nils-herrmann/clean_scirepeval huggingface dataset:
The Impact of Brand Awareness on Repurchase Intention of Customers With Trilogy of Emotions Approach ( Case Study for Cell Phones )

The purpose of this study is to evaluate the impact of brand awareness to repurchase intention of customers with trilogy of emotions approach. The study population consisted if all the people in Yazd. As the research sample, 384 people who went to cell phone shopping centers in Yazd province responded to the questionnaire. Cronbach's alpha was used to determine the reliability of the questionnaire, and its values was 0.87. To examine the effects of brand awareness on purchase intention, structural equation modeling and AMOUS and SPSS softwares were used. The results of this study show that consumers cognition does not affect the purchase intention, but the customers’ conation and affection affect the re-purchase intention. In addition, brand awareness affects emotions (cognition, affection, and conation) and consumer purchase intention.
Vehicular Cloud Computing : Trends and Challenges

Recently, Vehicular Ad Hoc Networks (VANET) have attracted the attention of research communities, leading car manufacturers, and governments due to their potential applications and specific characteristics. Their research outcome was started with awareness between vehicles for collision avoidance and Internet access and then expanded to vehicular multimedia communications. Moreover, vehicles’ high computation, communication, and storage resources set a ground for vehicular networks to deploy these applications in the near future. Nevertheless, on-board resources in vehicles are mostly underutilized.  is developed to utilize the VANET resources efficiently and provide subscribers safe infotainment services. In this chapter, the authors perform a survey of state-of-the-art vehicular cloud computing as well as the existing techniques that utilize cloud computing for performance improvements in VANET. The authors then classify the VCC based on the applications, service types, and vehicular cloud organization. They present the detail for each VCC application and formation. Lastly, the authors discuss the open issues and research directions related to VANET cloud computing. Kayhan Zrar Ghafoor Koya University, Iraq Marwan Aziz Mohammed Koya University, Iraq Kamalrulnizam Abu Bakar Universiti Teknologi Malaysia, Malaysia Ali Safa Sadiq Universiti Teknologi Malaysia, Malaysia Jaime Lloret Universidad Politecnica de Valencia, Spain DOI: 10.4018/978-1-4666-4781-7.ch014
Why do some patients keep hurting their back? Evidence of ongoing back muscle dysfunction during remission from recurrent back pain

Approximately thirty-four percent of people who experience acute low back pain (LBP) will have recurrent episodes. It remains unclear why some people experience recurrences and others do not, but one possible cause is a loss of normal control of the back muscles. We investigated whether the control of the short and long fibres of the deep back muscles was different in people with recurrent unilateral LBP from healthy participants. Recurrent unilateral LBP patients, who were symptom free during testing, and a group of healthy volunteers, participated. Intramuscular and surface electrodes recorded the electromyographic activity (EMG) of the short and long fibres of the lumbar multifidus and the shoulder muscle, deltoid, during a postural perturbation associated with a rapid arm movement. EMG onsets of the short and long fibres, relative to that of deltoid, were compared between groups, muscles, and sides. In association with a postural perturbation, short fibre EMG onset occurred later in participants with recurrent unilateral LBP than in healthy participants (p=0.022). The short fibres were active earlier than long fibres on both sides in the healthy participants (p<0.001) and on the non-painful side in the LBP group (p=0.045), but not on the previously painful side in the LBP group. Activity of deep back muscles is different in people with a recurrent unilateral LBP, despite the resolution of symptoms. Because deep back muscle activity is critical for normal spinal control, the current results provide the first evidence of a candidate mechanism for recurrent episodes.
Smart cities in the era of artificial intelligence and internet of things: literature review from 1990 to 2017

The concept of smart city is considered as the new engine for economic and social growths since it is supported by the rapid development of information and communication technologies. However, each technology not only brings its advantages, but also the challenges that cities have to face in order to implement it. So, this paper addresses two research questions : « What are the most important technologies that drive the development of smart cities ?» and « what are the challenges that cities will face when adopting these technologies ? » Relying on a literature review of studies published between 1990 and 2017, the ensuing results show that Artificial Intelligence and Internet of Things represent the most used technologies for smart cities. So, the focus of this paper will be on these two technologies by showing their advantages and their challenges.
Scheduling Fault Tolerant Cloud Applications using Component Ranking

Cloud is an emerging technology where the providers provide various services to Information Technology by adopting the concept of service oriented architecture, distributed, autonomic, and utility computing. In the present competitive world, building a highly dependable cloud application and opting for the optimal fault tolerant technique for cloud components has become crucial. In this paper, a component ranking framework is needed for identifying critical components along with the ranking prediction framework for selecting optimal cloud services. Additionally, Kernel Principal Component Ranking approach is proposed to have better accuracy in selecting the significant values for identifying critical components. Subsequent to the component ranking, an optimal fault-tolerance strategy is also proposed to automatically
Performance Managementand Performance Appraisal in the Public Sector 1

