nils-herrmann/clean_scirepeval huggingface dataset:
A System for Real-time Twitter Sentiment Analysis of 2012 U.S. Presidential Election Cycle

This paper describes a system for real-time analysis of public sentiment toward presidential candidates in the 2012 U.S. election as expressed on Twitter, a microblogging service. Twitter has become a central site where people express their opinions and views on political parties and candidates. Emerging events or news are often followed almost instantly by a burst in Twitter volume, providing a unique opportunity to gauge the relation between expressed public sentiment and electoral events. In addition, sentiment analysis can help explore how these events affect public opinion. While traditional content analysis takes days or weeks to complete, the system demonstrated here analyzes sentiment in the entire Twitter traffic about the election, delivering results instantly and continuously. It offers the public, the media, politicians and scholars a new and timely perspective on the dynamics of the electoral process and public opinion.
A0C: Alpha Zero in Continuous Action Space

A core novelty of Alpha Zero is the interleaving of tree search and deep learning, which has proven very successful in board games like Chess, Shogi and Go. These games have a discrete action space. However, many real-world reinforcement learning domains have continuous action spaces, for example in robotic control, navigation and self-driving cars. This paper presents the necessary theoretical extensions of Alpha Zero to deal with continuous action space. We also provide a preliminary experiment on the Pendulum swing-up task, empirically verifying the feasibility of our approach. Thereby, this work provides a first step towards the application of iterated search and learning in domains with a continuous action space.
Accuracy studies for TDOA and TOA localization

In sensor networks, passive localization can be performed by exploiting the received signals of unknown emitters. In this paper, the Time of Arrival (TOA) measurements are investigated. Often, the unknown time of emission is eliminated by calculating the difference between two TOA measurements where Time Difference of Arrival (TDOA) measurements are obtained. In TOA processing, additionally, the unknown time of emission is to be estimated. Therefore, the target state is extended by the unknown time of emission. A comparison is performed investigating the attainable accuracies for localization based on TDOA and TOA measurements given by the Cramér-Rao Lower Bound (CRLB). Using the Maximum Likelihood estimator, some characteristic features of the cost functions are investigated indicating a better performance of the TOA approach. But counterintuitive, Monte Carlo simulations do not support this indication, but show the comparability of TDOA and TOA localization.
Approximate computing for spiking neural networks

Spiking Neural Networks (SNNs) are widely regarded as the third generation of artificial neural networks, and are expected to drive new classes of recognition, data analytics and computer vision applications. However, large-scale SNNs (e.g., of the scale of the human visual cortex) are highly compute and data intensive, requiring new approaches to improve their efficiency. Complementary to prior efforts that focus on parallel software and the design of specialized hardware, we propose AxSNN, the first effort to apply approximate computing to improve the computational efficiency of evaluating SNNs. In SNNs, the inputs and outputs of neurons are encoded as a time series of spikes. A spike at a neuron's output triggers updates to the potentials (internal states) of neurons to which it is connected. AxSNN determines spike-triggered neuron updates that can be skipped with little or no impact on output quality and selectively skips them to improve both compute and memory energy. Neurons that can be approximated are identified by utilizing various static and dynamic parameters such as the average spiking rates and current potentials of neurons, and the weights of synaptic connections. Such a neuron is placed into one of many approximation modes, wherein the neuron is sensitive only to a subset of its inputs and sends spikes only to a subset of its outputs. A controller periodically updates the approximation modes of neurons in the network to achieve energy savings with minimal loss in quality. We apply AxSNN to both hardware and software implementations of SNNs. For hardware evaluation, we designed SNNAP, a Spiking Neural Network Approximate Processor that embodies the proposed approximation strategy, and synthesized it to 45nm technology. The software implementation of AxSNN was evaluated on a 2.7 GHz Intel Xeon server with 128 GB memory. Across a suite of 6 image recognition benchmarks, AxSNN achieves 1.4–5.5x reduction in scalar operations for network evaluation, which translates to 1.2–3.62x and 1.26–3.9x improvement in hardware and software energies respectively, for no loss in application quality. Progressively higher energy savings are achieved with modest reductions in output quality.
EMG-Based Neuro-Fuzzy Control of a 4DOF Upper-Limb Power-Assist Exoskeleton

We have been developing a 4DOF exoskeleton robot system in order to assist shoulder vertical motion, shoulder horizontal motion, elbow motion, and forearm motion of physically weak persons such as elderly, injured, or disabled persons. The robot is directly attached to a user's body and activated based on EMG (electromyogram) signals of the user's muscles, since the EMG signals directly reflect the user's motion intention. A neuro-fuzzy controller has been applied to control the exoskeleton robot system. In this paper, controller adaptation method to user's EMG signals is proposed. A motion indicator is introduced to indicate the motion intention of the user for the controller adaptation. The experimental results show the effectiveness of the proposed method.
Design of a new lower extremity orthosis for overground gait training with the WalkTrainer

