nils-herrmann/clean_scirepeval huggingface dataset:
Design of Microwave Filters

A survey of the major techniques used in the design of microwave filters is presented in this paper. It is shown that the basis for much fundamental microwave filter theory lies in the realm of lumped-element filters, which indeed are actually used directly for many applications at microwave frequencies as high as 18 GHz. Many types of microwave filters are discussed with the object of pointing out the most useful references, especially for a newcomer to the field.
A 0.18-$ \mu{\hbox {m}}$ Monolithic Li-Ion Battery Charger for Wireless Devices Based on Partial Current Sensing and Adaptive Reference Voltage

An Li-ion battery charger based on a charge-control buck regulator operating at 2.2 MHz is implemented in 180 nm CMOS technology. The novelty of the proposed charge-control converter consists of regulating the average output current by only sensing a portion of the inductor current and using an adaptive reference voltage. By adopting this approach, the charger average output current is set to a constant value of 900 mA regardless of the battery voltage variation. In constant-voltage (CV) mode, a feedback loop is established in addition to the preexisting current control loop, preserving the smoothness of the output voltage at the transition from constant-current (CC) to CV mode. A small-signal model has been developed to analyze the system stability and subharmonic oscillations at low current levels. Transistor-level simulations of the proposed switching charger are presented. The output voltage ranges from 2.1 to 4.2 V, and the power efficiency at 900 mA has been measured to be 86% for an input voltage of 10 V. The accuracy of the output current using the proposed sensing technique is 9.4% at 10 V.
Clothed and Naked Human Shapes Estimation from a Single Image

This paper presents a novel method to simultaneously estimate the clothed and naked 3D shapes of a person. The method needs only a single photograph of a person wearing clothing. Firstly, we learn a deformable model of human clothed body shapes from a database. Then, given an input image, the deformable model is initialized with a few user-specified 2D joints and contours of the person. And the correspondence between 3D shape and 2D contours is established automatically. Finally, we optimize the parameters of the deformable model in an iterative way, and then obtain the clothed and naked 3D shapes of the person simultaneously. The experimental results on real images demonstrate the effectiveness of our method.
Performance analysis of encryption algorithms for security

With the fast progression of digital data exchange information security has become an important issue in data communication. Encryption algorithms play an important role in information security system. These algorithms use techniques to enhance the data confidentiality and privacy by making the information indecipherable which can be only be decoded or decrypted by party those possesses the associated key. But at the same time, these algorithms consume a significant amount of computing resources such as CPU time, memory, and battery power. So we need to evaluate the performance of different cryptographic algorithms to find out best algorithm to use in future. This paper provides evaluation of both symmetric (AES, DES, Blowfish) as well as asymmetric (RSA) cryptographic algorithms by taking different types of files like Binary, text and image files. A comparison has been conducted for these encryption algorithms using evaluation parameters such as encryption time, decryption time and throughput. Simulation results are given to demonstrate the effectiveness of each.
Social and Parasocial Relationships on Social Network Sites and Their Differential Relationships with Users' Psychological Well-Being

With the advent of social network sites (SNSs), people can efficiently maintain preexisting social relationships and make online friendships without offline encounters. While such technological features of SNSs hold a variety of potential for individual and collective benefits, some scholars warn that use of SNSs might lead to socially negative consequences, such as social isolation, erosion of social cohesion, or SNS addiction. This study distinguishes types of SNS relationships, and investigates their relationships with social isolation, interpersonal trust, and SNS addiction. We classify SNS relationships into two types: (a) social relationships based on reciprocity between a user and his/her friends, and (b) parasocial relationships in which an ordinary user is aware of activities of a celebrity (e.g., famous actors, athletes, and others) but not vice versa. Based on achievements in studies of media effect and social psychology, we constructed a set of hypotheses, and tested them using a subsample of SNS users drawn from representative survey data in South Korea. We found that dependency on parasocial relationships is positively related with loneliness but negatively correlated with interpersonal distrust, while dependency on social relationship is negatively correlated with loneliness but positively related with trust. However, more dependency on both social and parasocial relationships are positively related with SNS addiction. Implications based on findings are also discussed.
Fusion of LiDAR and Camera Sensor Data for Environment Sensing in Driverless Vehicles

