nils-herrmann/clean_scirepeval huggingface dataset:
Paradox of richness: a cognitive model of media choice

Researchers have long studied the effects of social presence and media richness on media choice and the effects of media use. This focus on social presence and social psychological theories has led to valuable research on communication. However, little research (either empirical or theoretical) has been done to understand the ways in which media choices influence the cognitive processes that underlie communication. In this paper, we present a cognitive-based view of media choice and media use, based on dual process theories of cognition, which argue that in order for individuals to systematically process messages, they must be motivated to process the message and have the ability to process it. We argue that the use of rich media high in social presence induces increased motivation but decreases the ability to process information, while the use of lean media low in social presence induces decreased motivation but increases the ability to process information. The paradox of richness lies in its duality of impact: from a cognitive perspective, rich media high in social presence simultaneously acts to both improve and impair performance.
Analytic performance comparison of unsupervised LTE D 2 D and DSRC in a V 2 X safety context

3GPP LTE specified for 5G the support for Device-to-Device (D2D) communication in either supervised mode (controlled by the network) or unsupervised mode (independent from network). This article explores the potential of LTE D2D in a fully unsupervised mode for the broadcast of safety-of-life automotive messages. After an overview of the Proximity Service (ProSe) architecture and new D2D interfaces, it introduces a framework and the required mechanisms for unsupervised LTE D2D broadcast on the new SideLink (SL) interface, composed of (i) a multi-cell and panoperator resource reservation schema (ii) a distributed resource allocation mechanism (iii) decentralized channel congestion control for joint transmit power/scheduling optimization. The proposed scheme is first evaluated independently, then benchmarked against IEEE 802.11p. Complementary to IEEE 802.11p, unsupervised LTE D2D is an opportunity to provide redundancy for ultra-reliable broadcast of automotive safety-of-life messages.
Software testing in the real world - improving the process

Software Testing In The Real World provides the reader with a tool-box for effectively improving the software testing process. The book gives the practicing. Improving software practices, delivering more customer value, and. The outsourcing process, Martin shares a real-life case study, including a.This work offers a toolbox for the practical implementation of the software testing process and how to improve it. Based on real-world issues and examples.Software Testing in the Real World provides the reader with a tool-box for effectively improving the software testing process. The book contains many testing.Software testing is a process, or a series of processes, designed to make sure. From working with this example, that thoroughly testing a complex, real-world.
Pentylenetetrazole induced changes in zebrafish behavior, neural activity and c-fos expression

Rodent seizure models have significantly contributed to our basic understanding of epilepsy. However, medically intractable forms of epilepsy persist and the fundamental mechanisms underlying this disease remain unclear. Here we show that seizures can be elicited in a simple vertebrate system e.g. zebrafish larvae (Danio rerio). Exposure to a common convulsant agent (pentylenetetrazole, PTZ) induced a stereotyped and concentration-dependent sequence of behavioral changes culminating in clonus-like convulsions. Extracellular recordings from fish optic tectum revealed ictal and interictal-like electrographic discharges after application of PTZ, which could be blocked by tetrodotoxin or glutamate receptor antagonists. Epileptiform discharges were suppressed by commonly used antiepileptic drugs, valproate and diazepam, in a concentration-dependent manner. Up-regulation of c-fos expression was also observed in CNS structures of zebrafish exposed to PTZ. Taken together, these results demonstrate that chemically-induced seizures in zebrafish exhibit behavioral, electrographic, and molecular changes that would be expected from a rodent seizure model. Therefore, zebrafish larvae represent a powerful new system to study the underlying basis of seizure generation, epilepsy and epileptogenesis.
Clusters of Driving Behavior from Observational Smartphone Data

Understanding driving behaviors is essential for improving safety and mobility of our transportation systems. Data is usually collected via simulator-based studies or naturalistic driving studies. Those techniques allow for understanding relations between demographics, road conditions and safety. On the other hand, they are very costly and time consuming. Thanks to the ubiquity of smartphones, we have an opportunity to substantially complement more traditional data collection techniques with data extracted from phone sensors, such as GPS, accelerometer gyroscope and camera. We developed statistical models that provided insight into driver behavior in the San Francisco metro area based on tens of thousands of driver logs. We used novel data sources to support our work. We used cell phone sensor data drawn from five hundred drivers in San Francisco to understand the speed of traffic across the city as well as the maneuvers of drivers in different areas. Specifically, we clustered drivers based on their driving behavior. We looked at driver norms by street and flagged driving behaviors that deviated from the norm.
Fitness functions in evolutionary robotics: A survey and analysis

