nils-herrmann/clean_scirepeval huggingface dataset:
Dermoscopy of cutaneous leishmaniasis.

Dermoscopy has been proposed as a diagnostic tool in the case of skin infections and parasitosis but no specific dermoscopic criteria have been described for cutaneous leishmaniasis (CL).



To describe the dermoscopic features of CL.



Dermoscopic examination (using the DermLite Foto; 3Gen, LLC, Dana Point, CA, U.S.A.) of 26 CL lesions was performed to evaluate specific dermoscopic criteria.



We observed the following dermoscopic features: generalized erythema (100%), 'yellow tears' (53%), hyperkeratosis (50%), central erosion/ulceration (46%), erosion/ulceration associated with hyperkeratosis (38%) and 'white starburst-like pattern' (38%). Interestingly, at least one vascular structure described in skin neoplasms was observed in all cases: comma-shaped vessels (73%), linear irregular vessels (57%), dotted vessels (53%), polymorphous/atypical vessels (26%), hairpin vessels (19%), arborizing telangiectasia (11%), corkscrew vessels (7%) and glomerular-like vessels (7%). Combination of two or more different types of vascular structures was present in 23 of 26 CL lesions (88%), with a combination of two vascular structures in 13 cases (50%) and three or more in 10 cases (38%).



Characteristic dermoscopic structures have been identified in CL. Important vascular patterns seen in melanocytic and nonmelanocytic tumours are frequently observed in this infection.
High power and high efficiency GaN-HEMT for microwave communication applications

Microwaves have been widely used for the modern communication systems, which have advantages in high bit rate transmission and the easiness of compact circuit and antenna design. Gallium Nitride (GaN), featured with high breakdown and high saturation velocity, is one of the promising material for high power and high frequency devices, and a kW-class output power has already been achieved . We have developed the high power and high efficiency GaN HEMTs [2–5], targeting the amplifier for the base transceiver station (BTS). This presentation summarizes our recent works, focusing on the developments for the efficiency boosting and the robustness in high power RF operation.
Position-aware activity recognition with wearable devices

Reliable human activity recognition with wearable devices enables the development of human-centric pervasive applications.We aim to develop a robust wearable-based activity recognition system for real life situations where the device position is up to the user or where a user is unable to collect initial training data. Consequently, in this work we focus on the problem of recognizing the on-body position of the wearable device ensued by comprehensive experiments concerning subject-specific and cross-subjects activity recognition approaches that rely on acceleration data. We introduce a device localization method that predicts the on-body position with an F-measure of 89% and a cross-subjects activity recognition approach that considers common physical characteristics. In this context, we present a real world data set that has been collected from 15 participants for 8 common activities where they carried 7 wearable devices in different on-body positions. Our results show that the detection of the device position consistently improves the result of activity recognition for common activities. Regarding cross-subjects models, we identified the waist as the most suitable device location at which the acceleration patterns for the same activity across several people are most similar. In this context, our results provide evidence for the reliability of physical characteristics based cross-subjectsmodels.
Single-unit pattern generators for quadruped locomotion

Legged robots can potentially venture beyond the limits of wheeled vehicles. While creating controllers for such robots by hand is possible, evolutionary algorithms are an alternative that can reduce the burden of hand-crafting robotic controllers. Although major evolutionary approaches to legged locomotion can generate oscillations through popular techniques such as continuous time recurrent neural networks (CTRNNs) or sinusoidal input, they typically face a challenge in maintaining long-term stability. The aim of this paper is to address this challenge by introducing an effective alternative based on a new type of neuron called a single-unit pattern generator (SUPG). The SUPG, which is indirectly encoded by a compositional pattern producing network (CPPN) evolved by HyperNEAT, produces a flexible temporal activation pattern that can be reset and repeated at any time through an explicit trigger input, thereby allowing it to dynamically recalibrate over time to maintain stability. The SUPG approach, which is compared to CTRNNs and sinusoidal input, is shown to produce natural-looking gaits that exhibit superior stability over time, thereby providing a new alternative for evolving oscillatory locomotion.
Coordinated Scheduling of Residential Distributed Energy Resources to Optimize Smart Home Energy Services

We describe algorithmic enhancements to a decision-support tool that residential consumers can utilize to optimize their acquisition of electrical energy services. The decision-support tool optimizes energy services provision by enabling end users to first assign values to desired energy services, and then scheduling their available distributed energy resources (DER) to maximize net benefits. We chose particle swarm optimization (PSO) to solve the corresponding optimization problem because of its straightforward implementation and demonstrated ability to generate near-optimal schedules within manageable computation times. We improve the basic formulation of cooperative PSO by introducing stochastic repulsion among the particles. The improved DER schedules are then used to investigate the potential consumer value added by coordinated DER scheduling. This is computed by comparing the end-user costs obtained with the enhanced algorithm simultaneously scheduling all DER, against the costs when each DER schedule is solved separately. This comparison enables the end users to determine whether their mix of energy service needs, available DER and electricity tariff arrangements might warrant solving the more complex coordinated scheduling problem, or instead, decomposing the problem into multiple simpler optimizations.
Near or Far, Wide Range Zero-Shot Cross-Lingual Dependency Parsing

