nils-herrmann/clean_scirepeval huggingface dataset:
Analysis of Sentence Scoring Methods for Extractive Automatic Text Summarization

Automatic text summarization is a major area of research in the domain of information systems. Most of the methods requires domain knowledge in order to produce a coherent and meaningful summary. In Extractive text summarization, sentences are scored on some features. A large number of feature based scoring methods have been proposed for extractive automatic text summarization by researchers. This paper reviews features for sentence scoring. The results on combinations of various features for scoring are discussed. ROUGE-N is used to evaluate generated summary with abstractive summary of DUC 2002 dataset.
Challenges in Exploiting Conversational Memory in Human-Agent Interaction

In human interactions, language is used to project and maintain a social identity over time. The way people speak with others and revisit language across repeated interactions helps to create rapport and develop a feeling of coordination between conversational partners. Memory of past conversations is the main mechanism that allows us to exploit and explore ways of speaking, given knowledge acquired in previous encounters. As such, we introduce an agent that uses its conversational memory to revisit shared history with users to maintain a coherent social relationship over time. In this paper, we describe the dialog management mechanisms to achieve these goals when applied to a robot that engages in social chit-chat. In a study lasting 14 days with 28 users, totaling 474 interactions, we find that it is difficult to leverage the shared history with individual users and to also accommodate to expected conversational coordination patterns. We discuss the implications of this finding for long-term human-agent interaction. In particular, we highlight the importance of topic modeling and signaling explicit recall of previous episodes. Moreover, the way that users contribute to interactions requires additional adaptation, indicating a significant challenge for language interaction designers.
A topological coverage algorithm for mobile robots

In applications such as vacuum cleaning, painting, demining and foraging, a mobile robot must cover an unknown surface. The efficiency and completeness of coverage is improved via the construction of a map of covered regions while the robot covers the surface. Existing methods generally use grid maps, which are susceptible to odometry error and may require considerable memory and computation. This paper proposes a topological map and presents a coverage algorithm in which natural landmarks are added as nodes in a partial map. The completeness of the algorithm is argued. Simulation tests show over 99% of the surface is covered; 85% for real (Khepera) robot tests. The path length is about 10% worse than optimal in simulation tests, and about 20% worse than optimal for the real robot, which are within theoretical upper bounds for approximate solutions to travelling salesman based coverage problems. The proposed algorithm generates shorter paths and covers a wider variety of environments than topological coverage based on Morse decompositions.
Text Encryption with Huffman Compression

Communication between a sender and receiver needs security. It can be done in any form, like plain text or binary data. Changing the information to some unidentifiable form, can save it from assailants. For example, plain text can be coded using schemes so that a stranger cannot apprehend it. Cryptography is a subject or field which deals with the secret transmission of messages/ data between two parties. Cryptography is the practice and study of hiding information. It enables you to store delicate information and transmit it across insecure networks so that it cannot be read by anyone except the authorized recipient. Applications of cryptography include ATM cards, computer passwords, and electronic commerce etc. Messages, which are being sent between two parties, should be of small size so that they occupy less space. Data compression involves encoding of information using fewer bits than the original representation. Compression algorithms reduce the redundancy in data representation to decrease the storage required for that data. Symmetric key cryptography algorithms [6] are fast and mostly used type of encryption. Many types of data compression techniques do exist. Huffman Compression  is a lossless data compression technique and can be considered as the most efficient algorithm. In this paper, a combination of new Symmetric key algorithm and existing Huffman compression algorithm has been proposed. Proposed method works on text data. Algorithms have been provided in the paper itself.
The Mediating Roles of Upward Social Comparison and Self-esteem and the Moderating Role of Social Comparison Orientation in the Association between Social Networking Site Usage and Subjective Well-Being

