nils-herrmann/clean_scirepeval huggingface dataset:
Generalizing Agile Software Development Life Cycle

In last decade, various agile methods have been introduced and used by software industry. It has been observed that many practitioners are using hybrid of agile methods and traditional methods. The knowledge of agile software development process about the theoretical grounds, applicability in large development settings and connections to establish software engineering disciplines remain mostly in dark. It has been reported that it is difficult for average manager to implement agile method in the organization. Further, every agile method has its own development cycle that brings technological, managerial and environmental changes in organization. A proper roadmap of agile software development in the form of agile software development life cycle can be developed to address the aforesaid issues of agile software development process. Thus, there is strong need of agile software development life cycle that clearly defines the phases included in any agile method and also describes the artifacts of each phase. This generalization of agile software development life cycle provides the guideline for average developers about usability, suitability, applicability of agile methods. Agile software Development; extreme Programming; Adaptive software developmen; Scrum; Agile Method;story.
The Role of Family in Teaching Religious and Moral Values to their Children in Urban areas : A Case Study of Lahore ( Pakistan )

In current era of modernization and globalization it is observed that individuals are in the quest for mental peace and spiritual comfort even though they have achieved many scientific advancements. The major reason behind this uncomfortable condition is that they have indulged in exterior world and became careless about the religion and its practices. The most alarming fact regarding faith inculcation is the transformation of wrong, vague and ambiguous concepts to children at early ages which prolongs for whole life. Childhood is the period when concepts of right and wrong are strongly developed and most important agent that contributes to this concept making is parents. Keeping in mind this fact, the present study has highlighted the role of family in providing religious and moral values to their children. The qualitative approach has been used by adopting purposive sampling method. The focus group discussion has been conducted with families having an urban background. Some surprising facts has led the researcher to the conclusion of deterioration in the family as the social institution as a major cause which is resulted The Role of Family in Teaching Religious Moral Values to their Children 259 into not only a moral decay in the society but also the reason of socio-economic problems in country.
An update to the systematic literature review of empirical evidence of the impacts and outcomes of computer games and serious games

Continuing interest in digital games indicated that it would be useful to update [Authors’, 2012] systematic literature review of empirical evidence about the positive impacts an d outcomes of games. Since a large number of papers was identified in th e period from 2009 to 2014, the current review focused on 143 papers that provided higher quality evidence about the positive outcomes of games.
Dynamic Wind Effects : A Comparative Study of Provisions in Codes and Standards with Wind Tunnel Data

An evaluation and comparison of seven of the world’s major building codes and standards is conducted in this study, with specific discussion of their estimations of the alongwind, acrosswind, and torsional response, where applicable, for a given building. The codes and standards highlighted by this study are those of the United States, Japan, Australia, the United Kingdom, Canada, China, and Europe. In addition, the response predicted by using the measured power spectra of the alongwind, acrosswind, and torsional responses for several building shapes tested in a wind tunnel are presented, and a comparison between the response predicted by wind tunnel data and that estimated by some of the standards is conducted. This study serves not only as a comparison of the response estimates by international codes and standards, but also introduces a new set of wind tunnel data for validation of wind tunnel-based empirical expressions.  The alongwind motion primarily results from pressure fluctuations on the windward and leeward faces, which generally follows the fluctuations in the approach flow, at least in the low frequency range. Therefore, alongwind aerodynamic loads may be quantified analytically utilizing quasi-steady and strip theories, with dynamic effects customarily represented by a random-vibrationbased “Gust Factor Approach” (Davenport 1967, Vellozzi & Cohen 1968, Vickery 1970, Simiu 1976, Solari 1982, ESDU 1989, Gurley & Kareem 1993). However, the acrosswind motion is introduced by pressure fluctuations on the side faces which are influenced by fluctuations in the separated shear layers and wake dynamics (Kareem 1982). This renders the applicability of strip and quasi-steady theories rather doubtful. Similarly, the wind-induced torsional effects result from an imbalance in the instantaneous pressure distribution on the building surface. These load effects are further amplified in asymmetric buildings as a result of inertial coupling (Kareem 1985). Due to the complexity of the acrosswind and torsional responses, physical modeling of fluid-structure interactions remains the only viable means of obtaining information on wind loads, though recently, research in the area of computational fluid dynam1. Graduate Student & Corresponding Author, NatHaz Modeling Laboratory, Department of Civil Engineering and Geological Sciences, University of Notre Dame, Notre Dame, IN, 46556.
Network Behavioral Analysis for Zero-Day Malware Detection - A Case Study