The purpose of this paper is to review and discuss public performance management in general and performance appraisal and pay for performance specifically. Performance is a topic that is a popular catch-cry and performance management has become a new organizational ideology. Under the global economic crisis, almost every public and private organization is struggling with a performance challenge, one way or another. Various aspects of performance management have been extensively discussed in the literature. Many researchers and experts assert that sets of guidelines for design of performance management systems would lead to high performance (Kaplan and Norton, 1996, 2006). A long time ago, the traditional performance measurement was developed from cost and management accounting and such purely financial perspective of performance measures was perceived to be inappropriate so that multi-dimensional performance management was development in the 1970s (Radnor and McGuire, 2004).
On the Equivalence of Holographic and Complex Embeddings for Link Prediction

We show the equivalence of two stateof-the-art models for link prediction/ knowledge graph completion: Nickel et al’s holographic embeddings and Trouillon et al.’s complex embeddings. We first consider a spectral version of the holographic embeddings, exploiting the frequency domain in the Fourier transform for efficient computation. The analysis of the resulting model reveals that it can be viewed as an instance of the complex embeddings with a certain constraint imposed on the initial vectors upon training. Conversely, any set of complex embeddings can be converted to a set of equivalent holographic embeddings.
Term Proximity Constraints for Pseudo-Relevance Feedback

Pseudo-relevance feedback (PRF) refers to a query expansion strategy based on top-retrieved documents, which has been shown to be highly effective in many retrieval models. Previous work has introduced a set of constraints (axioms) that should be satisfied by any PRF model. In this paper, we propose three additional constraints based on the proximity of feedback terms to the query terms in the feedback documents. As a case study, we consider the log-logistic model, a state-of-the-art PRF model that has been proven to be a successful method in satisfying the existing PRF constraints, and show that it does not satisfy the proposed constraints. We further modify the log-logistic model based on the proposed proximity-based constraints. Experiments on four TREC collections demonstrate the effectiveness of the proposed constraints. Our modification the log-logistic model leads to significant and substantial (up to 15%) improvements. Furthermore, we show that the proposed proximity-based function outperforms the well-known Gaussian kernel which does not satisfy all the proposed constraints.
Blockchain world - Do you need a blockchain? This chart will tell you if the technology can solve your problem

According to a study released this July by Juniper Research, more than half the world's largest companies are now researching blockchain technologies with the goal of integrating them into their products. Projects are already under way that will disrupt the management of health care records, property titles, supply chains, and even our online identities. But before we remount the entire digital ecosystem on blockchain technology, it would be wise to take stock of what makes the approach unique and what costs are associated with it. Blockchain technology is, in essence, a novel way to manage data. As such, it competes with the data-management systems we already have. Relational databases, which orient information in updatable tables of columns and rows, are the technical foundation of many services we use today. Decades of market exposure and well-funded research by companies like Oracle Corp. have expanded the functionality and hardened the security of relational databases. However, they suffer from one major constraint: They put the task of storing and updating entries in the hands of one or a few entities, whom you have to trust won't mess with the data or get hacked.
An Empirical Exploration of Recurrent Network Architectures

The Recurrent Neural Network (RNN) is an extremely powerful sequence model that is often difficult to train. The Long Short-Term Memory (LSTM) is a specific RNN architecture whose design makes it much easier to train. While wildly successful in practice, the LSTM’s architecture appears to be ad-hoc so it is not clear if it is optimal, and the significance of its individual components is unclear. In this work, we aim to determine whether the LSTM architecture is optimal or whether much better architectures exist. We conducted a thorough architecture search where we evaluated over ten thousand different RNN architectures, and identified an architecture that outperforms both the LSTM and the recently-introduced Gated Recurrent Unit (GRU) on some but not all tasks. We found that adding a bias of 1 to the LSTM’s forget gate closes the gap between the LSTM and the GRU.
FitLoc: Fine-Grained and Low-Cost Device-Free Localization for Multiple Targets Over Various Areas

Many emerging applications driven the fast development of the device-free localization DfL technique, which does not require the target to carry any wireless devices. Most current DfL approaches have two main drawbacks in practical applications. First, as the pre-calibrated received signal strength RSS in each location i.e., radio-map of a specific area cannot be directly applied to the new areas, the manual calibration for different areas will lead to a high human effort cost. Second, a large number of RSS are needed to accurately localize the targets, thus causes a high communication cost and the areas variety will further exacerbate this problem.  Extensive experimental results illustrate the effectiveness and robustness of FitLoc.
Beyond Textures: Learning from Multi-domain Artistic Images for Arbitrary Style Transfer