Driverless vehicles operate by sensing and perceiving its surrounding environment to make the accurate driving decisions. A combination of several different sensors such as LiDAR, radar, ultrasound sensors and cameras are utilized to sense the surrounding environment of driverless vehicles. The heterogeneous sensors simultaneously capture various physical attributes of the environment. Such multimodality and redundancy of sensing need to be positively utilized for reliable and consistent perception of the environment through sensor data fusion. However, these multimodal sensor data streams are different from each other in many ways, such as temporal and spatial resolution, data format, and geometric alignment. For the subsequent perception algorithms to utilize the diversity offered by multimodal sensing, the data streams need to be spatially, geometrically and temporally aligned with each other. In this paper, we address the problem of fusing the outputs of a Light Detection and Ranging (LiDAR) scanner and a wide-angle monocular image sensor. The outputs of LiDAR scanner and the image sensor are of different spatial resolutions and need to be aligned with each other. A geometrical model is used to spatially align the two sensor outputs, followed by a Gaussian Process (GP) regression based resolution matching algorithm to interpolate the missing data with quantifiable uncertainty. The results indicate that the proposed sensor data fusion framework significantly aids the subsequent perception steps, as illustrated by the performance improvement of a typical free space detection algorithm.
Sentiment Analysis of Short Informal Texts

We describe a state-of-the-art sentiment analysis system that detects (a) the sentiment of short informal textual messages such as tweets and SMS (message-level task) and (b) the sentiment of a word or a phrase within a message (term-level task). The system is based on a supervised statistical text classification approach leveraging a variety of surfaceform, semantic, and sentiment features. The sentiment features are primarily derived from novel high-coverage tweet-specific sentiment lexicons. These lexicons are automatically generated from tweets with sentiment-word hashtags and from tweets with emoticons. To adequately capture the sentiment of words in negated contexts, a separate sentiment lexicon is generated for negated words. The system ranked first in the SemEval-2013 shared task ‘Sentiment Analysis in Twitter’ (Task 2), obtaining an F-score of 69.02 in the message-level task and 88.93 in the term-level task. Post-competition improvements boost the performance to an F-score of 70.45 (message-level task) and 89.50 (term-level task). The system also obtains state-ofthe-art performance on two additional datasets: the SemEval-2013 SMS test set and a corpus of movie review excerpts. The ablation experiments demonstrate that the use of the automatically generated lexicons results in performance gains of up to 6.5 absolute percentage points.
Fire Pixel Classification using Fuzzy Logic and Statistical Color Model

In this paper, fuzzy logic enhanced generic color model for fire pixel classification is proposed. The model uses YCbCr color space to separate the luminance from the chrominance more effectively than color spaces such as RGB or rgb. Concepts from fuzzy logic are used to replace existing heuristic rules and make the classification more robust in effectively discriminating fire and fire like colored objects. Further discrimination between fire and non fire pixels are achieved by a statistically derived chrominance model which is expressed as a region in the chrominance plane. The performance of the model is tested on two large sets of images; one set contains fire while the other set contains no fire but has regions similar to fire color. The model achieves up to 99.00% correct fire detection rate with a 9.50% false alarm rate.
On the Use of Character Affinities for Story Plot Generation

One of the aspects that is used to keep the reader’s interest in a story is the network of relationships among the characters that take part in that story. We can model the relationship between two characters using their mutual affinities, which allow us to define which interactions are possible between two characters. In this paper we present a model to represent characters’ affinities and we describe how we have implemented this model using a multi-agent system that is used to generate stories. We also present the result of one experiment to measure the evolution of the affinities between two characters throughout a story.
A probabilistic approach to mixed open-loop and closed-loop control, with application to extreme autonomous driving

We consider the task of accurately controlling a complex system, such as autonomously sliding a car sideways into a parking spot. Although certain regions of this domain are extremely hard to model (i.e., the dynamics of the car while skidding), we observe that in practice such systems are often remarkably deterministic over short periods of time, even in difficult-to-model regions. Motivated by this intuition, we develop a probabilistic method for combining closed-loop control in the well-modeled regions and open-loop control in the difficult-to-model regions. In particular, we show that by combining 1) an inaccurate model of the system and 2) a demonstration of the desired behavior, our approach can accurately and robustly control highly challenging systems, without the need to explicitly model the dynamics in the most complex regions and without the need to hand-tune the switching control law. We apply our approach to the task of autonomous sideways sliding into a parking spot, and show that we can repeatedly and accurately control the system, placing the car within about 2 feet of the desired location; to the best of our knowledge, this represents the state of the art in terms of accurately controlling a vehicle in such a maneuver.
Identifying Pathological Findings in German Radiology Reports Using a Syntacto-semantic Parsing Approach

