nils-herrmann/clean_scirepeval huggingface dataset:
Theories and computational models of affordance and mirror systems: An integrative review

Neuroscientific and psychological data suggest a close link between affordance and mirror systems in the brain. However, we still lack a full understanding of both the individual systems and their interactions. Here, we propose that the architecture and functioning of the two systems is best understood in terms of two challenges faced by complex organisms, namely: (a) the need to select among multiple affordances and possible actions dependent on context and high-level goals and (b) the exploitation of the advantages deriving from a hierarchical organisation of behaviour based on actions and action-goals. We first review and analyse the psychological and neuroscientific literature on the mechanisms and processes organisms use to deal with these challenges. We then analyse existing computational models thereof. Finally we present the design of a computational framework that integrates the reviewed knowledge. The framework can be used both as a theoretical guidance to interpret empirical data and design new experiments, and to design computational models addressing specific problems debated in the literature.
Machine vision based liquid level inspection system using ISEF edge detection technique

We have developed a machine vision based liquid level inspection system which decides the liquid level of bottle to be under or overfilled using ISEF edge detection technique. The system is having a conveyer belt controlled by Siemens LOGO24RLC PLC. MATLAB image acquisition toolbox along with a normal web camera is used for image acquisition purpose. We apply ISEF Edge detection technique and an average distance algorithm to decide about the level of the liquid in the bottles. A GUI based interfacing software is made to display the over and under filled status of the bottles on the screen.
Automatic selection of social media responses to news

Social media responses to news have increasingly gained in importance as they can enhance a consumer's news reading experience, promote information sharing and aid journalists in assessing their readership's response to a story. Given that the number of responses to an online news article may be huge, a common challenge is that of selecting only the most interesting responses for display. This paper addresses this challenge by casting message selection as an optimization problem. We define an objective function which jointly models the messages' utility scores and their entropy. We propose a near-optimal solution to the underlying optimization problem, which leverages the submodularity property of the objective function. Our solution first learns the utility of individual messages in isolation and then produces a diverse selection of interesting messages by maximizing the defined objective function. The intuitions behind our work are that an interesting selection of messages contains diverse, informative, opinionated and popular messages referring to the news article, written mostly by users that have authority on the topic. Our intuitions are embodied by a rich set of content, social and user features capturing the aforementioned aspects. We evaluate our approach through both human and automatic experiments, and demonstrate it outperforms the state of the art. Additionally, we perform an in-depth analysis of the annotated ``interesting'' responses, shedding light on the subjectivity around the selection process and the perception of interestingness.
Design Pattern Detection Using Similarity Scoring

The identification of design patterns as part of the reengineering process can convey important information to the designer. However, existing pattern detection methodologies generally have problems in dealing with one or more of the following issues: identification of modified pattern versions, search space explosion for large systems and extensibility to novel patterns. In this paper, a design pattern detection methodology is proposed that is based on similarity scoring between graph vertices. Due to the nature of the underlying graph algorithm, this approach has the ability to also recognize patterns that are modified from their standard representation. Moreover, the approach exploits the fact that patterns reside in one or more inheritance hierarchies, reducing the size of the graphs to which the algorithm is applied. Finally, the algorithm does not rely on any pattern-specific heuristic, facilitating the extension to novel design structures. Evaluation on three open-source projects demonstrated the accuracy and the efficiency of the proposed method
The calculi of emergence: computation, dynamics and induction

Defining structure and detecting the emergence of complexity in nature are inherently subjective, though essential, scientific activities. Despite the difficulties, these problems can be analyzed in terms of how model-building observers infer from measurements the computational capabilities embedded in nonlinear processes. An observer’s notion of what is ordered, what is random, and what is complex in its environment depends directly on its computational resources: the amount of raw measurement data, of memory, and of time available for estimation and inference. The discovery of structure in an environment depends more critically and subtlely, though, on how those resources are organized. The descriptive power of the observer’s chosen (or implicit) computational model class, for example, can be an overwhelming determinant in finding regularity in data. This paper presents an overview of an inductive framework — hierarchical -machine reconstruction — in which the emergence of complexity is associated with the innovation of new computational model classes. Complexity metrics for detecting structure and quantifying emergence, along with an analysis of the constraints on the dynamics of innovation, are outlined. Illustrative examples are drawn from the onset of unpredictability in nonlinear systems, finitary nondeterministic processes, and cellular automata pattern recognition. They demonstrate how finite inference resources drive the innovation of new structures and so lead to the emergence of complexity.
Permanent hypopigmentation after triamcinolone injection for tennis elbow

