nils-herrmann/clean_scirepeval huggingface dataset:
An Investigation into the Challenges and Opportunities Associated with the Application of Internet of Things (IoT) in the Agricultural Sector-A Review

Sarfraz Fayaz Khan Department of Management Information Systems, College of Commerce and Business Administration, Dhofar University, Salalah, Sultanate of Oman Email: 1skhan@du.edu.om 2mismail@du.edu.om  Internet of things has acquired attention all over the globe. It has transformed the agricultural field and allowed farmers to compete with massive issues they face. The aim of this paper is to review the various challenges and opportunities associated with the applications of internet of things in agricultural sector. This research makes use of secondary sources that have been gathered from existing academic literature such as journals, books, articles, magazines, internet, newsletter, company publications and whitepapers. Applications reviewed in this research are about crop sensing, mapping and monitoring the croplands pattern, managing and controlling with the help of radio frequency identification and real-time monitoring of environment. Some of the challenges that were taken into consideration for reviewing the applications of internet of things are software complexity, security, lack of supporting infrastructure and technical skill requirement. Complexity in the software has to be rectified in order to support the IoT network. Therefore software must be developed as user-friendly for improving the farming, production and quality of the crop. Security is the major threat in the IoT applications. Security has to be enhanced through proper access control, data confidentiality and user authentication. Technical skill is required for farming to enhance the organizational abilities and to perform the farming functions, solving problems and more. Proper supporting infrastructure can be developed with proper internet availability and connectivity. Some of the opportunities were taken for reviewing the applications of internet of things are low power wireless sensor, better connectivity, operational efficiency and remote management.
HOTEL BRANDING STRATEGY : ITS RELATIONSHIP TO GUEST SATISFACTION AND ROOM REVENUE

U.S. hotel brands and international hotel brands headquartered in the United States have increasingly evolved away from being hotel operating companies to being brand management and franchise administration organizations. This trend has allowed for the accelerated growth and development of many major hotel brands, and the increasing growth of franchised hotels. There are numerous strategic implications related to this trend. This study seeks to analyze some of these strategic implications by evaluating longitudinal data regarding the performance of major hotel brands in the marketplace, both in terms of guest satisfaction and revenue indicators. Specifically, the authors test whether guest satisfaction at various U.S. and international brands influences both brand occupancy percentage and average daily room rate 3 years later. In addition, the authors investigate whether the percentage of franchised hotel properties influences both guest satisfaction and occupancy 3 years later. Also, they test whether overall brand size has a positive or detrimental effect on future hotel occupancy. Finally, whether the change in guest satisfaction for hotel brands effects the change in average daily rate during the same 3-year period is tested.
VoxSegNet: Volumetric CNNs for Semantic Part Segmentation of 3D Shapes

Volumetric representation has been widely used for 3D deep learning in shape analysis due to its generalization ability and regular data format. However, for fine-grained tasks like part segmentation, volumetric data has not been widely adopted compared to other representations. Aiming at delivering an effective volumetric method for 3D shape part segmentation, this paper proposes a novel volumetric convolutional neural network. Our method can extract discriminative features encoding detailed information from voxelized 3D data under limited resolution. To this purpose, a spatial dense extraction (SDE) module is designed to preserve spatial resolution during feature extraction procedure, alleviating the loss of details caused by sub-sampling operations such as max pooling. An attention feature aggregation (AFA) module is also introduced to adaptively select informative features from different abstraction levels, leading to segmentation with both semantic consistency and high accuracy of details. Experimental results demonstrate that promising results can be achieved by using volumetric data, with part segmentation accuracy comparable or superior to state-of-the-art non-volumetric methods.
Big Data Application in Biomedical Research and Health Care: A Literature Review