In order to integrate heterogeneous clinical information sources, semantically correlating information entities have to be linked. Our discussions with radiologists revealed that anatomical entities with pathological findings are of particular interest when linking radiology text and images. Previous research to identify pathological findings focused on simplistic approaches that recognize diseases or negated findings, but failed to establish a holistic approach. In this paper, we introduce our syntacto-semantic parsing approach to classify sentences in radiology reports as either pathological or non-pathological based on the findings they describe. Although we operate with an incomplete, RadLex-based linguistic resource, the obtained results show the effectiveness of our approach by identifying a recall value of 74.3% for the classification task.
Biometric Recognition : An Overview

Human identifi cation leads to mutual trust that is essential for the proper functioning of society. We have been identifying fellow humans based on their voice, appearance, or gait for thousands of years. However, a systematic and scientifi c basis for human identifi cation started in the nineteenth century when Alphonse Bertillon (Rhodes and Henry 1956 ) introduced the use of a number of anthropomorphic measurements to identify habitual criminals. The Bertillon system was short-lived: soon after its introduction, the distinctiveness of human fi ngerprints was established. Since the early 1900s, fi ngerprints have been an accepted method in forensic investigations to identify suspects and repeat criminals. Now, virtually all law enforcement agencies worldwide use Automatic Fingerprint Identifi cation Systems (AFIS). With growing concerns about terrorist activities, security breaches, and fi nancial fraud, other physiological and behavioral human characteristics have been used for person identifi cation. These distinctive characteristics, or biometric traits, include features such as face, iris, palmprint, and voice. Biometrics (Jain et al. 2006, 2007 ) is now a mature technology that is widely used in a variety of applications ranging from border crossings (e.g., the US-VISIT program) to visiting Walt Disney Parks.
ARTIFICIAL NEURAL NETWORK APPLICATIONS IN GEOTECHNICAL ENGINEERING

Over the last few years or so, the use of artificial neural networks (ANNs) has increased in many areas of engineering. In particular, ANNs have been applied to many geotechnical engineering problems and have demonstrated some degree of success. A review of the literature reveals that ANNs have been used successfully in pile capacity prediction, modelling soil behaviour, site characterisation, earth retaining structures, settlement of structures, slope stability, design of tunnels and underground openings, liquefaction, soil permeability and hydraulic conductivity, soil compaction, soil swelling and classification of soils. The objective of this paper is to provide a general view of some ANN applications for solving some types of geotechnical engineering problems. It is not intended to describe the ANNs modelling issues in geotechnical engineering. The paper also does not intend to cover every single application or scientific paper that found in the literature. For brevity, some works are selected to be described in some detail, while others are acknowledged for reference purposes. The paper then discusses the strengths and limitations of ANNs compared with the other modelling approaches.
Software Ecosystems' Architectural Health: Another View

Nowadays the software ecosystem health has received increasing attention as an important field for managing software ecosystems. Researchers have intensified studies in this area with the goal of assessing ecosystem health and possible causes for the success achieved. However, none of them have considered directly the effects of the software architecture on the ecosystem health. There are some architectural aspects weighting considerably on the balance of the whole ecosystem. So, software ecosystem health should be addressed based on different perspectives, aligned with novel assumptions and ways of thinking. In this paper, we exploit intersections between architecture and software ecosystem health. We consider the concept of the "software ecosystem architectural health" from the perspective of key areas and practices, which affect the state of the ecosystem health. We also identify some research challenges and outline future work.
Development of 1-axis torque sensor with different shape of support and measurement spoke

This paper describes the design of a new 1-axis torque sensor that consists of two different type spokes. The sensor has three trapezoidal shape spokes and three support spokes. It serves a double purpose that the sensitivity and stiffness for driving torque. We suggest trapezoid-typed spokes rather than conventional rectangular-typed spoke for sensitivity, and support spokes are used to tolerate to decrease the deformation. The sensor is verified by finite element model analysis and experiments. The result showed the linear relationship between the applied weight and the measured value.
DYNAMIC MODEL OF PM SYNCHRONOUS MOTORS

In a permanent magnet synchronous motor where inductances vary as a function of rotor angle, the 2 phase (d-q) equivalent circuit model is commonly used for simplicity and intuition. In this article, a two phase model for a PM synchronous motor is derived and the properties of the circuits and variables are discussed in relation to the physical 3 phase entities. Moreover, the paper suggests methods of obtaining complete model parameters from simple laboratory tests. Due to the lack of developed procedures in the past, obtaining model parameters were very difficult and uncertain, because some model parameters are not directly measurable and vary depending on the operating conditions. Formulation is mainly for interior permanent magnet synchronous motors but can also be applied to surface permanent magnet motors.
Affective ranking of movie scenes using physiological signals and content analysis