A 52-year-old Latino male without significant medical history reported 1 month of left lateral elbow pain that worsened with repetitive movement of his wrist. He was diagnosed with lateral epicondylitis (tennis elbow) and initially treated with non-steroidal anti-inflammatory drugs and rest. Due to lack of improvement, he received a local triamcinolone injection with symptoms relieved after a few days. Approximately 6 weeks after the procedure, he noticed discoloration of the skin in the treated area (see image); at this time, this hypopigmentation has remained unchanged for 2 years . Injection of inflamed joints and soft tissues is a very common practice in the primary care office and is preferred over oral therapy due to high local efficacy and less systemic side effects. Among reported side effects, dermatological conditions are infrequent but can include irritation, perilymphatic atrophy, soft tissue calcification, sterile abscess, ecchymosis, allergic rash, and transitory or permanent hypopigmentation (1). Previously, case reports have documented local skin hypopigmentation after intralesional or intraarticular injection of steroids in different sites (2, 3). Hypopigmentation usually develops after a few weeks and, in the majority of cases, resolves after a few months (3, 4). The exact mechanism that causes this type of lesion is unknown, but a previous report indicated that melanocytes are intact, so it has been theorized that steroids potentially impair some of the functions of these cells (4, 5). The reports are more frequently in dark-skinned individuals and can present with a linear pattern due to possible lymphogenous or vascular spread of the corticosteroid (6). Triamcinolone is one of the preferred steroids for these injections due to specific pharmacologic properties that increase the duration of action. However, as a macromolecule with suspended crystals, it can spread along lymphatic channels in the superficial skin tissues and has been noted to cause hypopigmentation more frequently than other commonly used steroids, such as methylprednisolone and hydrocortisone (7, 8).
Metacognition in human decision-making: confidence and error monitoring

People are capable of robust evaluations of their decisions: they are often aware of their mistakes even without explicit feedback, and report levels of confidence in their decisions that correlate with objective performance. These metacognitive abilities help people to avoid making the same mistakes twice, and to avoid overcommitting time or resources to decisions that are based on unreliable evidence. In this review, we consider progress in characterizing the neural and mechanistic basis of these related aspects of metacognition-confidence judgements and error monitoring-and identify crucial points of convergence between methods and theories in the two fields. This convergence suggests that common principles govern metacognitive judgements of confidence and accuracy; in particular, a shared reliance on post-decisional processing within the systems responsible for the initial decision. However, research in both fields has focused rather narrowly on simple, discrete decisions-reflecting the correspondingly restricted focus of current models of the decision process itself-raising doubts about the degree to which discovered principles will scale up to explain metacognitive evaluation of real-world decisions and actions that are fluid, temporally extended, and embedded in the broader context of evolving behavioural goals.
Analysis on Credit Card Fraud Detection Techniques: Based on Certain Design Criteria

Financial fraud is increasing significantly with the development of modern technology and the global superhighways of communication, resulting in the loss of billions of dollars worldwide each year. The companies and financial institution loose huge amounts due to fraud and fraudsters continuously try to find new rules and tactics to commit illegal actions. Thus, fraud detection systems have become essential for all credit card issuing banks to minimize their losses. The most commonly used fraud detection methods are Neural Network (NN), rule-induction techniques, fuzzy system, decision trees, Support Vector Machines (SVM), Artificial Immune System (AIS), genetic algorithms, K-Nearest Neighbor algorithms. These techniques can be used alone or in collaboration using ensemble or meta-learning techniques to build classifiers. This paper presents a survey of various techniques used in credit card fraud detection and evaluates each methodology based on certain design criteria. And this survey enables us to build a hybrid approach for developing some effective algorithms which can perform well for the classification problem with variable misclassification costs and with higher accuracy.
Surrogate Scoring Rules and a Dominant Truth Serum for Information Elicitation

Strictly proper scoring rules (SPSR) are widely used when designing incentive mechanisms to elicit private information from strategic agents using realized ground truth signals, and they can help quantify the value of elicited information. In this paper, we extend such scoring rules to settings where a mechanism designer does not have access to ground truth. We consider two such settings: (i) a setting when the mechanism designer has access to a noisy proxy version of the ground truth, with known biases; and (ii) the standard peer prediction setting where agents’ reports are the only source of information that the mechanism designer has. We introduce surrogate scoring rules (SSR) for the first setting, which use the noisy ground truth to evaluate quality of elicited information. We show that SSR preserves the strict properness of SPSR. Using SSR, we then develop a multi-task scoring mechanism – called dominant truth serum (DTS) – to achieve strict properness when the mechanism designer only has access to agents’ reports. In comparison to standard equilibrium concepts in peer prediction, we show that DTS can achieve truthfulness in a multitask dominant strategy. A salient feature of SSR and DTS is that they quantify the quality of information despite lack of ground truth, just as proper scoring rules do for the with verification setting. Our method is verified both theoretically and empirically using data collected from real human participants.
Software process improvement in small and medium software enterprises: a systematic review