The increased pervasiveness of social media use has raised questions about potential effects on users' subjective well-being, with studies reaching contrasting conclusions. To reconcile these discrepancies and shed new light on this phenomenon, the current study examined: (1) whether upward social comparison and self-esteem mediate the association between social networking site (SNS) usage and users' subjective well-being, and (2) whether the association between SNS usage and upward social comparison is moderated by users' social comparison orientation. Data from 696 participants were collected. Structural equation modeling revealed that upward social comparison and self-esteem mediated the relationship between SNS usage and users' subjective well-being. We found that social comparison orientation moderated the association between passive SNS usage and users' upward social comparison. Specifically, social comparison orientation strengthened the association between passive SNS usage and upward social comparison. The results might suggest a process through which passive SNS usage is related to subjective well-being, and identify a context under which these associations may differ.
Low-power energy harvester for wiegand transducers

This paper discusses the use of Wiegand magnetic sensors as energy harvesters for powering low-power electronic equipments. A Wiegand device typically releases a ~10 μs voltage pulse several volts wide when subject to an external, time-varying magnetic field. Due to the sharpness of the magnetic transition, pulse generation occurs regardless of how slow the magnetic field variation is, an attractive feature which enables its use in energy harvesting scenarios even when low-frequency sources are considered. The paper first identifies the theoretical conditions for maximum energy extraction. An efficient energy harvesting circuit is then proposed which interfaces the Wiegand source to a fixed DC voltage provided, for instance, by a rechargeable Lithium-Ion battery. Simulations and experimental results are provided supporting the developed theoretical framework and the effectiveness of the proposed implementation.
A machine learning approach for product matching and categorization

Consumers today have the option to purchase products from thousands of e-shops. However, the completeness of the product specifications and the taxonomies used for organizing the products differ across different e-shops. To improve the consumer experience, e.g., by allowing for easily comparing offers by different vendors, approaches for product integration on the Web are needed. In this paper, we present an approach that leverages neural language models and deep learning techniques in combination with standard classification approaches for product matching and categorization. In our approach we use structured product data as supervision for training feature extraction models able to extract attribute-value pairs from textual product descriptions. To minimize the need for lots of data for supervision, we use neural language models to produce word embeddings from large quantities of publicly available product data marked up with Microdata, which boost the performance of the feature extraction model, thus leading to better product matching and categorization performances. Furthermore, we use a deep Convolutional Neural Network to produce image embeddings from product images, which further improve the results on both tasks.
Modified phase shifted PWM for cascaded H bridge multilevel inverter

This paper proposes a modified phase shift pulse width modulation technique which is suitable for both single phase and three phase system for a conventional cascaded H bridge multilevel inverter. The carrier phase shift PWM method which is proposed in this paper improves the output voltage waveform and reduces the total harmonic distortion when compared with the conventional phase shift PWM method. The modified pwm is analyzed using a seven level cascaded H bridge multilevel inverter for both single phase as well as for three phase system, and this pwm can also be applied to N number of levels for cascaded H bridge inverter depending upon their angle calculations. The comparative analysis of this proposed pwm is done with respect to conventional pwm and the results are shown in terms of the harmonic spectrum analysis. This is done using the MATLAB/SIMULINK.
Map Matching with Inverse Reinforcement Learning

We study map-matching, the problem of estimating the route that is traveled by a vehicle, where the points observed with the Global Positioning System are available. A state-of-the-art approach for this problem is a Hidden Markov Model (HMM). We propose a particular transition probability between latent road segments by the use of the number of turns in addition to the travel distance between the latent road segments. We use inverse reinforcement learning to estimate the importance of the number of turns relative to the travel distance. This estimated importance is incorporated in the transition probability of the HMM. We show, through numerical experiments, that the error of map-matching can be reduced substantially with the proposed transition probability.
Indirect Image Registration with Large Diffeomorphic Deformations

The paper adapts the large deformation diffeomorphic metric mapping framework for image registration to the indirect setting where a template is registered against a target that is given through indirect noisy observations. The registration uses diffeomorphisms that transform the template through a (group) action. These diffeomorphisms are generated by solving a flow equation that is defined by a velocity field with certain regularity. The theoretical analysis includes a proof that indirect image registration has solutions (existence) that are stable and that converge as the data error tends so zero, so it becomes a well-defined regularization method. The paper concludes with examples of indirect image registration in 2D tomography with very sparse and/or highly noisy data.
Steering angle estimation for autonomous vehicle navigation using hough and Euclidean transform

