nils-herrmann/clean_scirepeval huggingface dataset:
Mapping the moral domain.

The moral domain is broader than the empathy and justice concerns assessed by existing measures of moral competence, and it is not just a subset of the values assessed by value inventories. To fill the need for reliable and theoretically grounded measurement of the full range of moral concerns, we developed the Moral Foundations Questionnaire on the basis of a theoretical model of 5 universally available (but variably developed) sets of moral intuitions: Harm/Care, Fairness/Reciprocity, Ingroup/Loyalty, Authority/Respect, and Purity/Sanctity. We present evidence for the internal and external validity of the scale and the model, and in doing so we present new findings about morality: (a) Comparative model fitting of confirmatory factor analyses provides empirical justification for a 5-factor structure of moral concerns; (b) convergent/discriminant validity evidence suggests that moral concerns predict personality features and social group attitudes not previously considered morally relevant; and (c) we establish pragmatic validity of the measure in providing new knowledge and research opportunities concerning demographic and cultural differences in moral intuitions. These analyses provide evidence for the usefulness of Moral Foundations Theory in simultaneously increasing the scope and sharpening the resolution of psychological views of morality.
HCI Education: Innovation, Creativity and Design Thinking

Human-Computer Interaction (HCI) education needs re-thinking. In this paper, we explore how and what creativity and design thinking could contribute with, if included as a part of the HCI curriculum. The findings from courses where design thinking was included, indicate that design thinking contributed to increased focus on innovation and creativity, as well as prevented too early fixation on a single solution in the initial phases of HCI design processes, fostering increased flexibility and adaptability in learning processes. The creativity and adaptability may be the best long-term foci that HCI education can add to its curriculums and offer to students when preparing them for future work
Making resource decisions for software projects

Software metrics should support managerial decisionmaking in software projects. We explain how traditionalmetrics approaches, such as regression-based models forcost estimation fall short of this goal. Instead, wedescribe a causal model (using a Bayesian network)which incorporates empirical data, but allows it to beinterpreted and supplemented using expert judgement.We show how this causal model is used in a practicaldecision-support tool, allowing a project manager totrade-off the resources used against the outputs(delivered functionality, quality achieved) in a softwareproject. The model and toolset have evolved in a numberof collaborative projects and hence capture significantcommercial input. Extensive validation trials are takingplace among partners on the EC funded projectMODIST (this includes Philips, Israel Aircraft Industriesand ) and the feedback so far has been verygood. The estimates are sensible and the causalmodelling approach enables decision-makers to reasonin a way that is not possible with other projectmanagement and resource estimation tools. To ensurewide dissemination and validation a version of thetoolset with the full underlying model is being madeavailable for free to researchers.
A scoping review of scoping reviews: advancing the approach and enhancing the consistency

The scoping review has become an increasingly popular approach for synthesizing research evidence. It is a relatively new approach for which a universal study definition or definitive procedure has not been established. The purpose of this scoping review was to provide an overview of scoping reviews in the literature.



A scoping review was conducted using the Arksey and O'Malley framework. A search was conducted in four bibliographic databases and the gray literature to identify scoping review studies. Review selection and characterization were performed by two independent reviewers using pretested forms.



The search identified 344 scoping reviews published from 1999 to October 2012. The reviews varied in terms of purpose, methodology, and detail of reporting. Nearly three-quarter of reviews (74.1%) addressed a health topic. Study completion times varied from 2 weeks to 20 months, and 51% utilized a published methodological framework. Quality assessment of included studies was infrequently performed (22.38%).



Scoping reviews are a relatively new but increasingly common approach for mapping broad topics. Because of variability in their conduct, there is a need for their methodological standardization to ensure the utility and strength of evidence.
Tweet Sarcasm Detection Using Deep Neural Network

Sarcasm detection has been modeled as a binary document classification task, with rich features being defined manually over input documents. Traditional models employ discrete manual features to address the task, with much research effect being devoted to the design of effective feature templates. We investigate the use of neural network for tweet sarcasm detection, and compare the effects of the continuous automatic features with discrete manual features. In particular, we use a bi-directional gated recurrent neural network to capture syntactic and semantic information over tweets locally, and a pooling neural network to extract contextual features automatically from history tweets. Results show that neural features give improved accuracies for sarcasm detection, with different error distributions compared with discrete manual features.
Fixed-Rate Compressed Floating-Point Arrays