The number of cyber threats is constantly increasing. In 2013, 200,000 malicious tools were identified each day by antivirus vendors. This figure rose to 800,000 per day in 2014 and then to 1.8 million per day in 2016! The bar of 3 million per day will be crossed in 2017. Traditional security tools (mainly signature-based) show their limits and are less and less effective to detect these new cyber threats. Detecting never-seen-before or zero-day malware, including ransomware, efficiently requires a new approach in cyber security management. This requires a move from signature-based detection to behavior-based detection. We have developed a data breach detection system named CDS using Machine Learning techniques which is able to identify zero-day malware by analyzing the network traffic. In this paper, we present the capability of the CDS to detect zero-day ransomware, particularly WannaCry.
Report on the SIGIR 2016 Workshop on Neural Information Retrieval (Neu-IR)

The SIGIR 2016 workshop on Neural Information Retrieval (Neu-IR) took place on 21 July, 2016 in Pisa. The goal of the Neu-IR (pronounced "New IR") workshop was to serve as a forum for academic and industrial researchers, working at the intersection of information retrieval (IR) and machine learning, to present new work and early results, compare notes on neural network toolkits, share best practices, and discuss the main challenges facing this line of research. In total, 19 papers were presented, including oral and poster presentations. The workshop program also included a session on invited "lightning talks" to encourage participants to share personal insights and negative results with the community. The workshop was well-attended with more than 120 registrations.
A TEACHER STUDENT NETWORK FOR FASTER VIDEO CLASSIFICATION

Over the past few years, various tasks involving videos such as classification, description, summarization and question answering have received a lot of attention. Current models for these tasks compute an encoding of the video by treating it as a sequence of images and going over every image in the sequence, which becomes computationally expensive for longer videos. In this paper, we focus on the task of video classification and aim to reduce the computational cost by using the idea of distillation. Specifically, we propose a Teacher-Student network wherein the teacher looks at all the frames in the video but the student looks at only a small fraction of the frames in the video. The idea is to then train the student to minimize (i) the difference between the final representation computed by the student and the teacher and/or (ii) the difference between the distributions predicted by the teacher and the student. This smaller student network which involves fewer computations but still learns to mimic the teacher can then be employed at inference time for video classification. We experiment with the YouTube-8M dataset and show that the proposed student network can reduce the inference time by upto 30% with a negligent drop in the performance.
Practical Linear Models for Large-Scale One-Class Collaborative Filtering

Collaborative filtering has emerged as the de facto approach to personalized recommendation problems. However, a scenario that has proven difficult in practice is the one-class collaborative filtering case (OC-CF), where one has examples of items that a user prefers, but no examples of items they do not prefer. In such cases, it is desirable to have recommendation algorithms that are personalized, learning-based, and highly scalable. Existing linear recommenders for OC-CF achieve good performance in benchmarking tasks, but they involve solving a large number of a regression subproblems, limiting their applicability to large-scale problems. We show that it is possible to scale up linear recommenders to big data by learning an OCCF model in a randomized low-dimensional embedding of the user-item interaction matrix. Our algorithm, Linear-FLow, achieves state-of-the-art performance in a comprehensive set of experiments on standard benchmarks as well as real data.
Learning Composition Models for Phrase Embeddings

Lexical embeddings can serve as useful representations for words for a variety of NLP tasks, but learning embeddings for phrases can be challenging. While separate embeddings are learned for each word, this is infeasible for every phrase. We construct phrase embeddings by learning how to compose word embeddings using features that capture phrase structure and context. We propose efficient unsupervised and task-specific learning objectives that scale our model to large datasets. We demonstrate improvements on both language modeling and several phrase semantic similarity tasks with various phrase lengths. We make the implementation of our model and the datasets available for general use.
A Self-Training Method for Semi-Supervised GANs

Since the creation of Generative Adversarial Networks (GANs), much work has been done to improve their training stability, their generated image quality, their range of application but nearly none of them explored their self-training potential. Self-training has been used before the advent of deep learning in order to allow training on limited labelled training data and has shown impressive results in semi-supervised learning. In this work, we combine these two ideas and make GANs self-trainable for semi-supervised learning tasks by exploiting their infinite data generation potential. Results show that using even the simplest form of self-training yields an improvement. We also show results for a more complex self-training scheme that performs at least as well as the basic self-training scheme but with significantly less data augmentation.
Interpreting EEG alpha activity

