nils-herrmann/clean_scirepeval huggingface dataset:
Implicit cognition and addiction: a tool for explaining paradoxical behavior.

Research on implicit cognition and addiction has expanded greatly during the past decade. This research area provides new ways to understand why people engage in behaviors that they know are harmful or counterproductive in the long run. Implicit cognition takes a different view from traditional cognitive approaches to addiction by assuming that behavior is often not a result of a reflective decision that takes into account the pros and cons known by the individual. Instead of a cognitive algebra integrating many cognitions relevant to choice, implicit cognition assumes that the influential cognitions are the ones that are spontaneously activated during critical decision points. This selective review highlights many of the consistent findings supporting predictive effects of implicit cognition on substance use and abuse in adolescents and adults; reveals a recent integration with dual-process models; outlines the rapid evolution of different measurement tools; and introduces new routes for intervention.
Empirical evaluation of clone detection using syntax suffix trees

Reusing software through copying and pasting is a continuous plague in software development despite the fact that it creates serious maintenance problems. Various techniques have been proposed to find duplicated redundant code (also known as software clones). A recent study has compared these techniques and shown that token-based clone detection based on suffix trees is fast but yields clone candidates that are often not syntactic units. Current techniques based on abstract syntax trees—on the other hand—find syntactic clones but are considerably less efficient. This paper describes how we can make use of suffix trees to find syntactic clones in abstract syntax trees. This new approach is able to find syntactic clones in linear time and space. The paper reports the results of a large case study in which we empirically compare the new technique to other techniques using the Bellon benchmark for clone detectors. The Bellon benchmark consists of clone pairs validated by humans for eight software systems written in C or Java from different application domains. The new contributions of this paper over the conference paper are the additional analysis of Java programs, the exploration of an alternative path that uses parse trees instead of abstract syntax trees, and the investigation of the impact on recall and precision when clone analyses insist on consistent parameter renaming.
Chapter 2 Information Extraction : Past , Present and Future

In this chapter we present a brief overview of Information Extraction, which is an area of natural language processing that deals with finding factual information in free text. In formal terms, facts are structured objects, such as database records. Such a record may capture a real-world entity with its attributes mentioned in text, or a real-world event, occurrence, or state, with its arguments or actors: who did what to whom, where and when. Information is typically sought in a particular target setting, e.g., corporate mergers and acquisitions. Searching for specific, targeted factual information constitutes a large proportion of all searching activity on the part of information consumers. There has been a sustained interest in Information Extraction for over two decades, due to its conceptual simplicity on one hand, and to its potential utility on the other. Although the targeted nature of this task makes it more tractable than some of the more open-ended tasks in NLP, it is replete with challenges as the information landscape evolves, which also makes it an exciting research subject.
Management of the infected, abscessed primary tooth--a report.

Despite modern advances in the prevention of dental caries and an increased understanding of the importance of maintaining the natural dentition in children, many abscessed and infected primary teeth, especially the deciduous molars, are still being prematurely lost through extractions. This report describes a simple, quick and effective technique that has been successfully used to manage infected, abscessed primary teeth. Results indicate that the non-vital primary pulp therapy technique is both reliable and effective. Not only is the procedure painless, it also helps to relieve the child of his immediate pain and achieves the primary goals of elimination of infection and retention of the tooth in a functional state without endangering the developing permanent tooth germ.
Boosting KNN text classification accuracy by using supervised term weighting schemes

The increasing availability of digital documents in the last decade has prompted the development of machine learning techniques to automatically classify and organize text documents. The majority of text classification systems rely on the vector space model, which represents the documents as vectors in the term space. Each vector component is assigned a weight that reflects the importance of the term in the document. Typically, these weights are assigned using an information retrieval (IR) approach, such as the famous tf-idf function. In this work, we study two weighting schemes based on information gain and chi-square statistics. These schemes take advantage of the category label information to weight the terms according to their distributions across the different categories. We show that using these supervised weights instead of conventional unsupervised weights can greatly improve the performance of the k-nearest neighbor (KNN) classifier. Experimental evaluations, carried out on multiple text classification tasks, demonstrate the benefits of this approach in creating accurate text classifiers.
Mucocele of the upper lip: case report of an uncommon presentation and its differential diagnosis.