Cross-lingual transfer is the major means to leverage knowledge from high-resource languages to help low-resource languages. In this paper, we investigate cross-lingual transfer across a broad spectrum of language distances. We posit that Recurrent Neural Networks (RNNs)-based encoders, since explicitly incorporating surface word order, are brittle for transferring across distant languages, while self-attentive models are more flexible on modeling word order information; thus they would be more robust in the cross-lingual transfer setting. We test our hypothesis by training dependency parsers on only English corpus and evaluating them on 31 other languages. With detailed analysis, we find interesting patterns showing that RNNs-based architectures can transfer well for languages that are close to English, while self-attentive models are have better cross-lingual transferability across a wide range of languages.
Dual-Band and Wideband Dual-Polarized Cylindrical Dielectric Resonator Antennas

This letter investigates dual-band and wideband designs of dual-polarized cylindrical dielectric resonator antennas (DRAs). These designs make use of the fundamental HEM111 mode and higher-order HEM113 mode of the DRA. The strip- and slot-fed excitation methods are used for Ports 1 and 2 of the antennas, respectively. For demonstration, a dual-band dual-polarized DRA for DCS (1.71-1.88 GHz) and WLAN (2.4-2.48 GHz) bands and a wideband version that covers the 2.4-GHz WLAN band were designed. The S-parameters, radiation patterns, antenna gains, antenna efficiencies, and envelope correlations of the two designs are studied. It was found that the dual-band and wideband designs have port isolations of higher than 36 and 37 dB, respectively. Good agreement between the measured and simulated results is observed.
On the Analysis and Design of Low-Loss Single-Pole Double-Throw W-Band Switches Utilizing Saturated SiGe HBTs

This paper describes the analysis and design of saturated silicon-germanium (SiGe) heterojunction bipolar transistor (HBT) switches for millimeter-wave applications. A switch optimization procedure is developed based on detailed theoretical analysis and is then used to design multiple switch variants. The switches utilize IBM's 90-nm 9HP technology, which features SiGe HBTs with peak f T/ fmax of 300/350 GHz. Using a reverse-saturated configuration, a single-pole double-throw switch with a measured insertion loss of 1.05 dB and isolation of 22 dB is achieved at 94 GHz after de-embedding pad losses. The switch draws 5.2 mA from a 1.1-V supply, limiting power consumption to less than 6 mW. The switching speed is analyzed and the simulated turn-on and turn-off times are found to be less than 200 ps. A technique is also introduced to significantly increase the power-handling capabilities of saturated SiGe switches up to an input-referred 1-dB compression point of 22 dBm. Finally, the impact of RF stress on this novel configuration is investigated and initial measurements over a 48-h period show little performance degradation. These results demonstrate that SiGe-based switches may provide significant benefits to millimeter-wave systems.
AdaCluster : Adaptive Clustering for Heterogeneous Data

Clustering algorithms start with a fixed divergence, which captures the possibly asymmetric distance between a sample and a centroid. In the mixture model setting, the sample distribution plays the same role. When all attributes have the same topology and dispersion, the data are said to be homogeneous. If the prior knowledge of the distribution is inaccurate or the set of plausible distributions is large, an adaptive approach is essential. The motivation is more compelling for heterogeneous data, where the dispersion or the topology differs among attributes. We propose an adaptive approach to clustering using classes of parametrized Bregman divergences. We first show that the density of a steep exponential dispersion model (EDM) can be represented with a Bregman divergence. We then propose AdaCluster, an expectation-maximization (EM) algorithm to cluster heterogeneous data using classes of steep EDMs. We compare AdaCluster with EM for a Gaussian mixture model on synthetic data and nine UCI data sets. We also propose an adaptive hard clustering algorithm based on Generalized Method of Moments. We compare the hard clustering algorithm with k-means on the UCI data sets. We empirically verified that adaptively learning the underlying topology yields better clustering of heterogeneous data.
Supporting Situationally Aware Cybersecurity Systems 30 th September 2015

