“Familiar strangers” in the big data era: An exploratory study of Beijing metro encounters
"غریبه های آشنا" در عصر داده های بزرگ: یک مطالعه اکتشافی از برخورد مترو پکن-2020
Traditionally, familiar strangers are defined as those we encounter and observe repeatedly in the city but never interact with. They are common to most urban dwellers. They also have various socioeconomic, sociopsychological and public-policy implications, which have only been sporadically mentioned and/or examined in existing studies across different disciplines. In this manuscript, we first summarize fragmental existing studies on familiar strangers that are defined in the traditional manner based on “small data” such as survey responses. Then we reconceptualize “familiar strangers” against the backdrop of the emergence and increased availability of big and open data. Such familiar strangers are called “familiar strangers in the big data era” (FSiBDE). After this, we have done the following: (a) synthesized and hypothesized factors influencing the distribution and quantity of the FSiBDE; (b) conducted an empirical study in the context of Beijing to embody and operationalize a special type of the FSiBDE among metro riders and to study its possible influencers. We find that across metro stations, it is spatial structure, population distribution, and transport network that significantly influence the count and odds of FSiBDE among millions of metro riders. In addition, the FSiBDE also can have important policy and planning implications for operating metro services and managing metro station.
Keywords: Familiar stranger | Big data era | Implications | Odds | Distribution | Beijing
Big data analytics in health sector: Theoretical framework, techniques and prospects
تجزیه و تحلیل داده های بزرگ در بخش بهداشت و درمان: چارچوب نظری ، تکنیک ها و چشم انداز-2020
Clinicians, healthcare providers-suppliers, policy makers and patients are experiencing exciting opportunities in light of new information deriving from the analysis of big data sets, a capability that has emerged in the last decades. Due to the rapid increase of publications in the healthcare industry, we have conducted a structured review regarding healthcare big data analytics. With reference to the resource-based view theory we focus on how big data resources are utilised to create organization values/capabilities, and through content analysis of the selected publications we discuss: the classification of big data types related to healthcare, the associate analysis techniques, the created value for stakeholders, the platforms and tools for handling big health data and future aspects in the field. We present a number of pragmatic examples to show how the advances in healthcare were made possible. We believe that the findings of this review are stimulating and provide valuable information to practitioners, policy makers and researchers while presenting them with certain paths for future research.
Keywords: Big data analytics | Health-Medicine | Decision-making | Machine learning | Operations research (OR) techniques
The praxis of studying interorganizational practices in B2B marketing and purchasing : A critical literature review
اهداف مطالعه شیوه های بین سازمانی در بازاریابی و خرید B2B: بررسی ادبیات انتقادی-2020
Practice has developed into a key concept in management research, including B2B marketing and purchasing studies. However, the adoption of the term in B2B marketing and purchasing is characterized by some difficulties. Research on B2B practices is growing, but seems marked by fragmentation, inconsistency, and lack of precision among others. Without conceptual consistency and integrity, B2B practice studies are at risk of becoming derailed, compromising the developments of future theory and practice within B2B. In this critical review paper, we therefore seek to create an overview of B2B practice research as perceived through a practice lens. Based on a review of 116 identified practice papers from key B2B journals, we map the topic areas where the practice concept has been applied for investigations, and we also investigate how well the applied practice conceptualization in these papers align with a recognized practice theory conceptualization. We find that the majority of B2B studies align poorly with the three elements of practice: managerial action, habitual behavior, and action-structure duality. Since many of the alignment issues in the review are caused by methodological problems, we propose a series of methodological tools that can provide a more accurate understanding of B2B practices in future research.
