nils-herrmann/clean_scirepeval huggingface dataset:
Detection Method of the DC Bias in Distribution Power Transformers

Nonlinear loads such as ac drives, switch-mode power supplies and grid-connected converters can cause, in addition to the generation of unwanted current harmonics, a dc current component injection into the grid. This dc current component can lead to magnetic saturation of the distribution power transformers, leading to higher current distortion and overheating. This paper presents a method to detect the dc current component flowing into the distribution power transformer with a precise measurement of the dc voltage component at the transformer winding. A magnetic sensor has been developed and implemented in a closed-loop control system to achieve a high sensitivity and guarantees a good linearity with a high rejection ratio to grid voltage variations. Simulation and experimental results confirm the effectiveness of the proposed approach.
Antineutrophil cytoplasmic antibody-associated vasculitides and IgG4-related disease: A new overlap syndrome.

Atypical manifestations have been described in patients with ANCA-associated vasculitides (AAV), such as pachymeningitis, orbital mass or chronic periaortitis. Because these manifestations have been associated to the spectrum of IgG4-related disease (IgG4-RD), we hypothesized that both diseases could overlap.



We conducted a European retrospective multicenter observational study including patients fulfilling ACR and Chapel Hill criteria for AAV and IgG4-RD Comprehensive Diagnostic Criteria.



Eighteen patients were included (median age 55.5years, 13 men). AAV and IgG4-RD were diagnosed concomitantly in 13/18 (72%) patients; AAV preceded IgG4-RD in 3/18 (17%) while IgG4-RD preceded AAV in 2/18 (11%). AAV diagnoses included granulomatosis with polyangiitis in 14 (78%), microscopic polyangiitis in 3 (17%), and eosinophilic granulomatosis with polyangiitis in one case. IgG4-RD diagnosis included definite IgG4-RD in 5 (28%) cases, probable IgG4-RD in 5 (28%) and possible IgG4-RD in 8 (44%). IgG4-RD manifestations were chronic periaortitis in 9/18 (50%) patients, orbital mass and tubulointerstitial nephritis in 4 (22%) cases, prevertebral fibrosis in 3 (17%), pachymeningitis and autoimmune pancreatitis in 2 (11%) cases. Patients required median number of 2 (range 0-4) lines of immunosuppressants in combination with glucocorticoids. During the follow-up (median 49,8months, range 17,25-108months), AAV manifestations relapsed in 10/18 (56%) cases and IgG4-RD lesions in 5/18 (28%). When used, mainly for relapses, rituximab showed response in all cases.



AAV and IgG4-RD may overlap. Clinicians should consider that atypical manifestations during AAV could be related to IgG4-RD rather than to refractory granulomatous or vasculitic lesions.
Shaping the Narrative Arc: An Information-Theoretic Approach to Collaborative Dialogue

We consider the problem of designing an artificial agent capable of interacting with humans in collaborative dialogue to produce creative, engaging narratives. In this task, the goal is to establish universe details, and to collaborate on an interesting story in that universe, through a series of natural dialogue exchanges. Our model can augment any probabilistic conversational agent by allowing it to reason about universe information established and what potential next utterances might reveal. Ideally, with each utterance, agents would reveal just enough information to add specificity and reduce ambiguity without limiting the conversation. We empirically show that our model allows control over the rate at which the agent reveals information and that doing so significantly improves accuracy in predicting the next line of dialogues from movies. We close with a case-study with four professional theatre performers, who preferred interactions with our model-augmented agent over an unaugmented agent.
An Efficient Meta Heuristic Algorithm for POS-tagging

Tagging is an increasingly important task in natural language processing domains. As there are many natural language processing tasks which can be improved by applying disambiguation to the text, fast and high quality tagging algorithms are a crucial task in information retrieval and question answering. Tagging aims to assigning to each word of a text its correct tag according to the context in which the word is used. Part Of Speech (POS) tagging is a difficult problem by itself, since many words has a number of possible tags associated to it. In this paper we present a novel algorithm that deals with POS-tagging problem based on Harmony Search (HS) optimization method. This paper analyzes the relative advantages of HS metaheuristic approache to the well-known natural language processing problem of POS-tagging. In the experiments we conducted, we applied the proposed algorithm on linguistic corpora and compared the results obtained against other optimization methods such as genetic and simulated annealing algorithms. Experimental results reveal that the proposed algorithm provides more accurate results compared to the other algorithms.
Analysis and Optimization of IEEE 33 Bus Radial Distributed System Using Optimization Algorithm