Current compression schemes for floating-point data commonly take fixed-precision values and compress them to a variable-length bit stream, complicating memory management and random access. We present a fixed-rate, near-lossless compression scheme that maps small blocks of 4d values in d dimensions to a fixed, user-specified number of bits per block, thereby allowing read and write random access to compressed floating-point data at block granularity. Our approach is inspired by fixed-rate texture compression methods widely adopted in graphics hardware, but has been tailored to the high dynamic range and precision demands of scientific applications. Our compressor is based on a new, lifted, orthogonal block transform and embedded coding, allowing each per-block bit stream to be truncated at any point if desired, thus facilitating bit rate selection using a single compression scheme. To avoid compression or decompression upon every data access, we employ a software write-back cache of uncompressed blocks. Our compressor has been designed with computational simplicity and speed in mind to allow for the possibility of a hardware implementation, and uses only a small number of fixed-point arithmetic operations per compressed value. We demonstrate the viability and benefits of lossy compression in several applications, including visualization, quantitative data analysis, and numerical simulation.
Social-aware hybrid mobile offloading

Mobile offloading is a promising technique to aid the constrained resources of a mobile device. By offloading a computational task, a device can save energy and increase the performance of the mobile applications. Unfortunately, in existing offloading systems, the opportunistic moments to offload a task are often sporadic and short-lived. We overcome this problem by proposing a social-aware hybrid offloading system (HyMobi), which increases the spectrum of offloading opportunities. As a mobile device is always co-located to at least one source of network infrastructure throughout of the day, by merging cloudlet, device-to-device and remote cloud offloading, we increase the availability of offloading support. Integrating these systems is not trivial. In order to keep such coupling, a strong social catalyst is required to foster user’s participation and collaboration. Thus, we equip our system with an incentive mechanism based on credit and reputation, which exploits users’ social aspects to create offload communities. We evaluate our system under controlled and in-the-wild scenarios. With credit, it is possible for a device to create opportunistic moments based on user’s present need. As a result, we extended the widely used opportunistic model with a long-term Email addresses: hflores@ee.oulu.fi (Huber Flores), rajesh.sharma@unibo.it (Rajesh Sharma), denzil.ferreira@ee.oulu.fi (Denzil Ferreira), vassilis@ee.oulu.fi (Vassilis Kostakos), jukka.manner@aalto.fi (Jukka Manner), sasu.tarkoma@helsinki.fi (Sasu Tarkoma), panhui@cse.ust.hk (Pan Hui), liyong07@tsinghua.edu.cn (Yong Li)  to Pervasive and Mobile Computing September 23, 2016 perspective that significantly improves the offloading process and encourages unsupervised offloading adoption in the wild.
Comprehensive learning particle swarm optimizer for global optimization of multimodal functions

This paper presents a variant of particle swarm optimizers (PSOs) that we call the comprehensive learning particle swarm optimizer (CLPSO), which uses a novel learning strategy whereby all other particles' historical best information is used to update a particle's velocity. This strategy enables the diversity of the swarm to be preserved to discourage premature convergence. Experiments were conducted (using codes available from http://www.ntu.edu.sg/home/epnsugan) on multimodal test functions such as Rosenbrock, Griewank, Rastrigin, Ackley, and Schwefel and composition functions both with and without coordinate rotation. The results demonstrate good performance of the CLPSO in solving multimodal problems when compared with eight other recent variants of the PSO.
Performance Evaluation of a U-Shaped Less-Rare-Earth Hybrid Permanent Magnet Assisted Synchronous Reluctance Motor

In order to solve the rare-earth crisis emerged in recent years, a new permanent magnet assisted synchronous reluctance motor with less rare-earth materials is proposed in this paper. The novelty of the motor is the less rare-earth PMs and more ferrite PMs are artfully inset in its rotor with U shapes. Furthermore, an improved rotor structure with sectorial flux barriers is designed for less magnetic flux leakage. By using finite-element analysis method, the electromagnetic performances are evaluated, including flux density distribution, back-EMF and harmonic frequency analysis. In particular, the torque production characteristics are investigated in details. Finally, the analysis results indicate that the proposed motor can offer a desirable torque capability with relatively less rare earth materials.
Galvanic Skin Response : A Physiological Sensor System for Affective Computing

system is the change in electrical properties of skin due to variation in physiological and psychological conditions. The change is caused by the degree to which a person's sweat glands are active. Psychological status of a person tends to make the glands active and this change the skin resistance. Drier the skin is, higher will be the skin resistance. This variation in skin resistance ranges from 5kΩ to 25kΩ. In the current work, a subject whose Galvanic skin response (GSR) is to be measured, has been shown/played a movie clipping, images or recorded audio signals. Depending upon the theme, emotion will be evoked in the subject. Due to the change in emotion, GSR varies. This variation in GSR is recorded for a fixed time interval. In the present work, a total of 75 subjects are selected. People from different age groups and social background, both male and female have been carefully considered. Data acquisition and analysis is carried out by using LabVIEW. The results obtained are convincing and follows the ground reality with a high level of accuracy. As the subject also gives the feedback about the emotions he/she experienced, the results obtained are validated.
Variance Regularized Counterfactual Risk Minimization via Variational Divergence Minimization