This paper surveys fitness functions used in the field of evolutionary robotics (ER). Evolutionary robotics is a field of research that applies artificial evolution to generate control systems for autonomous robots. During evolution, robots attempt to perform a given task in a given environment. The controllers in the better performing robots are selected, altered and propagated to perform the task again in an iterative process that mimics some aspects of natural evolution. A key component of this process – one might argue, the key component – is the measurement of fitness in the evolving controllers. ER is one of a host of machine learning methods that rely on interaction with, and feedback from, a complex dynamic environment to drive synthesis of controllers for autonomous agents. These methods have the potential to lead to the development of robots that can adapt to uncharacterized environments and which may be able to perform tasks that human designers do not completely understand. In order to achieve this, issues regarding fitness evaluation must be addressed. In this paper we survey current ER research and focus on work that involved real robots. The surveyed research is organized according to the degree of a priori knowledge used to formulate the various fitness functions employed during evolution. The underlying motivation for this is to identify methods that allow the development of the greatest degree of novel control, while requiring the minimum amount of a priori task knowledge from the designer.
The Use of Technology for Second Language Learning and Teaching : A Retrospective

As one of the oldest and most influential foreign language pedagogical journals, The Modern Language Journal (MLJ) offers valuable insights into how technological advances have affected language teaching and learning at various points in history. The present article will review the proposed pedagogical use of technological resources by means of a critical analysis of articles published in the MLJ since its first edition in 1916. The assessment of how previous technical capabilities have been implemented for pedagogical purposes represents a necessary background for the assessment of the pedagogical potential of present-day technologies. In this article I argue that, whereas most “new technologies” (radio, television, VCR, computers) may have been revolutionary in the overall context of human interaction, it is not clear that they have achieved equal degrees of pedagogical benefit in the realm of second language teaching. I further claim that the pedagogical effectiveness of different technologies is related to four major questions: (a) Is increased technological sophistication correlated to increased pedagogical effectiveness? (b) Which technical attributes specific to new technologies can be profitably exploited for pedagogical purposes? (c) How can new technologies be successfully integrated into the curriculum? and (d) Do new technologies provide for an efficient use of human and material resources?
Bayesian phylogenetic analysis of combined data.

The recent development of Bayesian phylogenetic inference using Markov chain Monte Carlo (MCMC) techniques has facilitated the exploration of parameter-rich evolutionary models. At the same time, stochastic models have become more realistic (and complex) and have been extended to new types of data, such as morphology. Based on this foundation, we developed a Bayesian MCMC approach to the analysis of combined data sets and explored its utility in inferring relationships among gall wasps based on data from morphology and four genes (nuclear and mitochondrial, ribosomal and protein coding). Examined models range in complexity from those recognizing only a morphological and a molecular partition to those having complex substitution models with independent parameters for each gene. Bayesian MCMC analysis deals efficiently with complex models: convergence occurs faster and more predictably for complex models, mixing is adequate for all parameters even under very complex models, and the parameter update cycle is virtually unaffected by model partitioning across sites. Morphology contributed only 5% of the characters in the data set but nevertheless influenced the combined-data tree, supporting the utility of morphological data in multigene analyses. We used Bayesian criteria (Bayes factors) to show that process heterogeneity across data partitions is a significant model component, although not as important as among-site rate variation. More complex evolutionary models are associated with more topological uncertainty and less conflict between morphology and molecules. Bayes factors sometimes favor simpler models over considerably more parameter-rich models, but the best model overall is also the most complex and Bayes factors do not support exclusion of apparently weak parameters from this model. Thus, Bayes factors appear to be useful for selecting among complex models, but it is still unclear whether their use strikes a reasonable balance between model complexity and error in parameter estimates.
Detecting Domain-Specific Ambiguities: An NLP Approach Based on Wikipedia Crawling and Word Embeddings

In the software process, unresolved natural language (NL) ambiguities in the early requirements phases may cause problems in later stages of development. Although methods exist to detect domain-independent ambiguities, ambiguities are also influenced by the domain-specific background of the stakeholders involved in the requirements process. In this paper, we aim to estimate the degree of ambiguity of typical computer science words (e.g., system, database, interface) when used in different application domains. To this end, we apply a natural language processing (NLP) approach based on Wikipedia crawling and word embeddings, a novel technique to represent the meaning of words through compact numerical vectors. Our preliminary experiments, performed on five different domains, show promising results. The approach allows an estimate of the variation of meaning of the computer science words when used in different domains. Further validation of the method will indicate the words that need to be carefully defined in advance by the requirements analyst to avoid misunderstandings when editing documents and dealing with experts in the considered domains.
The first asylums in Canada: a response to neglectful community care and current trends.

