nils-herrmann/clean_scirepeval huggingface dataset:
Core self-evaluations and job and life satisfaction: the role of self-concordance and goal attainment.

The present study tested a model explaining how the core self-evaluations (i.e., positive self-regard) concept is linked to job and life satisfaction. The self-concordance model, which focuses on motives underlying goal pursuit, was used as an explanatory framework. Data were collected from 2 samples: (a) 183 university students (longitudinal measures of goal attainment and life satisfaction were used) and (b) 251 employees (longitudinal measures of goal attainment and job satisfaction were utilized). In both studies, the core self-evaluations concept was positively related to goal self-concordance, meaning that individuals with positive self-regard were more likely to pursue goals for intrinsic and identified (value-congruent) reasons. Furthermore, in both studies, goal self-concordance was related to satisfaction (job satisfaction in Study 1 and life satisfaction in Study 2).
A Study on the CBOW Model's Overfitting and Stability

Word vectors are distributed representations of word features. Continuous Bag-of-Words Model(CBOW) is a state-of-the-art model for learning word vectors, yet can be ameliorated for learning better word vectors because we find that CBOW is vulnerable to be overfitted and unstable. We use two methods to solve these two problems so that CBOW can learn better word vectors. In this study, we add the regularized structure risk summation to the objective function of the CBOW model and propose inverse word frequency encoding for the CBOW model. Our proposed methods substantially improve the quality of word vectors, boosting r from 0.638 to 0.696 for word relatedness and total accuracy from 30.80% to 38.43% for word pairs relationship relatedness regarding to 52 million training words with 200 dimensionality.
Adversarial Generative Nets: Neural Network Attacks on State-of-the-Art Face Recognition

In this paper we show that misclassification attacks against face-recognition systems based on deep neural networks (DNNs) are more dangerous than previously demonstrated, even in contexts where the adversary can manipulate only her physical appearance (versus directly manipulating the image input to the DNN). Specifically, we show how to create eyeglasses that, when worn, can succeed in targeted (impersonation) or untargeted (dodging) attacks while improving on previous work in one or more of three facets: (i) inconspicuousness to onlooking observers, which we test through a user study; (ii) robustness of the attack against proposed defenses; and (iii) scalability in the sense of decoupling eyeglass creation from the subject who will wear them, i.e., by creating “universal” sets of eyeglasses that facilitate misclassification. Central to these improvements are adversarial generative nets, a method we propose to generate physically realizable attack artifacts (here, eyeglasses) automatically.
Cognitive radar: a way of the future

This article discusses a new idea called cognitive radar. Three ingredients are basic to the constitution of cognitive radar: 1) intelligent signal processing, which builds on learning through interactions of the radar with the surrounding environment; 2) feedback from the receiver to the transmitter, which is a facilitator of intelligence; and 3) preservation of the information content of radar returns, which is realized by the Bayesian approach to target detection through tracking. All three of these ingredients feature in the echo-location system of a bat, which may be viewed as a physical realization (albeit in neurobiological terms) of cognitive radar. Radar is a remote-sensing system that is widely used for surveillance, tracking, and imaging applications, for both civilian and military needs. In this article, we focus on future possibilities of radar with particular emphasis on the issue of cognition. As an illustrative case study along the way, we consider the problem of radar surveillance applied to an ocean environment.
Challenges and Success Factors of ERP Systems in Australian SMEs

Today, great potential is envisaged for ERP systems in small and medium-sized enterprises (SMEs), and software vendors have been repackaging their ERP systems for SMEs with a recent focus on cloud-based systems. While cloud ERP offers the best solution for SMEs without the overheads of the huge investment and management costs that are associated with traditional ERP systems, the SME sector faces many challenges in their adoption. Traditional ERP studies have predominantly focused on large organizations, and gaps in the literature indicate that both vendor and consumer perspectives require more understanding with new technology offerings for SMEs. This paper describes some of the common challenges, such as cost effectiveness, alignment between software and business processes, customized governance and training, which form the major SME constraints for ERP system adoption. Due to the dynamic nature of SME businesses, best practice guidelines for an SME’s ERP implementation could be arrived at through closer investigation of its business requirements in order to avoid misfits. This forms the main objective of the study. We identify key success factors of ERP implementation in an Australian SME as a case study. These target success factors are then compared to the actual outcomes achieved. Factors such as business process alignment with the ERP system, meeting customer and stakeholder needs and reducing recurring and maintenance costs were key to the success of ERP implementation for the Australian SME. In particular, the IT and business strategy alignment with a customer focus and flexible reporting features of ERP systems has resulted in business agility.
MaCow: Masked Convolutional Generative Flow

