nils-herrmann/clean_scirepeval huggingface dataset:
A review of enterprise agility : Concepts , frameworks , and attributes

Existing knowledge about agile manufacturing (AM) and an agile workforce was reviewed in order to extend the concept of agility to the whole enterprise. The frameworks that describe other elements of the enterprise besides manufacturing, or contain attributes that could be applied to all enterprise structures were also included into the review. The review identified a large number of papers related to the AM. However, there was little empirical research done on the agile workforce and agile organization. This review identified the global characteristics of agility which can be applied to all aspects of enterprise: flexibility, responsiveness, speed, culture of change, integration and low complexity, high quality and customized products, and mobilization of core competencies. The need for further research in order to empirically establish and validate the attributes and indices of the agile workforce and agile enterprise has also been discussed. Purchase Export Previous article Next article Check if you have access through your login credentials or your institution.
Privacy-Preserving Smart Grid Tariff Decisions with Blockchain-Based Smart Contracts

The smart grid changes the way how energy and information are exchanged and offers opportunities for incentive-based load balancing. For instance, customers may shift the time of energy consumption of household appliances in exchange for a cheaper energy tariff. This paves the path towards a full range of modular tariffs and dynamic pricing that incorporate the overall grid capacity as well as individual customer demands. This also allows customers to frequently switch within a variety of tariffs from different utility providers based on individual energy consumption and provision forecasts. For automated tariff decisions it is desirable to have a tool that assists in choosing the optimum tariff based on a prediction of individual energy need and production. However, the revelation of individual load patterns for smart grid applications poses severe privacy threats for customers as analyzed in depth in literature. Similarly, accurate and fine-grained regional load forecasts are sensitive business information of utility providers that are not supposed to be released publicly. This paper extends previous work in the domain of privacy-preserving load profile matching where load profiles from utility providers and load profile forecasts from customers are transformed in a distance-preserving embedding in order to find a matching tariff. The embeddings neither reveal individual contributions of customers, nor those of utility providers. Prior work requires a dedicated entity that needs to be trustworthy at least to some extent for determining the matches. In this paper we propose an adaption of this protocol, where we use blockchains and smart contracts for this matching process, instead. Blockchains are gaining widespread adaption in the smart grid domain as a powerful tool for public commitments and accountable calculations. While the use of a decentralized and trust-free blockchain for this protocol comes at the price of some privacy degradation (for which a mitigation is outlined), this drawback is outweighed for it enables verifiability, reliability and transparency. Fabian Knirsch, Andreas Unterweger, Günther Eibl and Dominik Engel Salzburg University of Applied Sciences, Josef Ressel Center for User-Centric Smart Grid Privacy, Security and Control, Urstein Süd 1, 5412 Puch bei Hallein, Austria.
What Can Police Do to Reduce Crime , Disorder , and Fear ?

The authors review research on police effectiveness in reducing crime, disorder, and fear in the context of a typology of innovation in police practices. That typology emphasizes two dimensions: one concerning the diversity of approaches, and the other, the level of focus. The authors find that little evidence supports the standard model of policing—low on both of these dimensions. In contrast, research evidence does support continued investment in police innovations that call for greater focus and tailoring of police efforts, combined with an expansion of the tool box of policing beyond simple law enforcement. The strongest evidence of police effectiveness in reducing crime and disorder is found in the case of geographically focused police practices, such as hot-spots policing. Community policing practices are found to reduce fear of crime, but the authors do not find consistent evidence that community policing (when it is implemented without models of problem-oriented policing) affects either crime or disorder. A developing body of evidence points to the effectiveness of problemoriented policing in reducing crime, disorder, and fear. More generally, the authors find that many policing practices applied broadly throughout the United States either have not been the subject of systematic research or have been examined in the context of research designs that do not allow practitioners or policy makers to draw very strong conclusions.
Regression Facial Attribute Classification via simultaneous dictionary learning

Recently, many researchers have attempted to classify Facial Attributes (FAs) by representing characteristics of FAs such as attractiveness, age, smiling and so on. In this context, recent studies have demonstrated that visual FAs are a strong background for many applications such as face verification, face search and so on. However, Facial Attribute Classification (FAC) in a wide range of attributes based on the regression representation -predicting of FAs as real-valued labelsis still a significant challenge in computer vision and psychology. In this paper, a regression model formulation is proposed for FAC in a wide range of FAs (e.g. 73 FAs). The proposed method accommodates real-valued scores to the probability of what percentage of the given FAs is present in the input image. To this end, two simultaneous dictionary learning methods are proposed to learn the regression and identity feature dictionaries simultaneously. Accordingly, a multi-level feature extraction is proposed for FAC. Then, four regression classification methods are proposed using a regression model formulated based on dictionary learning, SRC and CRC. Convincing results are
Choosing the right business process maturity model

