nils-herrmann/clean_scirepeval huggingface dataset:
"This is a Throwaway Account": Temporary Technical Identities and Perceptions of Anonymity in a Massive Online Community

This paper explores temporary identities on social media platforms and individuals' uses of these identities with respect to their perceptions of anonymity. Given the research on multiple profile maintenance, little research has examined the role that some social media platforms play in affording users with temporary identities. Further, most of the research on anonymity stops short of the concept of varying perceptions of anonymity. This paper builds on these research areas by describing the phenomenon of temporary "throwaway accounts" and their uses on reddit.com, a popular social news site. In addition to ethnographic trace analysis to examine the contexts in which throwaway accounts are adopted, this paper presents a predictive model that suggests that perceptions of anonymity significantly shape the potential uses of throwaway accounts and that women are much more likely to adopt temporary identities than men.
Kinect cane: An assistive system for the visually impaired based on three-dimensional object recognition

This paper proposes a novel assistive system for the visually impaired. The system is composed of a Microsoft Kinect sensor, keypad-type controller, tactile device, laptop computer and so on. The system can recognize three-dimensional objects from depth data generated by the Kinect sensor, and inform visually impaired users not only about the existence of objects, but also about their classes such as chairs and upward stairs. Ordinarily, the system works as a conventional white cane. When a user instructs the system to find the object of a particular class, the system executes the recognition scheme that is designed to find the instructed object. If the object is found in the field of view of the Kinect sensor, the tactile device provides vibration feedback. The recognition schemes are applied to actual scenes. The experimental results indicate that the system is promising as means of helping the visually impaired find the desired objects.
Context-Aware Recommender Systems: A Service-Oriented Approach

Recommender systems are efficient tools that overcome the information overload problem by providing users with the most relevant contents. This is generally done through user’s preferences/ratings acquired from log files of his former sessions. Besides these preferences, taking into account the interaction context of the user will improve the relevancy of recommendation process. In this paper, we propose a context-aware recommender system based on both user profile and context. The approach we present is based on a previous work on data personalization which leads to the definition of a Personalized Access Model that provides a set of personalization services. We show how these services can be deployed in order to provide advanced context-aware recommender systems.
Affective Body Expression Perception and Recognition: A Survey

Thanks to the decreasing cost of whole-body sensing technology and its increasing reliability, there is an increasing interest in, and understanding of, the role played by body expressions as a powerful affective communication channel. The aim of this survey is to review the literature on affective body expression perception and recognition. One issue is whether there are universal aspects to affect expression perception and recognition models or if they are affected by human factors such as culture. Next, we discuss the difference between form and movement information as studies have shown that they are governed by separate pathways in the brain. We also review psychological studies that have investigated bodily configurations to evaluate if specific features can be identified that contribute to the recognition of specific affective states. The survey then turns to automatic affect recognition systems using body expressions as at least one input modality. The survey ends by raising open questions on data collecting, labeling, modeling, and setting benchmarks for comparing automatic recognition systems.
Development of Implantable Medical Devices: From an Engineering Perspective

From the first pacemaker implant in 1958, numerous engineering and medical activities for implantable medical device development have faced challenges in materials, battery power, functionality, electrical power consumption, size shrinkage, system delivery, and wireless communication. With explosive advances in scientific and engineering technology, many implantable medical devices such as the pacemaker, cochlear implant, and real-time blood pressure sensors have been developed and improved. This trend of progress in medical devices will continue because of the coming super-aged society, which will result in more consumers for the devices. The inner body is a special space filled with electrical, chemical, mechanical, and marine-salted reactions. Therefore, electrical connectivity and communication, corrosion, robustness, and hermeticity are key factors to be considered during the development stage. The main participants in the development stage are the user, the medical staff, and the engineer or technician. Thus, there are three different viewpoints in the development of implantable devices. In this review paper, considerations in the development of implantable medical devices will be presented from the viewpoint of an engineering mind.
Optimal relay path selection and cooperative communication protocol for a swarm of UAVs

In many applications based on the use of unmanned aerial vehicles (UAVs), it is possible to establish a cluster of UAVs in which each UAV knows the other vehicle's position. Assuming that the common channel condition between any two nodes of UAVs is line-of-sight (LOS), the time and energy consumption for data transmission on each path that connecting two nodes may be estimated by a node itself. In this paper, we use a modified Bellman-Ford algorithm to find the best selection of relay nodes in order to minimize the time and energy consumption for data transmission between any UAV node in the cluster and the UAV acting as the cluster head. This algorithm is applied with a proposed cooperative MAC protocol that is compatible with the IEEE 802.11 standard. The evaluations under data saturation conditions illustrate noticeable benefits in successful packet delivery ratio, average delay, and in particular the cost of time and energy.
Data Flow and Validation in Workflow Modelling

