nils-herrmann/clean_scirepeval huggingface dataset:
Women's emotional adjustment to IVF: a systematic review of 25 years of research.

This review provides an overview of how women adjust emotionally to the various phases of IVF treatment in terms of anxiety, depression or general distress before, during and after different treatment cycles. A systematic scrutiny of the literature yielded 706 articles that paid attention to emotional aspects of IVF treatment of which 27 investigated the women's emotional adjustment with standardized measures in relation to norm or control groups. Most studies involved concurrent comparisons between women in different treatment phases and different types of control groups. The findings indicated that women starting IVF were only slightly different emotionally from the norm groups. Unsuccessful treatment raised the women's levels of negative emotions, which continued after consecutive unsuccessful cycles. In general, most women proved to adjust well to unsuccessful IVF, although a considerable group showed subclinical emotional problems. When IVF resulted in pregnancy, the negative emotions disappeared, indicating that treatment-induced stress is considerably related to threats of failure. The concurrent research reviewed, should now be underpinned by longitudinal studies to provide more information about women's long-term emotional adjustment to unsuccessful IVF and about indicators of risk factors for problematic emotional adjustment after unsuccessful treatment, to foster focused psychological support for women at risk.
Making Knowledge Visible through Intranet Knowledge Maps: Concepts, Elements, Cases

This paper seeks to establish the conceptual and empirical basis for an innovative instrument of corporate knowledge management: the knowledge map. It begins by briefly outlining the rationale for knowledge mapping, i.e., providing a common context to access expertise and experience in large companies. It then conceptualizes five types of knowledge maps that can be used in managing organizational knowledge. They are knowledge-sources, assets, -structures, -applications, and -development maps. In order to illustrate these five types of maps, a series of examples will be presented (from a multimedia agency, a consulting group, a market research firm, and a mediumsized services company) and the advantages and disadvantages of the knowledge mapping technique for knowledge management will be discussed. The paper concludes with a series of quality criteria for knowledge maps and proposes a five step procedure to implement knowledge maps in a corporate intranet.
Noisy training for deep neural networks in speech recognition

Deep neural networks (DNNs) have gained remarkable success in speech recognition, partially attributed to the flexibility of DNN models in learning complex patterns of speech signals. This flexibility, however, may lead to serious over-fitting and hence miserable performance degradation in adverse acoustic conditions such as those with high ambient noises. We propose a noisy training approach to tackle this problem: by injecting moderate noises into the training data intentionally and randomly, more generalizable DNN models can be learned. This ‘noise injection’ technique, although known to the neural computation community already, has not been studied with DNNs which involve a highly complex objective function. The experiments presented in this paper confirm that the noisy training approach works well for the DNN model and can provide substantial performance improvement for DNN-based speech recognition.
Failure Analysis of Jobs in Compute Clouds: A Google Cluster Case Study

In this paper, we analyze a workload trace from the Google cloud cluster and characterize the observed failures. The goal of our work is to improve the understanding of failures in compute clouds. We present the statistical properties of job and task failures, and attempt to correlate them with key scheduling constraints, node operations, and attributes of users in the cloud. We also explore the potential for early failure prediction, and anomaly detection for the jobs. Based on our results, we speculate that there are many opportunities to enhance the reliability of the applications running in the cloud, such as pro-active maintenance of nodes or limiting job resubmissions. We further find that resource usage patterns of the jobs can be leveraged by failure prediction techniques. Finally, we find that the termination statuses of jobs and tasks can be clustered into six dominant categories based on the user profiles.
Massively parallel KD-tree construction and nearest neighbor search algorithms

This paper presents parallel algorithms for the construction of k dimensional tree (KD-tree) and nearest neighbor search (NNS) on massively parallel architecture (MPA) of graphics processing unit (GPU). Unlike previous parallel algorithms for KD-tree, for the first time, our parallel algorithms integrate high dimensional KD-tree construction and NNS on an MPA platform. The proposed massively parallel algorithms are of comparable quality as traditional sequential counterparts on CPU, while achieve high speedup performance on both low and high dimensional KD-tree. Low dimensional KD-tree construction and NNS algorithms, presented in this paper, outperform their serial CPU counterparts by a factor of up to 24 and 218, respectively. For high dimensional KD-tree, the speedup factors are even higher, raising to 30 and 242, respectively. Our implementations will potentially benefit real time three-dimensional (3D) image registration and high dimensional descriptor matching.
A Wideband L-Probes Fed Circularly-Polarized Reconfigurable Microstrip Patch Antenna