Keywords: Practice | Managerial action | B2B marketing and purchasing | Interorganizational | Review paper
Identification of animal individuals using deep learning: A case study of giant panda
شناسایی فردی حیوانی با استفاده از یادگیری عمیق: یک مطالعه موردی از پاندا غول پیکر-2020
Giant panda (Ailuropoda melanoleuca) is an iconic species of conservation. However, long-term monitoring of wild giant pandas has been a challenge, largely due to the lack of appropriate method for the identification of target panda individuals. Although there are some traditional methods, such as distance-bamboo stem fragments methods, molecular biological method, and manual visual identification, they all have some limitations that can restrict their application. Therefore, it is urgent to explore a reliable and efficient approach to identify giant panda individuals. Here, we applied the deep learning technology and developed a novel face-identification model based on convolutional neural network to identify giant panda individuals. The model was able to identify 95% of giant panda individuals in the validation dataset. In all simulated field situations where the quality of photo data was degraded, the model still accurately identified more than 90% of panda individuals. The identification accuracy of our model is robust to brightness, small rotation, and cleanness of photos, although large rotation angle (> 20°) of photos has significant influence on the identification accuracy of the model (P < 0.01). Our model can be applied in future studies of giant panda such as long-term monitoring, big data analysis for behavior and be adapted for individual identification of other wildlife species.
Keywords: Deep learning | convolutional neural network | Individual identification | Giant panda
Security-preserving social data sharing methods in modern social big knowledge systems
روشهای به اشتراک گذاری داده های اجتماعی حفظ سیستم در سیستم های دانش اجتماعی بزرگ مدرن-2020
In recent decades, the development of social computing systems has realized the efficient information exchange between large groups of people. Nowadays, social computing sys- tems are rather complex platforms supported by not only traditional sociology theory but also computer science and big data based applications. With the increase of the social computing systems’ complexities, serious issues of social digital security and privacy have shown up since, in recent years, more and more social data leakage incidents are happen- ing. This fact is due to reasons on many different aspects since there are many sources threatening the security and privacy of the social data in such a complex social comput- ing system. In this paper, we improve the traditional social data protection schemes by combining the information fragmentation concepts with the distributed system architec- tures to build a novel social data protection scheme. We use social photo protection as the fundamental scenario and deploy our novel scheme to illustrate the improvement on the protection level with the protection analysis in detail. A security analysis of practically realizing such a scheme is also evaluated in this paper.
Keywords: Data security | Social computing | Big knowledge | Selective encryption
A new pyramidal opponent color-shape model based video shot boundary detection
A new pyramidal opponent color-shape model based video shot boundary detection-2020
Video shot boundary detection (VSBD) is one of the most essential criteria for many intelligent video analysis-related applications, such as video retrieval, indexing, browsing, categorization and summarization. VSBD aims to segment big video data into meaningful fragments known as shots. This paper put forwards a new pyramidal opponent colour-shape (POCS) model which can detect abrupt transition (AT) and gradual transition (GT) simultaneously, even in the presence of illumination changes, huge object movement between frames, and fast camera motion. First, the content of frames in the video subjected to VSBD is represented by the proposed POCS model. Consequently, the temporal nature of the POCS model is subjected to a suitable segment (SS) selection procedure in order to minimize the complexity of VSBD method. The SS from the video frames is examined for transitions within it using a bagged-trees classifier (BTC) learned on a balanced training set via parallel processing. To prove the superiority of the proposed VSBD algorithm, it is evaluated on the TRECVID 2001, TRECVID2007 and VIDEOSEG2004 data sets for classifying the basic units of video according to no transition (NT), AT and GT. The experimental evaluation results in an F1-score of 95.13%, 98.13% and 97.11% on the TRECVID 2001, TRECVID2007 and VIDEOSEG2004 data sets, respectively.
Keywords: Shot Boundary Detection | Abrupt Transition | Gradual Transition | Opponent Color space | Ensemble Algorithm
A Framework for Digital Marketing Research: Investigating the Four Cultural Eras of Digital Marketing
چارچوبی برای تحقیقات بازاریابی دیجیتال: بررسی چهار دوره فرهنگی بازاریابی دیجیتال-2020
The digital marketing discipline is facing growing fragmentation; the proliferation of different subareas of research impedes the accumulation of knowledge. This fragmentation seems logically tied to the inherent complexity of the Internet, itself resulting from 50 years of evolution. Thus, our aim is to provide an integrative framework for research in digital marketing derived from the historical analysis of the Internet. Using practice theory and institutional theory, we outline a new type of institutional work: imprinting work. We apply this framework to the analysis of historical secondary sources. We find four cultural repertoires on the Internet (collaborative systems, traditional market systems, co-creation systems, and prosumption market systems) and describe the dynamics of imprinting work leading to their creation, showing how new systems are created by appropriating and assimilating existing cultural repertoires. We contribute to the digital marketing literature by providing a cultural framework and a theory explaining the dynamics of the creation of four cultural repertoires. Moreover, we outline three paths of potential evolution of the digital landscape. Our framework may help managers make sense of their digital strategy and navigate the various Internet systems.
