nils-herrmann/clean_scirepeval huggingface dataset:
Automatic face caricatures synthesis and exaggeration

Face caricatures are widely used in political cartoons and generating caricatures from images has become a popular research topic recently. The main challenge lies in achieving nice artistic effect and capturing face characteristics by exaggerating the most featured parts while keeping the resemblance to the original image. In this paper, a sketch-based face caricature synthesis framework is proposed to generate and exaggerate the face caricature from a single near-frontal picture. We first present an effective and robust face component rendering method using Adaptive Thresholding to eliminate the influence of illumination by separating face components into layers. Then, we propose an automatic exaggeration method, in which face component features are trained using Support Vector Machine (SVM) and then amplified using image processing techniques to make the caricature more hilarious and thus more impressive. After that, a hair rendering method is presented, which synthesizes hair in the same caricature style using edge-detection techniques. Practical results show that the synthesized face caricatures are of great artistic effect and well characterized, and our method is robust and efficient even under unfavorable lighting conditions.
Medicinal plants and antimicrobial activity.

In the present paper, we analyze the past, present and future of medicinal plants, both as potential antimicrobial crude drugs as well as a source for natural compounds that act as new anti-infection agents. In the past few decades, the search for new anti-infection agents has occupied many research groups in the field of ethnopharmacology. When we reviewed the number of articles published on the antimicrobial activity of medicinal plants in PubMed during the period between 1966 and 1994, we found 115; however, in the following decade between 1995 and 2004, this number more than doubled to 307. In the studies themselves one finds a wide range of criteria. Many focus on determining the antimicrobial activity of plant extracts found in folk medicine, essential oils or isolated compounds such as alkaloids, flavonoids, sesquiterpene lactones, diterpenes, triterpenes or naphtoquinones, among others. Some of these compounds were isolated or obtained by bio-guided isolation after previously detecting antimicrobial activity on the part of the plant. A second block of studies focuses on the natural flora of a specific region or country; the third relevant group of papers is made up of specific studies of the activity of a plant or principle against a concrete pathological microorganism. Some general considerations must be established for the study of the antimicrobial activity of plant extracts, essential oils and the compounds isolated from them. Of utmost relevance is the definition of common parameters, such as plant material, techniques employed, growth medium and microorganisms tested.
Coordination Control of Wheeled Mobile Robot Using MPC

In the last decades Model predictive control (MPC) is the leading paradigm for high-performance and cost-effective control of complex systems. MPC contains powerful technique for optimizing the performance of constrained systems on state and control variables. For this reason, we use a distributed controller to coordinate a fleet of moving robots with unicycle kinematics. The proposed algorithm is based on the concept of robust MPC control with tube based strategy (i.e. It imposes low complexity and conservativeness). Using this approach we are able to guarantee that the real controlled system, on which a bounded disturbance is supposed to act, remains constrained in a neighborhood of the trajectory of the ideal system, which is defined by neglecting the effect of uncertainties. Obstacle avoidance function introduced for prevent collision between robot coordination. Matlab implementation and real robot results are used to show the efficacy of the proposed control strategy.
Inferring User Routes and Locations Using Zero-Permission Mobile Sensors

Leakage of user location and traffic patterns is a serious security threat with significant implications on privacy as reported by recent surveys and identified by the US Congress Location Privacy Protection Act of 2014. While mobile phones can restrict the explicit access to location information to applications authorized by the user, they are ill-equipped to protect against side-channel attacks. In this paper, we show that a zero-permissions Android app can infer vehicular users' location and traveled routes, with high accuracy and without the users' knowledge, using gyroscope, accelerometer, and magnetometer information. We modeled this problem as a maximum likelihood route identification on a graph. The graph is generated from the OpenStreetMap publicly available database of roads. Our route identification algorithms output both a ranked list of potential routes as well a ranked list of route-clusters. Through extensive simulations over 11 cities, we show that for most cities with probability higher than 50% it is possible to output a short list of 10 routes containing the traveled route. In real driving experiments (over 980 Km) in the cities of Boston (resp. Waltham), Massachusetts, we report a probability of 30% (resp. 60%) of inferring a list of 10 routes containing the true route.
Androids as an Experimental Apparatus : Why Is There an Uncanny Valley and Can We Exploit It ?