Exploring EEG alpha oscillations has generated considerable interest, in particular with regards to the role they play in cognitive, psychomotor, psycho-emotional and physiological aspects of human life. However, there is no clearly agreed upon definition of what constitutes 'alpha activity' or which of the many indices should be used to characterize it. To address these issues this review attempts to delineate EEG alpha-activity, its physical, molecular and morphological nature, and examine the following indices: (1) the individual alpha peak frequency; (2) activation magnitude, as measured by alpha amplitude suppression across the individual alpha bandwidth in response to eyes opening, and (3) alpha "auto-rhythmicity" indices: which include intra-spindle amplitude variability, spindle length and steepness. Throughout, the article offers a number of suggestions regarding the mechanism(s) of alpha activity related to inter and intra-individual variability. In addition, it provides some insights into the various psychophysiological indices of alpha activity and highlights their role in optimal functioning and behavior.
GPU based Single-Pass Ray Casting of Large Heightfields Using Clipmaps

Heightfields have proved to be useful for rendering terrains or polygonal surfaces with finestructured details. While GPU-based ray casting has become popular for the latter setting, terrains are commonly rendered by using mesh-based techniques, because the heightfields can be very large and hence ray casting on these data is usually less efficient. Compared to mesh-based techniques, ray casting is attractive, for it does not require to deal with mesh related problems such as tessellation of the heightfield, frustum culling or mesh optimizations. In this paper we present an approach to render heightfields of almost arbitrary size at real-time frame rates by means of GPU-based ray casting and clipmaps. Our technique uses level-of-detail dependent early ray termination to accelerate ray casting and avoids aliasing caused by texture sampling or spatial sampling. Furthermore, we use two different methods to improve the visual quality of the reconstructed surfaces obtained from point sampled data. We evaluate our implementation for four different data sets and two different hardware configurations.
Overeducation and skill mismatch : a dynamic analysis

Half of American workers have a level of education that does not match the level of education required for their job. Of these, a majority are overeducated, i.e. have more schooling than necessary to perform their job (see, e.g., Leuven & Oosterbeek, 2011). In this paper, we use data from the National Longitudinal Survey of Youth 1979 (NLSY79) combined with the pooled 1989-1991 waves of the CPS to provide some of the first evidence regarding the dynamics of overeducation over the life cyle. Shedding light on this question is key to disentangle the role played by labor market frictions versus other factors such as selection on unobservables, compensating differentials or career mobility prospects. Overall, our results suggest that overeducation is a fairly persistent phenomenon, with 79% of workers remaining overeducated after one year. Initial overeducation also has an impact on wages much later in the career, which points to the existence of scarring effects. Finally, we find some evidence of duration dependence, with a 6.5 point decrease in the exit rate from overeducation after having spent five years overeducated.
GraphVAE: Towards Generation of Small Graphs Using Variational Autoencoders

Deep learning on graphs has become a popular research topic with many applications. However, past work has concentrated on learning graph embedding tasks, which is in contrast with advances in generative models for images and text. Is it possible to transfer this progress to the domain of graphs? We propose to sidestep hurdles associated with linearization of such discrete structures by having a decoder output a probabilistic fully-connected graph of a predefined maximum size directly at once. Our method is formulated as a variational autoencoder. We evaluate on the challenging task of molecule generation.
Channel coordination in green supply chain management

Environmental consciousness has become increasingly important in everyday life and business practice. The effort to reduce the impact of business activities on the environment has been labelled as green supply chain management. Any major greening project would require efforts on the part of the entire supply chain. However, very few studies have addressed the issue of coordinating the green supply chain. We consider the problem of coordination of a manufacturer and a retailer in a vertical supply chain, who put in efforts for ‘greening’ their operations. We address some pertinent questions in this regard such as extent of effort in greening of operations by manufacturer or retailer, level of cooperation between the two parties, and how to coordinate their operations in a supply chain. The greening efforts by the manufacturer and retailer result in demand expansion at the retail end. The decision variables of the manufacturer are wholesale price and greening effort, while those of the retailer are retail price and its greening effort. We find that the ratio of the optimal greening efforts put in by the manufacturer and retailer is equal to the ratio of their green sensitivity ratios and greening cost ratios. Further, profits and efforts are higher in the integrated channel as compared to the case of the decentralized channel. Finally, a two-part tariff contract is found to produce channel coordination in this problem. A numerical example illustrates the results.  (2013) 64, 336–351. doi:10.1057/jors.2012.44 Published online 9 May 2012
Using Educational Games for Sign Language Learning - A SignWriting Learning Game: Case Study