A new set of lower limb orthoses was developed for the WalkTrainer project. This mobile reeducation device for paralyzed people allows overground gait training combining closed loop electrical muscle stimulation and lower limb guiding while walking. An active body weight support system offers precise body weight unloading during locomotion. A 6 DOF parallel robot moves the pelvis in any desired position and orientation. The lower extremity orthosis is composed of two key parts. First, a purely passive lightweight exoskeleton acts as the interface between the human leg and the machine. A 1 DOF knee orthotic joint is also designed to prevent hyperextension. Second, the active part - composed of a mechanical leg equipped with motors and sensors - is located behind each human leg, with its base fixed to the WalkTrainer base frame. The two kinematic chains are connected with appropriate linkages at the thigh and the ankle joint. Actuation of the hip, knee and ankle joints is thus provided for their flexion/extension axis. The active mechanism operates only within the sagittal plane and guides the ankle-foot subsystem. Thigh and shank add/abduction movements are possible and even essential since the pelvis moves in a 3D space. This achievement prevents the scissors effect while allowing natural walking motion at the other joints. This paper describes the design and development of the lower extremity orthosis. Starting from a biomechanical approach, the needed actuation and the mechanical structure are discussed as well as the interface between the patient and the robot.
A non-linear switching controller for ball and plate system

The ball and plate system is a typical multi-variable non-linear plant. By extension of the traditional ball and beam system, it is used as a standard benchmark to inspect diverse control schemes. In this paper, a problem with trajectory-tracking of ball and plate systems is studied based on non-linear control theory. Firstly, we briefly summarise control problems of ball and plate systems and introduce a ball and plate system named BPVS-JLU developed in authors’ laboratory. Then the system’s mathematical model is derived using the Lagrange method. Lastly, a non-linear switching control scheme is put forward to deal with the trajectory-tracking problem after the non-linear control analysis of ball and plate system. Experimental results have shown that the proposed scheme can accomplish the task of trajectory-tracking effectively.
Unskilled, unaware, or both? The better-than-average heuristic and statistical regression predict errors in estimates of own performance.

People who score low on a performance test overestimate their own performance relative to others, whereas high scorers slightly underestimate their own performance. J. Kruger and D. Dunning (1999) attributed these asymmetric errors to differences in metacognitive skill. A replication study showed no evidence for mediation effects for any of several candidate variables. Asymmetric errors were expected because of statistical regression and the general better-than-average (BTA) heuristic. Consistent with this parsimonious model, errors were no longer asymmetric when either regression or the BTA effect was statistically removed. In fact, high rather than low performers were more error prone in that they were more likely to neglect their own estimates of the performance of others when predicting how they themselves performed relative to the group.
A 1 mW 71.5 dB SNDR 50 MS/s 13 bit Fully Differential Ring Amplifier Based SAR-Assisted Pipeline ADC

This paper presents a 13 bit 50 MS/s fully differential ring amplifier based SAR-assisted pipeline ADC, implemented in 65 nm CMOS. We introduce a new fully differential ring amplifier, which solves the problems of single-ended ring amplifiers while maintaining the benefits of high gain, fast slew based charging and an almost rail-to-rail output swing. We implement a switched-capacitor (SC) inter-stage residue amplifier that uses this new fully differential ring amplifier to give accurate amplification without calibration. In addition, a new floated detect-and-skip (FDAS) capacitive DAC (CDAC) switching method reduces the switching energy and improves linearity of first-stage CDAC. With these techniques, the prototype ADC achieves measured SNDR, SNR, and SFDR of 70.9 dB (11.5b), 71.3 dB and 84.6 dB, respectively, with a Nyquist frequency input. The prototype achieves 13 bit linearity without calibration and consumes 1 mW. This measured performance is equivalent to Walden and Schreier FoMs of 6.9 fJ/conversion ·step and 174.9 dB, respectively.
Soft Robotics Technologies to Address Shortcomings in Today ’ s Minimally Invasive Surgery : The STIFF-FLOP Approach