A wideband circularly polarized reconfigurable microstrip patch antenna fed by L-shaped probes is presented. Right hand circular polarization and left hand circular polarization could be excited by L-shaped probes feeding a perturbed square patch. The L-shaped probes are connected to a switch which is fabricated underneath the ground plane, such that circularly polarized radiation pattern reconfiguration could be realized. An antenna prototype was fabricated and it attains a bandwidth of over 10% with both SWR < 2 and axial ratio < 3 dB.
Vehicle-to-grid power implementation : From stabilizing the grid to supporting large-scale renewable energy

Vehicle-to-grid power (V2G) uses electric-drive vehicles (battery, fuel cell, or hybrid) to provide power for specific electric markets. This article examines the systems and processes needed to tap energy in vehicles and implement V2G. It quantitatively compares today’s light vehicle fleet with the electric power system. The vehicle fleet has 20 times the power capacity, less than one-tenth the utilization, and one-tenth the capital cost per prime mover kW. Conversely, utility generators have 10–50 times longer operating life and lower operating costs per kWh. T id manager. T er the initial h calculations s ind, plus 8 identified.
Towards a model of privacy and security for smart homes

The domain of smart home environments is viewed as a key element of the future Internet, and many homes are becoming “smarter” by using Internet of Things (IoT) technology to improve home security, energy efficiency and comfort. At the same time, enforcing privacy in IoT environments has been identified as one of the main barriers for realizing the vision of the smart home. Based on the results of a risk analysis of a smart home automation system developed in collaboration with leading industrial actors, we outline the first steps towards a general model of privacy and security for smart homes. As such, it is envisioned as support for enforcing system security and enhancing user privacy, and it can thus help to further realize the potential in smart home environments.
Iris Recognition With Off-the-Shelf CNN Features: A Deep Learning Perspective

Iris recognition refers to the automated process of recognizing individuals based on their iris patterns. The seemingly stochastic nature of the iris stroma makes it a distinctive cue for biometric recognition. The textural nuances of an individual’s iris pattern can be effectively extracted and encoded by projecting them onto Gabor wavelets and transforming the ensuing phasor response into a binary code - a technique pioneered by Daugman. This textural descriptor has been observed to be a robust feature descriptor with very low false match rates and low computational complexity. However, recent advancements in deep learning and computer vision indicate that generic descriptors extracted using convolutional neural networks (CNNs) are able to represent complex image characteristics. Given the superior performance of CNNs on the ImageNet large scale visual recognition challenge and a large number of other computer vision tasks, in this paper, we explore the performance of state-of-the-art pre-trained CNNs on iris recognition. We show that the off-the-shelf CNN features, while originally trained for classifying generic objects, are also extremely good at representing iris images, effectively extracting discriminative visual features and achieving promising recognition results on two iris datasets: ND-CrossSensor-2013 and CASIA-Iris-Thousand. We also discuss the challenges and future research directions in leveraging deep learning methods for the problem of iris recognition.
Image Processing System for Plant Disease Identification by Using FCM-Clustering Technique

Our paper focuses on providing information about plant diseases and prevention methods. Plants have become an important source of energy, and are a fundamental piece of the puzzle to solve the problem of global warming. There are many types of diseases which are present in plants. Diseases weaken trees and shrubs by interrupting chemical change, the method by that plants produce energy that sustains growth and defense systems and influences survival. This paper presents an improved method for plant disease detection using an adaptive approach. This approach helps to increase the accuracy of the disease level, it provides various prevention method (type and amount of pesticides to be used), the level of destruction and helps to check whether the disease spreads or not.
The Use and Effectiveness of User Stories in Practice

User stories are an increasingly popular textual notation to capture requirements in agile software development.  To date there is no scientific evidence on the effectiveness of user stories. The goal of this paper is to explore how practicioners perceive this artifact in the context of requirements engineering.  We explore perceived effectiveness of user stories by reporting on a survey with 182 responses from practitioners and 21 follow-up semi-structured interviews. The data shows that practitioners agree that using user stories, a user story template and quality guidelines such as the INVEST mnemonic improve their productivity and the quality of their work deliverables.  By combining the survey data with 21 semi-structured follow-up interviews, we present 12 findings on the usage and perception of user stories by practitioners that employ user stories in their everyday work environment.
Human Decision Making and Recommender Systems

