nils-herrmann/clean_scirepeval huggingface dataset:
Comparative Study on Agile software development methodologies

Today‘s business environment is very much dynamic, and organizations are constantly changing their software requirements to adjust with new environment. They also demand for fast delivery of software products as well as for accepting changing requirements. In this aspect, traditional plan-driven developments fail to meet up these requirements. Though traditional software development methodologies, such as life cycle-based structured and object oriented approaches, continue to dominate the systems development few decades and much research has done in traditional methodologies, Agile software development brings its own set of novel challenges that must be addressed to satisfy the customer through early and continuous delivery of the valuable software. It‘s a set of software development methods based on iterative and incremental development process, where requirements and development evolve through collaboration between self-organizing, cross-functional teams that allows rapid delivery of high quality software to meet customer needs and also accommodate changes in the requirements. In this paper, we significantly indentify and describe the major factors, that Agile development approach improves software development process to meet the rapid changing business environments. We also provide a brief comparison of agile development methodologies with traditional systems development methodologies, and discuss current state of adopting agile methodologies.
Gene networks inference using dynamic Bayesian networks

This article deals with the identification of gene regulatory networks from experimental data using a statistical machine learning approach. A stochastic model of gene interactions capable of handling missing variables is proposed. It can be described as a dynamic Bayesian network particularly well suited to tackle the stochastic nature of gene regulation and gene expression measurement. Parameters of the model are learned through a penalized likelihood maximization implemented through an extended version of EM algorithm. Our approach is tested against experimental data relative to the S.O.S. DNA Repair network of the Escherichia coli bacterium. It appears to be able to extract the main regulations between the genes involved in this network. An added missing variable is found to model the main protein of the network. Good prediction abilities on unlearned data are observed. These first results are very promising: they show the power of the learning algorithm and the ability of the model to capture gene interactions.
A comparison of behaviour in test and in everyday life : evidence of three consistent boldness-related personality traits in dogs

Six specific personality traits – playfulness, chase-proneness, curiosity/fearlessness, sociability, aggressiveness, and distance-playfulness – and a broad boldness dimension have been suggested for dogs in previous studies based on data collected in a standardized behavioural test (‘‘dog mentality assessment’’, DMA). In the present study I investigated the validity of the specific traits for predicting typical behaviour in everyday life. A questionnaire with items describing the dog’s typical behaviour in a range of situations was sent to owners of dogs that had carried out the DMA behavioural test 1–2 years earlier. Of the questionnaires that were sent out 697 were returned, corresponding to a response rate of 73.3%. Based on factor analyses on the questionnaire data, behavioural factors in everyday life were suggested to correspond to the specific personality traits from the DMA. Correlation analyses suggested construct validity for the traits playfulness, curiosity/ fearlessness, sociability, and distance-playfulness. Chase-proneness, which I expected to be related to predatory behaviour in everyday life, was instead related to human-directed play interest and nonsocial fear. Aggressiveness was the only trait from the DMA with low association to all of the behavioural factors from the questionnaire. The results suggest that three components of dog personality are measured in the DMA: (1) interest in playing with humans; (2) attitude towards strangers (interest in, fear of, and aggression towards); and (3) non-social fearfulness. These three components correspond to the traits playfulness, sociability, and curiosity/fearlessness, respectively, all of which were found to be related to a higher-order shyness–boldness dimension. www.elsevier.com/locate/applanim Applied Animal Behaviour Science 91 (2005) 103–128 * Present address: Department of Anatomy and Physiology, Faculty of Veterinary Medicine and Animal Science, Swedish University of Agricultural Sciences, Box 7011, SE-750 07 Uppsala,  21; fax: +46 18 67 21 11. E-mail address: kenth.svartberg@afys.slu.se. 0168-1591/$ – see front matter  Chase-proneness and distance-playfulness seem to be mixed measures of these personality components, and are not related to any additional components. Since the time between the behavioural test and the questionnaire was 1–2 years, the results indicate long-term consistency of the personality components. Based on these results, the DMA seems to be useful in predicting behavioural problems that are related to social and non-social fear, but not in predicting other potential behavioural problems. However, considering this limitation, the test seems to validly assess important aspects of dog personality, which supports the use of the test as an instrument in dog breeding and in selection of individual dogs for different purposes.
Stance Classification in Out-of-Domain Rumours: A Case Study Around Mental Health Disorders