This paper mainly focusses on the impact of distributed generation and best feeder reconfiguration of distribution system, in order to improve the quality of power in the distribution system. Primarily the goal of this paper is to mitigate as much as possible the losses in power system and improve the voltage profile. The optimization of the system constrained by feeder capability limit, radial configuration format, no load point interruption and load-point voltage limits. Using Hybrid Genetic Algorithm Particle Swarm Optimization (HGAPSO) to find the best configuration. This hybrid optimization or search algorithm has more efficiency and accuracy. Proposed methodology comprises of demonstration of 33-bus radial distribution with and without distributed generation. Study finding consist of the best possible configuration of switches given by optimization algorithm in order to minimize the losses but at the same time respecting all the constrain mention above.
Quantization of Fully Convolutional Networks for Accurate Biomedical Image Segmentation

With pervasive applications of medical imaging in health-care, biomedical image segmentation plays a central role in quantitative analysis, clinical diagnosis, and medical intervention. Since manual annotation suffers limited reproducibility, arduous efforts, and excessive time, automatic segmentation is desired to process increasingly larger scale histopathological data. Recently, deep neural networks (DNNs), particularly fully convolutional networks (FCNs), have been widely applied to biomedical image segmentation, attaining much improved performance. At the same time, quantization of DNNs has become an active research topic, which aims to represent weights with less memory (precision) to considerably reduce memory and computation requirements of DNNs while maintaining acceptable accuracy. In this paper, we apply quantization techniques to FCNs for accurate biomedical image segmentation. Unlike existing literatures on quantization which primarily targets memory and computation complexity reduction, we apply quantization as a method to reduce overfitting in FCNs for better accuracy. Specifically, we focus on a state-of-the-art segmentation framework, suggestive annotation [26], which judiciously extracts representative annotation samples from the original training dataset, obtaining an effective small-sized balanced training dataset. We develop two new quantization processes for this framework: (1) suggestive annotation with quantization for highly representative training samples, and (2) network training with quantization for high accuracy. Extensive experiments on the MICCAI Gland dataset show that both quantization processes can improve the segmentation performance, and our proposed method exceeds the current state-of-the-art performance by up to 1%. In addition, our method has a reduction of up to 6.4x on memory usage.
A wearable Malossi alphabet interface for deafblind people

Deafblind people have a severe degree of combined visual and auditory impairment resulting in problems with communication, (access to) information and mobility. Moreover, in order to interact with other people, most of them need the constant presence of a caregiver who plays the role of an interpreter with an external world organized for hearing and sighted people. As a result, they usually live behind an invisible wall of silence, in a unique and inexplicable condition of isolation.
 In this paper, we describe DB-HAND, an assistive hardware/software system that supports users to autonomously interact with the environment, to establish social relationships and to gain access to information sources without an assistant. DB-HAND consists of an input/output wearable peripheral (a glove equipped with sensors and actuators) that acts as a natural interface since it enables communication using a language that is easily learned by a deafblind: Malossi method. Interaction with DB-HAND is managed by a software environment, whose purpose is to translate text into sequences of tactile stimuli (and vice-versa), to execute commands and to deliver messages to other users. It also provides multi-modal feedback on several standard output devices to support interaction with the hearing and the sighted people.
Snake-Based Segmentation of Teeth from Virtual Dental Casts

During the past years several innovative and technological developments have been made in oral surgery. Today, digitized dental casts are common for simulation and planning of orthodontic interventions. In order to work with these models, knowing the exact position of the teeth is of high importance. In this paper, we present a new method for tooth segmentation with minimal user interaction. At the beginning, an initial estimate for the separating curve between the teeth and the gum is computed and optimized by use of an active contour. The second step calculates the dental arch and the interstices between the teeth. In order to detect each tooth surface exactly, we finally position a snake around the cusp of each tooth.
Innovation in Underwater Robots: Biologically Inspired Swimming Snake Robots

Increasing efficiency by improving locomotion methods is a key issue for underwater robots. Moreover, a number of different control design challenges must be solved to realize operational swimming robots for underwater tasks. This article proposes and experimentally validates a straightline-path-following controller for biologically inspired swimming snake robots. In particular, a line-of-sight (LOS) guidance law is presented, which is combined with a sinusoidal gait pattern and a directional controller that steers the robot toward and along the desired path. The performance of the path-following controller is investigated through experiments with a physical underwater snake robot for both lateral undulation and eel-like motion. In addition, fluid parameter identification is performed, and simulation results based on the identified fluid coefficients are presented to obtain a back-to-back comparison with the motion of the physical robot during the experiments. The experimental results show that the proposed control strategy successfully steers the robot toward and along the desired path for both lateral undulation and eel-like motion patterns.
Nanomaterial-Based Electrochemical Immunosensors for Clinically Significant Biomarkers

