nils-herrmann/clean_scirepeval huggingface dataset:
Culture in business process management: a literature review

Business process management (BPM) is a management approach that developed with a strong focus on the adoption of information technology (IT). However, there is a growing awareness that BPM requires a holistic organizational perspective especially since culture is often considered a key element in BPM practice. Therefore, the purpose of this paper is to provide an overview of existing research on culture in BPM.  This literature review builds on major sources of the BPM community including the BPM Journal, the BPM Conference and central journal/conference databases. Forward and backward searches additionally deepen the analysis. Based on the results, a model of culture’s role in BPM is developed.  The results of the literature review provide evidence that culture is still a widely under-researched topic in BPM. Furthermore, a framework on culture’s role in BPM is developed and areas for future research are revealed.  The analysis focuses on the concepts of BPM and culture. Thus, results do not include findings regarding related concepts such as business process reengineering or change management.  The framework provides an orientation for managerial practice. It helps identify dimensions of possible conflicts based on cultural aspects. It thus aims at raising awareness regarding potentially neglected cultural factors.  Although culture has been recognized in both theory and practice as an important aspect of BPM, researchers have not systematically engaged with the specifics of the culture phenomenon in BPM. This literature review provides a frame of reference that serves as a basis for future research regarding culture’s role in BPM.
A gameplay loops formal language

In this paper we present an approach of procedural game content generation that focuses on a gameplay loops formal language (GLFL). In fact, during an iterative game design process, game designers suggest modifications that often require high development costs. The proposed language and its operational semantic allow reducing the gap between game designers' requirement and game developers' needs, enhancing therefore video games productivity. Using gameplay loops concept for game content generation offers a low cost solution to adjust game challenges, objectives and rewards in video games. A pilot experiment have been conducted to study the impact of this approach on game development.
RECIPE: A Grammar-Based Framework for Automatically Evolving Classification Pipelines

Automatic Machine Learning is a growing area of machine learning that has a similar objective to the area of hyper-heuristics: to automatically recommend optimized pipelines, algorithms or appropriate parameters to specific tasks without much dependency on user knowledge. The background knowledge required to solve the task at hand is actually embedded into a search mechanism that builds personalized solutions to the task. Following this idea, this paper proposes RECIPE (REsilient ClassifIcation Pipeline Evolution), a framework based on grammar-based genetic programming that builds customized classification pipelines. The framework is flexible enough to receive different grammars and can be easily extended to other machine learning tasks. RECIPE overcomes the drawbacks of previous evolutionary-based frameworks, such as generating invalid individuals, and organizes a high number of possible suitable data pre-processing and classification methods into a grammar. Results of f-measure obtained by RECIPE are compared to those two state-of-the-art methods, and shown to be as good as or better than those previously reported in the literature. RECIPE represents a first step towards a complete framework for dealing with different machine learning tasks with the minimum required human intervention.
Cost effective video streaming using server push over HTTP 2.0

The Hypertext Transfer Protocol (HTTP) has been widely adopted and deployed as the key protocol for video streaming over the Internet. One of the consequences of leveraging traditional HTTP for video streaming is the significantly increased request overhead due to the segmentation of the video content into HTTP resources. The overhead becomes even more significant when non-multiplexed video and audio segments are deployed. In this paper, we investigate and address the request overhead problem by employing the server push technology in the new HTTP 2.0 protocol. In particular, we develop a set of push strategies that actively deliver video and audio content from the HTTP server without requiring a request for each individual segment. We evaluate our approach in a Dynamic Adaptive Streaming over HTTP (DASH) streaming system. We show that the request overhead can be significantly reduced by using our push strategies. Also, we validate that the server push based approach is compatible with the existing HTTP streaming features, such as adaptive bitrate switching.
Diagnosis of diabetes using classification mining techniques

Diabetes has affected over 246 million people worldwide with a majority of them being women. According to the WHO report, by 2025 this number is expected to rise to over 380 million. The disease has been named the fifth deadliest disease in the United States with no imminent cure in sight. With the rise of information technology and its continued advent into the medical and healthcare sector, the cases of diabetes as well as their symptoms are well documented. This paper aims at finding solutions to diagnose the disease by analyzing the patterns found in the data through classification analysis by employing Decision Tree and Naïve Bayes algorithms. The research hopes to propose a quicker and more efficient technique of diagnosing the disease, leading to timely treatment of the patients.
Exploiting approximate computing for deep learning acceleration

