nils-herrmann/clean_scirepeval huggingface dataset:
Software risk management barriers: An empirical study

This paper reports results from a survey of experienced project managers on their perception of software risk management. From a sample of 18 experienced project managers, we have found good awareness of risk management, but low tool usage. We offer evidence that the main barriers to performing risk management are related to its perceived high cost and comparative low value. Psychological issues are also important, but less so. Risk identification and monitoring, in particular, are perceived as effort intensive and costly. The perception is that risk management is not prioritised highly enough. Our conclusion is that more must be done to visibly prove the value : cost ratio for risk management activities.
Ontology mediation , merging and aligning Jos

Ontology mediation is a broad field of research which is concerned with determining and overcoming differences between ontologies in order to allow the reuse of such ontologies, and the data annotated using these ontologies, throughout different heterogeneous applications. Ontology mediation can be subdivided into three areas: ontology mapping, which is mostly concerned with the representation of correspondences between ontologies; ontology alignment, which is concerned with the (semi-)automatic discovery of correspondences between ontologies; and ontology merging, which is concerned with creating a single new ontology, based on a number of source ontologies. This chapter reviews the work which has been done in the three mentioned areas and proposes an integrated approach to ontology mediation in the area of knowledge management. A language is developed for the representation of correspondences between ontologies. An algorithm, which generalizes current state-of-the-art alignment algorithms, is developed for the (semi-)automated discovery of such mappings. A tool is presented for browsing and editing ontology mappings. An ontology mapping can be used for a variety of different tasks, such as transforming data between different representations and querying different heterogeneous knowledge
Review of visual odometry: types, approaches, challenges, and applications

Accurate localization of a vehicle is a fundamental challenge and one of the most important tasks of mobile robots. For autonomous navigation, motion tracking, and obstacle detection and avoidance, a robot must maintain knowledge of its position over time. Vision-based odometry is a robust technique utilized for this purpose. It allows a vehicle to localize itself robustly by using only a stream of images captured by a camera attached to the vehicle. This paper presents a review of state-of-the-art visual odometry (VO) and its types, approaches, applications, and challenges. VO is compared with the most common localization sensors and techniques, such as inertial navigation systems, global positioning systems, and laser sensors. Several areas for future research are also highlighted.
Cause related marketing campaigns and consumer purchase intentions : The mediating role of brand awareness and corporate image

The purpose of this research is to investigate the kind of relationship between Cause Related Marketing (CRM) campaigns, brand awareness and corporate image as possible antecedents of consumer purchase intentions in the less developed country of Pakistan. An initial conceptualization was developed from mainstream literature to be validated through empirical research. The conceptualization was then tested with primary quantitative survey data collected from 203 students studying in different universities of Rawalpindi and Islamabad. Correlation and regression analysis were used to test the key hypothesis derived from literature positing brand awareness and corporate image as mediating the relationship between CRM and consumer purchase intentions. The findings indicate that consumer purchase intentions are influenced by the cause related marketing campaigns. Furthermore it was observed that the brand awareness and corporate image partially mediate the impact of CRM campaigns on consumer purchase intentions. The data was gathered from universities situated in Rawalpindi and Islamabad only. Hence, future research could extend these findings to other cities in Pakistan to test their generalizability. Further research can be carried out through data collection from those people who actually participated in cause related marketing campaigns to identify the original behavior of customers instead of their purchase intentions. This research and the claims made are limited to the FMCG industry. The key implications cause related marketing of these findings for marketing managers lend support for the use of campaigns in Pakistan. The findings also suggest some measures which can be taken in to consideration in order to enhance brand awareness and to improve corporate image as both variables mediate the impact of CRM campaigns on consumer purchase intentions. The study contributes to cause related marketing literature by indicating a mediating role of brand awareness and corporate image on CRM campaigns and consumer purchase intentions. This mediating role was ignored in previous studies. Moreover, it contributes to close the gap of empirical research in this field, which exists particularly due to the diverse attitude of customers in less developed countries such as Pakistan.
Testing Programs with the Aid of a Compiler

if finite input-output specifications are added to the syntax of programs, these specifications can be verified at compile time. Programs which carry adequate tests with them in this way should be resistant to maintenance errors. If the specifications are independent of program details they are easy to give, and unlikely to contain errors in common with the program. Furthermore, certain finite specifications are maximal in that they exercise the control and expression structure of a program as well as any tests can.
Enlisting Hardware Architecture to Thwart Malicious Code Injection