Humane treatment and care of mentally ill people can be viewed from a historical perspective. Intramural (the institution) and extramural (the community) initiatives are not mutually exclusive.



The evolution of the psychiatric institution in Canada as the primary method of care is presented from an historical perspective. A province-by-province review of provisions for mentally ill people prior to asylum construction reveals that humanitarian motives and a growing sensitivity to social and medical problems gave rise to institutional psychiatry. The influence of Great Britain, France, and, to a lesser extent, the United States in the construction of asylums in Canada is highlighted. The contemporary redirection of the Canadian mental health system toward "dehospitalization" is discussed and delineated.



Early promoters of asylums were genuinely concerned with alleviating human suffering, which led to the separation of mental health services from the community and from those proffered to the criminal and indigent populations. While the results of the past institutional era were mixed, it is hoped that the "care" cycle will not repeat itself in the form of undesirable community alternatives.



Severely psychiatrically disabled individuals can be cared for in the community if appropriate services exist.
A two-stage hybrid credit scoring model using artificial neural networks and multivariate adaptive regression splines

The objective of the proposed study is to explore the performance of credit scoring using a two-stage hybrid modeling procedure with artificial neural networks and multivariate adaptive regression splines (MARS). The rationale under the analyses is firstly to use MARS in building the credit scoring model, the obtained significant variables are then served as the input nodes of the neural networks model. To demonstrate the effectiveness and feasibility of the proposed modeling procedure, credit scoring tasks are performed on one bank housing loan dataset using cross-validation approach. As the results reveal, the proposed hybrid approach outperforms the results using discriminant analysis, logistic regression, artificial neural networks and MARS and hence provides an alternative in handling credit scoring tasks. q 2005
Development of a pneumatic soft actuator as a hand finger for a collaborative robot

Pneumatic soft actuators produce flexion and meet the new needs of collaborative robotics, which is rapidly emerging in the industry landscape 4.0. The soft actuators are not only aimed at industrial progress, but their application ranges in the field of medicine and rehabilitation. Safety and reliability are the main requirements for coexistence and human-robot interaction; such requirements, together with the versatility and lightness, are the precious advantages that is offered by this new category of actuators. The objective is to develop an actuator with high compliance, low cost, high versatility and easy to produce, aimed at the realization of the fingers of a robotic hand that can faithfully reproduce the motion of a real hand. The proposed actuator is equipped with an intrinsic compliance thanks to the hyper-elastic silicone rubber used for its realization; the bending is allowed by the high compliance of the silicone and by a square-meshed gauze which contains the expansion and guides the movement through appropriate cuts in correspondence of the joints. A numerical model of the actuator is developed and an optimal configuration of the five fingers of the hand is achieved; finally, the index finger is built, on which the experimental validation tests are carried out.
Obesity and mobility disability in the older adult.

Mobility disability is becoming prevalent in the obese older population (> or = 60 years of age). We included a total of 13 cross-sectional and 15 longitudinal studies based on actual physical assessments of mobility in the obese older population in this review. We systematically examined existing evidence of which adiposity estimate best predicted mobility disability. Cross-sectional studies (82-4000 participants) showed poorer lower extremity mobility with increasing obesity severity in both men and women. All longitudinal studies (1-22 years) except for one, reported relationships between adiposity and declining mobility. While different physical tests made interpretation challenging, a consistent finding was that walking, stair climbing and chair rise ability were compromised with obesity, especially if the body mass index (BMI) exceeded 35 kg m(-2). More studies found that obese women were at an increased risk for mobility impairment than men. Existing evidence suggests that BMI and waist circumference are emerging as the more consistent predictors of the onset or worsening of mobility disability. Limited interventional evidence shows that weight loss is related with increased mobility and lower extremity function. Additional longitudinal studies are warranted that address overall body composition fat and muscle mass or change on future disability.
Speech emotion recognition using Support Vector Machines

