nils-herrmann/clean_scirepeval huggingface dataset:
Web Service SWePT: A Hybrid Opinion Mining Approach

The increasing use of social networks and online sites where people can express their opinions has created a growing interest in Opinion Mining. One of the main tasks of Opinion Mining is to determine whether an opinion is positive or negative. Therefore, the role of the feelings expressed on the web has become crucial, mainly due to the concern of businesses and government to automatically identify the semantic orientation of the views of customers or citizens. This is also a concern, in the area of health to identify psychological disorders. This research focuses on the development of a web application called SWePT (Web Service for Polarity detection in Spanish Texts), which implements the Sequential Minimal Optimization (SMO) algorithm, extracting its features from an affective lexicon in Mexican Spanish. For this purpose, a corpus and an affective lexicon in Mexican Spanish were created. The experiments using three (positive, neutral, negative) and five categories (very positive, positive, neutral, negative, and very negative) allow us to demonstrate the effectiveness of the presented method. SWePT has also been implemented in the Emotion-bracelet interface, which shows the opinion of a user graphically.
Long-term working memory.

To account for the large demands on working memory during text comprehension and expert performance, the traditional models of working memory involving temporary storage must be extended to include working memory based on storage in long-term memory. In the proposed theoretical framework cognitive processes are viewed as a sequence of stable states representing end products of processing. In skilled activities, acquired memory skills allow these end products to be stored in long-term memory and kept directly accessible by means of retrieval cues in short-term memory, as proposed by skilled memory theory. These theoretical claims are supported by a review of evidence on memory in text comprehension and expert performance in such domains as mental calculation, medical diagnosis, and chess.
Tuning of PID controller using Ziegler-Nichols method for speed control of DC motor

In this paper, a weighted tuning methods of a PID speed controller for separately excited Direct current motor is presented, based on Empirical Ziegler-Nichols tuning formula and modified Ziegler-Nichol PID tuning formula. Both these methods are compared on the basis of output response, minimum settling time, and minimum overshoot for speed demand application of DC motor. Computer simulation shows that the performance of PID controller using Modified Ziegler-Nichols technique is better than that of traditional Ziegler-Nichols technique.
Portable Option Discovery for Automated Learning Transfer in Object-Oriented Markov Decision Processes

We introduce a novel framework for option discovery and learning transfer in complex domains that are represented as object-oriented Markov decision processes (OO-MDPs) [Diuk et al., 2008]. Our framework, Portable Option Discovery (POD), extends existing option discovery methods, and enables transfer across related but different domains by providing an unsupervised method for finding a mapping between object-oriented domains with different state spaces. The framework also includes heuristic approaches for increasing the efficiency of the mapping process. We present the results of applying POD to Pickett and Barto’s [2002] PolicyBlocks and MacGlashan’s [2013] Option-Based Policy Transfer in two application domains. We show that our approach can discover options effectively, transfer options among different domains, and improve learning performance with low computational overhead.
Irony Detection in Twitter: The Role of Affective Content

Irony has been proven to be pervasive in social media, posing a challenge to sentiment analysis systems. It is a creative linguistic phenomenon where affect-related aspects play a key role. In this work, we address the problem of detecting irony in tweets, casting it as a classification problem. We propose a novel model that explores the use of affective features based on a wide range of lexical resources available for English, reflecting different facets of affect. Classification experiments over different corpora show that affective information helps in distinguishing among ironic and nonironic tweets. Our model outperforms the state of the art in almost all cases.
A 0.35 /spl mu/m CMOS comparator circuit for high-speed ADC applications

A high-speed differential clocked comparator circuit is presented. The comparator consists of a preamplifier and a latch stage followed by a dynamic latch that operates as an output sampler. The output sampler circuit consists of a full transmission gate (TG) and two inverters. The use of this sampling stage results in a reduction in the power consumption of this high-speed comparator. Simulations show that charge injection of the TG adds constructively to the sampled signal value, therefore amplifying the sampled signal with a modest gain of 1.15. Combined with the high gain of the inverters, the sampled signals are amplified toward the rail voltages. This comparator is designed and fabricated in a 0.35 /spl mu/m standard digital CMOS technology. Measurement results show a sampling frequency of 1 GHz with 16 mV resolution for a 1 V input signal range and 2 mW power consumption from a 3.3 V supply. The architecture can be scaled down to smaller feature sizes and lower supply voltages.
OPTIMIZING THE PERFORMANCE OF VEHICLE-TO-GRID ( V 2 G ) ENABLED BATTERY ELECTRIC VEHICLES THROUGH A SMART CHARGE SCHEDULING MODEL