Software vulnerabilities that enable the injection and executi on of malicious code in pervasive Internet-connected computing devices pose serious threats to cyber security. In a common type of attack, a hostile party induces a software buffer overflow in a susceptible computing device in order to corrupt a procedure return address and transfer control to malicious code. Th se buffer overflow attacks are often employed to recruit oblivious ho ts into distributed denial of service (DDoS) attack networks, which ultimately launch devastating DDoS attacks against victim networks or machines. In spite of xisting software countermeasures that seek to prevent buffer overflo w exploits, many systems remain vulnerable. In this paper, we describe a hardware-based secure return address stack (SRAS), which prevents malicious code injection involving proc edure return address corruption. Implementing this special hardware stack onl y requires low cost modifications to the processor and operating syste m. This enables the hardware protection to be applied to both legacy executable code an new programs. Also, this hardware defense has a negligible impac t on performance in the applications examined. The security offered by this hardwa re solution complements rather than replaces that provided by existing stati c of ware techniques. Thus, we detail how the combination of the proposed se cure r turn address stack and software defenses enables comprehensive multilayer protection against buffer overflow attacks and malicious code injection.
Extracting Product Features and Opinions from Reviews

Consumers are often forced to wade through many on-line reviews in order to make an informed product choice. This paper introduces OPINE, an unsupervised informationextraction system which mines reviews in order to build a model of important product features, their evaluation by reviewers, and their relative quality across products. Compared to previous work, OPINE achieves 22% higher precision (with only 3% lower recall) on the feature extraction task.OPINE’s novel use of relaxation labelingfor finding the semantic orientation of words in context leads to strong performance on the tasks of finding opinion phrases and their polarity.
Ensemble methods for spoken emotion recognition in call-centres

Machine-based emotional intelligence is a requirement for more natural interaction between humans and computer interfaces and a basic level of accurate emotion perception is needed for computer systems to respond adequately to human emotion. Humans convey emotional information both intentionally and unintentionally via speech patterns. These vocal patterns are perceived and understood by listeners during conversation. This research aims to improve the automatic perception of vocal emotion in two ways. First, we compare two emotional speech data sources: natural, spontaneous emotional speech and acted or portrayed emotional speech. This comparison demonstrates the advantages and disadvantages of both acquisition methods and how these methods affect the end application of vocal emotion recognition. Second, we look at two classification methods which have not been applied in this field: stacked generalisation and unweighted vote. We show how these techniques can yield an improvement over traditional classification methods.
Defining Technology Entrepreneurship

Over the last years Technology Entrepreneurship has developed as a distinct stream of research at the nexus of Entrepreneurship and the Management of Technology and Innovation. Yet, despite several special issues and an increased level of interest, no comprehensive definition of the field has been developed so far. This paper attempts to be a starting point for this discussion by deriving a framework based on elements identified in a review of the articles in the special issues. It structures Technology Entrepreneurship along the entrepreneurial process into a formation, exploitation and renewal phase and applies different levels of investigation at the product, firm and at the systems level.
RECOGNITION AND RETRIEVAL PROCESSES IN FREE RECALL

A model of free recall is described which identifies two processes in free recall: a retrieval process by which the subject accesses the words, and a recognition process by which the subject decides whether an implicitly retrieved word is a to-be-recalled word. Submodels for the recognition process and the retrieval process are described. The recognition model assumes that during the study phase, the subject associates "list markers" to the to-be-recalled words. The establishment of such associates is postulated to be an all-or-none stochastic process. In the test phase, the subject recognizes to-be-recalled words by deciding which words have relevant list markers as associates. A signal detectability model is developed for this decision process. The retrieval model is introduced as a computer program that tags associative paths between list words. In several experiments, subjects studied and were tested on a sequence of overlapping sublists sampled from a master set of common nouns. The twoprocess model predicts that the subject's ability to retrieve the words should increase as more overlapping sublists are studied, but his ability to differentiate the words on the most recent list should deteriorate. Experiments confirmed this predicted dissociation of recognition and retrieval. Further predictions derived from the free recall model were also supported.
An intelligent decision support system for skin cancer detection from dermoscopic images