Off-policy learning, the task of evaluating and improving policies using historic data collected from a logging policy, is important because onpolicy evaluation is usually expensive and has adverse impacts. One of the major challenge of off-policy learning is to derive counterfactual estimators that also has low variance and thus low generalization error. In this work, inspired by learning bounds for importance sampling problems, we present a new counterfactual learning principle for off-policy learning with bandit feedbacks. Our method regularizes the generalization error by minimizing the distribution divergence between the logging policy and the new policy, and removes the need for iterating through all training samples to compute sample variance regularization in prior work. With neural network policies, our end-to-end training algorithms using variational divergence minimization showed significant improvement over conventional baseline algorithms and is also consistent with our theoretical results.
Big Data and Transformational Government

The big data phenomenon is growing throughout private and public sector domains. Profit motives make it urgent for companies in the private sector to learn how to leverage big data. However, in the public sector, government services could also be greatly improved through the use of big data. Here, the authors describe some drivers, barriers, and best practices affecting the use of big data and associated analytics in the government domain. They present a model that illustrates how big data can result in transformational government through increased efficiency and effectiveness in the delivery of services. Their empirical basis for this model uses a case vignette from the US Department of Veterans Affairs, while the theoretical basis is a balanced view of big data that takes into account the continuous growth and use of such data. This article is part of a special issue on big data and business analytics.
MODELING AND FORECASTING CALL CENTER ARRIVALS : A LITERATURE SURVEY by Rouba

The effective management of call centers is a challenging task mainly because managers are consistently facing considerable uncertainty. Among important sources of uncertainty are call arrival rates which are typically time-varying, stochastic, dependent across time periods and across call types, and often affected by external events. Accurately modeling and forecasting future call arrival volumes is a complicated issue which is critical for making important operational decisions, such as staffing and scheduling, in the call center. In this paper, we review the existing literature on modeling and forecasting call arrivals. We also discuss the key issues in building good statistical arrival models. Additionally, we evaluate the forecasting accuracy of selected models in an empirical study with real-life call center data. We conclude by summarizing future research directions in this important field.
Novel selection policies for container-based cloud deployment models

Application deployment models have been introduced over the last decades. These models are characterized by low resource utilization efficiencies which are improved by the appearance of virtualization technologies. Currently, a more efficient utilization model called containers type virtualization has appeared. This allows different applications to share the same operating system kernel resulting in a superior application density. However, the developed infrastructures and frameworks contribute to the energy consumption and violation rates of the service level agreement. In this paper, a novel container and host selection policies for cloud deployment models are proposed. Experimental results show that the proposed policies achieve better energy and services level agreement commitment compared to the other competitors. This paper, demonstrate the existence of an association between container and host selection policies.
Probabilistic reasoning in intelligent systems - networks of plausible inference

Probabilistic methods to create the areas, of computational tools. But I needed to get canned, bayesian networks worked recently strongly. Recently I tossed this book was published. In intelligent systems is researchers in, ai operations research excellence award for graduate. Too concerned about how it i've been. Apparently daphne koller and learning structures evidential reasoning. Pearl is a language for i've. Despite its early publication date it, is not great give the best references.
HANDGESTURERECOGNITION: A LITERATURE REVIEW

Hand gesture recognition system received great attention in the recent few years because of its manifoldness applications and the ability to interact with machine efficiently through human computer interaction. In this paper a survey of recent hand gesture recognition systems is presented. Key issues of hand gesture recognition system are presented with challenges of gesture system. Review methods of recent postures and gestures recognition system presented as well. Summary of research results of hand gesture methods, databases, and comparison between main gesture recognition phases are also given. Advantages and drawbacks of the discussed systems are explained finally.
A review of affective computing: From unimodal analysis to multimodal fusion

