nils-herrmann/clean_scirepeval huggingface dataset:
Renewable and sustainable approaches for desalination

Freshwater and energy are essential commodities for well being of mankind. Due to increasing population growth on the one hand, and rapid industrialization on the other, today’s world is facing unprecedented challenge of meeting the current needs for these two commodities as well as ensuring the needs of future generations. One approach to this global crisis of water and energy supply is to utilize renewable energy sources to produce freshwater from impaired water sources by desalination. Sustainable practices and innovative desalination technologies for water reuse and energy recovery (staging, waste heat utilization, hybridization) have the potential to reduce the stress on the existing water and energy sources with a minimal impact to the environment. This paper discusses existing and emerging desalination technologies and possible combinations of renewable energy sources to drive them and associated desalination costs. It is suggested that a holistic approach of coupling renewable energy sources with technologies for recovery, reuse, and recycle of both energy and water can be a sustainable and environment friendly approach to meet the world’s energy and water needs. High capital costs for renewable energy sources for small-scale applications suggest that a hybrid energy source comprising both grid-powered energy and renewable energy will reduce the desalination costs considering present economics of energy.
Next-generation of virtual personal assistants (Microsoft Cortana, Apple Siri, Amazon Alexa and Google Home)

One of the goals of Artificial intelligence (AI) is the realization of natural dialogue between humans and machines. in recent years, the dialogue systems, also known as interactive conversational systems are the fastest growing area in AI. Many companies have used the dialogue systems technology to establish various kinds of Virtual Personal Assistants(VPAs) based on their applications and areas, such as Microsoft's Cortana, Apple's Siri, Amazon Alexa, Google Assistant, and Facebook's M. However, in this proposal, we have used the multi-modal dialogue systems which process two or more combined user input modes, such as speech, image, video, touch, manual gestures, gaze, and head and body movement in order to design the Next-Generation of VPAs model. The new model of VPAs will be used to increase the interaction between humans and the machines by using different technologies, such as gesture recognition, image/video recognition, speech recognition, the vast dialogue and conversational knowledge base, and the general knowledge base. Moreover, the new VPAs system can be used in other different areas of applications, including education assistance, medical assistance, robotics and vehicles, disabilities systems, home automation, and security access control.
Deep Metric Learning with Angular Loss

The modern image search system requires semantic understanding of image, and a key yet under-addressed problem is to learn a good metric for measuring the similarity between images. While deep metric learning has yielded impressive performance gains by extracting high level abstractions from image data, a proper objective loss function becomes the central issue to boost the performance. In this paper, we propose a novel angular loss, which takes angle relationship into account, for learning better similarity metric. Whereas previous metric learning methods focus on optimizing the similarity (contrastive loss) or relative similarity (triplet loss) of image pairs, our proposed method aims at constraining the angle at the negative point of triplet triangles. Several favorable properties are observed when compared with conventional methods. First, scale invariance is introduced, improving the robustness of objective against feature variance. Second, a third-order geometric constraint is inherently imposed, capturing additional local structure of triplet triangles than contrastive loss or triplet loss. Third, better convergence has been demonstrated by experiments on three publicly available datasets.
DATA MINING FOR PREDICTING CUSTOMER SATISFACTION IN FAST-FOOD

Managing customer satisfaction has become a crucial issue in fast-food industry. This study aims at identifying determinant factor related to customer satisfaction in fast-food restaurant. Customer data are analyzed by using data mining method with two classification techniques such as decision tree and neural network. Classification models are developed using decision tree and neural network to determine underlying attributes of customer satisfaction. Generated rules are beneficial for managerial and practical implementation in fast-food industry. Decision tree and neural network yield more than 80% of predictive accuracy.
SeaShips: A Large-Scale Precisely Annotated Dataset for Ship Detection

In this paper, we introduce a new large-scale dataset of ships, called SeaShips, which is designed for training and evaluating ship object detection algorithms. The dataset currently consists of 31 455 images and covers six common ship types (ore carrier, bulk cargo carrier, general cargo ship, container ship, fishing boat, and passenger ship). All of the images are from about 10 080 real-world video segments, which are acquired by the monitoring cameras in a deployed coastline video surveillance system. They are carefully selected to mostly cover all possible imaging variations, for example, different scales, hull parts, illumination, viewpoints, backgrounds, and occlusions. All images are annotated with ship-type labels and high-precision bounding boxes. Based on the SeaShips dataset, we present the performance of three detectors as a baseline to do the following: 1) elementarily summarize the difficulties of the dataset for ship detection; 2) show detection results for researchers using the dataset; and 3) make a comparison to identify the strengths and weaknesses of the baseline algorithms. In practice, the SeaShips dataset would hopefully advance research and applications on ship detection.
Relationships among grit, academic performance, perceived academic failure, and stress in associate degree students.