It is challenging to develop an intelligent agent-based or robotic system to conduct long-term automatic health monitoring and robust efficient disease diagnosis as autonomous e-Carers in real-world applications. In this research, we aim to deal with such challenges by presenting an intelligent decision support system for skin lesion recognition as the initial step, which could be embedded into an intelligent service robot for health monitoring in home environments to promote early diagnosis. The system is developed to identify benign and malignant skin lesions using multiple steps, including pre-processing such as noise removal, segmentation, feature extraction from lesion regions, feature selection and classification. After extracting thousands of raw shape, colour and texture features from the lesion areas, a Genetic Algorithm (GA) is used to identify the most discriminating significant feature subsets for healthy and cancerous cases. A Support Vector Machine classifier has been employed to perform benign and malignant lesion recognition. Evaluated with 1300 images from the Dermofit dermoscopy image database, the empirical results indicate that our approach achieves superior performance in comparison to other related research reported in the literature.
Robotic Detection of Marine Litter Using Deep Visual Detection Models

Trash deposits in aquatic environments have a destructive effect on marine ecosystems and pose a long-term economic and environmental threat. Autonomous underwater vehicles (AUVs) could very well contribute to the solution of this problem by finding and eventually removing trash. This paper evaluates a number of deep-learning algorithms preforming the task of visually detecting trash in realistic underwater environments, with the eventual goal of exploration, mapping, and extraction of such debris by using AUVs. A large and publicly-available dataset of actual debris in open-water locations is annotated for training a number of convolutional neural network architectures for object detection. The trained networks are then evaluated on a set of images from other portions of that dataset, providing insight into approaches for developing the detection capabilities of an AUV for underwater trash removal. In addition, the evaluation is performed on three different platforms of varying processing power, which serves to assess these algorithms’ fitness for real-time applications.
The community garden hack: participatory experiments in facilitating primary school teacher's appropriation of technology

1As technology increasingly pervades the daily life of teachers and students, finding sustainable ways to successfully integrate innovative technologies in classrooms remains a challenge. This paper reports on our experiences designing and running workshops with (and for) teachers. Collectively, the aims of the workshops were geared ultimately at encouraging teachers to work with technologies in more `designerly' ways in the classroom, i.e. looking at technology as a working material, rather than as an off-the-shelf tool for certain activities, educational content or as an isolated part of the curriculum. We present a case study of one of the workshops, the Community Garden, designed in the format of a hackathon. We report on how teachers adopted the workshop concept, appropriated the technologies and, more interestingly, how six months later they appropriated the design process as a pedagogy to engage students in their learning.
Perceptual category mapping between English and Korean obstruents in non-CV positions: Prosodic location effects in second language identification skills

This study examines the degree to which mapping patterns between native language (L1) and second language (L2) categories for one prosodic context will generalize to other prosodic contexts, and how position-specific neutralization in the L1 influences the category mappings. Forty L1-Korean learners of English listened to English nonsense words consisting of /p b t d f v h ð/ and /ɑ/, with the consonants appearing in pre-stressed intervocalic, poststressed intervocalic, or coda context, and were asked to identify the consonant with both Korean and English labeling and to give gradient evaluations of the goodness of each label to the stimuli. Results show that the mapping patterns differ extensively from those found previously with the same subjects for consonants in initial, onset context. The mapping patterns for the intervocalic context also differed by position with respect to stress location. Coda consonants elicited poor goodness-of-fit and noisier mapping patterns for all segments, suggesting that an L1 coda neutralization process put all L2-English sounds in codas as “new” sounds under the Speech Learning Model (SLM) framework (Flege, 1995). Taken together, the results indicate that consonant learning needs to be evaluated in terms of position-by-position variants, rather than just being a general property of the overall
A New Clustering Method Based On Type-2 Fuzzy Similarity and Inclusion Measures

Similarity and inclusion measures between type-2 fuzzy sets have a wide range of applications. New similarity and inclusion measures between type-2 fuzzy sets are respectively defined in this paper. The properties of the measures are discussed. Some examples are used to compare the proposed measures with the existing results. Numerical results show that the proposed measures are more reasonable. Similarity measures and Yang and Shih’s algorithm are combined as a clustering method for type-2 fuzzy data. Clustering results demonstrate that the proposed similarity measures are better than the existing measures.
Selective Pooling Vector for Fine-Grained Recognition