A complete workflow specification requires careful integration of many different process characteristics. Decisions must be made as to the definitions of individual activities, their scope, the order of execution that maintains the overall business process logic, the rules governing the discipline of work list scheduling to performers, identification of time constraints and more. The goal of this paper is to address an important issue in workflows modelling and specification, which is data flow, its modelling, specification and validation. Researchers have neglected this dimension of process analysis for some time, mainly focussing on structural considerations with limited verification checks. In this paper, we identify and justify the importance of data modelling in overall workflows specification and verification. We illustrate and define several potential data flow problems that, if not detected prior to workflow deployment may prevent the process from correct execution, execute process on inconsistent data or even lead to process suspension. A discussion on essential requirements of the workflow data model in order to support data validation is also given.
Analysis, Design, and Implementation of Multiple Parallel Ozone Chambers for High Flow Rate

It is well known that ozone concentration depends on air/oxygen input flow rate and power consumed by the ozone chamber. For every chamber, there exists a unique optimum flow rate that results in maximum ozone concentration. If the flow rate is increased (beyond) or decreased (below) from this optimum value, the ozone concentration drops. This paper proposes a technique whereby the concentration can be maintained even if the flow rate increases. The idea is to connect n number of ozone chambers in parallel, with each chamber designed to operate at its optimum point. Aside from delivering high ozone concentration at high flow rate, the proposed system requires only one power supply to drive all these (multiple) chambers simultaneously. In addition, due to its modularity, the system is very flexible, i.e., the number of chambers can be added or removed as demanded by the (output) ozone requirements. This paper outlines the chamber design using mica as dielectric and the determination of its parameters. To verify the concept, three chambers are connected in parallel and driven by a single transformer-less LCL resonant power supply. Moreover, a closed-loop feedback controller is implemented to ensure that the voltage gain remains at the designated value even if the number of chambers is changed or there is a variation in the components. It is shown that the flow rate can be increased linearly with the number of chambers while maintaining a constant ozone concentration.
Millimeter-wave CMOS digital controlled artificial dielectric differential mode transmission lines for reconfigurable ICs

Digital control of the effective dielectric constant of a differential mode transmission line is shown up to 60GHz in standard CMOS technology. The effective dielectric constant is shown to increase from 5 to over 50 for the fixed artificial dielectric case. The digital controlled artificial dielectric transmission line (DiCAD) uses MOS switches to dynamically control the phase. DiCAD achieves 50% of the physically available tuning range with effective dielectric constants varying between 7 and 28. Measured results favorably agree with full-wave electromagnetic simulations.
Integration Strategies for Enterprise Knowledge Graphs

Semantic computing and enterprise Linked Data have recently gained traction in enterprises. Although the concept of Enterprise Knowledge Graphs (EKGs) has meanwhile received some attention, a formal conceptual framework for designing such graphs has not yet been developed. By EKG we refer to a semantic network of concepts, properties, individuals and links representing and referencing foundational and domain knowledge relevant for an enterprise. Through the efforts reported in this paper, we aim to bridge the gap between the increasing need for EKGs and the lack of formal methods for realising them. We present a thorough study of the key concepts of knowledge graphs design along with an analysis of the advantages and disadvantages of various design decisions. In particular, we distinguish between two polar approaches towards data fusion, i.e., the unified and the federated approach, describe their benefits and point out shortages.
Comparative Performance Analysis of XOR- XNOR Function Based High-Speed CMOS Full Adder Circuits For Low Voltage VLSI Design

This paper presents comparative study of high-speed, low-power and low voltage full adder circuits. Our approach is based on XOR-XNOR design full adder circuits in a single unit. A low power and high performance 9T full adder cell using a design style called “XOR (3T)” is discussed. The designed circuit commands a high degree of regularity and symmetric higher density than the conventional CMOS design style as well as it lowers power consumption by using XOR (3T) logic circuits. Gate Diffusion Input (GDI) technique of low-power digital combinatorial circuit design is also described. This technique helps in reducing the power consumption and the area of digital circuits while maintaining low complexity of logic design. This paper analyses, evaluates and compares the performance of various adder circuits. Several simulations conducted using different voltage supplies, load capacitors and temperature variation demonstrate the superiority of the XOR (3T) based full adder designs in term of delay, power and power delay product (PDP) compared to the other full adder circuits. Simulation results illustrate the superiority of the designed adder circuits against the conventional CMOS, TG and Hybrid full adder circuits in terms of power, delay and power delay product (PDP). .
LaSTUS/TALN @ CLSciSumm-17: Cross-document Sentence Matching and Scientific Text Summarization Systems