Most devices for single-site or natural orifice transluminal surgery are very application specific and, hence, capable of effectively carrying out specific surgical tasks only. However, most of these instruments are rigid, lack a sufficient number of degrees of freedom (DOFs), and/or are incapable of modifying their mechanical properties based on the tasks to be performed. The current philosophy in commercial instrument design is mainly focused on creating minimally invasive surgical systems using rigid tools equipped with dexterous tips. Only few research efforts are aimed at developing flexible surgical systems, with many DOFs or even continuum kinematics. The authors propose a radical change in surgical instrument design: away from rigid tools toward a new concept of soft and stiffness-controllable instruments. Inspired by biology, we envision creating such soft and stiffnesscontrollable medical devices using the octopus as a model. The octopus presents all the capabilities requested and can be viewed as a precious source of inspiration. Several soft technologies are suitable for meeting the aforementioned capabilities, and in this article a brief review of the most promising ones is presented. Then we illustrate how specific technologies can be applied in the design of a novel manipulator for flexible surgery by discussing its potential and by presenting feasibility tests of a prototype responding to this new design philosophy. Our aim is to investigate the feasibility of applying these technologies in the field of minimally invasive surgery and at the same time to stimulate the creativeness of others who could take the proposed concepts further to achieve novel solutions and generate specific application scenarios for the devised technologies.
Hybrid Approach for Emotion Classification of Audio Conversation Based on Text and Speech Mining

One of the greatest challenges in speech technology is estimating the speaker’s emotion. Most of the existing approaches concentrate either on audio or text features. In this work, we propose a novel approach for emotion classification of audio conversation based on both speech and text. The novelty in this approach is in the choice of features and the generation of a single feature vector for classification. Our main intention is to increase the accuracy of emotion classification of speech by considering both audio and text features. In this work we use standard methods such as Natural Language Processing, Support Vector Machines, WordNet Affect and SentiWordNet. The dataset for this work have been taken from Semval -2007 and eNTERFACE'05 EMOTION Database.  Peer-review under responsibility of organizing committee of the International Conference on Information and Communication Technologies (ICICT 2014).
Food Recognition for Dietary Assessment Using Deep Convolutional Neural Networks

Table 2 – Results of the proposed method for different voting schemes and variants compared to a method from the literature Diet management is a key factor for the prevention and treatment of diet-related chronic diseases. Computer vision systems aim to provide automated food intake assessment using meal images. We propose a method for the recognition of food items in meal images using a deep convolutional neural network (CNN) followed by a voting scheme. Our approach exploits the outstanding descriptive ability of a CNN, while the patch-wise model allows the generation of sufficient training samples, provides additional spatial flexibility for the recognition and ignores background pixels.
Brain function in coma, vegetative state, and related disorders

We review the nosological criteria and functional neuroanatomical basis for brain death, coma, vegetative state, minimally conscious state, and the locked-in state. Functional neuroimaging is providing new insights into cerebral activity in patients with severe brain damage. Measurements of cerebral metabolism and brain activations in response to sensory stimuli with PET, fMRI, and electrophysiological methods can provide information on the presence, degree, and location of any residual brain function. However, use of these techniques in people with severe brain damage is methodologically complex and needs careful quantitative analysis and interpretation. In addition, ethical frameworks to guide research in these patients must be further developed. At present, clinical examinations identify nosological distinctions needed for accurate diagnosis and prognosis. Neuroimaging techniques remain important tools for clinical research that will extend our understanding of the underlying mechanisms of these disorders.
Developing Business Models for Ebusiness

The paper introduces first insights into a methodology for developing eBusiness business models, which was elaborated at evolaris and is currently validated in various business cases. This methodology relies upon a definition of the term business model, which is first examined and upon which prerequisites for such a methodology are presented. A business model is based on a mental representation of certain aspects of the real world that are relevant for the business. Supporting this change of the mental model is therefore a major prerequisite for a methodology for developing business models. This paper demonstrates that it addition, a business model discussion should be theory based, able to handle complex systems, provide a way for risk free experiments and be practically applicable. In order to fulfill the above critieria, the evolaris methodology is grounded on system theory and combines aspects of system dynamics and action research.
Optimal Sensor Data Fusion Architecture for Object Detection in Adverse Weather Conditions

A good and robust sensor data fusion in diverse weather conditions is a quite challenging task. There are several fusion architectures in the literature, e.g. the sensor data can be fused right at the beginning (Early Fusion), or they can be first processed separately and then concatenated later (Late Fusion). In this work, different fusion architectures are compared and evaluated by means of object detection tasks, in which the goal is to recognize and localize predefined objects in a stream of data. Usually, state-of-the-art object detectors based on neural networks are highly optimized for good weather conditions, since the well-known benchmarks only consist of sensor data recorded in optimal weather conditions. Therefore, the performance of these approaches decreases enormously or even fails in adverse weather conditions. In this work, different sensor fusion architectures are compared for good and adverse weather conditions for finding the optimal fusion architecture for diverse weather situations. A new training strategy is also introduced such that the performance of the object detector is greatly enhanced in adverse weather scenarios or if a sensor fails. Furthermore, the paper responds to the question if the detection accuracy can be increased further by providing the neural network with a-priori knowledge such as the spatial calibration of the sensors.
Crowdsourcing in logistics: concepts and applications using the social crowd