−A smart charge scheduling model is presented for potential (1) vehicle-to-grid (V2G) enabled battery electric vehicle (BEV) owners who are willing to participate in the grid ancillary services, and (2) grid operators. Unlike most V2G implementations, which are considered from the perspective of power grid systems, this model includes a communication network architecture for connecting system components that supports both BEV owners and grid operators to efficiently monitor and manage the charging and ancillary service activities. This model maximizes the net profit to each BEV participant while simultaneously satisfying energy demands for his/her trips. The performance of BEVs using the scheduling model is validated by estimating optimal annual financial benefits under different scenarios. An analysis of popular BEV models revealed that one of the existing BEVs considered in the study can generate an annual regulation profit of $454, $394 and $318 when the average daily driving distance is 20 miles, 40 miles and 60 miles, respectively. All popular BEV models can completely compensate the energy cost and generate a positive net profit, through the application of the scheduling model presented in this paper, with an annual driving distance of approximately 15,000 miles. Simulation analysis indicated that the extra load distribution from the optimized BEV charging operations were well balanced compared to the unmanaged BEV
Routing protocols in Vehicular Delay Tolerant Networks: A comprehensive survey

This article presents a comprehensive survey of routing protocols proposed for routing in Vehicular Delay Tolerant Networks (VDTN) in vehicular environment. DTNs are utilized in various operational environments, including those subject to disruption and disconnection and those with high-delay, such as Vehicular Ad-Hoc Networks (VANET). We focus on a special type of VANET, where the vehicular traffic is sparse and direct end-to-end paths between communicating parties do not always exist. Thus, communication in this context falls into the category of Vehicular Delay Tolerant Network (VDTN). Due to the limited transmission range of an RSU (Road Side Unit), remote vehicles, in VDTN, may not connect to the RSU directly and thus have to rely on intermediate vehicles to relay the packets. During the message relay process, complete end-to-end paths may not exist in highly partitioned VANETs. Therefore, the intermediate vehicles must buffer and forward messages opportunistically. Through buffer, carry and forward, the message can eventually be delivered to the destination even if an end-to-end connection never exists between source and destination. The main objective of routing protocols in DTN is to maximize the probability of delivery to the destination while minimizing the end-to-end delay. Also, vehicular traffic models are important for DTN routing in vehicle networks because the performance of DTN routing protocols is closely related to population and mobility models of the network.
Beneficial effects of a probiotic VSL#3 on parameters of liver dysfunction in chronic liver diseases.

To evaluate whether chronic therapy with probiotics affects plasma levels of cytokines and oxidative/nitrosative stress parameters, as well as liver damage, in patients with various types of chronic liver disease.



A total of 22 nonalcoholic fatty liver disease (NAFLD) and 20 alcoholic liver cirrhosis (AC) patients were enrolled in the study and compared with 36 HCV-positive patients with chronic hepatitis without (20, CH) or with (16, CC) liver cirrhosis. All patients were treated with the probiotic VSL#3. Routine liver tests, plasma levels of tumor necrosis factor alpha (TNF-alpha), interleukin (IL)-6 and -10, malondialdehyde (MDA), and 4-hydroxynonenal (4-HNE), S-nitrosothiols (S-NO), were evaluated on days -30, 0, 90, and 120.



Treatment with VSL#3 exerted different effects in the various groups of patients: in NAFLD and AC groups, it significantly improved plasma levels of MDA and 4-HNE, whereas cytokines (TNF-alpha, IL-6, and IL-10) improved only in AC patients. No such effects were observed in HCV patients. Routine liver damage tests and plasma S-NO levels were improved at the end of treatment in all groups.



Results of the study suggest that manipulation of intestinal flora should be taken into consideration as possible adjunctive therapy in some types of chronic liver disease.
Surface reconstruction and path planning for industrial inspection with a climbing robot

Inspection and maintenance tasks in the power industry often require tools to be moved smoothly in direct contact or close proximity over surfaces of curved tube-like structures. This paper addresses the problem of navigating robotic devices, namely a wheeled climbing robot carrying an inspection tool, on surfaces as they typically appear in steam chests of power plants. The surfaces are modeled by triangle meshes. Several state-of-the-art surface reconstruction methods are evaluated, and meshes are constructed for 3D point clouds taken with different laser scanners and for tube structures varying in shape, dimension and surface property. Each mesh represents an embedded graph, and thus directly enables graph search methods to plan paths of triangle strips over the surface. Discrete path planning and continuous robot control are combined to a hybrid system, which steers the robot smoothly along the triangle strip to the target regions on the surface. The navigation approach applies to robotic surface inspection, and is verified in simulation experiments by moving a robot on triangle meshes reconstructed from real data.
Physical and Graphical Simulation of an Ackermann Steered Vehicle BACHELOR ’ S