We have built and tested a decision tool which will help organisations properly select one business process maturity model (BPMM) over another. This prototype consists of a novel questionnaire with decision criteria for BPMM selection, linked to a unique data set of 69 BPMMs. Fourteen criteria (questions) were elicited from an international Delphi study, and weighed by the analytical hierarchy process. Case studies have shown (non-)profit and academic applications. Our purpose was to describe criteria that enable an informed BPMM choice (conform to decision-making theories, rather than ad hoc). Moreover, we propose a design process for building BPMM decision tools.
Visualizing Social Media Sentiment in Disaster Scenarios

Recently, social media, such as Twitter, has been successfully used as a proxy to gauge the impacts of disasters in real time. However, most previous analyses of social media during disaster response focus on the magnitude and location of social media discussion. In this work, we explore the impact that disasters have on the underlying sentiment of social media streams. During disasters, people may assume negative sentiments discussing lives lost and property damage, other people may assume encouraging responses to inspire and spread hope. Our goal is to explore the underlying trends in positive and negative sentiment with respect to disasters and geographically related sentiment. In this paper, we propose a novel visual analytics framework for sentiment visualization of geo-located Twitter data. The proposed framework consists of two components, sentiment modeling and geographic visualization. In particular, we provide an entropy-based metric to model sentiment contained in social media data. The extracted sentiment is further integrated into a visualization framework to explore the uncertainty of public opinion. We explored Ebola Twitter dataset to show how visual analytics techniques and sentiment modeling can reveal interesting patterns in disaster scenarios.
Design for the Value of Human Well-Being

This chapter studies how and to what extent it is possible to design for well-being. Well-being is rarely considered in the design literature, and is rarely linked to technology and design in philosophy and the social sciences. A few approaches to design for well-being have recently materialized, however, including Emotional Design, capability approaches, positive psychology approaches, and Life-Based Design. In this chapter, the notion of well-being will first be clarified and contemporary theories of and approaches to wellbeing will be reviewed. Next, theoretical and methodological issues in design for well-being will be discussed that must be accounted for in any successful approach. This will be followed by a review of the abovementioned four approaches to design for well-being. The chapter will conclude by considering open issues and future work in the development of design approaches for well-being.
Review of Three-Dimensional Human-Computer Interaction with Focus on the Leap Motion Controller

Modern hardware and software development has led to an evolution of user interfaces from command-line to natural user interfaces for virtual immersive environments. Gestures imitating real-world interaction tasks increasingly replace classical two-dimensional interfaces based on Windows/Icons/Menus/Pointers (WIMP) or touch metaphors. Thus, the purpose of this paper is to survey the state-of-the-art Human-Computer Interaction (HCI) techniques with a focus on the special field of three-dimensional interaction. This includes an overview of currently available interaction devices, their applications of usage and underlying methods for gesture design and recognition. Focus is on interfaces based on the Leap Motion Controller (LMC) and corresponding methods of gesture design and recognition. Further, a review of evaluation methods for the proposed natural user interfaces is given.
Biology of Consciousness

The Dynamic Core and Global Workspace hypotheses were independently put forward to provide mechanistic and biologically plausible accounts of how brains generate conscious mental content. The Dynamic Core proposes that reentrant neural activity in the thalamocortical system gives rise to conscious experience. Global Workspace reconciles the limited capacity of momentary conscious content with the vast repertoire of long-term memory. In this paper we show the close relationship between the two hypotheses. This relationship allows for a strictly biological account of phenomenal experience and subjectivity that is consistent with mounting experimental evidence. We examine the constraints on causal analyses of consciousness and suggest that there is now sufficient evidence to consider the design and construction of a conscious artifact.
Friction Stir Welding of 5 xxx series Aluminium Alloys A Literature Survey

As the concept of Friction stir welding is relatively new, there are many areas, which need thorough investigation to optimize and make it commercially viable. In order to obtain the desired mechanical properties, certain process parameters, like rotational and translation speeds, tool tilt angle, tool geometry etc. are to be controlled. Aluminum alloys of 5xxx series and their welded joints show good resistance to corrosion in sea water. Here, a literature survey has been carried out for the friction stir welding of 5xxx series aluminum alloys.
Mining Electronic Health Records (EHRs): A Survey

The continuously increasing cost of the US healthcare system has received significant attention. Central to the ideas aimed at curbing this trend is the use of technology in the form of the mandate to implement electronic health records (EHRs). EHRs consist of patient information such as demographics, medications, laboratory test results, diagnosis codes, and procedures. Mining EHRs could lead to improvement in patient health management as EHRs contain detailed information related to disease prognosis for large patient populations. In this article, we provide a structured and comprehensive overview of data mining techniques for modeling EHRs. We first provide a detailed understanding of the major application areas to which EHR mining has been applied and then discuss the nature of EHR data and its accompanying challenges. Next, we describe major approaches used for EHR mining, the metrics associated with EHRs, and the various study designs. With this foundation, we then provide a systematic and methodological organization of existing data mining techniques used to model EHRs and discuss ideas for future research.
Systematic review of the surgery-first approach in orthognathic surgery.

