Echo. Two case studies were then performed, one for the hands-free feature that customers raved about and the other for speech recognition and understanding which is one key feature of the Echo. To automate the analyzing on a large scale of reviews, we used natural language processing techniques for sentiment analysis and compared Echo reviews with reviews of two traditional electronic devices, Fire Tablet and the popular Bluetooth speaker Dknight MagicBox II. Interestingly we found a significant number of reviewers personify Echo as an assistant, a friend, or a family member. Some reviewers even compare Echo with their wives or girlfriends. Using emotion analysis, we found the reviews personifying Echo showed more positive emotions than those simply treated Echo as a device. In the future work, we plan to conduct field studies to understand interesting use cases of Echo and the novel applications enabled by such powerful ubicomp devices.
Meme representations for game agents

The advancement in game technology has served to enrich player’s gaming experience in a substantial way. Nowadays, it is common to have blockbuster quality games, with realistic graphics and engaging stories. Despite this, the progress made in incorporating Artificial Intelligence has been slow, and realistic human-like intelligence in games is hardly to be found. There have been some attempts to use Machine Learning in games, but such attempts often ended up impractical or affecting the players enjoyment due to several constraining factors. In this paper, we describe Meme War as a proof-of-concept for practical usage of Machine Learning in games. We introduce Extreme Learning Machine (ELM) as one approach to achieve a better experience in employing Machine Learning in games. Advantages of ELM over Multilayer Perceptron (MLP) are presented in terms of what ELM can offer in a practical point of view of the player.
Methods of combining multiple classifiers and their applications to handwriting recognition

Method of combining the classification powers of several classifiers is regarded as a general problem in various application areas of pattern recognition, and a systematic investigation has been made. Possible solutions to the problem can be divided into three categories according to the levels of information available from the various classifiers. Four approaches are proposed based on different methodologies for solving this problem. One is suitable for combining individual classifiers such as Bayesian, k-NN and various distance classifiers. The other three could be used for combining any kind of individual classifiers. On applying these methods to combine several classifiers for recognizing totally unconstrained handwritten numerals, the experimental results show that the performance of individual classifiers could be improved significantly. For example, on the U.S. zipcode database, the result of 98.9% recognition with 0.90% substitution and 0.2% rejection can be obtained, as well as a high reliability with 95% recognition, 0% substitution and 5% rejection. These results compared favorably to other research p u p s in Europe, Asia, and North America.
Making Bertha Drive—An Autonomous Journey on a Historic Route

125 years after Bertha Benz completed the first overland journey in automotive history, the Mercedes Benz S-Class S 500 INTELLIGENT DRIVE followed the same route from Mannheim to Pforzheim, Germany, in fully autonomous manner. The autonomous vehicle was equipped with close-to-production sensor hardware and relied solely on vision and radar sensors in combination with accurate digital maps to obtain a comprehensive understanding of complex traffic situations. The historic Bertha Benz Memorial Route is particularly challenging for autonomous driving. The course taken by the autonomous vehicle had a length of 103 km and covered rural roads, 23 small villages and major cities (e.g. downtown Mannheim and Heidelberg). The route posed a large variety of difficult traffic scenarios including intersections with and without traffic lights, roundabouts, and narrow passages with oncoming traffic. This paper gives an overview of the autonomous vehicle and presents details on vision and radar-based perception, digital road maps and video-based self-localization, as well as motion planning in complex urban scenarios.
SHAREK: A Scalable Dynamic Ride Sharing System

Due to its significant economic and environmental impact, sharing the ride among a number of drivers (i.e., Car pooling) has recently gained significant interest from industry and academia. Hence, a number of ride sharing services have appeared along with various algorithms on how to match a rider request to a driver who can provide the ride sharing service. However, existing techniques have several limitations that affect the quality of the ride sharing service, and hence hinder its wide applicability. This paper proposes SHAREK, a scalable and efficient ride sharing service that overcomes the limitations of existing approaches. SHAREK allows riders requesting the ride sharing service to indicate the maximum price they are willing to pay for the service and the maximum waiting time before being picked up. In the mean time, SHAREK computes the price of the service based on the distance of the rider trip and the detour that the driver will make to offer the service. Then, SHAREK returns a set of drivers that can make it to the rider within its price and temporal constraints. Since there could be many of such drivers, SHAREK internally prunes those drivers that are dominated by others, i.e., They provide higher price and higher waiting time than other drivers. To realize its efficiency and scalability, SHAREK employs a set of early pruning techniques that minimize the need for any actual shortest path computations.
Design, Analysis, and Test of a Novel 2-DOF Nanopositioning System Driven by Dual Mode