Automatic recognition of emotional states from human speech is a current research topic with a wide range. In this paper an attempt has been made to recognize and classify the speech emotion from three language databases, namely, Berlin, Japan and Thai emotion databases. Speech features consisting of Fundamental Frequency (F0), Energy, Zero Crossing Rate (ZCR), Linear Predictive Coding (LPC) and Mel Frequency Cepstral Coefficient (MFCC) from short-time wavelet signals are comprehensively investigated. In this regard, Support Vector Machines (SVM) is utilized as the classification model. Empirical experimentation shows that the combined features of F0, Energy and MFCC provide the highest accuracy on all databases provided using the linear kernel. It gives 89.80%, 93.57% and 98.00% classification accuracy for Berlin, Japan and Thai emotions databases, respectively.
Spatio-Temporal Conceptual Models: Data Structures + Space + Time

Nowadays, many applications need data modeling facilities for the description of complex objects with spatial and/or temporal facilities. Responses to such requirements may be found in Geographic Information Systems (GIS), in some DBMS, or in the research literature. However, most f existing models cover only partly the requirements (they address either spatial or temporal modeling), and most are at the logical level, h nce not well suited for database design. This paper proposes a spatiotemporal modeling approach at the conceptual level, called MADS. The proposal stems from the identification of the criteria to be met for a conceptual model. It is advocated that orthogonality is the key issue for achieving a powerful and intuitive conceptual model. Thus, the proposal focuses on highlighting similarities in the modeling of space and time, which enhance readability and understandability of the model.
Digital Forensic Identification, Collection, Examination and Decoding of Windows Registry Keys for Discovering User Activities Patterns

The Key study in this paper is to begin the investigation process with the initial forensic analysis in the segments of the storage media which would definitely contain the digital forensic evidences. These Storage media Locations is referred as the Windows registry. Identifying the forensic evidence from windows registry may take less time than required in the case of all locations of a storage media. Our main focus in this research will be to study the registry structure of Windows 7 and identify the useful information within the registry keys of windows 7 that may be extremely useful to carry out any task of digital forensic analysis. The main aim is to describe the importance of the study on computer & digital forensics. The Idea behind the research is to implement a forensic tool which will be very useful in extracting the digital evidences and present them in usable form to a forensic investigator. The work includes identifying various events registry keys value such as machine last shut down time along with machine name, List of all the wireless networks that the computer has connected to; List of the most recently used files or applications, List of all the USB devices that have been attached to the computer and many more. This work aims to point out the importance of windows forensic analysis to extract and identify the hidden information which shall act as an evidence tool to track and gather the user activities pattern. All Research was conducted in a Windows 7 Environment. Windows Registry, Windows 7 Forensic Analysis, Windows Registry Structure, Analysing Registry Key, Digital Forensic Identification, Forensic data Collection, Examination of Windows Registry, Decoding of Windows Registry Keys, Discovering User Activities Patterns, Computer Forensic Investigation Tool.
Comparative Analysis of Data Mining Tools and Techniques for Evaluating Performance of Database System

Data mining, the extraction of hidden predictive information from large databases, is a powerful new technology with great potential to help companies focus on the most important information in their data warehouses. Nowadays, large amount of data and information are available, Data can now be stored in many different kinds of databases and information repositories, being available on the Internet. There is a need for powerful techniques for better interpretation of these data that exceeds the human's ability for comprehension and making decision in a better way. There are data mining, web mining and knowledge discovery tools and software packages such as WEKA Tool and RapidMiner tool. The work deals with analysis of WEKA, RapidMiner and NetTools spider tools KNIME and Orange. There are various tools available for data mining and web mining. Therefore awareness is required about the quantitative investigation of these tools. This paper focuses on various functional, practical, cognitive as well as analysis aspects that users may be looking for in the tools. Complete study addresses the usefulness and importance of these tools including various aspects. Analysis presents various benefits of these data mining tools along with desired aspects and the features of current tools. KEYWORDSData Mining, KDD, Data Mining Tools.
Wireless data links for next-generation networked micro-implantables

Miniaturized minimally-invasive implants with wireless power and communication links have the potential to enable closed-loop treatments and precise diagnostics. As with wireless power transfer, robust wireless communication between implants and external transceivers presents challenges and tradeoffs with miniaturization and increasing depth. Both link efficiency and available bandwidth need to be considered for communication capacity. This paper analyzes and reviews active electromagnetic and ultrasonic communication links for implants. Example transmitter designs are presented for both types of links. Electromagnetic links for mm-sized implants have demonstrated high data rates sufficient for most applications up to Mbps range; nonetheless, they have so far been limited to depths under 5 cm. Ultrasonic links, on the other hand, have shown much deeper transmission depths, but with limited data rate due to their low operating frequency. Spatial multiplexing techniques are proposed to increase ultrasonic data rates without additional power or bandwidth.