The surgery-first approach in orthognathic surgery has recently created a broader interest in completely eliminating time-consuming preoperative orthodontic treatment. Available evidence on the surgery-first approach should be appraised to support its use in orthognathic surgery. A MEDLINE search using the keywords "surgery first" and "orthognathic surgery" was conducted to select studies using the surgery-first approach. We also manually searched the reference list of the selected keywords to include articles not selected by the MEDLINE search. The search identified 18 articles related to the surgery-first approach. There was no randomized controlled clinical trial. Four papers were excluded as the content was only personal opinion or basic scientific research. Three studies were retrospective cohort studies in nature. The other 11 studies were case reports. For skeletal Class III surgical correction, the final long-term outcomes for maxillofacial and dental relationship were not significantly different between the surgery-first approach and the orthodontics-first approach in transverse (e.g., intercanine or intermolar width) dimension, vertical (e.g., anterior open bite, lower anterior facial height) dimension, and sagittal (e.g., anterior-posterior position of pogonion and lower incisors) dimension. Total treatment duration was substantially shorter in cases of surgery-first approach use. In conclusion, most published studies related to the surgery-first approach were mainly on orthognathic correction of skeletal Class III malocclusion. Both the surgery-first approach and orthodontics-first approach had similar long-term outcomes in dentofacial relationship. However, the surgery-first approach had shorter treatment time.
FP-DNN: An Automated Framework for Mapping Deep Neural Networks onto FPGAs with RTL-HLS Hybrid Templates

DNNs (Deep Neural Networks) have demonstrated great success in numerous applications such as image classification, speech recognition, video analysis, etc. However, DNNs are much more computation-intensive and memory-intensive than previous shallow models. Thus, it is challenging to deploy DNNs in both large-scale data centers and real-time embedded systems. Considering performance, flexibility, and energy efficiency, FPGA-based accelerator for DNNs is a promising solution. Unfortunately, conventional accelerator design flows make it difficult for FPGA developers to keep up with the fast pace of innovations in DNNs. To overcome this problem, we propose FP-DNN (Field Programmable DNN), an end-to-end framework that takes TensorFlow-described DNNs as input, and automatically generates the hardware implementations on FPGA boards with RTL-HLS hybrid templates. FP-DNN performs model inference of DNNs with our high-performance computation engine and carefully-designed communication optimization strategies. We implement CNNs, LSTM-RNNs, and Residual Nets with FPDNN, and experimental results show the great performance and flexibility provided by our proposed FP-DNN framework.
Balinese Script’s Character Reconstruction Using Linear Discriminant Analysis

Balinese people have one of the civilization histories and cultural heritage are handwritten in Balinese script on palm leaves known as Balinese Papyrus (LontarAksara Bali). Until now that cultural heritage is still continuously strived its preservation along with the implementation begin to be abandoned in public life. Some of Balinese Papyrus now begins to rot and fade under influenced by age. Information technology utilization can be a tool to solve the problems faced in the preservation of the Balinese papyrus. By using digital image processing techniques, the papyrus script can be reconstructed digitally so that it can be retrieved and store the content in the digital media. Balinese papyrus reconstructed through several processes from scanning into a digital image, performing preprocessing for image quality improvement, segmenting the Balinese characters on image, doing character recognition using LDA algorithm, rearranging the result of recognition in accordance with the original content in papyrus, and translating that characters result into Latin. LDA algorithm quite successfully performs the classification associated with handwritten character recognition.
How stress influences the immune response.