Flow-based generative models, conceptually attractive due to tractability of both the exact log-likelihood computation and latent-variable inference, and efficiency of both training and sampling, has led to a number of impressive empirical successes and spawned many advanced variants and theoretical investigations. Despite their computational efficiency, the density estimation performance of flow-based generative models significantly falls behind those of state-of-the-art autoregressive models. In this work, we introduce masked convolutional generative flow (MACOW), a simple yet effective architecture of generative flow using masked convolution. By restricting the local connectivity in a small kernel, MACOW enjoys the properties of fast and stable training, and efficient sampling, while achieving significant improvements over Glow for density estimation on standard image benchmarks, considerably narrowing the gap to autoregressive models.
GCap: Graph-based Automatic Image Captioning

Given an image, how do we automatically assign keywords to it? In this paper, we propose a novel, graph-based approach (GCap) which outperforms previously reported methods for automatic image captioning. Moreover, it is fast and scales well, with its training and testing time linear to the data set size. We report auto-captioning experiments on the "standard" Corel image database of 680 MBytes, where GCap outperforms recent, successful auto-captioning methods by up to 10 percentage points in captioning accuracy (50% relative improvement).
A Systematic Approach of Dataset Definition for a Supervised Machine Learning Using NFR Framework

Non-functional requirements describe important constraints upon the software development and should therefore be considered and specified as early as possible during the system analysis. Effective elicitation of requirements is arguably among the most important of the resulting recommended RE practices. Recent research has shown that artificial intelligence techniques such as Machine Learning and Text Mining perform the automatic extraction and classification of quality attributes from text documents with relevant results. This paper aims to define a systematic process of dataset generation through NFR Framework catalogues improving the NFR's classification process using Machine Learning techniques. A well-known dataset (Promise) was used to evaluate the precision of our approach reaching interesting results. Regarding to security and performance we obtained a precision and recall ranging between ~85% and ~98%. And we achievement a F1 above ~79% when classified the security, performance and usability together.
Learning Scalable Deep Kernels with Recurrent
Structure

Many applications in speech, robotics, finance, and biology deal with sequential data, where ordering matters and recurrent structures are common. However, this structure cannot be easily captured by standard kernel functions. To model such structure, we propose expressive closed-form kernel functions for Gaussian processes. The resulting model, GP-LSTM, fully encapsulates the inductive biases of long short-term memory (LSTM) recurrent networks, while retaining the non-parametric probabilistic advantages of Gaussian processes. We learn the properties of the proposed kernels by optimizing the Gaussian process marginal likelihood using a new provably convergent semi-stochastic gradient procedure, and exploit the structure of these kernels for scalable training and prediction. This approach provides a practical representation for Bayesian LSTMs. We demonstrate state-of-the-art performance on several benchmarks, and thoroughly investigate a consequential autonomous driving application, where the predictive uncertainties provided by GP-LSTM are uniquely valuable.
An ultrawideband conformal loop antenna for ingestible capsule endoscope system

In this paper, an ultra wideband loop antenna is presented for capsule endoscope system operating around 433 MHz. The antenna occupies the outer-wall of the capsule and leaves the inner space for the other components of the capsule endoscope module. The proposed capsule antenna has a -10 dB impedance matching bandwidth of 744 MHz (from 336 to 1080 MHz). The ultra wideband characteristic enables the capsule antenna to compensate for detuning effects due to electronics components in the capsule endoscope module, different tissues in the gastrointestinal tract, and implant locations. The antenna has a nearly omnidirectional radiation pattern, which is usually required for such applications. The resonance performance of the fabricated antenna is validated by comparing the measured results with that of simulations when the antenna is placed in a human equivalent liquid phantom.
A fuzzing test for dynamic vulnerability detection on Android Binder mechanism

Binder, which helps to package the functional codes of system processes into inter-process invocable interfaces for application-level processes, is the core mechanism to implement the Inter-Process Communication(IPC) in Android. This paper, for the first time, attempts to study the system-level security properties of this mechanism. The universal injection interface and the model of IPC data are proposed to implement a fuzzing test. A test case generation technique based on mutation algorithm of pre-captured IPC data is introduced in order to improve the fuzzing test efficiency. Two high-risk vulnerabilities are detected in Android 5.1.0. Analysis of these vulnerabilities highlights a critical design issue in the system services of Binder mechanism.
Real-Time Recognition of Physical Activities and Their Intensities Using Wireless Accelerometers and a Heart Rate Monitor

In this paper, we present a real-time algorithm for automatic recognition of not only physical activities, but also, in some cases, their intensities, using five triaxial wireless accelerometers and a wireless heart rate monitor. The algorithm has been evaluated using datasets consisting of 30 physical gymnasium activities collected from a total of 21 people at two different labs. On these activities, we have obtained a recognition accuracy performance of 94.6% using subject-dependent training and 56.3% using subject-independent training. The addition of heart rate data improves subject-dependent recognition accuracy only by 1.2% and subject-independent recognition only by 2.1%. When recognizing activity type without differentiating intensity levels, we obtain a subject-independent performance of 80.6%. We discuss why heart rate data has such little discriminatory power.
Marital processes predictive of later dissolution: behavior, physiology, and health.