Recommender systems have already proved to be valuable for coping with the information overload problem in several application domains. They provide people with suggestions for items which are likely to be of interest for them; hence, a primary function of recommender systems is to help people make good choices and decisions. However, most previous research has focused on recommendation techniques and algorithms, and less attention has been devoted to the decision making processes adopted by the users and possibly supported by the system. There is still a gap between the importance that the community gives to the assessment of recommendation algorithms and the current range of ongoing research activities concerning human decision making. Different decision-psychological phenomena can influence the decision making of users of recommender systems, and research along these lines is becoming increasingly important and popular. This special issue highlights how the coupling of recommendation algorithms with the understanding of human choice and decision making theory has the potential to benefit research and practice on recommender systems and to enable users to achieve a good balance between decision accuracy and decision effort.
Design and Performance Analysis of Superconducting Rim-Driven Synchronous Motors for Marine Propulsion

Rim-driven thrusters have received much attention concerning the potential benefits in vibration and hydrodynamic characteristics, which are of great importance in marine transportation systems. In this sense, the rim-driven permanent magnet, brushless dc, and induction motors have been recently suggested to be employed as marine propulsion motors. On the other hand, high-temperature superconducting (HTS) synchronous motors are becoming much fascinating, particularly in transport applications, regarding some considerable advantages such as low loss, high efficiency, and compactness. However, the HTS-type rim-driven synchronous motor has not been studied yet. Therefore, this paper is devoted to a design practice of rim-driven synchronous motors with HTS field winding. A detailed design procedure is developed for the HTS rim-driven motors, and the design algorithm is validated applying the finite element (FE) method. The FE model of a three-phase 2.5-MW HTS rim-driven synchronous motor is utilized, and the electromagnetic characteristics of the motor are then evaluated. The goal is to design an HTS machine fitted in a thin duct to minimize the hydrodynamic drag force. The design problem exhibits some difficulties while considering various constraints.
Sexuality in adolescent boys with autism spectrum disorder: self-reported behaviours and attitudes.

Differences in sexual functioning of adolescents with and without autism spectrum disorder (ASD) are understudied. In the current study, self-reported sexual behaviours, interests and attitudes of 50 adolescent boys, aged 15-18, with at least average intelligence and diagnosed with ASD, were compared with a matched general population control group of 90 boys. Results demonstrated substantial similarity between the groups in terms of sexual behaviours. The only significant difference was that boys with ASD reacted more tolerant towards homosexuality compared to the control group. Results reveal that sexuality is a normative part of adolescent development in high-functioning boys with ASD. Hence, attention should be given to this topic in education and mental health care.
A sentiment analysis model for hotel reviews based on supervised learning

As the widespread use of computers and the high-speed development of the Internet, E-Commerce has already penetrated as a part of our daily life. For a popular product, there are a large number of reviews. This makes it difficult for a potential customer to make an informed decision on purchasing the product, as well as for the manufacturer of the product to keep track and to manage customer opinions. In this paper, we pay attention to online hotel reviews, and propose a supervised machine learning approach using unigram feature with two types of information (frequency and TF-IDF) to realize polarity classification of documents. As shown in our experimental results, the information of TF-IDF is more effective than frequency.
Privacy Concerns and Internet Use – A Model of Trade-off Factors

In this paper we report on the development and validation of a model that assesses the tradeoffs in the delicate balance between the perceived personal benefits and privacy costs associated with Internet use in conducting e-commerce transactions. The model assessment uses Structural Equations Modeling (SEM) with LISREL. Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA) were used to develop and validate the instrument involving two pilot studies and a final survey consisting of 301 individuals. The results indicate that trust influences decisions to engage in e-commerce transactions. Also, when personal interest and perceived ability to control surrendered personal information outweigh privacy concerns and perceptions of vulnerability, individuals are encouraged to use the Internet for e-commerce purposes. The findings are important for a number of reasons including their general support for the arguments made by researchers who have advocated the need to creating “procedural fairness” with respect to information disclosure.
Modelling of overcurrent relay using digital signal processor

Protective relays are used to detect any abnormalities in a power system and isolate the faulty section of the system in the shortest time, in order to minimize disturbances caused by failure in the system to ensure continuous power delivery. Protective relays for overcurrent protection are the most widely used relays in a power system. The implementation of overcurrent relay on digital signal processor (DSP) proficient to mitigate problems such as lack of memory capacity, lower speed and functionality, compared to conventional approach using microprocessor. The modelling of overcurrent relay for inverse definite minimum time (IDMT) type using DSP board TMS320F2812 will be discussed in the paper. Simulation results of the overcurrent relay from MATLAB/ Simulink and execution on TMS320F2812 will be presented.
Graphene-based Plasmonic Nano-Antenna for Terahertz Band Communication in Nanonetworks