It seems natural to assume that the more It seems natural to assume that the more closely robots come to resemble people, the more likely they are to elicit the kinds of responses people direct toward each other. However, subtle flaws in appearance and movement only seem eerie in very humanlike robots. This uncanny phenomenon may be symptomatic of entities that elicit a model of a human other but do not measure up to it. If so, a very humanlike robot may provide the best means of finding out what kinds of behavior are perceived as human, since deviations from a human other are more obvious. In pursuing this line of inquiry, it is essential to identify the mechanisms involved in evaluations of human likeness. One hypothesis is that an uncanny robot elicits an innate fear of death and culturally-supported defenses for coping with death’s inevitability. An experiment, which borrows from the methods of terror management research, was performed to test this hypothesis. Across all questions subjects who were exposed to a still image of an uncanny humanlike robot had on average a heightened preference for worldview supporters and a diminished preference for worldview threats relative to the control group.
Dust Removal with Boundary and Spatial Constraint for Videos Captured in Car

Videos captured in car often suffer from duston the wind screen glass. The dust particles on wind screendecrease the video quality and make them blur. Removingdust and restoring high quality dust-free video is a challengingtask in the field of video stream processing. In this paper, we propose an improved iterative optimization pipeline toremove dust from the videos. Our method employs boundaryconstraint to keep transmission map in a reasonable rangeand use spatial constraint on the transmission map to avoidintroduction of significant halo artifacts into the resultantvideo. With optimized transmission map as an initial condition, our method can separate dust layer and background layer frominput video frames and keeps the background frames beingcolor faithful with fine details. Test results demonstrate thatour proposed method can recover dust-free videos from thedust contaminated input videos and keep the resultant video color faithful.
IPv6 over LoRaWAN™

Although short-range wireless communication explicitly targets local and regional applications, range continues to be a highly important issue. The range directly depends on the so-called link budget, which can be increased by the choice of modulation and coding schemes. The recent transceiver generation in particular comes with extensive and flexible support for software-defined radio (SDR). The SX127× family from Semtech Corp. is a member of this device class and promises significant benefits for range, robust performance, and battery lifetime compared to competing technologies. This contribution gives a short overview of the technologies to support Long Range (LoRa™) and the corresponding Layer 2 protocol (LoRaWAN™). It particularly describes the possibility to combine the Internet Protocol, i.e. IPv6, into LoRaWAN™, so that it can be directly integrated into a full-fledged Internet of Things (IoT). The proposed solution, which we name 6LoRaWAN, has been implemented and tested; results of the experiments are also shown in this paper.
Assistive grasping with an augmented reality user interface

Assisting impaired individuals with robotic devices is an emerging and potentially transformative technology. This paper describes the design of an assistive robotic grasping system that allows impaired individuals to interact with the system in a human-in-the-loop manner, including the use of a novel cranio-facial electromyography input device. The system uses an augmented reality interface that allows users to plan grasps online that match their task-oriented intents. The system uses grasp quality measurements that generate more robust grasps by considering the local geometry of the object and the effect of uncertainty during grasp acquisition. This interface is validated by testing with real users, both healthy and impaired. This work forms the foundation for a flexible, fully featured human-in-the-loop system that allows users to grasp known and unknown objects in cluttered spaces using novel, practical human–robot interaction paradigms that have the potential to bring human-in-the-loop assistive devices out of the research environment and into the lives of those that need them.
Networked privacy: How teenagers negotiate context in social media

While much attention is given to young people’s online privacy practices on sites like Facebook, current theories of privacy fail to account for the ways in which social media alter practices of information-sharing and visibility. Traditional models of privacy are individualistic, but the realities of privacy reflect the location of individuals in contexts and networks. The affordances of social technologies, which enable people to share information about others, further preclude individual control over privacy. Despite this, social media technologies primarily follow technical models of privacy that presume individual information control. We argue that the dynamics of sites like Facebook have forced teens to alter their conceptions of privacy to account for the networked nature of social media. Drawing on their practices and experiences, we offer a model of networked privacy to explain how privacy is achieved in networked publics.
Coupled Inductor Characterization for a High Performance Interleaved Boost Converter

Interleaved power converter topologies have received increasing attention in recent years for high power and high performance applications. The advantages of interleaved boost converters include increased efficiency, reduced size, reduced electromagnetic emission, faster transient response, and improved reliability. The front end inductors in an interleaved boost converter are magnetically coupled to improve electrical performance and reduce size and weight. Compared to a direct coupled configuration, inverse coupling provides the advantages of lower inductor ripple current and negligible dc flux levels in the core. In this paper, we explore the possible advantages of core geometry on core losses and converter efficiency. Analysis of FEA simulation and empirical characterization data indicates a potential superiority of a square core, with symmetric 45deg energy storage corner gaps, for providing both ac flux balance and maximum dc flux cancellation when wound in an inverse coupled configuration.
Deep learning for automated skeletal bone age assessment in X-ray images