Nanotechnology has played a crucial role in the development of biosensors over the past decade. The development, testing, optimization, and validation of new biosensors has become a highly interdisciplinary effort involving experts in chemistry, biology, physics, engineering, and medicine. The sensitivity, the specificity and the reproducibility of biosensors have improved tremendously as a result of incorporating nanomaterials in their design. In general, nanomaterials-based electrochemical immunosensors amplify the sensitivity by facilitating greater loading of the larger sensing surface with biorecognition molecules as well as improving the electrochemical properties of the transducer. The most common types of nanomaterials and their properties will be described. In addition, the utilization of nanomaterials in immunosensors for biomarker detection will be discussed since these biosensors have enormous potential for a myriad of clinical uses. Electrochemical immunosensors provide a specific and simple analytical alternative as evidenced by their brief analysis times, inexpensive instrumentation, lower assay cost as well as good portability and amenability to miniaturization. The role nanomaterials play in biosensors, their ability to improve detection capabilities in low concentration analytes yielding clinically useful data and their impact on other biosensor performance properties will be discussed. Finally, the most common types of electroanalytical detection methods will be briefly touched upon.
Anatomy of a Vulnerable Fitness Tracking System: Dissecting the Fitbit Cloud, App, and Firmware

Fitbit fitness trackers record sensitive personal information, including daily step counts, heart rate profiles, and locations visited. By design, these devices gather and upload activity data to a cloud service, which provides aggregate statistics to mobile app users. The same principles govern numerous other Internet-of-Things (IoT) services that target different applications. As a market leader, Fitbit has developed perhaps the most secure wearables architecture that guards communication with end-to-end encryption. In this article, we analyze the complete Fitbit ecosystem and, despite the brand's continuous efforts to harden its products, we demonstrate a series of vulnerabilities with potentially severe implications to user privacy and device security. We employ a range of techniques, such as protocol analysis, software decompiling, and both static and dynamic embedded code analysis, to reverse engineer previously undocumented communication semantics, the official smartphone app, and the tracker firmware. Through this interplay and in-depth analysis, we reveal how attackers can exploit the Fitbit protocol to extract private information from victims without leaving a trace, and wirelessly flash malware without user consent. We demonstrate that users can tamper with both the app and firmware to selfishly manipulate records or circumvent Fitbit's walled garden business model, making the case for an independent, user-controlled, and more secure ecosystem. Finally, based on the insights gained, we make specific design recommendations that can not only mitigate the identified vulnerabilities, but are also broadly applicable to securing future wearable system architectures.
Security and Privacy Consideration for Internet of Things in Smart Home Environments

Of Things(IOT) has emerged as a trustworthy technology to improve the quality of life in smart homes through offering various automated, interactive and comfortable services. Sensors integrated at different places in homes, offices, and even in clothes, equipment, and utilities are used to sense and monitor owners’ positions, movements, required signs, valuable usage, temperature and humidity levels of rooms, etc. Along with sensing and monitoring capabilities, sensors cooperate and communicate with themselves to deliver; share and process sensed information and help real-time decision making procedures through activate suitable alerts and actions. However, ensuring privacy and providing enough security in these required services provided by IOTs is a major issue in smart home environments. In this paper, we examine the privacy and security challenges of IOTs and survey its possibilities for smart home environments. We discuss the unique characteristics that differentiate a smart environment from the rest, elaborate on security and privacy issues and their respective solution measures. A number of challenges and interesting research issues appearing from this study have been reported for further analysis.
A User-Centric View of Intelligent Environments: User Expectations, User Experience and User Role in Building Intelligent Environments

Our everyday environments are gradually becoming intelligent, facilitated both by technological development and user activities. Although large-scale intelligent environments are still rare in actual everyday use, they have been studied for quite a long time, and several user studies have been carried out. In this paper, we present a user-centric view of intelligent environments based on published research results and our own experiences from user studies with concepts and prototypes. We analyze user acceptance and users’ expectations that affect users’ willingness to start using intelligent environments and to continue using them. We discuss user experience of interacting with intelligent environments where physical and virtual elements are intertwined. Finally, we touch on the role of users in shaping their own intelligent environments instead of just using ready-made environments. People are not merely “using” the intelligent environments but they live in them, and they experience the environments via embedded services and new interaction tools as well as the physical and social environment. Intelligent environments should provide emotional as well as instrumental value to the people who live in them, and the environments should be trustworthy and controllable both by regular users and occasional visitors. Understanding user expectations and user experience in intelligent environments,
Model predictive torque control of a Switched Reluctance Motor