Nanonetworks, i.e., networks of nano-sized devices, are the enabling technology of long-awaited applications in the biological, industrial and military fields. For the time being, the size and power constraints of nano-devices limit the applicability of classical wireless communication in nanonetworks. Alternatively, nanomaterials can be used to enable electromagnetic (EM) communication among nano-devices. In this paper, a novel graphene-based nano-antenna, which exploits the behavior of Surface Plasmon Polariton (SPP) waves in semi-finite size Graphene Nanoribbons (GNRs), is proposed, modeled and analyzed. First, the conductivity of GNRs is analytically and numerically studied by starting from the Kubo formalism to capture the impact of the electron lateral confinement in GNRs. Second, the propagation of SPP waves in GNRs is analytically and numerically investigated, and the SPP wave vector and propagation length are computed. Finally, the nano-antenna is modeled as a resonant plasmonic cavity, and its frequency response is determined. The results show that, by exploiting the high mode compression factor of SPP waves in GNRs, graphene-based plasmonic nano-antennas are able to operate at much lower frequencies than their metallic counterparts, e.g., the Terahertz Band for a one-micrometer-long ten-nanometers-wide antenna. This result has the potential to enable EM communication in nanonetworks.
Internet of Things-Aware Process Modeling: Integrating IoT Devices as Business Process Resources

The Internet of Things (IoT) has grown in recent years to a huge branch of research: RFID, sensors and actuators as typical IoT devices are increasingly used as resources integrated into new value added applications of the Future Internet and are intelligently combined using standardised software services. While most of the current work on IoT integration focuses on areas of the actual technical implementation, little attention has been given to the integration of the IoT paradigm and its devices coming with native software components as resources in business processes of traditional enterprise resource planning systems. In this paper, we identify and integrate IoT resources as a novel automatic resource type on the business process layer beyond the classical human resource task-centric view of the business process model in order to face expanding resource planning challenges of future enterprise environments.
VisReduce: Fast and responsive incremental information visualization of large datasets

Performance and responsiveness of visual analytics sytems for exploratory data analysis of large datasets has been a long standing problem. We propose a method for incrementally computing visualizations in a distributed fashion by combining a modified MapReduce-style algorithm with a compressed columnar data store, resulting in significant improvements in performance and responsiveness for constructing commonly encountered information visualizations, e.g. bar charts, scatterplots, heat maps, cartograms and parallel coordinate plots. We compare our method with one that queries three other readily available database and data warehouse systems - PostgreSQL, Cloudera Impala and the MapReduce-based Apache Hive - in order to build visualizations. We show that our end-to-end approach allows for greater speed and guaranteed end-user responsiveness, even in the face of large, long-running queries.
A Review of Combined TMS-EEG Studies to Characterize Lasting Effects of Repetitive TMS and Assess Their Usefulness in Cognitive and Clinical Neuroscience

Transcranial magnetic stimulation (TMS) has developed into a powerful tool for studying human brain physiology and brain–behavior relations. When applied in sessions of repeated stimulation, TMS can lead to changes in neuronal activity/excitability that outlast the stimulation itself. Such aftereffects are at the heart of the offline TMS protocols in cognitive neuroscience and neurotherapeutics. However, whether these aftereffects are of applied interest critically depends on their magnitude and duration, which should fall within an experimentally or clinically useful range without increasing risks and adverse effects. In this short review, we survey combined TMS-EEG studies to characterize the TMS-aftereffects as revealed by EEG to contribute to the characterization of the most effective and promising repetitive TMS-parameters. With one session of conventional repetitive TMS (of fixed pulse frequency), aftereffects were consistently comparable in magnitude to EEG-changes reported after learning or with fatigue, and were short-lived (<70 min). The few studies using recently developed protocols (such as theta burst stimulation) suggest comparable effect-size but longer effect-durations. Based on the reviewed data, it is expected that TMS-efficacy can be further promoted by repeating TMS-sessions, by using EEG-gated TMS to tailor TMS to current neuronal state, or by other, non-conventional TMS-protocols. Newly emerging developments in offline TMS research for cognitive neuroscience and neurotherapeutics are outlined.
Believe ItA Simple Model of Cognitive Consistency

Many observations from psychology, political science, and organizational behavior indicate that people exhibit a taste for consistency. Individuals are inclined to interpret new evidence in ways that con...rm their pre-existing beliefs. They also tend to change their beliefs to enhance the desirability of their past actions. The current paper explores the implications of a simple model incorporating these e¤ects into an agent’s utility function. The model allows a characterization of when: 1. agents become underand over-con...dent, 2. agents prefer less accurate signals, i.e., they are willing to pay in order to forgo information, and 3. agents exhibit excess stickiness or excess volatility in action choices.