Seventy-three married couples were studied in 1983 and 1987. To identify marital processes associated with dissolution, a balance theory of marriage was used to generate 1 variable for dividing couples into regulated and nonregulated groups. For studying the precursors of divorce, a "cascade" model of marital dissolution, which forms a Guttman-like scale, received preliminary support. Compared with regulated couples, nonregulated couples had (a) marital problems rated as more severe (Time 1); (b) lower marital satisfaction (Time 1 and Time 2); (c) poorer health (Time 2); (d) smaller finger pulse amplitudes (wives); (e) more negative ratings for interactions; (f) more negative emotional expression; (g) less positive emotional expression; (h) more stubbornness and withdrawal from interaction; (i) greater defensiveness; and (j) greater risk for marital dissolution (lower marital satisfaction and higher incidence of consideration of dissolution and of actual separation).
Privacy-Preserving Attribute-Based Access Control Model for XML-Based Electronic Health Record System

Cloud-based electronic health record (EHR) systems enable medical documents to be exchanged between medical institutions; this is expected to contribute to improvements in various medical services in the future. However, as the system architecture becomes more complicated, cloud-based EHR systems may introduce additional security threats when compared with the existing singular systems. Thus, patients may experience exposure of private data that they do not wish to disclose. In order to protect the privacy of patients, many approaches have been proposed to provide access control to patient documents when providing health services. However, most current systems do not support fine-grained access control or take into account additional security factors such as encryption and digital signatures. In this paper, we propose a cloud-based EHR model that performs attribute-based access control using extensible access control markup language. Our EHR model, focused on security, performs partial encryption and uses electronic signatures when a patient’s document is sent to a document requester. We use XML encryption and XML digital signature technology. Our proposed model works efficiently by sending only the necessary information to the requesters who are authorized to treat the patient in question.
Neural correlates of experimentally induced flow experiences

Flow refers to a positive, activity-associated, subjective experience under conditions of a perceived fit between skills and task demands. Using functional magnetic resonance perfusion imaging, we investigated the neural correlates of flow in a sample of 27 human subjects. Experimentally, in the flow condition participants worked on mental arithmetic tasks at challenging task difficulty which was automatically and continuously adjusted to individuals' skill level. Experimental settings of "boredom" and "overload" served as comparison conditions. The experience of flow was associated with relative increases in neural activity in the left anterior inferior frontal gyrus (IFG) and the left putamen. Relative decreases in neural activity were observed in the medial prefrontal cortex (MPFC) and the amygdala (AMY). Subjective ratings of the flow experience were significantly associated with changes in neural activity in the IFG, AMY, and, with trend towards significance, in the MPFC. We conclude that neural activity changes in these brain regions reflect psychological processes that map on the characteristic features of flow: coding of increased outcome probability (putamen), deeper sense of cognitive control (IFG), decreased self-referential processing (MPFC), and decreased negative arousal (AMY).
Type Checking Type Classes

We study the type inference problem for a system with type classes as in the functional programming language Haskell. Type classes are an extension of ML-style polymorphism with overloading. We generalize Milner's work on polymorphism by introducing a separate context constraining the type variables in a typing judgement. This lead to simple type inference systems and algorithms which closely resemble those for ML. In particular we present a new unification algorithm which is an extension of syntactic unification with constraint solving. The existence of principal types follows from an analysis of this unification algorithm.
FC^4: Fully Convolutional Color Constancy with Confidence-Weighted Pooling

Improvements in color constancy have arisen from the use of convolutional neural networks (CNNs). However, the patch-based CNNs that exist for this problem are faced with the issue of estimation ambiguity, where a patch may contain insufficient information to establish a unique or even a limited possible range of illumination colors. Image patches with estimation ambiguity not only appear with great frequency in photographs, but also significantly degrade the quality of network training and inference. To overcome this problem, we present a fully convolutional network architecture in which patches throughout an image can carry different confidence weights according to the value they provide for color constancy estimation. These confidence weights are learned and applied within a novel pooling layer where the local estimates are merged into a global solution. With this formulation, the network is able to determine what to learn and how to pool automatically from color constancy datasets without additional supervision. The proposed network also allows for end-to-end training, and achieves higher efficiency and accuracy. On standard benchmarks, our network outperforms the previous state-of-the-art while achieving 120x greater efficiency.
Aware LSTM model for Semantic Role Labeling