Big data technologies are increasingly used for biomedical and health-care informatics research. Large amounts of biological and clinical data have been generated and collected at an unprecedented speed and scale. For example, the new generation of sequencing technologies enables the processing of billions of DNA sequence data per day, and the application of electronic health records (EHRs) is documenting large amounts of patient data. The cost of acquiring and analyzing biomedical data is expected to decrease dramatically with the help of technology upgrades, such as the emergence of new sequencing machines, the development of novel hardware and software for parallel computing, and the extensive expansion of EHRs. Big data applications present new opportunities to discover new knowledge and create novel methods to improve the quality of health care. The application of big data in health care is a fast-growing field, with many new discoveries and methodologies published in the last five years. In this paper, we review and discuss big data application in four major biomedical subdisciplines: (1) bioinformatics, (2) clinical informatics, (3) imaging informatics, and (4) public health informatics. Specifically, in bioinformatics, high-throughput experiments facilitate the research of new genome-wide association studies of diseases, and with clinical informatics, the clinical field benefits from the vast amount of collected patient data for making intelligent decisions. Imaging informatics is now more rapidly integrated with cloud platforms to share medical image data and workflows, and public health informatics leverages big data techniques for predicting and monitoring infectious disease outbreaks, such as Ebola. In this paper, we review the recent progress and breakthroughs of big data applications in these health-care domains and summarize the challenges, gaps, and opportunities to improve and advance big data applications in health care.
Understanding the Impact of Subclinical Ketosis

Dairy cattle experience a remarkable shift in metabolism after calving, after which milk production typically increases so rapidly that feed intake alone cannot meet energy requirements (Bauman and Currie, 1980; Baird, 1982). Cows with a poor adaptive response to negative energy balance may develop hyperketonemia (ketosis) in early lactation. Cows that develop ketosis in early lactation lose milk yield and are at higher risk for other postpartum diseases and early removal from the herd.
Text Classification with Heterogeneous Information Network Kernels

Text classification is an important problem with many applications. Traditional approaches represent text as a bagof-words and build classifiers based on this representation. Rather than words, entity phrases, the relations between the entities, as well as the types of the entities and relations carry much more information to represent the texts. This paper presents a novel text as network classification framework, which introduces 1) a structured and typed heterogeneous information networks (HINs) representation of texts, and 2) a meta-path based approach to link texts. We show that with the new representation and links of texts, the structured and typed information of entities and relations can be incorporated into kernels. Particularly, we develop both simple linear kernel and indefinite kernel based on metapaths in the HIN representation of texts, where we call them HIN-kernels. Using Freebase, a well-known world knowledge base, to construct HIN for texts, our experiments on two benchmark datasets show that the indefinite HIN-kernel based on weighted meta-paths outperforms the state-of-theart methods and other HIN-kernels.
Big data and extreme-scale computing

Over the past four years, the Big Data and Exascale Computing (BDEC) project organized a series of five international workshops that aimed to explore the ways in which the new forms of data-centric discovery introduced by the ongoing revolution in high-end data analysis (HDA) might be integrated with the established, simulation-centric paradigm of the high-performance computing (HPC) community. Based on those meetings, we argue that the rapid proliferation of digital data generators, the unprecedented growth in the volume and diversity of the data they generate, and the intense evolution of the methods for analyzing and using that data are radically reshaping the landscape of scientific computing. The most critical problems involve the logistics of wide-area, multistage workflows that will move back and forth across the computing continuum, between the multitude of distributed sensors, instruments and other devices at the networks edge, and the centralized resources of commercial clouds and HPC centers. We suggest that the prospects for the future integration of technological infrastructures and research ecosystems need to be considered at three different levels. First, we discuss the convergence of research applications and workflows that establish a research paradigm that combines both HPC and HDA, where ongoing progress is already motivating efforts at the other two levels. Second, we offer an account of some of the problems involved with creating a converged infrastructure for peripheral environments, that is, a shared infrastructure that can be deployed throughout the network in a scalable manner to meet the highly diverse requirements for processing, communication, and buffering/storage of massive data workflows of many different scientific domains. Third, we focus on some opportunities for software ecosystem convergence in big, logically centralized facilities that execute large-scale simulations and models and/or perform large-scale data analytics. We close by offering some conclusions and recommendations for future investment and policy review.
Emoticons and social interaction on the Internet: the importance of social context

The present study examines the influence of social context on the use of emoticons in Internet communication. Secondary school students (N = 158) responded to short internet chats. Social context (task-oriented vs. socio-emotional) and valence of the context (positive vs. negative) were manipulated in these chats. Participants were permitted to respond with text, emoticon or a combination of both. Results showed that participants used more emoticons in socio-emotional than in task-oriented social contexts. Furthermore, students used more positive emoticons in positive contexts and more negative emoticons in negative contexts. An interaction was found between valence and kind of context; in negative, task-oriented contexts subjects used the least emoticons. Results are related to research about the expression of emotions in face-to-face interaction.
A Survey on Hidden Markov Model for Credit Card Fraud Detection