In response to a stressor, physiological changes are set into motion to help an individual cope with the stressor. However, chronic activation of these stress responses, which include the hypothalamic–pituitary–adrenal axis and the sympathetic–adrenal–medullary axis, results in chronic production of glucocorticoid hormones and catecholamines. Glucocorticoid receptors expressed on a variety of immune cells bind cortisol and interfere with the function of NF-kB, which regulates the activity of cytokine-producing immune cells. Adrenergic receptors bind epinephrine and norepinephrine and activate the cAMP response element binding protein, inducing the transcription of genes encoding for a variety of cytokines. The changes in gene expression mediated by glucocorticoid hormones and catecholamines can dysregulate immune function. There is now good evidence (in animal and human studies) that the magnitude of stress-associated immune dysregulation is large enough to have health implications.
Internet and video game addictions : a cognitive behavioral approach

While several benefits are attributed to the Internet and video games, an important proportion of the population presents symptoms related to possible new technological addictions and there has been little discussion of treatment of problematic technology use. Although demand for knowledge is growing, only a small number of treatments have been described.  To conduct a systematic review of the literature, to establish Cognitive Behavioral Therapy (CBT) as a possible strategy for treating Internet and video game addictions.  The review was conducted in the following databases: Science Direct on Line, PubMed, PsycINFO, Cochrane Clinical Trials Library, BVS and SciELO. The keywords used were: Cognitive Behavioral Therapy; therapy; treatment; with association to the terms Internet addiction and video game addiction. Given the scarcity of studies in the field, no restrictions to the minimum period of publication were made, so that articles found until October 2013 were accounted.  Out of 72 articles found, 23 described CBT as a psychotherapy for Internet and video game addiction. The manuscripts showed the existence of case studies and protocols with satisfactory efficacy.  Despite the novelty of technological dependencies, CBT seems to be applicable and allows an effective treatment for this population. Lemos IL, et al. / Rev Psiq Clín. 2014;41(3):82-8
Semantic Web

ion is one dimension of context; the other is culture. The term culture is used both to describe socio-cultural systems, which are artifactual and knowable, and ideational systems, which are systems of shared ideas, rules and meanings that underlie and are expressed in the way that humans live (Keesing & Strathern, 1998). Both cultures are key to the flow of knowledge within an organization. We need to transfer to new members, in both society and the organization, knowledge that has been painfully created at cost over previous generations.  Diversity over Time and Space The dimensions of abstraction and culture create the sensemaking model, shown below in Figure 1. Feature Cynefin (pronounced kun-ev’in) is a Welsh word with no direct equivalent in English. As a noun it is translated as habitat, as an adjective acquainted or familiar, but dictionary definitions fail to do it justice. It links a community into its shared history – or histories – in a way that paradoxically both limits the perception of that community while enabling an instinctive and intuitive ability to adapt to conditions of profound uncertainty. In general, if a community is not physically, temporally and spiritually rooted, then it is alienated from its environment and will focus on survival rather than creativity and collaboration. In such conditions, knowledge hoarding will predominate and the community will close itself to the external world. If the alienation becomes extreme, the community may even turn in on itself, atomizing into an incoherent babble of competing self interests. Critically it emphasizes that we never start from a zero base when we design a knowledge system, all players in that system come with the baggage, positive and negative derived from multiple histories.  Common Sense Making Informal Interdependent The informal organization
3D Segmentation with Exponential Logarithmic Loss for Highly Unbalanced Object Sizes

With the introduction of fully convolutional neural networks, deep learning has raised the benchmark for medical image segmentation on both speed and accuracy, and different networks have been proposed for 2D and 3D segmentation with promising results. Nevertheless, most networks only handle relatively small numbers of labels (<10), and there are very limited works on handling highly unbalanced object sizes especially in 3D segmentation. In this paper, we propose a network architecture and the corresponding loss function which improve segmentation of very small structures. By combining skip connections and deep supervision with respect to the computational feasibility of 3D segmentation, we propose a fast converging and computationally efficient network architecture for accurate segmentation. Furthermore, inspired by the concept of focal loss, we propose an exponential logarithmic loss which balances the labels not only by their relative sizes but also by their segmentation difficulties. We achieve an average Dice coefficient of 82% on brain segmentation with 20 labels, with the ratio of the smallest to largest object sizes as 0.14%. Less than 100 epochs are required to reach such accuracy, and segmenting a 128×128×128 volume only takes around 0.4 s.
Semantic Frames as Interlingual Representations for Multilingual Lexical Databases

This paper presents a novel approach to constructing multilingual lexical databases using semantic frames. Starting with the conceptual information contained in the English FrameNet database, we propose a corpus-based procedure for producing parallel lexicon fragments for Spanish, German, and Japanese, which mirror the English entries in breadth and depth. The resulting lexicon fragments are linked to each other via semantic frames, which function as interlingual representations. The resulting parallel FrameNets differ from other multilingual databases in three significant points: (1) They provide for each entry an exhaustive account of the semantic and syntactic combinatorial possibilities of each lexical unit; (2) They offer for each entry semantically annotated example sentences from large electronic corpora; (3) By employing semantic frames as interlingual representations, the parallel FrameNets make use of independently existing linguistic concepts that can be empirically verified.