nils-herrmann/clean_scirepeval huggingface dataset:
Social behavior and brand devotion among iPhone innovators

Convergence of communication technologies and innovative product features are expanding the markets for technological products and services. Prior literature on technology acceptance and use has focused on utilitarian belief factors as predictors of rational adoption decisions and subsequent user behavior. This presupposes that consumers’ intentions to use technology are based on functional or utilitarian needs. Using netnographic evidence on iPhone usage, this study suggests that innovative consumers adopt and use new technology for not just utilitarian but also for experiential outcomes. The study presents an interpretive analysis of the consumption behavior of very early iPhone users. Apple introduced iPhone as a revolutionary mobile handset offering integrated features and converged services—a handheld computercum-phone with a touch-screen web browser, a music player, an organizer, a note-taker, and a camera. This revolutionary product opened up new possibilities to meld functional tasks, hedonism, and social signaling. The study suggests that even utilitarian users have hedonic and social factors present in their consumption patterns.
The Security and Performance of the Galois/Counter Mode (GCM) of Operation

The recently introduced Galois/Counter Mode (GCM) of operation for block ciphers provides both encryption and message authentication, using universal hashing based on multiplication in a binary finite field. We analyze its security and performance, and show that it is the most efficient mode of operation for high speed packet networks, by using a realistic model of a network crypto module and empirical data from studies of Internet traffic in conjunction with software experiments and hardware designs. GCM has several useful features: it can accept IVs of arbitrary length, can act as a stand-alone message authentication code (MAC), and can be used as an incremental MAC. We show that GCM is secure in the standard model of concrete security, even when these features are used. We also consider several of its important system-security aspects.
Evaluation of Computerized Physician Order Entry System—A Satisfaction Survey in Taiwan

In the rapidly developing world of information technology, computers have been used in various settings for clinical medicine application. Studies have focused on computerized physician order entry (CPOE) system interface design and functional development to achieve a successful technology adoption process. Therefore, the purpose of this study was to evaluate physician satisfaction with the CPOE system. This survey included user attitude toward interface design, operation functions/usage effectiveness, interface usability, and user satisfaction. We used questionnaires for data collection from June to August 2008, and 225 valid questionnaires were returned with a response rate of 84.5 %. Canonical correlation was applied to explore the relationship of personal attributes and usability with user satisfaction. The results of the data analysis revealed that certain demographic groups showed higher acceptance and satisfaction levels, especially residents, those with less pressure when using computers or those with less experience with the CPOE systems. Additionally, computer use pressure and usability were the best predictors of user satisfaction. Based on the study results, it is suggested that future CPOE development should focus on interface design and content links, as well as providing educational training programs for the new users; since a learning curve period should be considered as an indespensible factor for CPOE adoption.
Decision Tree Based Algorithm for Intrusion Detection

Panjab University, Chandigarh, India Email: kajalrai@pu.ac.in M. Syamala Devi Professor, Department of Computer Science and Applications, Panjab University, Chandigarh, India Email: syamala@pu.ac.in Ajay Guleria System Manager, Computer Center, Panjab University, Chandigarh, India Email: ag@pu.ac.in -----------------------------------------------------ABSTRACT-----------------------------------------------------An Intrusion Detection System (IDS) is a defense measure that supervises activities of the computer network and reports the malicious activities to the network administrator. Intruders do many attempts to gain access to the network and try to harm the organization’s data. Thus the security is the most important aspect for any type of organization. Due to these reasons, intrusion detection has been an important research issue. An IDS can be broadly classified as Signature based IDS and Anomaly based IDS. In our proposed work, the decision tree algorithm is developed based on C4.5 decision tree approach. Feature selection and split value are important issues for constructing a decision tree. In this paper, the algorithm is designed to address these two issues. The most relevant features are selected using information gain and the split value is selected in such a way that makes the classifier unbiased towards most frequent values. Experimentation is performed on NSL-KDD (Network Security Laboratory Knowledge Discovery and Data Mining) dataset based on number of features. The time taken by the classifier to construct the model and the accuracy achieved is analyzed. It is concluded that the proposed Decision Tree Split (DTS) algorithm can be used for signature based intrusion detection.
Case Study: Extreme Programming in a University Environment

Extreme Programming (XP) is a new and controversial software process for small teams. A practical training course at the university of Karlsruhe led to the following observations about the key practices of XP. First, it is unclear how to reap the potential benefits of pair programming, although pair programming produces high quality code. Second, designing in small increments appears problematic but ensures rapid feedback about the code. Third, while automated testing is helpful, writing test cases before coding is a challenge. And last, it is difficult to implement XP without coaching. This paper also provides some guidelines for those starting out with XP.
In-place Activated BatchNorm for Memory-Optimized Training of DNNs