We propose a fast feed-forward network for arbitrary style transfer, which can generate stylized image for previously unseen content and style image pairs. Besides the traditional content and style representation based on deep features and statistics for textures, we use adversarial networks to regularize the generation of stylized images. Our adversarial network learns the intrinsic property of image styles from large-scale multi-domain artistic images. The adversarial training is challenging because both the input and output of our generator are diverse multi-domain images. We use a conditional generator that stylized content by shifting the statistics of deep features, and a conditional discriminator based on the coarse category of styles. Moreover, we propose a mask module to spatially decide the stylization level and stabilize adversarial training by avoiding mode collapse. As a side effect, our trained discriminator can be applied to rank and select representative stylized images. We qualitatively and quantitatively evaluate the proposed method, and compare with recent style transfer methods.
DOPING: Generative Data Augmentation for Unsupervised Anomaly Detection with GAN

Recently, the introduction of the generative adversarial network (GAN) and its variants has enabled the generation of realistic synthetic samples, which has been used for enlarging training sets. Previous work primarily focused on data augmentation for semi-supervised and supervised tasks. In this paper, we instead focus on unsupervised anomaly detection and propose a novel generative data augmentation framework optimized for this task. In particular, we propose to oversample infrequent normal samples - normal samples that occur with small probability, e.g., rare normal events. We show that these samples are responsible for false positives in anomaly detection. However, oversampling of infrequent normal samples is challenging for real-world high-dimensional data with multimodal distributions. To address this challenge, we propose to use a GAN variant known as the adversarial autoencoder (AAE) to transform the high-dimensional multimodal data distributions into low-dimensional unimodal latent distributions with well-defined tail probability. Then, we systematically oversample at the 'edge' of the latent distributions to increase the density of infrequent normal samples. We show that our oversampling pipeline is a unified one: it is generally applicable to datasets with different complex data distributions. To the best of our knowledge, our method is the first data augmentation technique focused on improving performance in unsupervised anomaly detection. We validate our method by demonstrating consistent improvements across several real-world datasets.
Evolution, Detection and Analysis of Malware for Smart Devices

Smart devices equipped with powerful sensing, computing and networking capabilities have proliferated lately, ranging from popular smartphones and tablets to Internet appliances, smart TVs, and others that will soon appear (e.g., watches, glasses, and clothes). One key feature of such devices is their ability to incorporate third-party apps from a variety of markets. This poses strong security and privacy issues to users and infrastructure operators, particularly through software of malicious (or dubious) nature that can easily get access to the services provided by the device and collect sensory data and personal information. Malware in current smart devices -mostly smartphones and tablets- have rocketed in the last few years, in some cases supported by sophisticated techniques purposely designed to overcome security architectures currently in use by such devices. Even though important advances have been made on malware detection in traditional personal computers during the last decades, adopting and adapting those techniques to smart devices is a challenging problem. For example, power consumption is one major constraint that makes unaffordable to run traditional detection engines on the device, while externalized (i.e., cloud-based) techniques rise many privacy concerns. This article examines the problem of malware in smart devices and recent progress made in detection techniques. We first present a detailed analysis on how malware has evolved over the last years for the most popular platforms. We identify exhibited behaviors, pursued goals, infection and distribution strategies, etc. and provide numerous examples through case studies of the most relevant specimens. We next survey, classify and discuss efforts made on detecting both malware and other suspicious software (grayware), concentrating on the 20 most relevant techniques proposed between 2010 and 2013. Based on the conclusions extracted from this study, we finally provide constructive discussion on open research problems and areas where we believe that more work is needed.
Antiprotozoal activities determined in vitro and in vivo of certain plant extracts against Histomonas meleagridis, Tetratrichomonas gallinarum and Blastocystis sp.

A total of 43 plant substances provided as raw material and different kinds of extracts (aqueous, ethanol, and heptane) from 18 different organic wastes obtained from the food/feed industry were investigated for their in vitro activities against clonal cultures of Histomonas meleagridis, Tetratrichomonas gallinarum, and Blastocystis sp. Ethanolic extracts of thyme, saw palmetto, grape seed, and pumpkin fruit proved to be most efficacious. Thus, these extracts were further tested in vivo in turkeys experimentally infected with H. meleagridis by administrating the substances to the birds through the drinking water. Even though a delayed mortality was noticed in some birds medicated with the extracts of thyme, grape seed, and pumpkin fruit, all birds died or had to be euthanized the latest within 5 weeks post infection—with the exception of one bird which was probably never infected with histomonads—due to a severe typhlohepatitis indicative for histomonosis. In addition, none of the substances were able to prevent the spreading of H. meleagridis from infected to in-contact birds. Thus, these studies clearly demonstrate that in vitro studies are of limited value to assess the efficacy of plant substances against histomonosis.
A Survey of Distant Supervision Methods using PGMs