In recent years there has been an increasing interest in approaches to scientific summarization that take advantage of the citations a research paper has received in order to extract its main contributions. In this context, the CL-SciSumm 2017 Shared Task has been proposed to address citation-based information extraction and summarization. In this paper we present several systems to address three of the CL-SciSumm tasks. Notably, unsupervised systems to match citing and cited sentences (Task 1A), a supervised approach to identify the type of information being cited (Task 1B), and a supervised citation-based summarizer (Task 2).
Rectangular building extraction from stereoscopic airborne Radar images

From the recent availability of images recorded by synthetic aperture radar (SAR) airborne systems, automatic results of digital elevation models (DEMs) on urban structures have been published lately. This paper deals with automatic extraction of three-dimensional (3-D) buildings from stereoscopic high-resolution images recorded by the SAR airborne RAMSES sensor from the French Aerospace Research Center (ONERA). On these images, roofs are not very textured whereas typical strong L-shaped echoes are visible. These returns generally result from dihedral corners between ground and structures. They provide a part of the building footprints and the ground altitude, but not the building heights. Thus, we present an adapted processing scheme in two steps. First is stereoscopic structure extraction from L-shaped echoes. Buildings are detected on each image using the Hough transform. Then they are recognized during a stereoscopic refinement stage based on a criterion optimization. Second, is height measurement. As most of previous extracted footprints indicate the ground altitude, building heights are found by monoscopic and stereoscopic measures. Between structures, ground altitudes are obtained by a dense matching process. Experiments are performed on images representing an industrial area. Results are compared with a ground truth. Advantages and limitations of the method are brought out.
Image Processing and Image Mining using Decision Trees

Valuable information can be hidden in images, however, few research discuss data mining on them. In this paper, we propose a general framework based on the decision tree for mining and processing image data. Pixel-wised image features were extracted and transformed into a database-like table which allows various data mining algorithms to make explorations on it. Each tuple of the transformed table has a feature descriptor formed by a set of features in conjunction with the target label of a particular pixel. With the label feature, we can adopt the decision tree induction to realize relationships between attributes and the target label from image pixels, and to construct a model for pixel-wised image processing according to a given training image dataset. Both experimental and theoretical analyses were performed in this study. Their results show that the proposed model can be very efficient and effective for image processing and image mining. It is anticipated that by using the proposed model, various existing data mining and image processing methods could be worked on together in different ways. Our model can also be used to create new image processing methodologies, refine existing image processing methods, or act as a powerful image filter.
A Differential Game Approach to Multi-agent Collision Avoidance

A multi-agent system consisting of <inline-formula><tex-math notation="LaTeX">$N$</tex-math></inline-formula> agents is considered. The problem of steering each agent from its initial position to a desired goal while avoiding collisions with obstacles and other agents is studied. This problem, referred to as the <italic>multi-agent collision avoidance problem</italic>, is formulated as a differential game. Dynamic feedback strategies that approximate the feedback Nash equilibrium solutions of the differential game are constructed and it is shown that, provided certain assumptions are satisfied, these guarantee that the agents reach their targets while avoiding collisions.
Chest diseases diagnosis using artificial neural networks

Chronic obstructive pulmonary, pneumonia, asthma, tuberculosis, lung cancer diseases are the most important chest diseases. These chest diseases are important health problems in the world. In this study, a comparative chest diseases diagnosis was realized by using multilayer, probabilistic, learning vector quantization, and generalized regression neural networks. The chest diseases dataset were prepared by using patient’s epicrisis reports from a chest diseases hospital’s database.
A new method for monitoring of distribution transformers

Distribution transformers are one of the most important equipment in power network. Because of, the large number of transformers distributed over a wide area in power electric systems, the data acquisition and condition monitoring is a important issue. This paper presents design and implementation of a mobile embedded system and a novel software to monitor and diagnose condition of transformers, by record key operation indictors of a distribution transformer like load currents, transformer oil, ambient temperatures and voltage of three phases. The proposed on-line monitoring system integrates a Global Service Mobile (GSM) Modem, with stand alone single chip microcontroller and sensor packages. Data of operation condition of transformer receives in form of SMS (Short Message Service) and will be save in computer server. Using the suggested online monitoring system will help utility operators to keep transformers in service for longer of time.
Creating Advice-Taking Reinforcement Learners