In this work we present In-Place Activated Batch Normalization (INPLACE-ABN) - a novel approach to drastically reduce the training memory footprint of modern deep neural networks in a computationally efficient way. Our solution substitutes the conventionally used succession of BatchNorm + Activation layers with a single plugin layer, hence avoiding invasive framework surgery while providing straightforward applicability for existing deep learning frameworks. We obtain memory savings of up to 50% by dropping intermediate results and by recovering required information during the backward pass through the inversion of stored forward results, with only minor increase (0.8-2%) in computation time. Also, we demonstrate how frequently used checkpointing approaches can be made computationally as efficient as INPLACE-ABN. In our experiments on image classification, we demonstrate on-par results on ImageNet-1k with state-of-the-art approaches. On the memory-demanding task of semantic segmentation, we report competitive results for COCO-Stuff and set new state-of-the-art results for Cityscapes and Mapillary Vistas. Code can be found at https://github.com/mapillary/inplace_abn.
Learning analytics beyond the LMS: the connected learning analytics toolkit

We present a Connected Learning Analytics (CLA) toolkit, which enables data to be extracted from social media and imported into a Learning Record Store (LRS), as defined by the new xAPI standard. A number of implementation issues are discussed, and a mapping that will enable the consistent storage and then analysis of xAPI verb/object/activity statements across different social media and online environments is introduced. A set of example learning activities are proposed, each facilitated by the Learning Analytics beyond the LMS that the toolkit enables.
Statistical Texture Measures Computed from Gray Level Coocurrence Matrices

The purpose of the present text is to present the theory and techniques behind the Gray Level Coocurrence Matrix (GLCM) method, and the stateof-the-art of the field, as applied to two dimensional images. It does not present a survey of practical results. 1 Gray Level Coocurrence Matrices In statistical texture analysis, texture features are computed from the statistical distribution of observed combinations of intensities at specified positions relative to each other in the image. According to the number of intensity points (pixels) in each combination, statistics are classified into first-order, second-order and higher-order statistics. The Gray Level Coocurrence Matrix (GLCM) method is a way of extracting second order statistical texture features. The approach has been used in a number of applications, e.g. [5],[6],[14],[5],[7],[12],[2],[8],[10],[1]. A GLCM is a matrix where the number of rows and colums is equal to the number of gray levels, G, in the image. The matrix element P (i, j | ∆x, ∆y) is the relative frequency with which two pixels, separated by a pixel distance (∆x, ∆y), occur within a given neighborhood, one with intensity i and the other with intensity j. One may also say that the matrix element P (i, j | d, θ) contains the second order 1 Albregtsen : Texture Measures Computed from GLCM-Matrices 2 statistical probability values for changes between gray levels i and j at a particular displacement distance d and at a particular angle (θ). Given an M ×N neighborhood of an input image containing G gray levels from 0 to G − 1, let f(m, n) be the intensity at sample m, line n of the neighborhood. Then P (i, j | ∆x, ∆y) = WQ(i, j | ∆x, ∆y) (1) where W = 1 (M − ∆x)(N − ∆y) Q(i, j | ∆x, ∆y) = N−∆y ∑
Customer Loyalty and Customer Loyalty Programs

Customer loyalty presents a paradox. Many see it as primarily an attitude-based phenomenon that can be influenced significantly by Customer Relationship Management initiatives such as the increasingly popular loyalty and affinity programs. However, empirical research shows that loyalty in competitive repeat-purchase markets is shaped more by the passive acceptance of brands than by strongly-held attitudes about them. From this perspective, the demandenhancing potential of loyalty programs is more limited than might be hoped. We review three different perspectives on loyalty, and relate these to a framework for understanding customer loyalty that encompasses Customer Brand Commitment, Customer Brand Acceptance and Customer Brand Buying. This framework is used to analyze the demand-side potential of loyalty programs. We discuss where these programs might work and where they are unlikely to succeed on any large scale. A checklist for marketers is provided.
Hybrid text simplification using synchronous dependency grammars with hand-written and automatically harvested rules