Apart from being used as a means of entertainment, computer games have been adopted for a long time as a valuable tool for learning. Computer games can offer many learning benefits to students since they can consume their attention and increase their motivation and engagement which can then lead to stimulate learning. However, most of the research to date on educational computer games, in particular learning versions of existing computer games, focused only on learner with typical development. Rather less is known about designing educational games for learners with special needs. The current research presents the results of a pilot study. The principal aim of this pilot study is to examine the interest of learners with hearing impairments in using an educational game for learning the sign language notation system SignWriting. The results found indicated that, overall, the application is useful, enjoyable and easy to use: the game can stimulate the students’ interest in learning such notations.
Predictive Data Mining For Medical Diagnosis : An Overview Of Heart Disease Prediction

As large amount of data is generated in medical organizations (hospitals, medical centers) but this data is not properly used. There is a wealth of hidden information present in the datasets. The healthcare environment is still “information rich” but “knowledge poor”. There is a lack of effective analysis tools to discover hidden relationships and trends in data. Advanced data mining techniques can help remedy this situation. For this purpose we can use different data mining techniques. This research paper intends to provide a survey of current techniques of knowledge discovery in databases using data mining techniques that are in use in today’s medical research particularly in Heart Disease Prediction. This research has developed a prototype Heart Disease Prediction System (HDPS) using data mining techniques namely, Decision Trees, Naïve Bayes and Neural Network. This Heart disease prediction system can answer complex “what if” queries which traditional decision support systems cannot. Using medical profiles such as age, sex, blood pressure and blood sugar it can predict the likelihood of patients getting a heart disease. It enables significant knowledge, e.g. patterns, relationships between medical factors related to heart disease, to be established.
Relationship between gluteal muscle activation and upper extremity kinematics and kinetics in softball position players

As the biomechanical literature concerning softball pitching is evolving, there are no data to support the mechanics of softball position players. Pitching literature supports the whole kinetic chain approach including the lower extremity in proper throwing mechanics. The purpose of this project was to examine the gluteal muscle group activation patterns and their relationship with shoulder and elbow kinematics and kinetics during the overhead throwing motion of softball position players. Eighteen Division I National Collegiate Athletic Association softball players (19.2 ± 1.0 years; 68.9 ± 8.7 kg; 168.6 ± 6.6 cm) who were listed on the active playing roster volunteered. Electromyographic, kinematic, and kinetic data were collected while players caught a simulated hit or pitched ball and perform their position throw. Pearson correlation revealed a significant negative correlation between non-throwing gluteus maximus during the phase of maximum external rotation to maximum internal rotation (MIR) and elbow moments at ball release (r = −0.52). While at ball release, trunk flexion and rotation both had a positive relationship with shoulder moments at MIR (r = 0.69, r = 0.82, respectively) suggesting that the kinematic actions of the pelvis and trunk are strongly related to the actions of the shoulder during throwing.
Single Document Automatic Text Summarization using Term Frequency-Inverse Document Frequency (TF-IDF)

The increasing availability of online information has triggered an intensive research in the area of automatic text summarization within the Natural Language Processing (NLP). Text summarization reduces the text by removing the less useful information which helps the reader to find the required information quickly. There are many kinds of algorithms that can be used to summarize the text. One of them is TF-IDF (Term Frequency-Inverse Document Frequency). This research aimed to produce an automatic text summarizer implemented with TF-IDF algorithm and to compare it with other various online source of automatic text summarizer. To evaluate the summary produced from each summarizer, The F-Measure as the standard comparison value had been used. The result of this research produces 67% of accuracy with three data samples which are higher compared to the other online summarizers.
Developments in Ducted Water Current Turbines

Unlike conventional hydro and tidal barrage installations, water current turbines in open flow can generate power from flowing water with almost zero environmental impact, over a much wider range of sites than those available for conventional tidal power generation. Recent developments in current turbine design are reviewed and some potential advantages of ducted or “diffuser-augmented” current turbines are explored. These include improved safety, protection from weed growth, increased power output and reduced turbine and gearbox size for a given power output. Ducted turbines are not subject to the so-called Betz limit, which defines an upper limit of 59.3% of the incident kinetic energy that can be converted to shaft power by a single actuator disk turbine in open flow. For ducted turbines the theoretical limit depends on (i) the pressure difference that can be created between duct inlet and outlet, and (ii) the volumetric flow through the duct. These factors in turn depend on the shape of the duct and the ratio of duct area to turbine area. Previous investigations by others have found a theoretical limit for a diffuser-augmented wind turbine of about 3.3 times the Betz limit, and a model diffuseraugmented wind turbine has extracted 4.25 times the power extracted by the same turbine without a diffuser. In the present study, similar principles applied to a water turbine have so far achieved an augmentation factor of 3 at an early stage of the investigation.