Social media being a prolific source of rumours, stance classification of individual posts towards rumours has gained attention in the past few years. Classification of stance in individual posts can then be useful to determine the veracity of a rumour. Research in this direction has looked at rumours in different domains, such as politics, natural disasters or terrorist attacks. However, work has been limited to in-domain experiments, i.e. training and testing data belong to the same domain. This presents the caveat that when one wants to deal with rumours in domains that are more obscure, training data tends to be scarce. This is the case of mental health disorders, which we explore here. Having annotated collections of tweets around rumours emerged in the context of breaking news, we study the performance stability when switching to the new domain of mental health disorders. Our study confirms that performance drops when we apply our trained model on a new domain, emphasising the differences in rumours across domains. We overcome this issue by using a little portion of the target domain data for training, which leads to a substantial boost in performance. We also release the new dataset with mental health rumours annotated for stance.
A multirange architecture for collision-free off-road robot navigation

We present a multi-layered mapping, planning, and command execution system developed and tested on the LAGR mobile robot. Key to robust performance under uncertainty is the combination of a short-range perception system operating at high frame rate and low resolution and a long-range, adaptive vision system operating at lower frame rate and higher resolution. The short-range module performs local planning and obstacle avoidance with fast reaction times, while the long-range module performs strategic visual planning. Probabilistic traversability labels provided by the perception modules are combined and accumulated into a robot-centered hyperbolic-polar map with a 200 meter effective range. Instead of using a dynamical model of the robot for short-range planning, the system uses a large lookup table of physically-possible trajectory segments recorded on the robot in a wide variety of driving conditions. Localization is performed using a combination of GPS, wheel odometry, IMU, and a high-speed, low-complexity rotational visual odometry module. The end to end system was developed and tested on the LAGR mobile robot, and was verified in independent government tests.
Graphicionado: A high-performance and energy-efficient accelerator for graph analytics

Graphs are one of the key data structures for many real-world computing applications and the importance of graph analytics is ever-growing. While existing software graph processing frameworks improve programmability of graph analytics, underlying general purpose processors still limit the performance and energy efficiency of graph analytics. We architect a domain-specific accelerator, Graphicionado, for high-performance, energy-efficient processing of graph analytics workloads. For efficient graph analytics processing, Graphicionado exploits not only data structure-centric datapath specialization, but also memory subsystem specialization, all the while taking advantage of the parallelism inherent in this domain. Graphicionado augments the vertex programming paradigm, allowing different graph analytics applications to be mapped to the same accelerator framework, while maintaining flexibility through a small set of reconfigurable blocks. This paper describes Graphicionado pipeline design choices in detail and gives insights on how Graphicionado combats application execution inefficiencies on general-purpose CPUs. Our results show that Graphicionado achieves a 1.76-6.54x speedup while consuming 50-100x less energy compared to a state-of-the-art software graph analytics processing framework executing 32 threads on a 16-core Haswell Xeon processor.
Controlling Prominence Realisation in Parametric DNN-Based Speech Synthesis

This work aims to improve text-to-speech synthesis for Wikipedia by advancing and implementing models of prosodic prominence. We propose a new system architecture with explicit prominence modeling and test the first component of the architecture. We automatically extract a phonetic feature related to prominence from the speech signal in the ARCTIC corpus. We then modify the label files and train an experimental TTS system based on the feature using Merlin, a statistical-parametric DNN-based engine. Test sentences with contrastive prominence on the word-level are synthesised and separate listening tests a) evaluating the level of prominence control in generated speech, and b) naturalness, are conducted. Our results show that the prominence feature-enhanced system successfully places prominence on the appropriate words and increases perceived naturalness relative to the baseline.
Predicting football results with an evolutionary ensemble classier

In this research a new type of prediction model is introduced, where an ensemble of k-nn prediction models is created with the help of an evolutionary algorithm. This approach is thought to be effective because of the importance of diversity in both ensemble models and evolutionary algorithms. This model consists of two stages, where the first tries to find those models that work well together within an ensemble and the second tries to learn the optimal weights of these individuals. Both of these stages are evolutionary algorithms, where the first stage uses deterministic crowding to find diverse prediction models. This model is then trained and tested by predicting games in the Dutch Eredivisie. The base two stage model is tested, together with some additional features. Of these features only weighted voting improved the performance of the model. The effect of the size of the ensemble and the number of variables on the performance was also tested. The ensemble created by the first stage is consistently better than that created by the second stage and these predictions are comparable but slightly worse than that of the best benchmarks. However, another ensemble model trained on the same dataset was beaten by the evolutionary ensemble model.
Discourse Structure and Computation: Past, Present and Future