Small and medium enterprises are a very important cog in the gears of the world economy. The software industry in most countries is composed of an industrial scheme that is made up mainly of small and medium software enterprises—SMEs. To strengthen these types of organizations, efficient Software Engineering practices are needed—practices which have been adapted to their size and type of business. Over the last two decades, the Software Engineering community has expressed special interest in software process improvement (SPI) in an effort to increase software product quality, as well as the productivity of software development. However, there is a widespread tendency to make a point of stressing that the success of SPI is only possible for large companies. In this article, a systematic review of published case studies on the SPI efforts carried out in SMEs is presented. Its objective is to analyse the existing approaches towards SPI which focus on SMEs and which report a case study carried out in industry. A further objective is that of discussing the significant issues related to this area of knowledge, and to provide an up-to-date state of the art, from which innovative research activities can be thought of and planned.
Learning Convolutional Networks for Content-Weighted Image Compression

Lossy image compression is generally formulated as a joint rate-distortion optimization problem to learn encoder, quantizer, and decoder. Due to the non-differentiable quantizer and discrete entropy estimation, it is very challenging to develop a convolutional network (CNN)-based image compression system. In this paper, motivated by that the local information content is spatially variant in an image, we suggest that: (i) the bit rate of the different parts of the image is adapted to local content, and (ii) the content-aware bit rate is allocated under the guidance of a content-weighted importance map. The sum of the importance map can thus serve as a continuous alternative of discrete entropy estimation to control compression rate. The binarizer is adopted to quantize the output of encoder and a proxy function is introduced for approximating binary operation in backward propagation to make it differentiable. The encoder, decoder, binarizer and importance map can be jointly optimized in an end-to-end manner. And a convolutional entropy encoder is further presented for lossless compression of importance map and binary codes. In low bit rate image compression, experiments show that our system significantly outperforms JPEG and JPEG 2000 by structural similarity (SSIM) index, and can produce the much better visual result with sharp edges, rich textures, and fewer artifacts.
Memristor modeling in MATLAB® &Simulink®

The article deals with various ways of memristor modeling and simulation in the MATLAB&Simulink environment. Recently used and published mathematical memristor model serves as a base, regarding all known features of its behavior. Three different approaches in the MATLAB&Simulink system are used for the differential and other equations formulation. The first one employs the standard system core offer for the Ordinary Differential Equations solutions (ODE) in the form of an m-file. The second approach is the model construction in Simulink environment. The third approach employs so-called physical modeling using the built-in Simscape system. The output data are the basic memristor characteristics and appropriate time courses. The features of all models are discussed, especially regarding the computer simulation. Possible problems that may occur during modeling are pointed.  memristor, modeling and simulation, MATLAB, Simulink, Simscape, physical model
Artificial Intelligence and Soft Computing - ICAISC 2004

Information system design and optimum sizing is a very complex task. Theoretical research and practitioners often tackle the optimization problem by applying specific techniques for the optimization of individual design phases, usually leading to local optima. Conversely, this paper proposes the definition of a design methodology based on an evolutionary approach to the optimization of the client/server-farm distributed structure, which is typical of a distributed information technology (IT) architecture. The optimization problem consists of finding the minimum-cost physical systems that satisfy all architectural requirements given by the designer. The proposed methodology allows for the identification of the architectural solution that minimizes costs, against different information system requirements and multiple design alternatives, thorough a genetic-based exploration of the solution space. Experimental results show that costs can be significantly reduced with respect to conventional approaches adopted by IT designers and available in the professional literature.
Effect of Honey and Green Tea Solutions on Streptococcus mutans.

The aim of this cross-sectional in vivo study was to assess the effect of green tea and honey solutions on the level of salivary Streptococcus mutans.