The recent trend of proliferation of road accidents and fatalities, calls for a methodology that can appease the burden on the drivers during certain crucial moments, requiring fast response and decision making. This paper expatiates a novel and effective procedure for determining the steering angle required to keep the vehicle in the middle of the lane. The procedure used is divided in multiple stages. The first stage includes determination of the road's midpoint using Robert edge detection and morphological operations. This midpoint is then used to determine the angle by which a vehicle must steer to maintain its position at the center of the road. It is done using Euclidean distance transform and trigonometric equations. The input data suffers non-linearity, irregular intensity, non-uniform contrast difference which makes edge extraction ineffective. To tackle such situations, Hough transform and virtual road edge was used to determine the road and lane edges. The algorithm was tested rigorously using video samples, acquired from camera mounted on hood of a car, characterizing various road conditions. The results underscored the robustness and effectiveness of the same.
Prediction of Time-varying Musical Mood Distributions from Audio

The appeal of music lies in its ability to express emotions, and it is natural for us to organize music in terms of emotional associations. But the ambiguities of emotions make the determination of a single, unequivocal response label for the mood of a piece of music unrealistic. We address this lack of specificity by modeling human response labels to music in the arousal-valence (A-V) representation of affect as a stochastic distribution. Based upon our collected data, we present and evaluate methods using multiple sets of acoustic features to estimate these mood distributions parametrically using multivariate regression. Furthermore, since the emotional content of music often varies within a song, we explore the estimation of these A-V distributions in a time-varying context, demonstrating the ability of our system to track changes on a short-time basis.
Investigating How Student's Cognitive Behavior in MOOC Discussion Forum Affect Learning Gains

While MOOCs undoubtedly provide valuable learning resources for students, little research in the MOOC context has sought to evaluate students’ learning gains in the environment. It has been long acknowledged that conversation is a significant way for students to construct knowledge and learn. However, rather than studying learning in MOOC discussion forums, the thrust of current research in that context has been to identify factors that predict dropout. Thus, cognitively relevant student behavior in the forums has not been evaluated for its impact on cognitive processes and learning. In this paper, we adopt a content analysis approach to analyze students’ cognitively relevant behaviors in a MOOC discussion forum and further explore the relationship between the quantity and quality of that participation with their learning gains. As an integral part of our approach, we built a computational model to automate the analysis so that it is possible to extend the content analysis to all communication that occurred in the MOOC. We identified significant associations between discourse behavior and learning. Theoretical and practical implications are discussed.
PeopleNet: engineering a wireless virtual social network

People often seek information by asking other people even when they have access to vast reservoirs of information such as the Internet and libraries. This is because people are great sources of unique information, especially that which is location-specific, community-specific and time-specific. Social networking is effective because this type of information is often not easily available anywhere else. In this paper, we conceive a wireless virtual social network which mimics the way people seek information via social networking. PeopleNet is a simple, scalable and low-cost architecture for efficient information search in a distributed manner. It uses the infrastructure to propagate queries of a given type to users in specific geographic locations, called bazaars. Within each bazaar, the query is further propagated between neighboring nodes via peer-to-peer connectivity until it finds a matching query. The PeopleNet architecture can overlay easily on existing cellular infrastructure and entails minimal software installation. We identify three metrics for system performance: (i) probability of a match, (ii) time to find a match and (iii) number of matches found by a query. We describe two simple models, called the swap and spread models, for query propagation within a bazaar. We qualitatively argue that the swap model is better with respect to the performance metrics identified and demonstrate this via simulations. Next, we compute analytically the probability of match for the swap model. We show that the probability of match can be significantly improved if, prior to swapping queries, the nodes exchange some limited information about their buffer contents. We propose a simple greedy algorithm which uses this limited information to decide which queries to swap. We show via simulation that this algorithm achieves significantly better performance. Overall our results demonstrate that PeopleNet, with its bazaar concept and peer-to-peer query propagation, can provide a simple and efficient mechanism for seeking information.
Force-directed layout of origin-destination flow maps