Piezodriven flexure-based motion stages, with a large workspace and high positioning precision, are really attractive for the realization of high-performance atomic force microscope (AFM) scanning. In this paper, a modified lever displacement amplifier is proposed for the mechanism design of a novel compliant two-degree-of-freedom (2-DOF) nanopositioning stage, which can be selected to drive in dual modes. Besides, the modified double four-bar parallelogram, P (P denotes prismatic) joints are adopted in designing the flexure limbs. The established models for the mechanical performance evaluation of the stage, in terms of kinetostatics, dynamics, and workspace, are validated by the finite-element analysis. After a series of dimension optimizations carried out through the particle swarm optimization algorithm, a novel active disturbance rejection controller, including the nonlinearity tracking differentiator, the extended state observer, and the nonlinear state error feedback, is proposed to automatically estimate and suppress plant uncertainties arising from the hysteresis nonlinearity, creep effect, sensor noises, and unknown disturbances. The simulation and prototype test results indicate that the first natural frequency of the proposed stage is approximated to be 831 Hz, the amplification ratio in two axes is about 4.2, and the workspace is 119.7 μm × 121.4 μm, while the cross coupling between the two axes is kept within 2%. All the results prove that the developed stage possesses a good property for high-performance AFM scanning.
Virtual Reality and Education

Virtual Reality (VR), a new computer technology, has incredible potential in the education field. The reason for this assertion is that education is a field that requires the students to understand complex data, particularly in the study of science (see "Why is science hard to learn?" by Millar) and VR makes that task easier. VR presents information in a 3-dimensional form with the participant viewing the world from inside the world (an immersed viewpoint) with the ability to interact with the information or world. People can enter a VR world that is already created or build their own. VR’s style of presentation mimics the ways that we, as humans, have learned to interact with our physical world. The requirement of learning abstract concepts (such as written language or jargon of a particular field) in order to understand data is dramatically reduced. For example, a chemistry world can show participants how electrons hover around a nucleus depending on the amount of energy involved. The abstractness of the subject would not be eliminated, but metaphors could be employed that were based solidly in reality. For instance, in the electron world the color of the world could represent the amount of energy in the system with red representing more energy than blue. For the students, they would be able to relate to hot things looking red and cold things looking blue without the concept of "joules increasing" getting in the way.
Reduction of Overfitting in Diabetes Prediction Using Deep Learning Neural Network

Augmented accuracy in prediction of diabetes will open up new frontiers in health prognostics. Data overfitting is a performance-degrading issue in diabetes prognosis. In this study, a prediction system for the disease of diabetes is presented where the issue of overfitting is minimized by using the dropout method. Deep learning neural network is used where both fully connected layers are followed by dropout layers. The output performance of the proposed neural network is shown to have outperformed other state-of-art methods and it is recorded as by far the best performance for the Pima Indians Diabetes Data Set.
Interactive Ambiguity Resolution of Named Entities in Fictional Literature

Named entity recognition (NER) denotes the task to detect entities and their corresponding classes, such as person or location, in unstructured text data. For most applications, state of the art NER software is producing reasonable results. However, as a consequence of the methodological limitations and the well-known pitfalls when analyzing natural language data, the NER results are likely to contain ambiguities. In this paper, we present an interactive NER ambiguity resolution technique, which enables users to create (post-processing) rules for named entity recognition data based on the content and entity context of the analyzed documents. We specifically address the problem that in use-cases where ambiguities are problematic, such as the attribution of fictional characters with traits, it is often unfeasible to train models on custom data to improve state of the art NER software. We derive an iterative process model for improving NER results, show an interactive NER ambiguity resolution prototype, illustrate our approach with contemporary literature, and discuss our work and future research.
EMI: Exploration with Mutual Information

Reinforcement learning algorithms struggle when the reward signal is very sparse. In these cases, naive random exploration methods essentially rely on a random walk to stumble onto a rewarding state. Recent works utilize intrinsic motivation to guide the exploration via generative models, predictive forward models, or discriminative modeling of novelty. We propose EMI, which is an exploration method that constructs embedding representation of states and actions that does not rely on generative decoding of the full observation but extracts predictive signals that can be used to guide exploration based on forward prediction in the representation space. Our experiments show that the proposed method significantly outperforms a number of existing exploration methods on challenging locomotion task with continuous control and on image-based exploration tasks with discrete actions on Atari.
Switched Reluctance Motor Drive With External Rotor for Fan in Air Conditioner