The Institute of Computer Aided Automation at the Technical University of Vienna founded an interest group for autonomous driving. The idea is to attract students as well as teachers to this topic and provide them with easy access to the required hardware and software. Therefore a fleet of autonomous robots will be built. A prototype based on a RC-model car equipped with a Raspberry Pi as computation unit and an Arduino Uno as a micro controller to control the vehicle was already created . In the future, this robot will be extended with sensors to facilitate autonomous driving. In order to achieve this goal, validation of systems and algorithms is imperative. For that purpose, a physical simulation and visualization of the prototype is created. The creation of the real and the simulated robot as well as their common interface was presented within a student paper  at the ARW ( 2016. The focus of this thesis is on the creation of this simulation and its accuracy. Since the primary interest of the research is in the motion of the robot, it is sufficient to only simulate the chassis. Specifically, the simulation of the steering which is the defining part of Ackermann robots is explained.  is used as middle ware for the simulation and visualization software [10]. A ROS node able to communicate with the simulation and visualization tool is used to control the simulated robot. This node is responsible for the steering, powering the wheels, and the necessary calculations. Additionally, it enables communication between the robotic tools used. On the one hand, the node receives motion commands and on the other hand it publishes velocity motion model [9, 12] based odometry data as well as the ground truth based odometry data. Different ways to visualize these kinds of odometry data will be introduced. A ROS node is created to visualize the estimated pose and its uncertainty as well as the ground truth pose. A pose by definition holds both the position and the orientation of the robot. The comparative advantage of this node over previously existing nodes is the visualization of the pose uncertainty. This is required to determine whether the estimated pose is credible in comparison with the ground truth pose. Since the simulation and the tracking system aim to be used for self localization, this is a important issue. Conclusively, the comparison of the velocity motion model odometry data with the ground truth odometry data will show that the simulation meets the requirements of accuracy with respect to the tracking model. Ways to improve the accuracy of the simulation will be introduced as well.
Joint Computation Partitioning and Resource Allocation for Latency Sensitive Applications in Mobile Edge Clouds

The proliferation of mobile devices and ubiquitous access of the wireless network enables many new mobile applications such as augmented reality, mobile gaming and so on. As the applications are latency sensitive, researchers propose to offload the complex computations of these applications to the nearby mobile edge cloud, in order to reduce the latency. Existing works mostly consider the problem of partitioning the computations between the mobile device and the traditional cloud that has abundant resources. The proposed approaches can not be applied in the context of mobile edge cloud, because both the resources in the mobile edge cloud and the wireless access bandwidth to the edge cloud are constrained. In this paper, we study joint computation partitioning and resource allocation problem for latency sensitive applications in mobile edge clouds. The problem is novel in that we combine the computation partitioning and the two-dimensional resource allocations in both the computation resources and the network bandwidth. We develop a new and efficient method, namely Multi-Dimensional Search and Adjust (MDSA), to solve the problem. We compares MDSA with the classic list scheduling method and the SearchAdjust algorithm via comprehensive simulations. The results show that MDSA outperforms the benchmark algorithms in terms of the overall application latency.
Knowledge-aided adaptive radar at DARPA: an overview

For the past several years, the Defense Advanced Research Projects Agency (DARPA) has been pioneering the development of the first ever real-time knowledge-aided (KA) adaptive radar architecture. The impetus for this program is the ever increasingly complex missions and operational environments encountered by modern radars and the inability of traditional adaptation methods to address rapidly varying interference environments. The DARPA KA sensor signal processing and expert reasoning (KASSPER) program has as its goal the demonstration of a high performance embedded computing (HPEC) architecture capable of integrating high-fidelity environmental knowledge (i.e., priors) into the most computationally demanding subsystem of a modern radar: the adaptive space-time beamformer. This is no mean feat as environmental knowledge is a memory quantity that is inherently difficult (if not impossible) to access at the rates required to meet radar front-end throughput requirements. In this article, we will provide an overview of the KASSPER program highlighting both the benefits of KA adaptive radar, key algorithmic concepts, and the breakthrough look-ahead radar scheduling approach that is the keystone to the KASSPER HPEC architecture.
Automatic Question Generation using Discourse Cues

In this paper, we present a system that automatically generates questions from natural language text using discourse connectives. We explore the usefulness of the discourse connectives for Question Generation (QG) that looks at the problem beyond sentence level. Our work divides the QG task into content selection and question formation. Content selection consists of finding the relevant part in text to frame question from while question formation involves sense disambiguation of the discourse connectives, identification of question type and applying syntactic transformations on the content. The system is evaluated manually for syntactic and semantic correctness.
Design of an endoluminal NOTES robotic system