Credit card frauds are increasing day by day regardless of the various techniques developed for its detection. Fraudsters are so expert that they engender new ways for committing fraudulent transactions each day which demands constant innovation for its detection techniques as well. Many techniques based on Artificial Intelligence, Data mining, Fuzzy logic, Machine learning, Sequence Alignment, decision tree, neural network, logistic regression, naïve Bayesian, Bayesian network, metalearning, Genetic Programming etc., has evolved in detecting various credit card fraudulent transactions. A steady indulgent on all these approaches will positively lead to an efficient credit card fraud detection system. This paper presents a survey of various techniques used in credit card fraud detection mechanisms and Hidden Markov Model (HMM) in detail. HMM categorizes card holder’s profile as low, medium and high spending based on their spending behavior in terms of amount. A set of probabilities for amount of transaction is being assigned to each cardholder. Amount of each incoming transaction is then matched with card owner’s category, if it justifies a predefined threshold value then the transaction is decided to be legitimate else declared as fraudulent. Index Terms — Credit card, fraud detection, Hidden Markov Model, online shopping
MDMA enhances emotional empathy and prosocial behavior.

3,4-Methylenedioxymethamphetamine (MDMA, 'ecstasy') releases serotonin and norepinephrine. MDMA is reported to produce empathogenic and prosocial feelings. It is unknown whether MDMA in fact alters empathic concern and prosocial behavior. We investigated the acute effects of MDMA using the Multifaceted Empathy Test (MET), dynamic Face Emotion Recognition Task (FERT) and Social Value Orientation (SVO) test. We also assessed effects of MDMA on plasma levels of hormones involved in social behavior using a placebo-controlled, double-blind, random-order, cross-over design in 32 healthy volunteers (16 women). MDMA enhanced explicit and implicit emotional empathy in the MET and increased prosocial behavior in the SVO test in men. MDMA did not alter cognitive empathy in the MET but impaired the identification of negative emotions, including fearful, angry and sad faces, in the FERT, particularly in women. MDMA increased plasma levels of cortisol and prolactin, which are markers of serotonergic and noradrenergic activity, and of oxytocin, which has been associated with prosocial behavior. In summary, MDMA sex-specifically altered the recognition of emotions, emotional empathy and prosociality. These effects likely enhance sociability when MDMA is used recreationally and may be useful when MDMA is administered in conjunction with psychotherapy in patients with social dysfunction or post-traumatic stress disorder.
Bayesian optimization and attribute adjustment

Automatic design via Bayesian optimization holds great promise given the constant increase of available data across domains. However, it faces difficulties from high-dimensional, potentially discrete, search spaces. We propose to probabilistically embed inputs into a lower dimensional, continuous latent space, where we perform gradient-based optimization guided by a Gaussian process. Building on variational autoncoders, we use both labeled and unlabeled data to guide the encoding and increase its accuracy. In addition, we propose an adversarial extension to render the latent representation invariant with respect to specific design attributes, which allows us to transfer these attributes across structures. We apply the framework both to a functional-protein dataset and to perform optimization of drag coefficients directly over high-dimensional shapes without incorporating domain knowledge or handcrafted features.
Not all M&As are alike--and that matters.

Despite all that's been written about mergers and acquisitions, even the experts know surprisingly little about them. The author recently headed up a year-long study sponsored by Harvard Business School on the subject of M&A activity. In-depth findings will emerge over the next few years, but the research has already revealed some interesting results. Most intriguing is the notion that, although academics, consultants, and businesspeople lump M&As together, they represent very different strategic activities. Acquisitions occur for the following reasons: to deal with overcapacity through consolidation in mature industries; to roll up competitors in geographically fragmented industries; to extend into new products and markets; as a substitute for R&D; and to exploit eroding industry boundaries by inventing an industry. The different strategic intents present distinct integration challenges. For instance, if you acquire a company because your industry has excess capacity, you have to determine which plants to shut down and which people to let go. If, on the other hand, you buy a company because it has developed an important technology, your challenge is to keep the acquisition's best engineers from jumping ship. These scenarios require the acquiring company to engage in nearly opposite managerial behaviors. The author explores each type of M&A--its strategic intent and the integration challenges created by that intent. He underscores the importance of the acquiring company's assessment of the acquired group's culture. Depending on the type of M&A, approaches to the culture in place must vary, as will the level to which culture interferes with integration. He draws from the experiences of such companies as Cisco, Viacom, and BancOne to exemplify the different kinds of M&As.
Data mining Techniques for Predicting Crop Productivity – A review article