Deep Neural Networks (DNNs) have emerged as a powerful and versatile set of techniques to address challenging artificial intelligence (AI) problems. Applications in domains such as image/video processing, natural language processing, speech synthesis and recognition, genomics and many others have embraced deep learning as the foundational technique. DNNs achieve superior accuracy for these applications using very large models which require 100s of MBs of data storage, ExaOps of computation and high bandwidth for data movement. Despite advances in computing systems, training state-of-the-art DNNs on large datasets takes several days/weeks, directly limiting the pace of innovation and adoption. In this paper, we discuss how these challenges can be addressed via approximate computing. Based on our earlier studies demonstrating that DNNs are resilient to numerical errors from approximate computing, we present techniques to reduce communication overhead of distributed deep learning training via adaptive residual gradient compression (AdaComp), and computation cost for deep learning inference via Prameterized clipping ACTivation (PACT) based network quantization. Experimental evaluation demonstrates order of magnitude savings in communication overhead for training and computational cost for inference while not compromising application accuracy.
Channel Pruning for Accelerating Very Deep Neural Networks

In this paper, we introduce a new channel pruning method to accelerate very deep convolutional neural networks. Given a trained CNN model, we propose an iterative two-step algorithm to effectively prune each layer, by a LASSO regression based channel selection and least square reconstruction. We further generalize this algorithm to multi-layer and multi-branch cases. Our method reduces the accumulated error and enhance the compatibility with various architectures. Our pruned VGG-16 achieves the state-of-the-art results by 5× speed-up along with only 0.3% increase of error. More importantly, our method is able to accelerate modern networks like ResNet, Xception and suffers only 1.4%, 1.0% accuracy loss under 2× speedup respectively, which is significant.
Hacking the Nintendo Wii Remote

Since its introduction, the Nintendo Wii remote has become one of the world's most sophisticated and common input devices. Combining its impressive capability with a low cost and high degree of accessibility make it an ideal platform for exploring a variety of interaction research concepts. The author describes the technology inside the Wii remote, existing interaction techniques, what's involved in creating custom applications, and several projects ranging from multiobject tracking to spatial augmented reality that challenge the way its developers meant it to be used.
Adding Innovation Diffusion Theory to the Technology Acceptance Model: Supporting Employees' Intentions to use E-Learning Systems

This study intends to investigate factors affecting business employees’ behavioral intentions to use the elearning system. Combining the innovation diffusion theory (IDT) with the technology acceptance model (TAM), the present study proposes an extended technology acceptance model. The proposed model was tested with data collected from 552 business employees using the e-learning system in Taiwan. The results show that five perceptions of innovation characteristics significantly influenced employees’ e-learning system behavioral intention. The effects of the compatibility, complexity, relative advantage, and trialability on the perceived usefulness are significant. In addition, the effective of the complexity, relative advantage, trialability, and complexity on the perceived ease of use have a significant influence. Empirical results also provide strong support for the integrative approach. The findings suggest an extended model of TAM for the acceptance of the e-learning system, which can help organization decision makers in planning, evaluating and executing the use of e-learning systems.
Trusted VM Snapshots in Untrusted Cloud Infrastructures

A cloud customer’s inability to verifiably trust an infrastructure provider with the security of its data inhibits adoption of cloud computing. Customers could establish trust with secure runtime integrity measurements of their virtual machines (VMs). The runtime state of a VM, captured via a snapshot, is used for integrity measurement, migration, malware detection, correctness validation, and other purposes. However, commodity virtualized environments operate the snapshot service from a privileged VM. In public cloud environments, a compromised privileged VM or its potentially malicious administrators can easily subvert the integrity of a customer VMs snapshot. To this end, we present HyperShot, a hypervisor-based system that captures VM snapshots whose integrity cannot be compromised by a rogue privileged VM or its administrators. HyperShot additionally generates trusted snapshots of the privileged VM itself, thus contributing to the increased security and trustworthiness of the entire cloud infrastructure.
Measurement of Quality of Experience of Video-on-Demand Services: A Survey

Video-on-demand streaming services have gained popularity over the past few years. An increase in the speed of the access networks has also led to a larger number of users watching videos online. Online video streaming traffic is estimated to further increase from the current value of 57% to 69% by 2017, Cisco, 2014. In order to retain the existing users and attract new users, service providers attempt to satisfy the user's expectations and provide a satisfactory viewing experience. The first step toward providing a satisfactory service is to be able to quantify the users' perception of the current service level. Quality of experience (QoE) is a quality metric that provides a holistic measure of the users' perception of the quality. In this survey, we first present a tutorial overview of the popular video streaming techniques deployed for stored videos, followed by identifying various metrics that could be used to quantify the QoE for video streaming services; finally, we present a comprehensive survey of the literature on various tools and measurement methodologies that have been proposed to measure or predict the QoE of online video streaming services.
Big data analytics in E-commerce: a systematic review and agenda for future research