The present study examined the relationships among grit, academic performance, perceived academic failure, and stress levels of Hong Kong associate degree students using path analysis. Three hundred and forty-five students from a community college in Hong Kong voluntarily participated in the study. They completed a questionnaire that measured their grit (operationalized as interest and perseverance) and stress levels. The students also provided their actual academic performance and evaluated their perception of their academic performance as a success or a failure. The results of the path analysis showed that interest and perseverance were negatively associated with stress, and only perceived academic failure was positively associated with stress. These findings suggest that psychological appraisal and resources are more important antecedents of stress than objective negative events. Therefore, fostering students' psychological resilience may alleviate the stress experienced by associate degree students or college students in general.
Communication and information technology in medical education

The past few years have seen rapid advances in communication and information technology (C&IT), and the pervasion of the worldwide web into everyday life has important implications for education. Most medical schools provide extensive computer networks for their students, and these are increasingly becoming a central component of the learning and teaching environment. Such advances bring new opportunities and challenges to medical education, and are having an impact on the way that we teach and on the way that students learn, and on the very design and delivery of the curriculum. The plethora of information available on the web is overwhelming, and both students and staff need to be taught how to manage it effectively. Medical schools must develop clear strategies to address the issues raised by these technologies. We describe how medical schools are rising to this challenge, look at some of the ways in which communication and information technology can be used to enhance the learning and teaching environment, and discuss the potential impact of future developments on medical education.
Survey on Deep Learning Techniques for Wireless Communications

A transmitter, channel, and receiver make up a typical wireless communication system. The channel model describes the underlying physical phenomenon and informs the design of the transmitter and receiver. Signaling schemes are devised according to system objectives, such as robustness to channel impairments, ease of implementation, and minimization of transmit power. Similarly, detection algorithms are designed to optimize system performance subject to chosen constraints. Most deployed wireless communication systems use electromagnetic signals at frequencies below 10GHz. In these systems, the channels conform to tractable mathematical models that describe electromagnetic propagation with reasonable accuracy. However, in other systems, the channels do not have easily-described channel models. In this category, we highlight two categories of channels: channels describing the propagation of mmWave electromagnetic signals, and unknown channel models describing the propagation of non-electromagnetic signals. The former category, mmWave channels, offer large amounts of bandwidth to support high data rates in next-generation systems. In this category, channel models might be known, but they are too complex and/or change too fast to estimate with reasonable accuracy. This estimation of channel state information (CSI) is essential for many current detection algorithms. The latter category includes molecular channels (i.e. channels over which molecular signals are sent) , which offer a means to communicate when electromagnetic propagation is impossible or impractical. In this category, channel models are unknown or difficult to derive, so traditional detection algorithms cannot be used. These considerations prompted researchers to turn to estimation tools from other fields to deduce the channel or circumvent its explicit deduction. Deep learning techniques have recently exhibited unprecedented success in classification problems for which no welldefined mathematical model exists [2]. For example, the mapping of pixels to object, or of spoken language to meaning. In this survey, we explore current research on deep-learning based channel estimation and detection. These topics are discussed in Sections III and IV respectively. We also overview other research in Section V, although we do not dive into detail. We aim to provide an overview of current methods being explored and to highlight promising areas of further exploration.
Adult N orills for the Box and Block Test of Manual Dexterity ( hand evaluation , hand , motor skills , occupational therapy , tests )