Extraction refers to the task of populating a database with tuples of the form r(e1, e2), where r is a relation and e1, e2 are entities. Distant supervision is one such technique which tries to automatically generate training examples based on an existing KB such as Freebase. This paper is a survey of some of the techniques in distant supervision which primarily rely on Probabilistic Graphical Models (PGMs).
Seven major challenges for e-learning in developing countries : Case study eBIT , Sri Lanka

By using an extensive framework for e-learning enablers and disablers (including 37 factors) this paper sets out to identify which of these challenges are most salient for an e-learning course in Sri Lanka. The study includes 1887 informants and data has been collected from year 2004 to 2007, covering opinions of students and staff. A quantitative approach is taken to identify the most important factors followed by a qualitative analysis to explain why and how they are important. The study identified seven major challenges in the following areas: Student support, Flexibility, Teaching and Learning Activities, Access, Academic confidence, Localization and Attitudes. In this paper these challenges will be discussed and solutions suggested.
Incremental Subgraph Feature Selection for Graph Classification

Graph classification is an important tool for analyzing data with structure dependency, where subgraphs are often used as features for learning. In reality, the dimension of the subgraphs crucially depends on the threshold setting of the frequency support parameter, and the number may become extremely large. As a result, subgraphs may be incrementally discovered to form a feature stream and require the underlying graph classifier to effectively discover representative subgraph features from the subgraph feature stream. In this paper, we propose a primal-dual incremental subgraph feature selection algorithm (ISF) based on a max-margin graph classifier. The ISF algorithm constructs a sequence of solutions that are both primal and dual feasible. Each primal-dual pair shrinks the dual gap and renders a better solution for the optimal subgraph feature set. To avoid bias of ISF algorithm on short-pattern subgraph features, we present a new incremental subgraph join feature selection algorithm (ISJF) by forcing graph classifiers to join short-pattern subgraphs and generate long-pattern subgraph features. We evaluate the performance of the proposed models on both synthetic networks and real-world social network data sets. Experimental results demonstrate the effectiveness of the proposed methods.
The triple negative paradox: primary tumor chemosensitivity of breast cancer subtypes.

Gene expression analysis identifies several breast cancer subtypes. We examined the relationship of neoadjuvant chemotherapy response to outcome among these breast cancer subtypes.



We used immunohistochemical profiles [human epidermal growth factor receptor 2-positive (HER2+)/hormone receptor-negative for HER2+/estrogen receptor-negative (ER-), hormone receptor and HER2- for basal-like, hormone receptor-positive for luminal] to subtype a prospectively maintained data set of patients with breast cancer treated with neoadjuvant anthracycline-based (doxorubicin plus cyclophosphamide, AC) chemotherapy. We analyzed each subtype for clinical and pathologic response to neoadjuvant chemotherapy and examined the relationship of response to distant disease-free survival and overall survival.



Of the 107 patients tested, 34 (32%) were basal-like, 11 (10%) were HER2+/ER-, and 62 (58%) were luminal. After neoadjuvant AC, 75% received subsequent chemotherapy and all received endocrine therapy if hormone receptor-positive. The chemotherapy regimen and pretreatment stage did not differ by subtype. Clinical response to AC was higher among the HER2+/ER- (70%) and basal-like (85%) than the luminal subtypes (47%; P < 0.0001). Pathologic complete response occurred in 36% of HER2+/ER-, 27% of basal-like, and 7% of luminal subtypes (P = 0.01). Despite initial chemosensitivity, patients with the basal-like and HER2+/ER- subtypes had worse distant disease-free survival (P = 0.04) and overall survival (P = 0.02) than those with the luminal subtypes. Regardless of subtype, only 2 of 17 patients with pathologic complete response relapsed. The worse outcome among basal-like and HER+/ER- subtypes was due to higher relapse among those with residual disease (P = 0.003).



Basal-like and HER2+/ER- subtypes are more sensitive to anthracycline-based neoadjuvant chemotherapy than luminal breast cancers. Patients that had pathologic complete response to chemotherapy had a good prognosis regardless of subtype. The poorer prognosis of basal-like and HER2+/ER- breast cancers could be explained by a higher likelihood of relapse in those patients in whom pathologic complete response was not achieved.
A survey of qualitative spatial representations

Representation and reasoning with qualitative spatial relations is an important problem in artificial intelligence and has wide applications in the fields of geographic information system, computer vision, autonomous robot navigation, natural language understanding, and spatial databases etc. The reasons for this interest in using qualitative spatial relations include cognitive comprehensibility, efficiency and computational facility. This paper summarizes progress in qualitative spatial representation by describing key calculi representing different types of spatial relationships. The paper concludes with a discussion of current research and glimpse of future work.