There has been an increasing emphasis on big data analytics (BDA) in e-commerce in recent years. However, it remains poorly-explored as a concept, which obstructs its theoretical and practical development. This position paper explores BDA in e-commerce by drawing on a systematic review of the literature. The paper presents an interpretive framework that explores the definitional aspects, distinctive characteristics, types, business value and challenges of BDA in the e-commerce landscape. The paper also triggers broader discussions regarding future research challenges and opportunities in theory and practice. Overall, the findings of the study synthesize diverse BDA concepts (e.g., definition of big data, types, nature, business value and relevant theories) that provide deeper insights along the cross-cutting analytics applications in ecommerce.
Live Data Analytics With Collaborative Edge and Cloud Processing in Wireless IoT Networks

Recently, big data analytics has received important attention in a variety of application domains including business, finance, space science, healthcare, telecommunication and Internet of Things (IoT). Among these areas, IoT is considered as an important platform in bringing people, processes, data and things/objects together in order to enhance the quality of our everyday lives. However, the key challenges are how to effectively extract useful features from the massive amount of heterogeneous data generated by resource-constrained IoT devices in order to provide real-time information and feedback to the end-users, and how to utilize this data-aware intelligence in enhancing the performance of wireless IoT networks. Although there are parallel advances in cloud computing and edge computing for addressing some issues in data analytics, they have their own benefits and limitations. The convergence of these two computing paradigms, i.e., massive virtually shared pool of computing and storage resources from the cloud and real-time data processing by edge computing, could effectively enable live data analytics in wireless IoT networks. In this regard, we propose a novel framework for coordinated processing between edge and cloud computing/processing by integrating advantages from both the platforms. The proposed framework can exploit the network-wide knowledge and historical information available at the cloud center to guide edge computing units towards satisfying various performance requirements of heterogeneous wireless IoT networks. Starting with the main features, key enablers and the challenges of big data analytics, we provide various synergies and distinctions between cloud and edge processing. More importantly, we identify and describe the potential key enablers for the proposed edge-cloud collaborative framework, the associated key challenges and some interesting future research directions.
A Recommender System using Principal Component Analysis

In this paper we examine the use of a mathematical procedure, called Principal Component Analysis, in Recommender Systems. The resulting filtering algorithm applies PCA on user ratings and demographic data, aiming to improve various aspects of the recommendation process. After a brief introduction to PCA, we provide a discussion of the proposed PCADemog algorithm, along with possible ways of combining it with different sources of filtering data. The experimental part of this work tests distinct parameterizations for PCA-Demog, identifying those with the best performance. Finally, the paper compares their results with those achieved by other filtering approaches, and draws interesting conclusions.
PERSUASION BIAS , SOCIAL INFLUENCE , AND UNIDIMENSIONAL

We propose a boundedly-rational model of opinion formation in which individuals are subject to persuasion bias; that is, they fail to account for possible repetition in the information they receive. We show that persuasion bias implies the phenomenon of social influence, whereby one’s influence on group opinions depends not only on accuracy, but also on how well-connected one is in the social network that determines communication. Persuasion bias also implies the phenomenon of unidimensional opinions; that is, individuals’ opinions over a multidimensional set of issues converge to a single “left-right” spectrum. We explore the implications of our model in several natural settings, including political science and marketing, and we obtain a number of novel empirical implications.  A Model of Persuasion – With Implication for Financial Markets,” (first draft, May 1997). We are grateful to Nick Barberis, Gary Becker, Jonathan Bendor, Larry Blume, Simon Board, Eddie Dekel, Stefano DellaVigna, Darrell Duffie, David Easley, Glenn Ellison, Simon Gervais, Ed Glaeser, Ken Judd, David Kreps, Edward Lazear, George Loewenstein, Lee Nelson, Anthony Neuberger, Matthew Rabin, José Scheinkman, Antoinette Schoar, Peter Sorenson, Pietro Veronesi, Richard Zeckhauser, three anonymous referees, and seminar participants at the American Finance Association Annual Meetings, Boston University, Cornell, Carnegie-Mellon, ESSEC, the European Summer Symposium in Financial Markets at Gerzensee, HEC, the Hoover Institution, Insead, MIT, the NBER Asset Pricing Conference, the Northwestern Theory Summer Workshop, NYU, the Stanford Institute for Theoretical Economics, Stanford, Texas A&M, UCLA, U.C. Berkeley, Université Libre de Bruxelles, University of Michigan, University of Texas at Austin, University of Tilburg, and the Utah Winter Finance Conference for helpful comments and discussions. All errors are our own.
Some challenges and grand challenges for computational intelligence