Skeletal bone age assessment is a common clinical practice to investigate endocrinology, genetic and growth disorders in children. It is generally performed by radiological examination of the left hand by using either the Greulich and Pyle (G&P) method or the Tanner-Whitehouse (TW) one. However, both clinical procedures show several limitations, from the examination effort of radiologists to (most importantly) significant intra- and inter-operator variability. To address these problems, several automated approaches (especially relying on the TW method) have been proposed; nevertheless, none of them has been proved able to generalize to different races, age ranges and genders. In this paper, we propose and test several deep learning approaches to assess skeletal bone age automatically; the results showed an average discrepancy between manual and automatic evaluation of about 0.8 years, which is state-of-the-art performance. Furthermore, this is the first automated skeletal bone age assessment work tested on a public dataset and for all age ranges, races and genders, for which the source code is available, thus representing an exhaustive baseline for future research in the field. Beside the specific application scenario, this paper aims at providing answers to more general questions about deep learning on medical images: from the comparison between deep-learned features and manually-crafted ones, to the usage of deep-learning methods trained on general imagery for medical problems, to how to train a CNN with few images.
Ad Serving with Multiple KPIs

Ad-servers have to satisfy many different targeting criteria, and the combination can often result in no feasible solution. We hypothesize that advertisers may be defining these metrics to create a kind of "proxy target". We therefore reformulate the standard ad-serving problem to one where we attempt to get as close as possible to the advertiser's multi-dimensional target inclusive of delivery. We use a simple simulation to illustrate the behavior of this algorithm compared to Constraint and Pacing strategies. The system is then deployed in one of the largest video ad-servers in the United States and we show experimental results from live test ads, as well as 6 months of production performance across hundreds of ads. We find that the live ad-server tests match the simulation, and we report significant gains in multi-KPI performance from using the error minimization strategy.
Alternatives to Indwelling Catheters Cause Unintended Complications.

To reduce the risk of catheter-associated urinary tract infection (CAUTI), limiting use of indwelling catheters is encouraged with alternative collection methods and early removal. Adverse effects associated with such practices have not been described. We also determined if CAUTI preventative measures increase the risk of catheter-related complications. We hypothesized that there are complications associated with early removal of indwelling catheters. We described complications associated with indwelling catheterization and intermittent catheterization, and compared complication rates before and after policy updates changed catheterization practices. We performed retrospective cohort analysis of trauma patients admitted between August 1, 2009, and December 31, 2013 who required indwelling catheter. Associations between catheter days and adverse outcomes such as infection, bladder overdistention injury, recatheterization, urinary retention, and patients discharged with indwelling catheter were evaluated. The incidence of CAUTI and the total number of catheter days pre and post policy change were similar. The incidence rate of urinary retention and associated complications has increased since the policy changed. Practices intended to reduce the CAUTI rate are associated with unintended complications, such as urinary retention. Patient safety and quality improvement programs should monitor all complications associated with urinary catheterization practices, not just those that represent financial penalties.
Engineering Analysis and Recognition of Nigerian English: An Insight into Low Resource Languages

A comparative analysis between Nigerian English (NE) and American English (AE) is presented in this article. The study is aimed at highlighting differences in the speech parameters, and how they influence speech processing and automatic speech recognition (ASR). The UILSpeech corpus of Nigerian-Accented English isolated word recordings, read speech utterances, and video recordings are used as a reference for Nigerian English. The corpus captures the linguistic diversity of Nigeria with data collected from native speakers of Hausa, Igbo, and Yoruba languages. The UILSpeech corpus is intended to provide a unique opportunity for application and expansion of speech processing techniques to a limited resource language dialect. The acoustic-phonetic differences between American English (AE) and Nigerian English (NE) are studied in terms of pronunciation variations, vowel locations in the formant space, mean fundamental frequency, and phone model distances in the acoustic space, as well as through visual speech analysis of the speakers’ articulators. A strong impact of the AE–NE acoustic mismatch on ASR is observed. A combination of model adaptation and extension of the AE lexicon for newly established NE pronunciation variants is shown to substantially improve performance of the AE-trained ASR system in the new NE task. This study is a part of the pioneering efforts towards incorporating speech technology in Nigerian English and is intended to provide a development basis for other low resource language dialects and languages.
Tie strength prediction in OSN