In this paper an attempt has been made to review the research studies on application of data mining techniques in the field of agriculture. Some of the techniques, such asID3 algorithms, the k-means, the k nearest neighbor, artificial neural networks and support vector machines applied in the field of agriculture were presented. Data mining in application in agriculture is a relatively new approach for forecasting / predicting of agricultural crop/animal management. This article explores the applications of data mining techniques in the field of agriculture and allied sciences. I.  Data mining is 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. Data mining tools predict future trends and behaviours, allowing businesses to make proactive, knowledge-driven decisions. The automated, prospective analysis offered by data mining move beyond the analysis of past events provided by retrospective tools typical of decision support systems. Agriculture and allied activities constitute the single largest component of India's gross domestic product, contributing nearly 25% of the total and nearly 60% of Indian population depends on this profession. Due to vagaries of climate factors the agricultural productivities in India are continuously decreasing over a decade. The reasons for this were studied mostly using regression analysis. In this paper an attempt has been made to compile the research findings of different researchers who used data.
A cost-effective recommender system for taxi drivers

The GPS technology and new forms of urban geography have changed the paradigm for mobile services. As such, the abundant availability of GPS traces has enabled new ways of doing taxi business. Indeed, recent efforts have been made on developing mobile recommender systems for taxi drivers using Taxi GPS traces. These systems can recommend a sequence of pick-up points for the purpose of maximizing the probability of identifying a customer with the shortest driving distance. However, in the real world, the income of taxi drivers is strongly correlated with the effective driving hours. In other words, it is more critical for taxi drivers to know the actual driving routes to minimize the driving time before finding a customer. To this end, in this paper, we propose to develop a cost-effective recommender system for taxi drivers. The design goal is to maximize their profits when following the recommended routes for finding passengers. Specifically, we first design a net profit objective function for evaluating the potential profits of the driving routes. Then, we develop a graph representation of road networks by mining the historical taxi GPS traces and provide a Brute-Force strategy to generate optimal driving route for recommendation. However, a critical challenge along this line is the high computational cost of the graph based approach. Therefore, we develop a novel recursion strategy based on the special form of the net profit function for searching optimal candidate routes efficiently. Particularly, instead of recommending a sequence of pick-up points and letting the driver decide how to get to those points, our recommender system is capable of providing an entire driving route, and the drivers are able to find a customer for the largest potential profit by following the recommendations. This makes our recommender system more practical and profitable than other existing recommender systems. Finally, we carry out extensive experiments on a real-world data set collected from the San Francisco Bay area and the experimental results clearly validate the effectiveness of the proposed recommender system.
Consumer attitudes toward and intentions to accept mobile advertising

Received March 25, 2015 Received in revised format June 1 2015 Accepted June 27 2015 Available online July 1 2015 The objective of this study was to examine the drivers of consumers’ attitudes towards mobile advertisement. It also sought the relationship between consumers’ attitudes towards mobile advertisement and their willingness to accept mobile advertising. Confirmatory factor analysis was used to assess the measurement model while structural equation was conducted to assess the goodness-fit of the overall model. The findings indicate that entertainment, credibility and personalization had positive effects on consumers’ attitudes toward mobile advertising. Furthermore, the results show that, consumers’ attitude determines their willingness to accept mobile advertising.
Bidirectionally aligned sparse representation for single image super-resolution

It has been demonstrated that the sparse representation based framework is one of the most popular and promising ways to handle the single image super-resolution (SISR) issue. However, due to the complexity of image degradation and inevitable existence of noise, the coding coefficients produced by imposing sparse prior only are not precise enough for faithful reconstructions. In order to overcome it, we present an improved SISR reconstruction method based on the proposed bidirectionally aligned sparse representation (BASR) model. In our model, the bidirectional similarities are first modeled and constructed to form a complementary pair of regularization terms. The raw sparse coefficients are additionally aligned to this pair of standards to restrain sparse coding noise and therefore result in better recoveries. On the basis of fast iterative shrinkage-thresholding algorithm, a well-designed mathematic implementation is introduced for solving the proposed BASR model efficiently. Thorough experimental results indicate that the proposed method performs effectively and efficiently, and outperforms many recently published baselines in terms of both objective evaluation and visual fidelity.