In this paper, we propose an approach for affective ranking of movie scenes based on the emotions that are actually felt by spectators. Such a ranking can be used for characterizing the affective, or emotional, content of video clips. The ranking can for instance help determine which video clip from a database elicits, for a given user, the most joy. This in turn will permit video indexing and retrieval based on affective criteria corresponding to a personalized user affective profile.
 A dataset of 64 different scenes from 8 movies was shown to eight participants. While watching, their physiological responses were recorded; namely, five peripheral physiological signals (GSR - galvanic skin resistance, EMG - electromyograms, blood pressure, respiration pattern, skin temperature) were acquired. After watching each scene, the participants were asked to self-assess their felt arousal and valence for that scene. In addition, movie scenes were analyzed in order to characterize each with various audio- and video-based features capturing the key elements of the events occurring within that scene.
 Arousal and valence levels were estimated by a linear combination of features from physiological signals, as well as by a linear combination of content-based audio and video features. We show that a correlation exists between arousal- and valence-based rankings provided by the spectator's self-assessments, and rankings obtained automatically from either physiological signals or audio-video features. This demonstrates the ability of using physiological responses of participants to characterize video scenes and to rank them according to their emotional content. This further shows that audio-visual features, either individually or combined, can fairly reliably be used to predict the spectator's felt emotion for a given scene. The results also confirm that participants exhibit different affective responses to movie scenes, which emphasizes the need for the emotional profiles to be user-dependant.
A Detailed Qualitative Model for the Programming Physics of Silicided Polysilicon Fuses

This paper presents a detailed qualitative model for the programming physics of 90-nm silicided polysilicon fuses that is derived from a wide range of measurement data. These insights have led to a programming time of 100 ns, while achieving a resistance increase of times. This is an order of magnitude better than any previously published result for the programming time and resistance increase individually. Simple calculations and TEM-analyses substantiate the proposed programming mechanism. The insights explain the importance of the falling edge of the programming pulse. The advantage of a rectangular fuse head over a tapered fuse head is shown and explained. Polysilicon doping type is shown to have little influence on the programming result. Finally, the stability of fuses programmed with this method is shown to be very high.
Using Logistic Regression to Trace Multiple Sub-skills in a Dynamic Bayes Net

A challenge in estimating students’ changing knowledge from sequential observations of their performance arises when each observed step involves multiple subskills. To overcome this mismatch in grain size between modelled skills and observed actions, we use logistic regression over each step’s subskills in a dynamic Bayes net (LR-DBN) to model transition probabilities for the overall knowledge required by the step. Unlike previous methods, LR-DBN can trace knowledge of the individual subskills without assuming they are independent. We evaluate how well it fits children’s oral reading fluency data logged by Project LISTEN’s Reading Tutor, compared to other methods.
Robust lane detection in urban environments

Most of the lane marking detection algorithms reported in the literature are suitable for highway scenarios. This paper presents a novel clustered particle filter based approach to lane detection, which is suitable for urban streets in normal traffic conditions. Furthermore, a quality measure for the detection is calculated as a measure of reliability. The core of this approach is the usage of weak models, i.e. the avoidance of strong assumptions about the road geometry. Experiments were carried out in Sydney urban areas with a vehicle mounted laser range scanner and a ccd camera. Through experimentations, we have shown that a clustered particle filter can be used to efficiently extract lane markings.
Real-time system for monitoring driver vigilance

This paper presents a nonintrusive prototype computer vision system for monitoring a driver's vigilance in real time. It is based on a hardware system for the real-time acquisition of a driver's images using an active IR illuminator and the software implementation for monitoring some visual behaviors that characterize a driver's level of vigilance. Six parameters are calculated: Percent eye closure (PERCLOS), eye closure duration, blink frequency, nodding frequency, face position, and fixed gaze. These parameters are combined using a fuzzy classifier to infer the level of inattentiveness of the driver. The use of multiple visual parameters and the fusion of these parameters yield a more robust and accurate inattention characterization than by using a single parameter. The system has been tested with different sequences recorded in night and day driving conditions in a motorway and with different users. Some experimental results and conclusions about the performance of the system are presented