A decade earlier, work on modeling and analyzing social network, was primarily focused on manually collected datasets where the friendship links were sparse but relatively noise free (i.e. all links represented strong physical relation). With the popularity of online social networks, the notion of “friendship” changed dramatically. The data collection, now although automated, contains dense friendship links but the links contain noisier information (i.e. some weaker relationships). The aim of this study is to identify these weaker links and suggest how these links (identification) play a vital role in improving social media design elements such as privacy control, detection of auto-bots, friend introductions, information prioritization and so on. The binary metric used so far for modeling links in social network (i.e. friends or not) is of little importance as it groups all our relatives, close friends and acquaintances in the same category. Therefore a popular notion of tie-strength has been incorporated for modeling links. In this paper, a predictive model is presented that helps evaluate tie-strength for each link in network based on transactional features (e.g. communication, file transfer, photos). The model predicts tie strength with 76.4% efficiency. This work also suggests that important link properties manifest similarly across different social media sites.
Syslog processing for switch failure diagnosis and prediction in datacenter networks

Syslogs on switches are a rich source of information for both post-mortem diagnosis and proactive prediction of switch failures in a datacenter network. However, such information can be effectively extracted only through proper processing of syslogs, e.g., using suitable machine learning techniques. A common approach to syslog processing is to extract (i.e., build) templates from historical syslog messages and then match syslog messages to these templates. However, existing template extraction techniques either have low accuracies in learning the “correct” set of templates, or does not support incremental learning in the sense the entire set of templates has to be rebuilt (from processing all historical syslog messages again) when a new template is to be added, which is prohibitively expensive computationally if used for a large datacenter network. To address these two problems, we propose a frequent template tree (FT-tree) model in which frequent combinations of (syslog) words are identified and then used as message templates. FT-tree empirically extracts message templates more accurately than existing approaches, and naturally supports incremental learning. To compare the performance of FT-tree and three other template learning techniques, we experimented them on two-years' worth of failure tickets and syslogs collected from switches deployed across 10+ datacenters of a tier-1 cloud service provider. The experiments demonstrated that FT-tree improved the estimation/prediction accuracy (as measured by F1) by 155% to 188%, and the computational efficiency by 117 to 730 times.
Temporal Convolutional Neural Network for the Classification of Satellite Image Time Series

New remote sensing sensors now acquire high spatial and spectral Satellite Image Time Series (SITS) of the world. These series of images are a key component of classification systems that aim at obtaining up-to-date and accurate land cover maps of the Earth’s surfaces. More specifically, the combination of the temporal, spectral and spatial resolutions of new SITS makes possible to monitor vegetation dynamics. Although traditional classification algorithms, such as Random Forest (RF), have been successfully applied for SITS classification, these algorithms do not make the most of the temporal domain. Conversely, some approaches that take into account the temporal dimension have recently been tested, especially Recurrent Neural Networks (RNNs). This paper proposes an exhaustive study of another deep learning approaches, namely Temporal Convolutional Neural Networks (TempCNNs) where convolutions are applied in the temporal dimension. The goal is to quantitatively and qualitatively evaluate the contribution of TempCNNs for SITS classification. This paper proposes a set of experiments performed on one million time series extracted from 46 Formosat-2 images. The experimental results show that TempCNNs are more accurate than RF and RNNs, that are the current state of the art for SITS classification. We also highlight some differences with results obtained in computer vision, e.g. about pooling layers. Moreover, we provide some general guidelines on the network architecture, common regularization mechanisms, and hyper-parameter values such as batch size. Finally, we assess the visual quality of the land cover maps produced by TempCNNs.
Situational , Transformational , and Transactional Leadership and Leadership Development

In order to advance our knowledge of leadership, it is necessary to understand where the study of leadership has been. McCleskey (2014) argued that the study of leadership spans more than 100 years. This manuscript describes three seminal leadership theories and their development. Analysis of a sampling of recent articles in each theory is included. The manuscript also discusses the concept of leadership development in light of those three seminal theories and offers suggestions for moving forward both the academic study of leadership and the practical application of research findings on the field.
Hybrid Self Tuned Fuzzy PID controller for speed control of Brushless DC Motor

The objective of the proposed work is to investigate the performance of hybrid self tuned fuzzy proportional integral derivative (STFPID) controller for brushless DC (BLDC) motor drive. The proposed hybrid STFPID controller includes a proportional integral derivative (PID) controller at steady state, a PID type self tuned fuzzy logic (FL) controller (STFLC) at transient state thereby combining the merits of both the controllers. The switching function incorporated in the controller ensures desired control response at various operating conditions by appropriately switching between PID and STFPID based on speed error. A detailed simulation study and performance comparison with other control approaches is performed to highlight the merits of the proposed work. The simulation results indicate that the proposed controller is robust with fast tracking capability and less steady state error. The experimental results are provided to validate the simulation study.