This report describes a lesion of the upper lip that was definitively diagnosed by histologic examination as a mucocele or mucus retention phenomenon. The usual location of mucoceles is the lower lip. This case illustrates an uncommon presentation of mucocele with respect to symptoms, location and duration. The features of a variety of oral lesions are discussed and compared, to help clinicians in establishing an appropriate differential diagnosis.
An improved rice transformation system using the biolistic method

Immature embryos and embryogenic calli of rice, both japonica and indica subspecies, were bombarded with tungsten particles coated with plasmid DNA that contained a gene encoding hygromycin phosphotransferase (HPH, conferring hygromycin resistance) driven by the CaMV 35S promoter or Agrobactenum tumefaciens NOS promoter. Putatively transformed cell clusters were identified from the bombarded tissues 2 weeks after selection on hygromycin B. By separating these cell clusters from each other, and by stringent selection not only at the callus growth stage but also during regeneration and plantlet growth, the overall transformation and selection efficiencies were substantially improved over those previously reported. From the most responsive cultivar used in these studies, an average of one transgenic plant was produced from 1.3 immature embryos or from 5 pieces of embryogenic calli bombarded. Integration of the introduced gene into the plant genome, and inheritance to the offspring were demonstrated. By using this procedure, we have produced several hundred transgenic plants. The procedure described here provides a simple method for improving transformation and selection efficiencies in rice and may be applicable to other monocots.
Enthesitis in psoriatic disease.

Enthesitis is increasingly recognized as an important component in psoriatic arthritis (PsA). Improved imaging techniques have expanded our understanding of the role of enthesitis in PsA and provided methods for earlier detection and assessment. Increased knowledge about the extent of tendon and ligament involvement has led to the theory that enthesitis may be the primary event in PsA. Given the historical difficulties in detecting and measuring enthesitis, its inclusion as an endpoint in PsA trials has been limited. Current trial data suggest that tumour necrosis factor inhibitors can successfully treat PsA-related enthesitis, which may have implications for the long-term prognosis of PsA. In this article, we review methods for detecting and assessing enthesitis, current thinking regarding the role of enthesitis in the pathogenesis of PsA, and trial evidence for the treatment of PsA and, therefore, enthesitis.
On-line policy optimisation of Bayesian spoken dialogue systems via human interaction

A partially observable Markov decision process has been proposed as a dialogue model that enables robustness to speech recognition errors and automatic policy optimisation using reinforcement learning (RL). However, conventional RL algorithms require a very large number of dialogues, necessitating a user simulator. Recently, Gaussian processes have been shown to substantially speed up the optimisation, making it possible to learn directly from interaction with human users. However, early studies have been limited to very low dimensional spaces and the learning has exhibited convergence problems. Here we investigate learning from human interaction using the Bayesian Update of Dialogue State system. This dynamic Bayesian network based system has an optimisation space covering more than one hundred features, allowing a wide range of behaviours to be learned. Using an improved policy model and a more robust reward function, we show that stable learning can be achieved that significantly outperforms a simulator trained policy.
Learning without Memorizing

Incremental learning (IL) is an important task aimed to increase the capability of a trained model, in terms of the number of classes recognizable by the model. The key problem in this task is the requirement of storing data (e.g. images) associated with existing classes, while training the classifier to learn new classes. However, this is impractical as it increases the memory requirement at every incremental step, which makes it impossible to implement IL algorithms on the edge devices with limited memory. Hence, we propose a novel approach, called ‘Learning without Memorizing (LwM)’, to preserve the information with respect to existing (base) classes, without storing any of their data, while making the classifier progressively learn the new classes. In LwM, we present an information preserving penalty: Attention Distillation Loss (LAD), and demonstrate that penalizing the changes in classifiers’ attention maps helps to retain information of the base classes, as new classes are added. We show that adding LAD to the distillation loss which is an existing information preserving loss consistently outperforms the state-of-the-art performance in the iILSVRC-small and iCIFAR-100 datasets in terms of the overall accuracy of base and incrementally learned classes.
MODELLING OF MOBILE ROBOT DYNAMICS

This paper presents two approaches to modelling of mobile robot dynamics. First approach is based on physical modelling and second approach is based on experimental identification of mobile robot dynamics features. Model of mobile robot dynamics can then be used to improve the navigational system, especially path planing and localization modules. Localization module estimates mobile robot pose using its kinematic odometry model for pose prediction and additional sensor measurements for pose correction. Kinematic odometry models are simple, valid if mobile robot is travelling with low velocity, low acceleration and light load. Disadvantage is that they don’t take any dynamic constraints into account. This leads to errors in pose prediction, especially when significant control signal (translational and rotational velocity reference) changes occur. Problem lies in the fact that mobile robot can’t immediately change its current velocity to the desired value and mostly there exists a communication delay between the navigation computer and mobile robot micro-controller. Errors in predicted pose cause additional computations in path planning and localization modules. In order to reduce such pose prediction errors and considering that mobile robots are designed to travel at higher velocities and perform heavy duty work, mobile robot drive dynamics can be modelled and included as part of the navigational system. Proposed two modelling approaches are described and first results using a Pioneer 3DX mobile robot are presented. They are also compared regarding to complexity, accuracy and suitability of implementation as part of the mobile robot navigational system.
Calm Before the Storm: The Challenges of Cloud Computing in Digital Forensics