The discourse properties of text have long been recognized as critical to language technology, and over the past 40 years, our understanding of and ability to exploit the discourse properties of text has grown in many ways. This essay briefly recounts these developments, the technology they employ, the applications they support, and the new challenges that each subsequent development has raised. We conclude with the challenges faced by our current understanding of discourse, and the applications that meeting these challenges will promote. 1 Why bother with discourse? Research in Natural Language Processing (NLP) has long benefitted from the fact that text can often be treated as simply a bag of words or a bag of sentences. But not always: Position often matters — e.g., It is well-known that the first one or two sentences in a news report usually comprise its best extractive summary. Order often matters – e.g., very different events are conveyed depending on how clauses and sentences are ordered. (1) a. I said the magic words, and a genie appeared. b. A genie appeared, and I said the magic words. Adjacency often matters — e.g., attributed material may span a sequence of adjacent sentences, and contrasts are visible through sentence juxtaposition. Context always matters — e.g., All languages achieve economy through minimal expressions that can only convey intended meaning when understood in context. Position, order, adjacency and context are intrinsic features of discourse, and research on discourse processing attempts to solve the challenges posed by context-bound expressions and the discourse structures that give rise, when linearized, to position, order and adjacency. But challenges are not why Language Technology (LT) researchers should care about discourse: Rather, discourse can enable LT to overcome known obstacles to better performance. Consider automated summarization and machine translation: Humans regularly judge output quality in terms that include referential clarity and coherence. Systems can only improve here by paying attention to discourse — i.e., to linguistic features above the level of ngrams and single sentences. (In fact, we predict that as soon as cheap — i.e., non-manual – methods are found for reliably assessing these features — for example, using proxies like those suggested in (Pitler et al., 2010) — they will supplant, or at least complement today’s common metrics, Bleu and Rouge that say little about what matters to human text understanding (Callison-Burch et al., 2006).) Consider also work on automated text simplification: One way that human editors simplify text is by re-expressing a long complex sentence as a discourse sequence of simple sentences. Researchers should be able to automate this through understanding the various ways that information is conveyed in discourse. Other examples of LT applications already benefitting from recognizing and applying discourse-level information include automated assessment of student essays (Burstein and Chodorow, 2010); summarization (Thione et al., 2004), infor-
Control-flow checking by software signatures

This paper presents a new signature monitoring technique, CFCSS (Control Flow Checking by Software Signatures); CFCSS is a pure software method that checks the control flow of a program using assigned signatures. An algorithm assigns a unique signature to each node in the program graph and adds instructions for error detection. Signatures are embedded in the program during compilation time using the constant field of the instructions and compared with run-time signatures when the program is executed. Another algorithm reduces the code size and execution time overhead caused by checking instructions in CFCSS. A “branching fault injection experiment” was performed with benchmark programs. Without CFCSS, an average of 33.7% of the injected branching faults produced undetected incorrect outputs; however, with CFCSS, only 3.1% of branching faults produced undetected incorrect outputs. Thus it is possible to increase error detection coverage for control flow errors by an order of magnitude using CFCSS. The distinctive advantage of CFCSS over previous signature monitoring techniques is that CFCSS is a pure software method, i.e., it needs no dedicated hardware such as a watchdog processor for control flow checking. A watchdog task in multitasking environment also needs no extra hardware, but the advantage of CFCSS over a watchdog task is that CFCSS can be used even when the operating system does not support multitasking.
Towards a decision-making structure for selecting a research design in empirical software engineering

Several factors make empirical research in software engineering particularly challenging as it requires studying not only technology but its stakeholders’ activities while drawing concepts and theories from social science. Researchers, in general, agree that selecting a research design in empirical software engineering research is challenging, because the implications of using individual research methods are not well recorded. The main objective of this article is to make researchers aware and support them in their research design, by providing a foundation of knowledge about empirical software engineering research decisions, in order to ensure that researchers make well-founded and informed decisions about their research designs. This article provides a decision-making structure containing a number of decision points, each one of them representing a specific aspect on empirical software engineering research. The article provides an introduction to each decision point and its constituents, as well as to the relationships between the different parts in the decision-making structure. The intention is the structure should act as a starting point for the research design before going into the details of the research design chosen. The article provides an in-depth discussion of decision points in relation to the research design when conducting empirical research.
Comparison of NER Performance Using Word Embedding