We present an approach to text simplification based on synchronous dependency grammars. The higher level of abstraction afforded by dependency representations allows for a linguistically sound treatment of complex constructs requiring reordering and morphological change, such as conversion of passive voice to active. We present a synchronous grammar formalism in which it is easy to write rules by hand and also acquire them automatically from dependency parses of aligned English and Simple English sentences. The grammar formalism is optimised for monolingual translation in that it reuses ordering information from the source sentence where appropriate. We demonstrate the superiority of our approach over a leading contemporary system based on quasi-synchronous tree substitution grammars, both in terms of expressivity and performance.
Synthesis of Transportation Applications of Mobile LIDAR

A thorough review of available literature was conducted to inform of advancements in mobile LIDAR technology, techniques, and current and emerging applications in transportation. The literature review touches briefly on the basics of LIDAR technology followed by a more in depth description of current mobile LIDAR trends, including system components and software. An overview of existing quality control procedures used to verify the accuracy of the collected data is presented. A collection of case studies provides a clear description of the advantages of mobile LIDAR, including an increase in safety and efficiency. The final sections of the review identify current challenges the industry is facing, the guidelines that currently exist, and what else is needed to streamline the adoption of mobile LIDAR by transportation agencies. Unfortunately, many of these guidelines do not cover the specific challenges and concerns of mobile LIDAR use as many have been developed for airborne LIDAR acquisition and processing. From this review, there is a lot of discussion on “what” is being done in practice, but not a lot on “how” and “how well” it is being done. A willingness to share information going forward will be important for the successful use of mobile LIDAR.
Probabilistic models of information retrieval based on measuring the divergence from randomness

We introduce and create a framework for deriving probabilistic models of Information Retrieval. The models are nonparametric models of IR obtained in the language model approach. We derive term-weighting models by measuring the divergence of the actual term distribution from that obtained under a random process. Among the random processes we study the binomial distribution and Bose--Einstein statistics. We define two types of term frequency normalization for tuning term weights in the document--query matching process. The first normalization assumes that documents have the same length and measures the information gain with the observed term once it has been accepted as a good descriptor of the observed document. The second normalization is related to the document length and to other statistics. These two normalization methods are applied to the basic models in succession to obtain weighting formulae. Results show that our framework produces different nonparametric models forming baseline alternatives to the standard tf-idf model.
Attention-based Deep Multiple Instance Learning

Multiple instance learning (MIL) is a variation of supervised learning where a single class label is assigned to a bag of instances. In this paper, we state the MIL problem as learning the Bernoulli distribution of the bag label where the bag label probability is fully parameterized by neural networks. Furthermore, we propose a neural network-based permutation-invariant aggregation operator that corresponds to the attention mechanism. Notably, an application of the proposed attention-based operator provides insight into the contribution of each instance to the bag label. We show empirically that our approach achieves comparable performance to the best MIL methods on benchmark MIL datasets and it outperforms other methods on a MNIST-based MIL dataset and two real-life histopathology datasets without sacrificing interpretability.
A STRUCTURAL EQUATIONS MODELING OF PURCHASING DECISION THROUGH E-COMMERCE

The purpose of the study is to explore the factors influencing customer buying decision through Intern et shopping. Several factors such as information quali ty, firm’s reputation, perceived ease of payment, s ites design, benefit of online shopping, and trust that influence customer decision to purchase from e-comm erce sites were analyzed. Factors such as those mention d above, which are commonly considered influencing purhasing decision through online shopping in other countries were hypothesized to be true in the case of Indonesia. A random sample comprised of 171 Indone sia people who have been buying goods/services through e-commerce sites at least once, were collec ted via online questionnaires. To test the hypothes is, the data were examined using Structural Equations Model ing (SEM) which is basically a combination of Confirmatory Factor Analysis (CFA), and linear Regr ession. The results suggest that information qualit y, perceived ease of payment, benefits of online shopp ing, and trust affect online purchase decision significantly. Close attention need to be placed on these factors to increase online sales. The most significant influence comes from trust. Indonesian people still lack of trust toward online commerce, so it is very important to gain customer trust to increase s al s. E-commerce’s business owners are encouraged t o develop sites that can meet the expectation of pote ntial customer, provides ease of payment system, pr ovide detailed and actual information and responsible for customer personal information and transaction reco rds. This paper outlined the key factors influencing onl ine shopping intention in Indonesia and pioneered t he building of an integrated research framework to und erstand how consumers make purchase decision toward online shopping; a relatively new way of shopping i the country.
Hardware design for Hash functions