Cloud computing is a rapidly evolving information technology (IT) phenomenon. Rather than procure, deploy and manage a physical IT infrastructure to host their software applications, organizations are increasingly deploying their infrastructure into remote, virtualized environments, often hosted and managed by third parties. This development has significant implications for digital forensic investigators, equipment vendors, law enforcement, as well as corporate compliance and audit departments (among others). Much of digital forensic practice assumes careful control and management of IT assets (particularly data storage) during the conduct of an investigation. This paper summarises the key aspects of cloud computing and analyses how established digital forensic procedures will be invalidated in this new environment. Several new research challenges addressing this changing context are also identified and discussed.
The neurobiological link between OCD and ADHD

Obsessive compulsive disorder (OCD) and attention deficit hyperactivity disorder (ADHD) are two of the most common neuropsychiatric diseases in paediatric populations. The high comorbidity of ADHD and OCD with each other, especially of ADHD in paediatric OCD, is well described. OCD and ADHD often follow a chronic course with persistent rates of at least 40-50 %. Family studies showed high heritability in ADHD and OCD, and some genetic findings showed similar variants for both disorders of the same pathogenetic mechanisms, whereas other genetic findings may differentiate between ADHD and OCD. Neuropsychological and neuroimaging studies suggest that partly similar executive functions are affected in both disorders. The deficits in the corresponding brain networks may be responsible for the perseverative, compulsive symptoms in OCD but also for the disinhibited and impulsive symptoms characterizing ADHD. This article reviews the current literature of neuroimaging, neurochemical circuitry, neuropsychological and genetic findings considering similarities as well as differences between OCD and ADHD.
Control of Single-Phase-to-Three-Phase AC/DC/AC PWM Converters for Induction Motor Drives

This paper proposes a novel control scheme of single-phase-to-three-phase pulsewidth-modulation (PWM) converters for low-power three-phase induction motor drives, where a single-phase half-bridge PWM rectifier and a two-leg inverter are used. With this converter topology, the number of switching devices is reduced to six from ten in the case of full-bridge rectifier and three-leg inverter systems. In addition, the source voltage sensor is eliminated with a state observer, which controls the deviation between the model current and the system current to be zero. A simple scalar voltage modulation method is used for a two-leg inverter, and a new technique to eliminate the effect of the dc-link voltage ripple on the inverter output current is proposed. Although the converter topology itself is of lower cost than the conventional one, it retains the same functions such as sinusoidal input current, unity power factor, dc-link voltage control, bidirectional power flow, and variable-voltage and variable-frequency output voltage. The experimental results for the V/f control of 3-hp induction motor drives controlled by a digital signal processor TMS320C31 chip have verified the effectiveness of the proposed scheme
Haptic perception: a tutorial.

This tutorial focuses on the sense of touch within the context of a fully active human observer. It is intended for graduate students and researchers outside the discipline who seek an introduction to the rapidly evolving field of human haptics. The tutorial begins with a review of peripheral sensory receptors in skin, muscles, tendons, and joints. We then describe an extensive body of research on "what" and "where" channels, the former dealing with haptic perception of objects, surfaces, and their properties, and the latter with perception of spatial layout on the skin and in external space relative to the perceiver. We conclude with a brief discussion of other significant issues in the field, including vision-touch interactions, affective touch, neural plasticity, and applications.
ArteFill® Permanent Injectable for Soft Tissue Augmentation: II. Indications and Applications

Patients ask for procedures with long-lasting effects. ArteFill is the first permanent injectable approved in 2006 by the FDA for nasolabial folds. It consists of cleaned microspheres of polymethylmethacrylate (PMMA) suspended in bovine collagen. Over the development period of 20 years most of its side effects have been eliminated to achieve the same safety standard as today’s hyaluronic acid products. A 5-year follow-up study in U.S. clinical trial patients has shown the same wrinkle improvement as seen at 6 months. Long-term follow-up in European Artecoll patients has shown successful wrinkle correction lasting up to 15 years. A wide variety of off-label indications and applications have been developed that help the physician meet the individual needs of his/her patients. Serious complications after ArteFill injections, such as granuloma formation, have not been reported due to the reduction of PMMA microspheres smaller than 20 μm to less than 1% “by the number.” Minor technique-related side effects, however, may occur during the initial learning curve. Patient and physician satisfaction with ArteFill has been shown to be greater than 90%.
Adaptive One-Class Support Vector Machine