Learning from reinforcements is a promising approach for creating intelligent agents. However, reinforcement learning usually requires a large number of training episodes. We present and evaluate a design that addresses this shortcoming by allowing a connectionist Q-learner to accept advice given, at any time and in a natural manner, by an external observer. In our approach, the advice-giver watches the learner and occasionally makes suggestions, expressed as instructions in a simple imperative programming language. Based on techniques from knowledge-based neural networks, we insert these programs directly into the agent‘s utility function. Subsequent reinforcement learning further integrates and refines the advice. We present empirical evidence that investigates several aspects of our approach and shows that, given good advice, a learner can achieve statistically significant gains in expected reward. A second experiment shows that advice improves the expected reward regardless of the stage of training at which it is given, while another study demonstrates that subsequent advice can result in further gains in reward. Finally, we present experimental results that indicate our method is more powerful than a naive technique for making use of advice.
Static UML Model Generator from Analysis of Requirements (SUGAR)

In this paper, we propose a tool, named Static UML Model Generator from Analysis of Requirements (SUGAR), which generates both use-case and class models by emphasizing on natural language requirements. SUGAR aims at integrating both requirement analysis and design phases by identifying use-cases, actors, classes along with its attributes and methods with proper association among classes. This tool extends the idea of previously existing tools and implemented with the help of efficient natural language processing tools of Stanford NLP Group, WordNet and JavaRAP using the modified approach of Rational Unified Process with better accuracy. SUGAR has added new features and also able to incorporate solution for those problems existed in previous tools by developing both analysis and design class models. SUGAR generates all static UML models in Java in conjunction with Rational Rose and provides all functionalities of the system even though the developer is having less domain knowledge.
SSHLDA: A Semi-Supervised Hierarchical Topic Model

Supervised hierarchical topic modeling and unsupervised hierarchical topic modeling are usually used to obtain hierarchical topics, such as hLLDA and hLDA. Supervised hierarchical topic modeling makes heavy use of the information from observed hierarchical labels, but cannot explore new topics; while unsupervised hierarchical topic modeling is able to detect automatically new topics in the data space, but does not make use of any information from hierarchical labels. In this paper, we propose a semi-supervised hierarchical topic model which aims to explore new topics automatically in the data space while incorporating the information from observed hierarchical labels into the modeling process, called SemiSupervised Hierarchical Latent Dirichlet Allocation (SSHLDA). We also prove that hLDA and hLLDA are special cases of SSHLDA. We conduct experiments on Yahoo! Answers and ODP datasets, and assess the performance in terms of perplexity and clustering. The experimental results show that predictive ability of SSHLDA is better than that of baselines, and SSHLDA can also achieve significant improvement over baselines for clustering on the FScore measure.
CCM and DCM Operation of the Interleaved Two-Phase Boost Converter With Discrete and Coupled Inductors

Coupled-inductor interleaved boost converters are under development for high-current, high-power applications ranging from automotive to distributed generation. The operating modes of these coupled-inductor converters can be complex. This paper presents an investigation of the various continuous-current (CCM) and discontinuous-current (DCM) modes of operation of the coupled-inductor interleaved two-phase boost converter. The various CCM and DCM of the converter are identified together with their submodes of operation. The standard discrete-inductor interleaved two-phase boost can be seen as a subset of the coupled-inductor converter family with zero mutual coupling between the phases. The steady-state operating characteristics, equations and waveforms for the many CCM and DCM will be presented for the converter family. Mode maps will be developed to map the converter operation across the modes over the operating range. Experimental validation is presented from a 3.6 kW laboratory prototype. Design considerations and experimental results are presented for a 72 kW prototype.
Human-Robot Interaction in UVs Swarming : A Survey

The purpose of this paper is to review research pertaining to the limitations and advantages of User-Robot Interaction for Unmanned-Vehicles (UVs) swarming. We identify and discuss results showing technologies that mitigate the observed problems such as specialized level of automation and human factors in controlling a swarm of mobile agents. In the paper, we first present an overview of definitions and important terms of swarm robotics and its application in multiple UVs systems. Then, the discussion of human-swam interactions in controlling of multiple vehicles is provided with consideration of varies limitations and design guidelines. Finally, we discussed challenges and potential research aspects in the area of Human-robot interaction design in large swarm of UVs and robots.