The introduction of crowdsourcing offers numerous business opportunities. In recent years, manifold forms of crowdsourcing have emerged on the market -- also in logistics. Thereby, the ubiquitous availability and sensor-supported assistance functions of mobile devices support crowdsourcing applications, which promotes contextual interactions between users at the right place at the right time. This paper presents the results of an in-depth-analysis on crowdsourcing in logistics in the course of ongoing research in the field of location-based crowdsourcing (LBCS). This paper analyzes LBCS for both, 'classic' logistics as well as 'information' logistics. Real-world examples of crowdsourcing applications are used to underpin the two evaluated types of logistics using crowdsourcing. Potential advantages and challenges of logistics with the crowd ('crowd-logistics') are discussed. Accordingly, this paper aims to provide the necessary basis for a novel interdisciplinary research field.
Varieties of transgender/transsexual lives and their relationship with transphobia.

The conceptualization of gender variant populations within studies have consisted of imposed labels and a diversity of individual identities that preclude any attempt at examining the variations found among gender variant populations, while at the same time creating artificial distinctions between groups that may not actually exist. Data were collected from 90 transgender/transsexual people using confidential, self-administered questionnaires. Factors like age of transition, being out to others, and participant's race and class were associated with experiences of transphobic life events. Discrimination can have profound impact on transgender/transsexual people's lives, but different factors can influence one's experience of transphobia. Further studies are needed to examine how transphobia manifests, and how gender characteristics impact people's lives.
Applying k-Nearest Neighbour in Diagnosing Heart Disease Patients

—Heart disease is the leading cause of death in the world over the past 10 years. Researchers have been using several data mining techniques to help health care professionals in the diagnosis of heart disease. K-Nearest-Neighbour(KNN) is one of the successful data mining techniques used in classification problems. However, it is less used in the diagnosis of heart disease patients. Recently, researchers are showing that combining different classifiers through voting is outperforming other single classifiers. This paper investigates applying KNN to help healthcare professionals in the diagnosis of heart disease. It also investigates if integrating voting with KNN can enhance its accuracy in the diagnosis of heart disease patients. The results show that applying KNN could achieve higher accuracy than neural network ensemble in the diagnosis of heart disease patients. The results also show that applying voting could not enhance the KNN accuracy in the diagnosis of heart disease.
Exploring Motivations for Bitcoin Technology Usage

This paper presents an exploratory study focusing on user experience with Bitcoin technology. We describe interviews with 9 Bitcoin users and report findings related to users' motivations for buying and using bitcoins. Our initial findings capture three main motivations such as Bitcoin's predicted role in a monetary revolution, users' increased empowerment, and their perception of a real value of Bitcoin currency. We conclude with reflections on the value of these findings for HCI researchers.
On using machine learning to automatically classify software applications into domain categories

Software repositories hold applications that are often categorized to improve the effectiveness of various maintenance tasks. Properly categorized applications allow stakeholders to identify requirements related to their applications and predict maintenance problems in software projects. Manual categorization is expensive, tedious, and laborious – this is why automatic categorization approaches are gaining widespread importance. Unfortunately, for different legal and organizational reasons, the applications’ source code is often not available, thus making it difficult to automatically categorize these applications. In this paper, we propose a novel approach in which we use Application Programming Interface (API) calls from third-party libraries for automatic categorization of software applications that use these API calls. Our approach is general since it enables different categorization algorithms to be applied to repositories that contain both source code and bytecode of applications, since API calls can be extracted from both the source code and byte-code. We compare our approach to a state-of-the-art approach that uses machine learning algorithms for software categorization, and conduct experiments on two large Java repositories: an open-source repository containing 3,286 projects and a closed-source repository with 745 applications, where the source code was not available. Our contribution is twofold: we propose a new approach that makes it possible to categorize software projects without any source code using a small number of API calls as attributes, and furthermore we carried out a comprehensive empirical evaluation of automatic categorization approaches.
Improved Fusion of Visual and Language Representations by Dense Symmetric Co-attention for Visual Question Answering

A key solution to visual question answering (VQA) exists in how to fuse visual and language features extracted from an input image and question. We show that an attention mechanism that enables dense, bi-directional interactions between the two modalities contributes to boost accuracy of prediction of answers. Specifically, we present a simple architecture that is fully symmetric between visual and language representations, in which each question word attends on image regions and each image region attends on question words. It can be stacked to form a hierarchy for multi-step interactions between an image-question pair. We show through experiments that the proposed architecture achieves a new state-of-the-art on VQA and VQA 2.0 despite its small size. We also present qualitative evaluation, demonstrating how the proposed attention mechanism can generate reasonable attention maps on images and questions, which leads to the correct answer prediction.