This paper introduces a force-directed layout method for creating origin-destination flow maps. Design principles derived from manual cartography and automated graph drawing to increase readability of flow maps and graph layouts are taken into account. The origin-destination flow maps produced with our algorithm show flows with quadratic Bézier curves that reduce flow-on-flow and flow-on-node overlaps, and avoid sharp or irregular bends in flow lines. A survey of expert cartographers found that flow maps created with our automated method are similar in quality to manually produced flow maps. ARTICLE HISTORY Received 7 November 2016  March 2017
Experts ’ Views on Digital Parenting Strategies

American teenagers are spending much of their time online. Online communities offer teens perspective, acceptance, connection, and education that were once more limited by geography. However, the online world also opens teens to risks. Adults must decide how to guide teen online behavior in a space that they may not understand. We interviewed 16 experts about teen online behavior, related benefits and risks, and risk mitigation methods. We found that experts agree on certain mitigation methods, especially communication and education about online behavior, but are divided over monitoring. We propose a set of possible solutions that promote online safety while preserving teens’ privacy.
Using Ground Reaction Forces from Gait Analysis: Body Mass as a Weak Biometric

Ground reaction forces generated during normal walking have recently been used to identify and/or classify individuals based upon the pattern of the forces observed over time. One feature that can be extracted from vertical ground reaction forces is body mass. This single feature has identifying power comparable to other studies that use multiple and more complex features. This study contributes to understanding the role of body mass in identification by (1) quantifying the accuracy and precision with which body mass can be obtained using vertical ground reaction forces, (2) quantifying the distribution of body mass across a population larger than has previously been studied in relation to gait analysis, and (3) quantifying the expected identification capabilities of systems using body mass as a weak biometric. Our results show that body mass can be measured in a fraction of a second with less than a 1 kilogram standard deviation of error.
Bimaxillary protrusion with masseter muscle hypertrophy treated with titanium screw anchorage and masseter surgical reduction.

This case report describes the treatment of a patient with bimaxillary protrusion and masseter muscle hypertrophy. At age 21 years 7 months, this woman had temporomandibular disorder (TMD) symptoms, severe bimaxillary protrusion, and a prominent mandibular angle with facial asymmetry. After an attempt to alleviate the TMD symptoms with occlusal splint stabilization, portions of the masseter muscle and the mandible were surgically removed. Titanium screws were placed bilaterally in both arches, and a retraction force was applied. After active treatment for 38 months, the convexity of the facial profile with lip protrusion was improved remarkably, and good occlusion was achieved. The prominent mandibular angle with facial asymmetry was improved as a result of the surgical reduction of the masseter muscle and the modeling ostectomy near the masseteric tuberosity. The TMD symptoms disappeared, and the jaw movement pattern became normal. Therefore, our results suggest that this combination treatment would be useful for masseter muscle hypertrophy for morphologic and functional problems.
A four-component decomposition of POLSAR images based on the coherency matrix

A four-component decomposition scheme of the coherency matrix is presented here for the analysis of polarimetric synthetic aperture radar (SAR) images. The coherency matrix is used to deal with nonreflection symmetric scattering case, which is an extension of covariance matrix approach. The same decomposition results have been obtained. The advantage of this approach is explicit expressions of four scattering powers in terms of scattering matrix elements, which serve the interpretation of polarimetric SAR data quantitatively
Towards automatic assessment of government web sites

This paper presents an approach for automatic assessment of web sites in large scale e-Government surveys. The approach aims at supplementing and to some extent replacing human evaluation which is typically the core part of these surveys.
 The heart of the solution is a colony inspired algorithm, called the lost sheep, which automatically locates targeted governmental material online. The algorithm centers around classifying link texts to determine if a web page should be downloaded for further analysis.
 The proposed algorithm is designed to work with minimum human interaction and utilize the available resources as best possible. Using the lost sheep, the people carrying out a survey will only provide sample data for a few web sites for each type of material sought after. The algorithm will automatically locate the same type of material in the other web sites part of the survey. This way it significantly reduces the need for manual work in large scale e-Government surveys.