A convenient sample of 30 Saudi boys aged 7-10 years were randomly assigned into 2 groups of 15 each. Saliva sample was collected for analysis of level of S. mutans before rinsing. Commercial honey and green tea were prepared for use and each child was asked to rinse for two minutes using 10 mL of the prepared honey or green tea solutions according to their group. Saliva samples were collected again after rinsing. The collected saliva samples were prepared and colony forming unit (CFU) of S. mutans per mL of saliva was calculated.



The mean number of S. mutans before and after rinsing with honey and green tea solutions were 2.28* 10(8)(2.622*10(8)), 5.64 *10(7)(1.03*10(8)), 1.17*10(9)(2.012*10(9)) and 2.59*10(8) (3.668*10(8)) respectively. A statistically significant reduction in the average number of S. mutans at baseline and post intervention in the children who were assigned to the honey (P=0.001) and green tea (P=0.001) groups was found.



A single time mouth rinsing with honey and green tea solutions for two minutes effectively reduced the number of salivary S. mutans of 7-10 years old boys.
Aerobic endurance exercise improves executive functions in depressed patients.

Aerobic endurance exercise has been shown to improve higher cognitive functions such as executive control in healthy subjects. We tested the hypothesis that a 30-minute individually customized endurance exercise program has the potential to enhance executive functions in patients with major depressive disorder.



In a randomized within-subject study design, 24 patients with DSM-IV major depressive disorder and 10 healthy control subjects performed 30 minutes of aerobic endurance exercise at 2 different workload levels of 40% and 60% of their predetermined individual 4-mmol/L lactic acid exercise capacity. They were then tested with 4 standardized computerized neuropsychological paradigms measuring executive control functions: the task switch paradigm, flanker task, Stroop task, and GoNogo task. Performance was measured by reaction time. Data were gathered between fall 2000 and spring 2002.



While there were no significant exercise-dependent alterations in reaction time in the control group, for depressive patients we observed a significant decrease in mean reaction time for the congruent Stroop task condition at the 60% energy level (p = .016), for the incongruent Stroop task condition at the 40% energy level (p = .02), and for the GoNogo task at both energy levels (40%, p = .025; 60%, p = .048). The exercise procedures had no significant effect on reaction time in the task switch paradigm or the flanker task.



A single 30-minute aerobic endurance exercise program performed by depressed patients has positive effects on executive control processes that appear to be specifically subserved by the anterior cingulate.
Fast SLAM using polar scan matching and particle weight based occupancy grid map for mobile robot

Simultaneous Localization and Mapping is the one of essential techniques for mobile robot navigation. In this paper, we propose a fast SLAM method that uses particle weight based occupancy grid map, and also works well with in indoor environment. In addition, in the prediction step, our method that uses the polar scan matching and wheel odometry information improves the accuracy of prediction pose. In order to evaluate our proposed method, we use a simulation and the 2-wheeled mobile robot that is able to move in large-scale indoor environment. The experimental results show that our method is suitable for fast and accurate mapping and localization algorithm for mobile robot.
Electrochemical Biosensors - Sensor Principles and Architectures

Quantification of biological or biochemical processes are of utmost importance for medical, biological and biotechnological applications. However, converting the biological information to an easily processed electronic signal is challenging due to the complexity of connecting an electronic device directly to a biological environment. Electrochemical biosensors provide an attractive means to analyze the content of a biological sample due to the direct conversion of a biological event to an electronic signal. Over the past decades several sensing concepts and related devices have been developed. In this review, the most common traditional techniques, such as cyclic voltammetry, chronoamperometry, chronopotentiometry, impedance spectroscopy, and various field-effect transistor based methods are presented along with selected promising novel approaches, such as nanowire or magnetic nanoparticle-based biosensing. Additional measurement techniques, which have been shown useful in combination with electrochemical detection, are also summarized, such as the electrochemical versions of surface plasmon resonance, optical waveguide lightmode spectroscopy, ellipsometry, quartz crystal microbalance, and scanning probe microscopy. The signal transduction and the general performance of electrochemical sensors are often determined by the surface architectures that connect the sensing element to the biological sample at the nanometer scale. The most common surface modification techniques, the various electrochemical transduction mechanisms, and the choice of the recognition receptor molecules all influence the ultimate sensitivity of the sensor. New nanotechnology-based approaches, such as the use of engineered ion-channels in lipid bilayers, the encapsulation of enzymes into vesicles, polymersomes, or polyelectrolyte capsules provide additional possibilities for signal amplification. In particular, this review highlights the importance of the precise control over the delicate interplay between surface nano-architectures, surface functionalization and the chosen sensor transducer principle, as well as the usefulness of complementary characterization tools to interpret and to optimize the sensor response.