In this report, we describe the Unified Cyber Security ontology (UCO) to support situational awareness in cyber security systems. The ontology is an effort to incorporate and integrate heterogeneous information available from different cyber security systems and most commonly used cyber security standards for information sharing and exchange. The ontology has also been mapped to a number of existing cyber security ontologies as well as concepts in the Linked Open Data cloud. Similar to DBpedia which serves as the core for Linked Open Data cloud, we envision UCO to serve as the core for the specialized cyber security Linked Open Data cloud which would evolve and grow with the passage of time with additional cybersecurity data sets as they become available. We also present a prototype system and concrete use-cases supported by the UCO ontology. To the best of our knowledge, this is the first cyber security ontology that has been mapped to general world ontologies to support broader and diverse security use-cases. We compare the resulting ontology with previous efforts, discuss its strengths and limitations, and describe potential future work directions.
Loss Functions for Binary Class Probability Estimation and Classification : Structure and Applications

What are the natural loss functions or fitting criteria for binary class probability estimation? This question has a simple answer: so-called “proper scoring rules”, that is, functions that score probability estimates in view of data in a Fisher-consistent manner. Proper scoring rules comprise most loss functions currently in use: log-loss, squared error loss, boosting loss, and as limiting cases cost-weighted misclassification losses. Proper scoring rules have a rich structure: • Every proper scoring rules is a mixture (limit of sums) of cost-weighted misclassification losses. The mixture is specified by a weight function (or measure) that describes which misclassification cost weights are most emphasized by the proper scoring rule. • Proper scoring rules permit Fisher scoring and Iteratively Reweighted LS algorithms for model fitting. The weights are derived from a link function and the above weight function. • Proper scoring rules are in a 1-1 correspondence with information measures for tree-based classification. • Proper scoring rules are also in a 1-1 correspondence with Bregman distances that can be used to derive general approximation bounds for cost-weighted misclassification errors, as well as generalized bias-variance decompositions. We illustrate the use of proper scoring rules with novel criteria for 1) Hand and Vinciotti’s (2003) localized logistic regression and 2) for interpretable classification trees. We will also discuss connections with exponential loss used in boosting.
Server virtualization architecture and implementation

Virtual machine technology, or virtualization, is gaining momentum in the information technology community. While virtual machines are not a new concept, recent advances in hardware and software technology have brought virtualization to the forefront of IT management. Stability, cost savings, and manageability are among the reasons for the recent rise of virtualization. Virtual machine solutions can be classified by hardware, software, and operating system/containers. From its inception on the mainframe to distributed servers on x86, the virtual machine has matured and will play an increasing role in systems management.
An Industry-Based Survey of Reliability in Power Electronic Converters

A questionnaire survey was carried out to determine the industrial requirements and expectations of reliability in power electronic converters. The survey was subjective and conducted with a number of high-profile semiconductor manufacturers, integrators, and users in the aerospace, automation, motor drive, utility power, and other industry sectors. According to the survey, power semiconductor devices ranked the most fragile components. It was concluded that main stresses were from the environment, transients, and heavy loads, which should be considered during power electronic system design and normal operation. This paper has also highlighted that there is a significant need identified by the responders for better reliability-monitoring methods and indicators.
Sign Language to Speech Translation System Using PIC Microcontroller Gunasekaran

The advancement in embedded system, provides a space to design and develop a sign language translator system to assist the dumb people. This paper mainly addresses to facilitate dumb person's lifestyle. Dumb people throughout the world use sign language to communicate with others, this is possible for those who has undergone special trainings. Common people also face difficult to understand the gesture language. To overcome these real time issues, this system is developed. Whenever the proposed system senses any sign language, it plays corresponding recorded voice. This reduces the communication gap between dumb and ordinary people. This proposed model consist of four modules, they are sensing unit, processing unit, voice storage unit and wireless communication unit. It is achieved by integrating flux sensor and APR9600 with PIC16F877A. The flux sensors are placed in gloves, which respond to gesture. By using suitable circuit response of the sensor is given to the microcontroller based on the response microcontroller plays the recorded voice using APR9600. A snapshot of the entire system, advantage over existing methods and simulation output of the process is discussed in this work. This system offers high reliability and fast response. This method is more precise on hand movement and different languages can be installed without altering the code in PIC microcontroller.  Flux sensor, APR9600, Bluetooth, PIC Microcontroller
ImAxes: Immersive Axes as Embodied Affordances for Interactive Multivariate Data Visualisation

We introduce ImAxes immersive system for exploring multivariate data using fluid, modeless interaction. The basic interface element is an embodied data axis. The user can manipulate these axes like physical objects in the immersive environment and combine them into sophisticated visualisations. The type of visualisation that appears depends on the proximity and relative orientation of the axes with respect to one another, which we describe with a formal grammar. This straight-forward composability leads to a number of emergent visualisations and interactions, which we review, and then demonstrate with a detailed multivariate data analysis use case.
Guiding Wheelchair Motion Based on EOG Signals Using Tangent Bug Algorithm