In Semantic Role Labeling (SRL) task, the tree structured dependency relation is rich in syntax information, but it is not well handled by existing models. In this paper, we propose Syntax Aware Long Short Time Memory (SA-LSTM). The structure of SA-LSTM changes according to dependency structure of each sentence, so that SA-LSTM can model the whole tree structure of dependency relation in an architecture engineering way. Experiments demonstrate that on Chinese Proposition Bank (CPB) 1.0, SA-LSTM improves F1 by 2.06% than ordinary bi-LSTM with feature engineered dependency relation information, and gives state-of-the-art F1 of 79.92%. On English CoNLL 2005 dataset, SA-LSTM brings improvement (2.1%) to bi-LSTM model and also brings slight improvement (0.3%) when added to the stateof-the-art model.
Emotional memory formation is enhanced across sleep intervals with high amounts of rapid eye movement sleep.

Recent studies indicated a selective activation during rapid eye movement (REM) sleep of the amygdala known to play a decisive role in the processing of emotional stimuli. This study compared memory retention of emotional versus neutral text material over intervals covering either early sleep known to be dominated by nonREM slow wave sleep (SWS) or late sleep, in which REM sleep is dominant. Two groups of men were tested across 3-h periods of early and late sleep (sleep group) or corresponding retention intervals filled with wakefulness (wake group). Sleep was recorded polysomnographically. Cortisol concentrations in saliva were monitored at acquisition and retrieval testing. As expected, the amount of REM sleep was about three times greater during late than during early retention sleep, whereas a reversed pattern was observed for SWS distribution (P < 0.001). Sleep improved retention, compared with the effects of wake intervals (P < 0.02). However, this effect was substantial only in the late night (P < 0.005), during which retention was generally worse than during the early night (P < 0.02). Late sleep particularly enhanced memory for emotional texts. This effect was highly significant in comparison with memory for neutral texts (P < 0.01) and in comparison with memory after late and early wake intervals (P < 0.001). Cortisol concentration differed between early and late retention intervals but not between sleep and wake conditions. Results are consonant with a supportive function of REM sleep predominating late sleep for the formation of emotional memory in humans.
A lifespan database of adult facial stimuli.

Faces constitute a unique and widely used category of stimuli. In spite of their importance, there are few collections of faces for use in research, none of which adequately represent the different ages of faces across the lifespan. This lack of a range of ages has limited the majority of researchers to using predominantly young faces as stimuli even when their hypotheses concern both young and old participants. We describe a database of 575 individual faces ranging from ages 18 to 93. Our database was developed to be more representative of age groups across the lifespan, with a special emphasis on recruiting older adults. The resulting database has faces of 218 adults age 18-29, 76 adults age 30-49, 123 adults age 50-69, and 158 adults age 70 and older. These faces may be acquired for research purposes from http://agingmind.cns.uiuc.edu/facedb/. This will allow researchers interested in using facial stimuli access to a wider age range of adult faces than has previously been available.
Role of social media in online travel information search

Social media are playing an increasingly important role as information sources for travelers. The goal of this study is to investigate the extent to which social media appear in search engine results in the context of travel-related searches. The study employed a research design that simulates a traveler’s use of a search engine for travel planning by using a set of pre-defined keywords in combination with nine U.S. tourist destination names. The analysis of the search results showed that social media constitute a substantial part of the search results, indicating that search engines likely direct travelers to social media sites. This study confirms the growing importance of social media in the online tourism domain. It also provides evidence for challenges faced by traditional providers of travel-related information. Implications for tourism marketers in terms of online marketing strategies are discussed.
The Smart Car Parking System Based on Iot Commanded by Android Application

Parking the car is one of the difficult task that we are facing in our day to day life. The main issue is providing the sufficient parking system. Now a days it is very hard to find the availability of parking slots. The various places(public) that is shopping mall, cinema hall etc finds it difficult to search the available parking area. This calls for the situations of an Smart car parking system which is based on IoT and commanded by Android application which are equipped with IR sensors and microcontroller (arduinouno).In this paper a small prototype of smart car parking system which is based on IoT is implemented. The paper proposed a system that the user will automatically find the parking space throught an android application via server. In addition to this we can say that the its a new way of communication between humans and the things with the help of new technology based on IoT.