Affective computing is an emerging interdisciplinary research field bringing together researchers and practitioners from various fields, ranging from artificial intelligence, natural language processing, to cognitive and social sciences. With the proliferation of videos posted online (e.g., on YouTube, Facebook, Twitter) for product reviews, movie reviews, political views, and more, affective computing research has increasingly evolved from conventional unimodal analysis to more complex forms of multimodal analysis. This is the primary motivation behind our first of its kind, comprehensive literature review of the diverse field of affective computing. Furthermore, existing literature surveys lack a detailed discussion of state of the art in multimodal affect analysis frameworks, which this review aims to address. Multimodality is defined by the presence of more than one modality or channel, e.g., visual, audio, text, gestures, and eye gage. In this paper, we focus mainly on the use of audio, visual and text information for multimodal affect analysis, since around 90% of the relevant literature appears to cover these three modalities. Following an overview of different techniques for unimodal affect analysis, we outline existing methods for fusing information from different modalities. As part of this review, we carry out an extensive study of different categories of state-of-the-art fusion techniques, followed by a critical analysis of potential performance improvements with multimodal analysis compared to unimodal analysis. A comprehensive overview of these two complementary fields aims to form the building blocks for readers, to better understand this challenging and exciting research field.
Additively Manufactured Nanotechnology and Origami-Enabled Flexible Microwave Electronics

Inkjet printing on flexible paper and additive manufacturing technologies (AMT) are introduced for the sustainable ultra-low-cost fabrication of flexible radio frequency (RF)/microwave electronics and sensors. This paper covers examples of state-of-the-art integrated wireless sensor modules on paper or flexible polymers and shows numerous inkjet-printed passives, sensors, origami, and microfluidics topologies. It also demonstrates additively manufactured antennas that could potentially set the foundation for the truly convergent wireless sensor ad-hoc networks of the future with enhanced cognitive intelligence and “zero-power” operability through ambient energy harvesting and wireless power transfer. The paper also discusses the major challenges for the realization of inkjet-printed/3-D printed high-complexity flexible modules as well as future directions in the area of environmentally-friendly “Green”) RF electronics and “Smart-House” conformal sensors.
Defining Explainable AI for Requirements Analysis

Explainable artificial intelligence (XAI) has become popular in the last few years. The artificial intelligence (AI) community in general, and the machine learning (ML) community in particular, is coming to the realisation that in many applications, for AI to be trusted, it must not only demonstrate good performance in its decisionmaking, but it also must explain these decisions and convince us that it is making the decisions for the right reasons. However, different applications have different requirements on the information required of the underlying AI system in order to convince us that it is worthy of our trust. How do we define these requirements? In this paper, we present three dimensions for categorising the explanatory requirements of different applications. These are Source, Depth and Scope. We focus on the problem of matching up the explanatory requirements of different applications with the capabilities of underlying ML techniques to provide them. We deliberately avoid including aspects of explanation that are already well-covered by the existing literature and we focus our discussion on ML although the principles apply to AI more broadly.
Advances in learning analytics and educational data mining

The growing interest in recent years towards Learning Analytics (LA) and Educational Data Mining (EDM) has enabled novel approaches and advancements in educational settings. The wide variety of research and practice in this context has enforced important possibilities and applications from adaptation and personalization of Technology Enhanced Learning (TEL) systems to improvement of instructional design and pedagogy choices based on students needs. LA and EDM play an important role in enhancing learning processes by offering innovative methods of development and integration of more personalized, adaptive, and interactive educational environments. This has motivated the organization of the ESANN 2015 Special Session in Advances in Learning Analytics and Educational Data Mining. Here, a review of research and practice in LA and EDM is presented accompanied by the most central methods, benefits, and challenges of the field. Additionally, this paper covers a review of novel contributions into the Special Session.
A survey on attacks, security and trust management solutions in VANETs

Vehicular Ad hoc NETwork (VANET) is an emerging paradigm in networking. It is a new form of Mobile Ad hoc NETwork (MANET). VANET enables vehicle to vehicle (V2V) communication with the goal of providing road safety and reduce traffic congestion. However, dishonest (malicious) peers (vehicles) in a VANET many send out false information to maximize their own utility. Now a day, VANET has been taken more attention of researchers and automotive industries due to life saving factor. In this paper, we first discuss the applications and unique characteristics of VANETs that distinguish them from MANETs. We also discuss about different attacks and security issues in VANET. We then survey existing trust models in VANETs, and point out their key issues. Based on these studies, we suggest desired properties towards effective trust management in VANETs.