In this work, we propose new method beside the classic method, to control the motorized wheelchair using EOG signals. The new method allows the user to look around freely while the wheelchair navigates automatically to the desired goal point. Only EOG signals are used to control the wheelchair, eye gazing and blinking. The user can still choose to control the wheelchair using the classic manual method in case the environment and obstacles structure does not help with the auto navigation method. In the new auto navigation method the micro controller can know the goal point direction and distance by calculating the gaze angle that the user is gazing at. Gaze angle and blinks are measured and used as inputs for the controlling method. Tangent Bug algorithm is used to navigate the wheelchair in Auto controlling method.
Motivated software engineers are engaged and focused, while satisfied ones are happy

Motivation and job satisfaction are not the same thing, and although business organization research recognized this a long time ago, in Software Engineering research, we have not. As a result, thirty years of research on motivation in software engineering has produced knowledge on what makes software engineers generally happier, but not about how to increase their motivation. Goal -- In this article, we aim to identify visible signs of a software engineer who is motivated to work.  We describe a field study in which 62 practitioners in Brazil reported their view of "motivation" in the context of their practical work. Data was collected by means of audio-recorded semi-structured interviews, and a thematic analysis was applied to identify the most relevant descriptors of motivation.  Our data reveal that (1) motivated Software Engineers are engaged, focused, and collaborative; and (2) the term "motivation" is used as an umbrella term to cover several distinct organizational behaviours that are not necessarily related to the individual's desire to work.  Without a clear picture of the difference between these two concepts, work-based motivation programs may not be designed effectively to address either turnover or performance issues. Overall, this work indicates the need for a more effective conceptual system to investigate and encourage both job satisfaction and work motivation in software engineering research and practice.
A taxonomy of sequential pattern mining algorithms

Owing to important applications such as mining web page traversal sequences, many algorithms have been introduced in the area of sequential pattern mining over the last decade, most of which have also been modified to support concise representations like closed, maximal, incremental or hierarchical sequences. This article presents a taxonomy of sequential pattern-mining techniques in the literature with web usage mining as an application. This article investigates these algorithms by introducing a taxonomy for classifying sequential pattern-mining algorithms based on important key features supported by the techniques. This classification aims at enhancing understanding of sequential pattern-mining problems, current status of provided solutions, and direction of research in this area. This article also attempts to provide a comparative performance analysis of many of the key techniques and discusses theoretical aspects of the categories in the taxonomy.
Antecedents and consequences of customer-company identification: expanding the role of relationship marketing.

This article presents an empirical test of organizational identification in the context of customer-company (C-C) relationships. It investigates whether customers identify with companies and what the antecedents and consequences of such identification are. The model posits that perceived company characteristics, construed external image, and the perception of the company's boundary-spanning agent lead to C-C identification. In turn, such identification is expected to impact both in-role behavior (i.e., product utilization) as well as extra-role behavior (i.e., citizenship). The model was tested in a consultative selling context of pharmaceutical sales reps calling on physicians. Results from the empirical test indicated that customers do indeed identify with organizations and that C-C identification positively impacts both product utilization behavior and extra-role behavior even when the effect of brand perception is accounted for. Second, the study found that the organization's characteristics as well as the salesperson's characteristics contributed to the development of C-C identification.
Semi-supervised Named Entity Recognition in noisy-text

Many of the existing Named Entity Recognition (NER) solutions are built based on news corpus data with proper syntax. These solutions might not lead to highly accurate results when being applied to noisy, user generated data, e.g., tweets, which can feature sloppy spelling, concept drift, and limited contextualization of terms and concepts due to length constraints. The models described in this paper are based on linear chain conditional random fields (CRFs), use the BIEOU encoding scheme, and leverage random feature dropout for up-sampling the training data. The considered features include word clusters and pre-trained distributed word representations, updated gazetteer features, and global context predictions. The latter feature allows for ingesting the meaning of new or rare tokens into the system via unsupervised learning and for alleviating the need to learn lexicon based features, which usually tend to be high dimensional. In this paper, we report on the solution [ST] we submitted to the WNUT 2016 NER shared task. We also present an improvement over our original submission [SI], which we built by using semi-supervised learning on labelled training data and pre-trained resourced constructed from unlabelled tweet data. Our ST solution achieved an F1 score of 1.2% higher than the baseline (35.1% F1) for the task of extracting 10 entity types. The SI resulted in an increase of 8.2% in F1 score over the baseline (7.08% over ST). Finally, the SI model’s evaluation on the test data achieved a F1 score of 47.3% (~1.15% increase over the 2 best submitted solution). Our experimental setup and results are available as a standalone twitter NER tool at https://github.com/napsternxg/TwitterNER.