This paper describes the developed three-phase 6/8 poles switched reluctance external rotor motor drive for a fan in air conditioner. The external rotor core structure and the internal stator core structure, the three-phase windings arrangement, the slotted claw, and the setting structure of the photoelectric transducers on the rotor position detector are illustrated. The electromagnetic field calculation results are given. The three-phase asymmetric bridge power converter was used in the drive system. The block diagram of the switched reluctance external rotor motor drive with closed-loop rotor speed control is given. The closed-loop rotor speed control is implemented using a fuzzy logic algorithm. The experimental tests of the developed prototype are made for driving the fan from 200 to 950 r/min. The comparison results of the two systems show that the input power and input phase current RMS value are lower in the developed three-phase 6/8 poles external rotor switched reluctance motor drive prototype with the fan than those in the induction motor variable-frequency variable-speed drive with the fan.
HAND GESTURE RECOGNITION SYSTEM FOR DUMB PEOPLE Sagar P . More * 1 ,

Few years ago, there is no way to communicate between the dumb people in a normal speed. And also the normal people never try to communicate between dumb persons. But now a days so many facility are available for understanding to communicate a dumb person using machine translator, hidden grove method etc. But drawback of this method is that is requiring more power supply and this type of machine translator is very costly. To overcome this drawback, In this paper use the static hand gesture recognition system using digital image processing.
Compassion fatigue: psychotherapists' chronic lack of self care.

Psychotherapists who work with the chronic illness tend to disregard their own self-care needs when focusing on the needs of clients. The article discusses the concept of compassion fatigue, a form of caregiver burnout among psychotherapists and contrasts it with simple burnout and countertransference. It includes a multi-factor model of compassion fatigue that emphasizes the costs of caring, empathy, and emotional investment in helping the suffering. The model suggests that psychotherapists that limiting compassion stress, dealing with traumatic memories, and more effectively managing case loads are effective ways of avoiding compassion fatigue. The model also suggests that, to limit compassion stress, psychotherapists with chronic illness need to development methods for both enhancing satisfaction and learning to separate from the work emotionally and physically in order to feel renewed. A case study illustrates how to help someone with compassion fatigue.
PixelNet: Representation of the pixels, by the pixels, and for the pixels

We explore design principles for general pixel-level prediction problems, from low-level edge detection to midlevel surface normal estimation to high-level semantic segmentation. Convolutional predictors, such as the fullyconvolutional network (FCN), have achieved remarkable success by exploiting the spatial redundancy of neighboring pixels through convolutional processing. Though computationally efficient, we point out that such approaches are not statistically efficient during learning precisely because spatial redundancy limits the information learned from neighboring pixels. We demonstrate that stratified sampling of pixels allows one to (1) add diversity during batch updates, speeding up learning; (2) explore complex nonlinear predictors, improving accuracy; and (3) efficiently train state-of-the-art models tabula rasa (i.e., “from scratch”) for diverse pixel-labeling tasks. Our single architecture produces state-of-the-art results for semantic segmentation on PASCAL-Context dataset, surface normal estimation on NYUDv2 depth dataset, and edge detection on BSDS.
Knowledge Discovering using FrameNet , VerbNet and PropBank

In this paper, we present a high accurate system for FrameNet semantic role classification based on the innovative features derived from a combined use of FrameNet, VerbNet and PropBank. The main property of our approach is a unified view of the above three resources which is theoretically supported by the linking theory. Experiments on Support Vector Machines (SVM) show that our system classifies semantic information with high accuracy, enabling future work for the design of knowledge discovering FrameNet-based models.
Face Recognition with Local Binary Patterns

This paper is about providing efficient face recognition i.e. feature extraction and face matching system using local binary patterns (LBP) method. It is a texture based algorithm for face recognition which describes the texture and shape of digital images. The preprocessed or facial image is first divided into small blocks from which LBP histograms are formed and then concatenated into a single feature vector. This feature vector plays a vital role in efficient representation of the face and is used to measure similarities by calculating the distance between Images. This paper presents the principles of the method and implementation to perform face recognition. Experiments have been carried out on Yale data set; high recognition rates are obtained, especially compared to other face recognition methods. Also few extensions are investigated and implemented successfully to further improve the performance of the method.
Pulsed voltage converter with bipolar output voltages up to 10 kV for Dielectric Barrier Discharge