Natural orifice transluminal endoscopic surgery, or NOTES, allows for exceedingly minimally invasive surgery but has high requirements for the dexterity and force capabilities of the tools. An overview of the ViaCath System is presented. This system is a first generation teleoperated robot for endoluminal surgery and consists of a master console with haptic interfaces, slave drive mechanisms, and 6 degree-of-freedom, long-shafted flexible instruments that run alongside a standard gastroscope or colonoscope. The system was validated through animal studies. It was discovered that the devices were difficult to introduce into the GI tract and manipulation forces were insufficient. The design of a second generation system is outlined with improvements to the instrument articulation section and a steerable overtube. Results of basic evaluation tests performed on the tools are also presented.
Vehicle black box with 24GHz FMCW radar

This paper presents vehicle black box using camera image and 24GHz Frequency Modulation Continuous Wave (FMCW) radar. Currently, almost all of vehicle black boxes are recording driving data using a single camera. They have some problems on low image quality and narrow viewing angle. In addition, it is difficult to record a clean image when the weather is in bad condition due to heavy rain, snow and night. These problems may have some troubles to investigate car accident accurately. In this paper, we propose a novel black box fusing data from radar and cameras. We estimate the distance and velocity of obstacle using the Doppler Effect and FMCW radar. In particular, we compare various Direction of Arrival (DOA) algorithms for estimation of obstacle angle. The experimental results indicate that the Multiple Signal Classification (MUSIC) is more accurate compared to other methods. Moreover, we formulate the vehicle tracking image through the camera image processing and radar information. This image can be used to widen viewing angle and it may be helpful to investigate a car accident. The experimental study results indicate that the new black box overcome the disadvantages of the current black boxes and may be useful for safe driving and accurate accident investigation.
The FrameNet Database and Software Tools

The FrameNet Project is producing a lexicon of English for both human use and NLP applications, based on the principles of Frame Semantics, in which sentences are described on the basis of predicators which evoke semantic frames and other constituents which express the participants (frame elements) in these frames. Our lexicon contains detailed information about the possible syntactic realizations of frame elements, derived from annotated corpus examples. In the process, we have developed a suite of tools for the definition of semantic frames, for annotating sentences, for searching the results, and for creating a variety ofreports. We will discuss the conceptual basis ofour work and demonstrate the tools we work with, the results we produce, and how they may be ofuse to other NLP projects.
Informational Influence in Organizations: An Integrated Approach to Knowledge Adoption

This research investigates how knowledge workers are influenced to adopt the advice that they receive in mediated contexts. The research integrates the Technology Acceptance Model (Davis, 1989) with dual process models of informational influence (e.g., Petty and Cacioppo, 1986; Chaiken and Eagly, 1976) to build a theoretical model of information adoption. This model highlights the assessment of information usefulness as a mediator of the information adoption process. Importantly, the model draws on the dual process models to make predictions about the antecedents of informational usefulness under different processing conditions. The model is investigated first qualitatively using interviews of a sample of 40 consultants, and then quantitatively on another sample of 63 consultants from the same international consulting organization. Data reflect participants’ perceptions of actual e-mails they received from colleagues consisting of advice or recommendations. Results support the model, suggesting that the process models used to understand information adoption can be generalized to the field of knowledge management, and that usefulness serves a mediating role between influence processes and information adoption. Organizational knowledge work is becoming increasingly global. This research offers a model for understanding knowledge transfer using computer-mediated communication.
Comparative effects of Facebook and conventional media on body image dissatisfaction

Appearance comparison has consistently been shown to engender body image dissatisfaction. To date, most studies have demonstrated this relationship between appearance comparison and body image dissatisfaction in the context of conventional media images depicting the thin-ideal. Social comparison theory posits that people are more likely to compare themselves to similar others. Since social media forums such as Facebook involve one's peers, the current study aimed to determine whether the relationship between appearance comparison and body image dissatisfaction would be stronger for those exposed to social media images, compared to conventional media images.



A sample of 193 female first year university students were randomly allocated to view a series of either Facebook or conventional media thin-ideal images. Participants completed questionnaires assessing pre- and post- image exposure measures of thin-ideal internalisation, appearance comparison, self-esteem, Facebook use and eating disorder risk.



Type of exposure was not found to moderate the relationship between appearance comparison and changes in body image dissatisfaction. When analysed according to exposure type, appearance comparison only significantly predicted body image dissatisfaction change for those exposed to Facebook, but not conventional media. Facebook use was found to predict higher baseline body image dissatisfaction and was associated with higher eating disorder risk.



The findings suggest the importance of extending the body image dissatisfaction literature by taking into account emerging social media formats. It is recommended that interventions for body image dissatisfaction and eating disorders consider appearance comparison processes elicited by thin-ideal content on social media forums, such as Facebook, in addition to conventional media.