In this correspondence, we derive an online adaptive one-class support vector machine. The machine structure is updated via growing and pruning mechanisms and the weights are updated using structural risk minimization principles underlying support vector machines. Our approach leads to very compact machines compared to other online kernel methods whose size, unless truncated, grows almost linearly with the number of observed patterns. The proposed method is online in the sense that every pattern is only presented once to the machine and there is no need to store past samples and adaptive in the sense that it can forget past input patterns and adapt to the new characteristics of the incoming data. Thus, the characterizing properties of our algorithm are compactness, adaptiveness and real-time processing capabilities, making it especially well-suited to solve online novelty detection problems. Regarding algorithm performance, we have carried out experiments in a time series segmentation problem, obtaining favorable results in both accuracy and model complexity with respect to two existing state-of-the-art methods.
Electrical power distribution system (HV270DC), for application in more electric aircraft

In the new designs of military aircraft and unmanned aircraft there is a clear trend towards increasing demand of electrical power. This fact is mainly due to the replacement of mechanical, pneumatic and hydraulic equipments by partially or completely electrical systems. Generally, use of electrical power onboard is continuously increasing within the areas of communications, surveillance and general systems, such as: radar, cooling, landing gear or actuators systems. To cope with this growing demand for electric power, new levels of voltage (270 VDC), architectures and power electronics devices are being applied to the onboard electrical power distribution systems. The purpose of this paper is to present and describe the technological project HV270DC. In this project, one Electrical Power Distribution System (EPDS), applicable to the more electric aircrafts, has been developed. This system has been integrated by EADS in order to study the benefits and possible problems or risks that affect this kind of power distribution systems, in comparison with conventional distribution systems.
Attacking the baseband modem of mobile phones to breach the users' privacy and network security

As people are using their smartphones more frequently, cyber criminals are focusing their efforts on infecting smartphones rather than computers. This paper presents the design and implementation of a new type of mobile malware, named (U)SimMonitor for Android and iPhone devices, which attacks the baseband modem of mobile phones. In particular, the mobile malware is capable of stealing security credentials and sensitive information of the cellular technology including permanent and temporary identities, encryption keys and location of users. The developed malware operates in the background in a stealthy manner without disrupting the normal operation of the phone. We elaborate on the software architecture of (U)SimMonitor and provide implementation details for the specific AT commands used by the malware. We analyse the security impacts of (U)SimMonitor malware and we show that it can entirely breach the privacy of mobile users and the security of cellular networks. In particular, a mobile user with an infected phone can be identified and all his/her movements can be tracked. Moreover, all his/her encrypted phone calls and data sessions can be disclosed.
Deep Transfer Network with Joint Distribution Adaptation: A New Intelligent Fault Diagnosis Framework for Industry Application

In recent years, an increasing popularity of deep learning model for intelligent condition monitoring and diagnosis as well as prognostics used for mechanical systems and structures has been observed. In the previous studies, however, a major assumption accepted by default, is that the training and testing data are taking from same feature distribution. Unfortunately, this assumption is mostly invalid in real application, resulting in a certain lack of applicability for the traditional diagnosis approaches. Inspired by the idea of transfer learning that leverages the knowledge learnt from rich labeled data in source domain to facilitate diagnosing a new but similar target task, a new intelligent fault diagnosis framework, i.e., deep transfer network (DTN), which generalizes deep learning model to domain adaptation scenario, is proposed in this paper. By extending the marginal distribution adaptation (MDA) to joint distribution adaptation (JDA), the proposed framework can exploit the discrimination structures associated with the labeled data in source domain to adapt the conditional distribution of unlabeled target data, and thus guarantee a more accurate distribution matching. Extensive empirical evaluations on three fault datasets validate the applicability and practicability of DTN, while achieving many state-of-the-art transfer results in terms of diverse operating conditions, fault severities and fault types.
A Survey of Machine Learning for Big Code and Naturalness

Research at the intersection of machine learning, programming languages, and software engineering has recently taken important steps in proposing learnable probabilistic models of source code that exploit the abundance of patterns of code. In this article, we survey this work. We contrast programming languages against natural languages and discuss how these similarities and differences drive the design of probabilistic models. We present a taxonomy based on the underlying design principles of each model and use it to navigate the literature. Then, we review how researchers have adapted these models to application areas and discuss cross-cutting and application-specific challenges and opportunities.