The strongly nonlinear magnetic characteristic of Switched Reluctance Motors (SRMs) makes their torque control a challenging task. In contrast to standard current-based control schemes, we use Model Predictive Control (MPC) and directly manipulate the switches of the dc-link power converter. At each sampling time a constrained finite-time optimal control problem based on a discrete-time nonlinear prediction model is solved yielding a receding horizon control strategy. The control objective is torque regulation while winding currents and converter switching frequency are minimized. Simulations demonstrate that a good closed-loop performance is achieved already for short prediction horizons indicating the high potential of MPC in the control of SRMs.
Adolescent Alcohol Exposure Persistently Impacts Adult Neurobiology and Behavior

Adolescence is a developmental period when physical and cognitive abilities are optimized, when social skills are consolidated, and when sexuality, adolescent behaviors, and frontal cortical functions mature to adult levels. Adolescents also have unique responses to alcohol compared with adults, being less sensitive to ethanol sedative-motor responses that most likely contribute to binge drinking and blackouts. Population studies find that an early age of drinking onset correlates with increased lifetime risks for the development of alcohol dependence, violence, and injuries. Brain synapses, myelination, and neural circuits mature in adolescence to adult levels in parallel with increased reflection on the consequence of actions and reduced impulsivity and thrill seeking. Alcohol binge drinking could alter human development, but variations in genetics, peer groups, family structure, early life experiences, and the emergence of psychopathology in humans confound studies. As adolescence is common to mammalian species, preclinical models of binge drinking provide insight into the direct impact of alcohol on adolescent development. This review relates human findings to basic science studies, particularly the preclinical studies of the Neurobiology of Adolescent Drinking in Adulthood (NADIA) Consortium. These studies focus on persistent adult changes in neurobiology and behavior following adolescent intermittent ethanol (AIE), a model of underage drinking. NADIA studies and others find that AIE results in the following: increases in adult alcohol drinking, disinhibition, and social anxiety; altered adult synapses, cognition, and sleep; reduced adult neurogenesis, cholinergic, and serotonergic neurons; and increased neuroimmune gene expression and epigenetic modifiers of gene expression. Many of these effects are specific to adolescents and not found in parallel adult studies. AIE can cause a persistence of adolescent-like synaptic physiology, behavior, and sensitivity to alcohol into adulthood. Together, these findings support the hypothesis that adolescent binge drinking leads to long-lasting changes in the adult brain that increase risks of adult psychopathology, particularly for alcohol dependence.
The psychological profile and affective response of women diagnosed with unexplained infertility undergoing in vitro fertilization

It has been hypothesized that unexplained infertility may be related to specific personality and coping styles. We studied two groups of women with explained infertility (EIF, n = 63) and unexplained infertility (UIF, n = 42) undergoing an in vitro fertilization (IVF) cycle. Women completed personality and coping style questionnaires prior to the onset of the cycle, and state depression and anxiety scales before and at two additional time points during the cycle. Almost no in-between group differences were found at any of the measured time points in regards to the Minnesota Multiphasic Personality Inventory-2 validity and clinical scales, Illness Cognitions and Life Orientation Test, or for the situational measures. The few differences found suggest a more adaptive, better coping, and functioning defensive system in women with EIF. In conclusion, we did not find any clinically significant personality differences or differences in depression or anxiety levels between women with EIF and UIF during an IVF cycle. Minor differences found are probably a reaction to the ambiguous medical situation with its uncertain prognosis, amplifying certain traits which are not specific to one psychological structure but rather to the common experience shared by the group. The results of this study do not support the possibility that personality traits are involved in the pathophysiology of unexplained infertility.
Sparse Geometries Handling in Lattice Boltzmann Method Implementation for Graphic Processors

We describe a high-performance implementation of the lattice Boltzmann method (LBM) for sparse geometries on graphic processors. In our implementation we cover the whole geometry with a uniform mesh of small tiles and carry out calculations for each tile independently with proper data synchronization at the tile edges. For this method, we provide both a theoretical analysis of complexity and the results for real implementations involving two-dimensional (2D) and three-dimensional (3D) geometries. Based on the theoretical model, we show that tiles offer significantly smaller bandwidth overheads than solutions based on indirect addressing. For 2D lattice arrangements, a reduction in memory usage is also possible, although at the cost of diminished performance. We achieved a performance of 682 MLUPS on GTX Titan (72 percent of peak theoretical memory bandwidth) for the D3Q19 lattice arrangement and double-precision data.