Introduction to chapter. Due to its cryptographic and operational key features such as the one-way function property, high speed and a fixed output size independent of input size the hash algorithm is one of the most important cryptographic primitives. A critical drawback of most cryptographic algorithms is the large computational overheads. This is getting more critical since the data amount to process or communicate is dramatically increasing. In many of such cases, a proper use of the hash algorithm effectively reduces the computational overhead. Digital signature algorithm and the message authentication are the most common applications of the hash algorithms. The increasing data size also motivates hardware designers to have a throughput optimal architecture of a given hash algorithm. In this chapter, some popular hash algorithms and their cryptanalysis are briefly introduced, and a design methodology for throughput optimal architectures of MD4-based hash algorithms is described in detail.
Wi lights — A wireless solution to control headlight intensity

A number of factors are considered during the analysis of automobile transportation with respect to increasing safety. One of the vital factors for night-time travel is temporary blindness due to increase in the headlight intensity. While headlight intensity provides better visual acuity, it simultaneously affects oncoming traffic. This problem is encountered when both drivers are using a higher headlight intensity setting. Also, increased speed of the vehicles due to decreased traffic levels at night increases the severity of accidents. In order to reduce accidents due to temporary driver blindness, a wireless sensor network (WSN) based controller could be developed to transmit sensor data in a faster and an efficient way between cars. Low latency allows faster headlight intensity adjustment between the vehicles to drastically reduce the cause of temporary blindness. An attempt has been made to come up with a system which would sense the intensity of the headlight of the oncoming vehicle and depending on the threshold headlight intensity being set in the system it would automatically reduce the intensity of the headlight of the oncoming vehicle using wireless sensor network thus reducing the condition of temporary blindness caused due to excessive exposure to headlights.
Study of Web Crawler and its Different Types

Due to the current size of the Web and its dynamic nature, building an efficient search mechanism is very important. A vast number of web pages are continually being added every day, and information is constantly changing. Search engines are used to extract valuable Information from the internet. Web crawlers are the principal part of search engine, is a computer program or software that browses the World Wide Web in a methodical, automated manner or in an orderly fashion. It is an essential method for collecting data on, and keeping in touch with the rapidly increasing Internet. This Paper briefly reviews the concepts of web crawler, its architecture and its various types.  Crawling techniques, Web Crawler, Search engine, WWW
Folded Corrugated SIW (FCSIW) Slot Antenna for Backlobe Suppression

A novel substrate integrated waveguide (SIW) slot antenna having folded corrugated stubs is proposed for suppressing the backlobes of the SIW slot antenna associated with the diffraction of the spillover current. The longitudinal array of the folded stubs replacing the SIW via-holes effectively prevents the propagation of the surface spillover current. The measured front-to-back ratio (FTBR) has been greatly (15 dB) improved from that of the common SIW slot antenna. We expect that the proposed folded corrugated SIW (FCSIW) slot antenna plays an important role for reducing the excessive backside radiation of the SIW slot antenna and for decreasing mutual coupling in SIW slot antenna arrays.
XRCE: Hybrid Classification for Aspect-based Sentiment Analysis

In this paper, we present the system we have developed for the SemEval-2014 Task 4 dedicated to Aspect-Based Sentiment Analysis. The system is based on a robust parser that provides information to feed different classifiers with linguistic features dedicated to aspect categories and aspect categories polarity classification. We mainly present the work which has been done on the restaurant domain1 for the four subtasks, aspect term and category detection and aspect term and category polarity.
A geometric snake model for segmentation of medical imagery

We employ the new geometric active contour models, previously formulated, for edge detection and segmentation of magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound medical imagery. Our method is based on defining feature-based metrics on a given image which in turn leads to a novel snake paradigm in which the feature of interest may be considered to lie at the bottom of a potential well. Thus, the snake is attracted very quickly and efficiently to the desired feature.
A developmental perspective on antisocial behavior.

A developmental model of antisocial behavior is outlined. Recent findings are reviewed that concern the etiology and course of antisocial behavior from early childhood through adolescence. Evidence is presented in support of the hypothesis that the route to chronic delinquency is marked by a reliable developmental sequence of experiences. As a first step, ineffective parenting practices are viewed as determinants for childhood conduct disorders. The general model also takes into account the contextual variables that influence the family interaction process. As a second step, the conduct-disordered behaviors lead to academic failure and peer rejection. These dual failures lead, in turn, to increased risk for depressed mood and involvement in a deviant peer group. This third step usually occurs during later childhood and early adolescence. It is assumed that children following this developmental sequence are at high risk for engaging in chronic delinquent behavior. Finally, implications for prevention and intervention are discussed.