Keywords: Digital marketing | Historical method | Digital cultures | Institutional theory | Practice theory | Cultural framework | Prospective
Brands as relationship builders in the virtual world: A bibliometric analysis
مارک ها به عنوان سازندگان روابط در دنیای مجازی: یک تحلیل کتابشناختی-2020
Given the growing role of brands as relationship partners and relationship facilitators and the pre-eminence of the online environment for consumers, this article contributes to the understanding of virtual brand-centric relationships by presenting the first bibliometric mapping analysis of the academic research into the topic from its conception until 2018. Using keyword co-occurrence, it examines 585 records and identifies the most productive countries, journals, influential authors and papers, and research clusters. With 96% of the published records appearing between 2010 and 2018, this analysis revealed that the field is emergent. The research primarily originates from authors based in the USA, China and the UK. It also is highly fragmented, with papers being published in information management and marketing/branding journals, with cross-citations lacking. In addition, its foundations rest on a small number of works published in a handful of journals by just a few academics. The analysis also identified three main clusters of keywords: (a) identity, feelings and relationship outcomes; (b) relational elements; and (c) relationship facilitation. This bibliometric analysis brings insights together from different research streams, adds to the categorization of the literature on the topic, and provides promising future research directions in terms of research areas and strategies.
Keywords: Bibliometric analysis | Bibliographic coupling | Brand relationships | Keyword co-occurrence | Online brand communities | VOSviewer
A survey on clone refactoring and tracking
مروری بر فاکتورگیری مجدد و ردیابی کلون-2020
Code clones, identical or nearly similar code fragments in a software system’s code-base, have mixed impacts on software evolution and maintenance. Focusing on the issues of clones researchers suggest managing them through refactoring, and tracking. In this paper we present a survey on the state-of-the-art of clone refactoring and tracking techniques, and identify future research possibilities in these areas. We define the quality assessment features for the clone refactoring and tracking tools, and make a comparison among these tools considering these features. To the best of our knowledge, our survey is the first comprehensive study on clone refactoring and tracking. According to our survey on clone refactoring we realize that automatic refactoring cannot eradicate the necessity of manual effort regarding finding refactoring opportunities, and post refactoring testing of system behaviour. Post refactoring testing can require a significant amount of time and effort from the quality assurance engineers. There is a marked lack of research on the effect of clone refactoring on system performance. Future investigations in this direction will add much value to clone refactoring research. We also feel the necessity of future research towards real-time detection, and tracking of code clones in a big-data environment.
Keywords: Code clones |Clone-types |Clone refactoring |Clone tracking
Exploiting potential of deep neural networks by layer-wise fine-grained parallelism
بهره برداری از پتانسیل شبکه های عصبی عمیق با موازی سازی ریز دانه ای لایه ای خرد-2020
Deep neural networks (DNNs) have become more and more important for big data analysis. They usually use data parallelism or model parallelism for extreme scale computing. However, the two approaches realize the performance improvement mainly by using coarse-grained parallelization schemes. Neither can fully exploit the potentials of the parallelism of many-core systems (such as GPUs) for neural network models. Here, a new fine − grained parallelism strategy (named FiLayer) is presented based on layer-wise parallelization. It has two components: inter-layer parallelism and intralayer parallelism. The inter-layer parallelism makes several neighboring layers be processed by using a pipeline manner in a network model. For intra-layer parallelism, the operations in one layer are separated into several parts and processed concurrently. To implement above fine-grained parallelism methods, CUDA streams are used. A mathematical analysis is presented for the influence of fragment number on performance of the inter-layer parallelism, and also an analysis for the influence of CUDA stream number on the performance of the intra-layer parallelism is given. The proposed approach is realized based on Caffe. Some representative datasets including CIFAR100 and ImageNet, are applied for experiments. The evaluation results show that it can help Caffe realize remarkable speedups, which makes much sense to big data analysis.
Keywords: Deep learning | Fine-grained parallelism | CUDA stream