The Box and Block Test, a test of manual dexterity, has been used by occupational therapists and others to evaluate physically handicapped individuals. Because the test lacked normative data for adults, the results of the test have been interpreted subjectively. The purpose of this study was to develop normative data for adults. Test subjects were 628 Normal adults (310 males and 318 females)from the seven-county Milwaukee area. Data on males and females 20 to 94 years old were divided into 12 age groups. Means, standard deviations, standard error, and low and high scores are reported for each five-year age group. These data will enable clinicians to objectively compare a patient's score to a normal population parameter. Occupational therapists are frequently involved with increasing the manual dexterity of their patients. Often, these patients are unable to perform tests offine manual or finger dexterity, such as the Purdue Pegboard Test or the Crawford Small Parts Dexterity Test. Tests of manual dexterity, such as the Minnesota Rate of Manipulation Test, have limited clinical application because a) they require lengthy administration time, b) a standardized standing position must be used for testing, and c) the tests use normative samples that poorly represent the wide range of clinical patients. Because of the limitations of such standardized tests, therapists often evaluate dexterity subjectively. The Box and Block Test has been suggested as a measure of gross manual dexterity (1) and as a prevocational test for handicapped people (2). Norms have been collected on adults with neuromuscular involvement (2) and on normal children (7, 8, and 9 years old) (3). Standardized instructions along with reliability and validity data, are reported in the literature (2,3), but there are no norms for the normal adult population. Therefore, the purpose of this study was to collect normative data for adults.
Heart failure with preserved ejection fraction: pathophysiology, diagnosis, and treatment.

Half of patients with heart failure (HF) have a preserved left ventricular ejection fraction (HFpEF). Morbidity and mortality in HFpEF are similar to values observed in patients with HF and reduced EF, yet no effective treatment has been identified. While early research focused on the importance of diastolic dysfunction in the pathophysiology of HFpEF, recent studies have revealed that multiple non-diastolic abnormalities in cardiovascular function also contribute. Diagnosis of HFpEF is frequently challenging and relies upon careful clinical evaluation, echo-Doppler cardiography, and invasive haemodynamic assessment. In this review, the principal mechanisms, diagnostic approaches, and clinical trials are reviewed, along with a discussion of novel treatment strategies that are currently under investigation or hold promise for the future.
A Probabilistic Co-Bootstrapping Method for Entity Set Expansion

Entity Set Expansion (ESE) aims at automatically acquiring instances of a specific target category. Unfortunately, traditional ESE methods usually have the expansion boundary problem and the semantic drift problem. To resolve the above two problems, this paper proposes a probabilistic Co-Bootstrapping method, which can accurately determine the expansion boundary using both the positive and the discriminant negative instances, and resolve the semantic drift problem by effectively maintaining and refining the expansion boundary during bootstrapping iterations. Experimental results show that our method can achieve a competitive performance.
Salesforce Compensation Scheme and Consumer Inferences

Ajay Kalra • Mengze Shi • Kannan Srinivasan Graduate School of Industrial Administration, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213 Rotman School of Management, University of Toronto, Toronto, Ontario, Canada M5S 3G4 Graduate School of Industrial Administration, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213 kalra@andrew.cmu.edu • mshi@rotman.utoronto.ca •
A Conceptual Study on E-marketing and Its Operation on Firm ' s Promotion and Understanding Customer ’ s Response

The purpose of this conceptual paper is to discuss four main different tools which are: mobile marketing, E-mail marketing, web marketing and marketing through social networking sites, which use to distribute e-marketing promotion and understanding their different influence on consumers` perception. This study also highlighted the E-marketing, marketing through internet, mobile marketing, web marketing and role of social networks and their component in term of perceptual differences and features which are important to them according to the literatures. The review of the research contains some aspect of mobile marketing, terms like adaption, role of trust, and customers’ satisfaction. Moreover some attributes of marketing through E-mail like Permission issue in Email in aim of using for marketing activity and key success factor base on previous literatures.
An Algorithm for Trading and Portfolio Management Using Q-learning and Sharpe Ratio Maximization

A trading and portfolio management system called QSR is proposed. It uses Q-learning and Sharpe ratio maximization algorithm. We use absolute proot and relative risk-adjusted proot as performance function to train the system respectively, and employ a committee of two networks to do the testing. The new proposed algorithm makes use of the advantages of both parts and can be used in a more general case. We demonstrate with experimental results that the proposed approach generates appreciable proots from trading in the foreign exchange markets.
Dynamics of and on Complex Networks