We propose a new framework for image recognition by selectively pooling local visual descriptors, and show its superior discriminative power on fine-grained image classification tasks. The representation is based on selecting the most confident local descriptors for nonlinear function learning using a linear approximation in an embedded higher dimensional space. The advantage of our Selective Pooling Vector over the previous state-of-the-art Super Vector and Fisher Vector representations, is that it ensures a more accurate learning function, which proves to be important for classifying details in fine-grained image recognition. Our experimental results corroborate this claim: with a simple linear SVM as the classifier, the selective pooling vector achieves significant performance gains on standard benchmark datasets for various fine-grained tasks such as the CMU Multi-PIE dataset for face recognition, the Caltech-UCSD Bird dataset and the Stanford Dogs dataset for fine-grained object categorization. On all datasets we outperform the state of the arts and boost the recognition rates to 96.4%, 48.9%, 52.0% respectively.
Cooperative spectrum sensing in cognitive radio networks

In cognitive networks, cognitive (unlicensed) users need to continuously monitor spectrum to detect the presence of primary (licensed) users. In this paper, we illustrate the benefits of cooperation in cognitive radio. We show that by allowing the cognitive radios operating in the same band to cooperate we can reduce the detection time and thus increasing their agility. We first consider the case of two cognitive users and show how the inherent asymmetry in the network can be exploited to increase the probability of detection. We then extend our work to multiple cognitive user networks. We also propose a practical algorithm which allows cooperation in random networks
DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks

Probabilistic forecasting, i.e. estimating the probability distribution of a time series’ future given its past, is a key enabler for optimizing business processes. In retail businesses, for example, forecasting demand is crucial for having the right inventory available at the right time at the right place. In this paper we propose DeepAR, a methodology for producing accurate probabilistic forecasts, based on training an auto-regressive recurrent network model on a large number of related time series. We demonstrate how by applying deep learning techniques to forecasting, one can overcome many of the challenges faced by widely-used classical approaches to the problem. We show through extensive empirical evaluation on several real-world forecasting data sets that our methodology produces more accurate forecasts than other state-of-the-art methods, while requiring minimal manual work.
Mindfulness-based interventions in schools—a systematic review and meta-analysis

Mindfulness programs for schools are popular. We systematically reviewed the evidence regarding the effects of school-based mindfulness interventions on psychological outcomes, using a comprehensive search strategy designed to locate both published and unpublished studies. Systematic searches in 12 databases were performed in August 2012. Further studies were identified via hand search and contact with experts. Two reviewers independently extracted the data, also selecting information about intervention programs (elements, structure etc.), feasibility, and acceptance. Twenty-four studies were identified, of which 13 were published. Nineteen studies used a controlled design. In total, 1348 students were instructed in mindfulness, with 876 serving as controls, ranging from grade 1 to 12. Overall effect sizes were Hedge's g = 0.40 between groups and g = 0.41 within groups (p < 0.0001). Between group effect sizes for domains were: cognitive performance g = 0.80, stress g = 0.39, resilience g = 0.36, (all p < 0.05), emotional problems g = 0.19 third person ratings g = 0.25 (both n.s.). All in all, mindfulness-based interventions in children and youths hold promise, particularly in relation to improving cognitive performance and resilience to stress. However, the diversity of study samples, variety in implementation and exercises, and wide range of instruments used require a careful and differentiated examination of data. There is great heterogeneity, many studies are underpowered, and measuring effects of Mindfulness in this setting is challenging. The field is nascent and recommendations will be provided as to how interventions and research of these interventions may proceed.
Modeling how people extract color themes from images

Color choice plays an important role in works of graphic art and design. However, it can be difficult to choose a compelling set of colors, or color theme, from scratch. In this work, we present a method for extracting color themes from images using a regression model trained on themes created by people. We collect 1600 themes from Mechanical Turk as well as from artists. We find that themes extracted by Turk participants were similar to ones extracted by artists. In addition, people tended to select diverse colors and focus on colors in salient image regions. We show that our model can match human-extracted themes more closely compared to previous work. Themes extracted by our model were also rated higher as representing the image than previous approaches in a Mechanical Turk study.
Exploring Home and Work Locations in a City from Mobile Phone Data

Recently, mobile phone call detail records (CDR) have been used to study mobility patterns in cities. Since home and workplace are the most important places in people's lives and define the structure and activity patterns of the city, identifying home and work locations and home-work commuting patterns are of much interest. However, due to decreasing usage of voice calls and SMS, and low usage while people staying at home, identifying home and work locations are getting more difficult. In this paper, we develop a method to exploit daily-aggregated internet usage data (G-CDR) in addition to CDR for identifying work locations. We also develop a method for identifying home locations based on detecting sleeping time. This method can significantly increase home detection success rate and accuracy. From the identified home and work locations, we have explored the population distribution and commuting patterns of people in Bangkok.