For pulsed power applications special pulsed voltage converters are needed. This paper presents an approach for a pulsed power converter, which generates bipolar output voltages up to 10 kV with extremely fast voltage slopes and high repetition rates. The topology is based on a H-bridge with a 10 kV dc-link. The output voltage and current of the pulsed voltage converter are adapted for the operation with Dielectric Barrier Discharge. To avoid the use of spark gaps due to their limited lifetime and thus to the lifetime of the converter, series stacked MOSFETs are used to realize a switch with a high blocking voltage. A balancing network for the series stacked MOSFETs is introduced as well as an adequate gate drive circuit. A matching method for the capacitive load is described, to achieve a maximum voltage slope at this capacitive load. To validate the theoretical considerations a prototype and measurements are presented.
Design of Switched Reluctance Motor Competitive to 60-kW IPMSM in Third-Generation Hybrid Electric Vehicle

In this paper, the design of a switched reluctance motor (SRM) having torque, power, speed-range, and efficiency values competitive to those of the interior permanent-magnet synchronous motor (IPMSM) employed in the 2009 Toyota Prius has been investigated. The outer diameter and axial length are the same as those of the IPMSM. A maximum torque of 207 N·m is needed up to the knee speed of 2768 r/min. An output power of 60 kW is required in the speed range from 2768 to 13 900 r/min. It is shown that the aforementioned requirements can be satisfied by the designed SRM, although the current density and weight are slightly increased. At high speed, the simulation results show that the output of the designed SRM is greatly enhanced with respect to that of the IPMSM.
Detection of financial statement fraud and feature selection using data mining techniques

a r t i c l e i n f o Keywords: Data mining Financial fraud detection Feature selection t-statistic Neural networks SVM GP Recently, high profile cases of financial statement fraud have been dominating the news. This paper uses data mining techniques such as Multilayer to identify companies that resort to financial statement fraud. Each of these techniques is tested on a dataset involving 202 Chinese companies and compared with and without feature selection. PNN outperformed all the techniques without feature selection, and GP and PNN outperformed others with feature selection and with marginally equal accuracies. Financial fraud is a serious problem worldwide and more so in fast growing countries like China. Traditionally, auditors are responsible for detecting financial statement fraud. With the appearance of an increasing number of companies that resort to these unfair practices, auditors have become overburdened with the task of detection of fraud. Hence, various techniques of data mining are being used to lessen the workload of the auditors. Enron and Worldcom are the two major scandals involving corporate accounting fraud, which arose from the disclosure of misdeeds conducted by trusted executives of large public corporations. Enron Corporation  was an American energy company based in Houston, Texas. Before its bankruptcy in late 2001, Enron was one of the world's leading electricity, natural gas, pulp and paper, and communications companies, with revenues amounting to nearly $101 billion in 2000. Long Distance Discount Services, Inc. (LDDS) began its operations in Hattiesburg, Mississippi in 1983. The company's name was changed to LDDS WorldCom [18] in 1995, and later it became WorldCom. On July 21, 2002, WorldCom filed for Chapter 11 bankruptcy protection in the largest such filing in US history at that time. Financial statements are a company's basic documents to reflect its financial status . A careful reading of the financial statements can indicate whether the company is running smoothly or is in crisis. If the company is in crisis, financial statements can indicate if the most critical thing faced by the company is cash or profit or something else. All the listed companies are required to publish their financial statements every year and every quarter. The stockholders can form a good idea about the companies' financial future through the financial statements, and can decide whether the companies' stocks are worth investing. The bank also needs the companies' financial statements in order to decide whether to …
Design of Organic TFT Pixel Electrode Circuit for Active-Matrix OLED Displays

A new current-programming pixel circuit for active-matrix organic light-emitting diode (AM-OLED) displays, composed of four organic thin-film transistors (OTFTs) and one capacitor, has been designed, simulated and evaluated. The most critical issue in realizing AMOLED displays with OTFTs is the variation and aging of the driving-TFTs and degradation of OLEDs. This can cause image sticking or degradation of image quality. These problems require compensation methods for high-quality display applications, and pixel level approach is considered to be one of the most important factors for improving display image quality. Our design shows that the current OLED and OTFT technology can be implemented for AMOLED displays, compensating the degradation of OTFT device characteristics.