Complex networks are dynamic, evolving structures that can host a great number of dynamical processes. In this thesis, we address current challenges regarding the dynamics of and dynamical processes on complex networks. First, we study complex network dynamics from the standpoint of network growth. As a quantitative measure of the complexity and information content of networks generated by growing network models, we define and evaluate their entropy rate. We propose stochastic growth models inspired by the duplication-divergence mechanism to generate epistatic interaction networks and find that they exhibit the property of monochromaticity as a result of their dynamical evolution. Second, we explore the dynamics of quantum mechanical processes on complex networks. We investigate the Bose-Hubbard model on annealed and quenched scale-free networks as well as Apollonian networks and show that their phase diagram changes significantly in the presence of complex topologies, depending on the second degree of the degree distribution and the maximal eigenvalue of the adjacency matrix. We then study the Jaynes-Cummings-Hubbard model on various complex topologies and demonstrate the importance of the maximal eigenvalue of the hopping matrix in determining the phase diagram of the model. Third, we investigate dynamical processes on interacting and multiplex networks. We study opinion dynamics in a simulated setting of two antagonistically interacting networks and recover the importance of connectivity and committed agents. We propose a multiplex centrality measure that takes into account the connectivity patterns within and across different layers and find that the dynamics of biased random walks on multiplex networks gives rise to a centrality ranking that is different from univariate centrality measures. Finally, we study the statistical mechanics of multilayered spatial networks and demonstrate the emergence of significant link overlap and improved navigability in multiplex and interacting spatial networks.
Screen time use in children under 3 years old: a systematic review of correlates

A large percentage (68%) of children under age 3 use screen media, such as television, DVDs and video games, on a daily basis. Research suggests that increased screen time in young children is linked to negative health outcomes, including increased BMI, decreased cognitive and language development and reduced academic success. Reviews on correlates of screen time for young children have included preschool age children and children up to age 7; however, none have focused specifically on correlates among infants and toddlers. As research suggests that screen media use increases with age, examining correlates of early media exposure is essential to reducing exposure later in life. Thus, this paper systemically reviews literature published between January 1999 and January 2013 on correlates of screen time among children between 0 and 36 months of age.



Two methods were used to conduct this review: (1) Computerized searches of databases (PubMed, PsycINFO, ERIC, Medline); and (2) Reference sections of existing reviews and primary studies. Inclusion criteria were: (1) The article included separate data for children 36 months and younger, (2) English language, (3) peer reviewed article, (4) analysis reported for screen viewing as a dependent variable, (5) original research article and, (6) examined correlates or associations between screen time and other demographic, contextual or behavioral variables. Articles were compiled between 2011 and 2013 and evaluation occurred in 2012 and 2013.



The literature search identified 29 studies that met inclusion criteria. These studies investigated a total of 33 potential correlates, which were examined in this review. Findings suggest demographic variables most commonly correlated with high screen time among infants and toddlers are child's age (older) and race/ethnicity (minority). Child BMI, maternal distress/depression, television viewing time of the mother and cognitive stimulation in the home environment were also associated with screen media use. Studies reported that child sex, first born status, paternal education, non-English speaking family, two-parent household, number of children in the home and non-parental childcare were not associated with screen time among children aged 0-36 months. Associations were unclear (fewer than 60% of studies report an association) for maternal age, maternal education and household income. The remaining correlates were investigated in fewer than three studies and thus not coded for an association.



The correlates identified in this study point to avenues for intervention to reduce screen time use in young children. However, further research is necessary to explore a number of environmental, socio-cultural and behavioral correlates that are under-examined in this population and may further inform prevention and intervention strategies.
Visual SLAM algorithms: a survey from 2010 to 2016

SLAM is an abbreviation for simultaneous localization and mapping, which is a technique for estimating sensor motion and reconstructing structure in an unknown environment. Especially, Simultaneous Localization and Mapping (SLAM) using cameras is referred to as visual SLAM (vSLAM) because it is based on visual information only. vSLAM can be used as a fundamental technology for various types of applications and has been discussed in the field of computer vision, augmented reality, and robotics in the literature. This paper aims to categorize and summarize recent vSLAM algorithms proposed in different research communities from both technical and historical points of views. Especially, we focus on vSLAM algorithms proposed mainly from 2010 to 2016 because major advance occurred in that period. The technical categories are summarized as follows: feature-based, direct, and RGB-D camera-based approaches.
NEXUS Network: Connecting the Preceding and the Following in Dialogue Generation

Sequence-to-Sequence (seq2seq) models have become overwhelmingly popular in building end-to-end trainable dialogue systems. Though highly efficient in learning the backbone of human-computer communications, they suffer from the problem of strongly favoring short generic responses. In this paper, we argue that a good response should smoothly connect both the preceding dialogue history and the following conversations. We strengthen this connection through mutual information maximization. To sidestep the nondifferentiability of discrete natural language tokens, we introduce an auxiliary continuous code space and map such code space to a learnable prior distribution for generation purpose. Experiments on two dialogue datasets validate the effectiveness of our model, where the generated responses are closely related to the dialogue context and lead to more interactive conversations.