Recent studies in NER use the supervised machine learning. This study used CRF as a learning algorithm, and applied word embedding to feature for NER training. Word embedding is helpful in many learning algorithms of NLP, indicating that words in a sentence are mapped by a real vector in a lowdimension space. As a result of comparing the performance of multiple techniques for word embedding to NER, it was found that CCA (85.96%) in Test A and Word2Vec (80.72%) in Test B exhibited the best performance.
Sequential Projection-Based Metacognitive Learning in a Radial Basis Function Network for Classification Problems

In this paper, we present a sequential projection-based metacognitive learning algorithm in a radial basis function network (PBL-McRBFN) for classification problems. The algorithm is inspired by human metacognitive learning principles and has two components: a cognitive component and a metacognitive component. The cognitive component is a single-hidden-layer radial basis function network with evolving architecture. The metacognitive component controls the learning process in the cognitive component by choosing the best learning strategy for the current sample and adapts the learning strategies by implementing self-regulation. In addition, sample overlapping conditions and past knowledge of the samples in the form of pseudosamples are used for proper initialization of new hidden neurons to minimize the misclassification. The parameter update strategy uses projection-based direct minimization of hinge loss error. The interaction of the cognitive component and the metacognitive component addresses the what-to-learn, when-to-learn, and how-to-learn human learning principles efficiently. The performance of the PBL-McRBFN is evaluated using a set of benchmark classification problems from the University of California Irvine machine learning repository. The statistical performance evaluation on these problems proves the superior performance of the PBL-McRBFN classifier over results reported in the literature. Also, we evaluate the performance of the proposed algorithm on a practical Alzheimer's disease detection problem. The performance results on open access series of imaging studies and Alzheimer's disease neuroimaging initiative datasets, which are obtained from different demographic regions, clearly show that PBL-McRBFN can handle a problem with change in distribution.
Joint CSIT Acquisition Based on Low-Rank Matrix Completion for FDD Massive MIMO Systems

Channel state information at the transmitter (CSIT) is essential for frequency-division duplexing (FDD) massive MIMO systems, but conventional solutions involve overwhelming overhead both for downlink channel training and uplink channel feedback. In this letter, we propose a joint CSIT acquisition scheme to reduce the overhead. Particularly, unlike conventional schemes where each user individually estimates its own channel and then feed it back to the base station (BS), we propose that all scheduled users directly feed back the pilot observation to the BS, and then joint CSIT recovery can be realized at the BS. We further formulate the joint CSIT recovery problem as a low-rank matrix completion problem by utilizing the low-rank property of the massive MIMO channel matrix, which is caused by the correlation among users. Finally, we propose a hybrid low-rank matrix completion algorithm based on the singular value projection to solve this problem. Simulations demonstrate that the proposed scheme can provide accurate CSIT with lower overhead than conventional schemes.
An Auxiliary Self-Oscillating Preheating System for Self-Oscillating Fluorescent Lamp Electronic Ballasts

The fluorescent lamp lifetime is very dependent of the start-up lamp conditions. The lamp filament current and temperature during warm-up and at steady-state operation are important to extend the life of a hot-cathode fluorescent lamp, and the preheating circuit is responsible for attending to the start-up lamp requirements. The usual solution for the preheating circuit used in self-oscillating electronic ballasts is simple and presents a low cost. However, the performance to extend the lamp lifetime is not the most effective. This paper presents an effective preheating circuit for self-oscillating electronic ballasts as an alternative to the usual solution.
Fuzzy logic control of buck converter for photo voltaic emulator

This paper analyses the digital simulation of a buck converter to emulate the photovoltaic (PV) system with focus on fuzzy logic control of buck converter. A PV emulator is a DC-DC converter (buck converter in the present case) having same electrical characteristics as that of a PV panel. The emulator helps in the real analysis of PV system in an environment where using actual PV systems can produce inconsistent results due to variation in weather conditions. The paper describes the application of fuzzy algorithms to the control of dynamic processes. The complete system is modelled in MATLAB® Simulink SimPowerSystem software package. The results obtained from the simulation studies are presented and the steady state and dynamic stability of the PV emulator system is discussed.