When the terms “intelligence” or “intelligent” are used by scientists, they are referring to a large collection of human cognitive behaviors— people thinking. When life scientists speak of the intelligence of animals, they are asking us to call to mind a set of human behaviors that they are asserting the animals are (or are not) capable of. When computer scientists speak of artificial intelligence, machine intelligence, intelligent agents, or (as I chose to do in the title of this essay) computational intelligence, we are also referring to that set of human behaviors. Although intelligence meanspeople thinking, we might be able to replicate the same set of behaviors using computation. Indeed, one branch of modern cognitive psychology is based on the model that the human mind and brain are complex computational “engines,” that is, we ourselves are examples of computational intelligence.
The Effect of User Involvement on System Success: A Contingency Approach

Despite the fact that commercial computer systems have been in existence for almost three decades, many systems in the process of being implemented may be classed as failures. One of the factors frequently cited as important to successful system development is involving users in the design and implementation process. This paper reports the results of a field study, conducted on data from forty-two systems, that investigates the role of user involvement and factors affecting the employment of user involvement on the success of system development. Path analysis was used to investigate both the direct effects of the contingent variables on system success and the effect of user involvement as a mediating variable between the contingent variables and system success. The results show that high system complexity and constraints on the resources available for system development are associated with less successful systems.
Towards Optimising Non-functional Requirements

Non-functional requirements are an important, and often critical, aspect of any software system. However, determining the degree to which any particular software system meets such requirements and incorporating such considerations into the software design process is a difficult challenge. This paper presents a modification of the NFR framework that allows for the discovery of a set of system functionalities that optimally satisfice a given set of non-functional requirements. This new technique introduces an adaptation of softgoal interdependency graphs, denoted softgoal interdependency ruleset graphs, in which label propagation can be done consistently. This facilitates the use of optimisation algorithms to determine the best set of bottom-level operationalizing softgoals that optimally satisfice the highest-level NFR softgoals. The proposed method also introduces the capacity to incorporate both qualitative and quantitative information.
Smart Collaborative Caching for Information-Centric IoT in Fog Computing

The significant changes enabled by the fog computing had demonstrated that Internet of Things (IoT) urgently needs more evolutional reforms. Limited by the inflexible design philosophy; the traditional structure of a network is hard to meet the latest demands. However, Information-Centric Networking (ICN) is a promising option to bridge and cover these enormous gaps. In this paper, a Smart Collaborative Caching (SCC) scheme is established by leveraging high-level ICN principles for IoT within fog computing paradigm. The proposed solution is supposed to be utilized in resource pooling, content storing, node locating and other related situations. By investigating the available characteristics of ICN, some challenges of such combination are reviewed in depth. The details of building SCC, including basic model and advanced algorithms, are presented based on theoretical analysis and simplified examples. The validation focuses on two typical scenarios: simple status inquiry and complex content sharing. The number of clusters, packet loss probability and other parameters are also considered. The analytical results demonstrate that the performance of our scheme, regarding total packet number and average transmission latency, can outperform that of the original ones. We expect that the SCC will contribute an efficient solution to the related studies.
Solutionism, the Game: Design Fictions for Positive Aging

This paper reports a qualitative study of thriving older people and illustrates the findings with design fiction. Design research has been criticized as "solutionist" i.e. solving problems that don't exist or providing "quick fixes" for complex social, political and environmental problems. We respond to this critique by presenting a "solutionist" board game used to generate design concepts. Players are given data cards and technology dice, they move around the board by pitching concepts that would support positive aging. We argue that framing concept design as a solutionist game explicitly foregrounds play, irony and the limitations of technological intervention. Three of the game concepts are presented as design fictions in the form of advertisements for products and services that do not exist. The paper argues that design fiction can help create a space for design beyond solutionism.
LEA: A 128-Bit Block Cipher for Fast Encryption on Common Processors

We propose a new block cipher LEA, which has 128-bit block size and 128, 192, or 256-bit key size. It provides a high-speed software encryption on general-purpose processors. Our experiments show that LEA is faster than AES on Intel, AMD, ARM, and ColdFire platforms. LEA can be also implemented to have tiny code size. Its hardware implementation has a competitive throughput per area. It is secure against all the existing attacks on block ciphers.