کارابرن عزیز، مقالات isi بالاترین کیفیت ترجمه را دارند، ترجمه آنها کامل و دقیق می باشد (محتوای جداول و شکل های نیز ترجمه شده اند) و از بهترین مجلات isi انتخاب گردیده اند. همچنین تمامی ترجمه ها دارای ضمانت کیفیت بوده و در صورت عدم رضایت کاربر مبلغ عینا عودت داده خواهد شد.
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Differential Privacy Preserving of Training Model in Wireless Big Data with Edge Computing
حفظ حریم خصوصی دیفرانسیل حفظ مدل آموزش در داده های بزرگ بی سیم با محاسبات لبه-2018
With the popularity of smart devices and the widespread use of machine learning methods, smart edges have become the mainstream of dealing with wireless big data. When smart edges use machine learning models to analyze wireless big data, nevertheless, some models may unintentionally store a small portion of the training data with sensitive records. Thus, intruders can expose sensitive information by careful analysis of this model. To solve this privacy issue, in this paper, we propose and implement a machine learning strategy for smart edges using differential privacy. We focus our attention on privacy protection in training datasets in wireless big data scenario. Moreover, we guarantee privacy protection by adding Laplace mechanisms, and design two different algorithms Output Perturbation (OPP) and Objective Perturbation (OJP), which satisfy differential privacy. In addition, we consider the privacy preserving issues presented in the existing literatures for differential privacy in the correlated datasets, and further provided differential privacy preserving methods for correlated datasets, guaranteeing privacy by theoretical deduction. Finally, we implement the experiments on the TensorFlow, and evaluate our strategy on four datasets, i.e., MNIST, SVHN, CIFAR-10 and STL-10. The experiment results show that our methods can efficiently protect the privacy of training datasets and guarantee the accuracy on benchmark datasets.
Index Terms: Wireless Big Data, Smart Edges, Differential Privacy, Training Data Privacy, Machine Learning, Correlated Datasets, Laplacian Mechanism, TensorFlow
The impact of more frequent portfolio disclosure on mutual fund performance
تاثیر افشای متناوب تر دارایی روی عملکرد سرمایه ای دوطرفه-2018
This paper analyzes the impact of more frequent portfolio disclosures on performance of mutual funds. Since 2004, SEC requires all U.S. mutual funds to disclose their portfolio holdings on a quarterly basis from semi-annual previously. This change in regulation provides a natural setting to study the impact of frequency of disclosure on performance of mutual funds. Prior to the policy change, we find that successful semi-annual funds outperform successful quarterly funds by 17–20 basis points a month. After 2004, their performance goes down and they no longer outperform successful quarterly funds. This reduction in performance is higher for semi-annual funds holding illiquid assets. These results support our hypothesis that the performance of funds with more frequent disclosure, particularly of those holding illiquid assets, suffer more from front running activities. We also find complementary evidence that the profitability of a hypothetical front running strategy based on public disclosures goes up with the frequency of portfolio disclosures.
keywords: Portfolio disclosure frequency |Mutual fund performance |Front running |Free riding |SEC regulation |Difference-in-difference test |Illiquid funds
On the goodput of flows in heterogeneous mobile networks
قرارگیری مناسب جریان ها در شبکه های سیار ناهمگن-2018
In practice heterogeneous networks comprising of diverse nodes need to operate efficiently under a wide range of node mobility and link quality regimes. In this paper, we propose algorithms to determine the goodput of flows in heterogeneous mobile networks. We consider a scenario where some network nodes operate as routers while others operate as flooders, based on the underlying forwarding policy. When a node operates as a router, it forwards packets based on the routing table as determined by the underlying routing algorithm and when it operates as a flooder, it broadcasts packets to all its neighbors. We begin with the case of a single network flow and demonstrate that the problem of determining the goodput is challenging even for this simple setting. We construct a Bayesian network, and propose an algorithm based on the sum-product algorithm to determine the exact goodput. We extend the proposed Bayesian network model for exact goodput calculation to feed forward networks with multiple flows. For a general network with multiple flows, the problem becomes more challenging. The difficulty of the problem stems from the fact that node pairs can forward traffic to one another, resulting in cyclical dependencies. We propose a fixed-point approximation to determine the goodput in this case. Finally, we present an application scenario, where we leverage the fixed-point approximation to design a forwarding strategy adaptive-flood that adapts seamlessly to varying networking conditions. We perform simulations and show that adaptive-flood can effectively classify individual nodes as routers/flooders, achieving performance equivalent to, and in some cases significantly better than that of network-wide routing or flooding alone.
Food Trend Based on Social Media for Big Data Analysis Using K-Mean Clustering and SAW
روند تغذیه بر اساس رسانه های اجتماعی برای تجزیه و تحلیل داده های بزرگ با استفاده از خوشه بندی K-Mean و SAW-2018
tracking customer preferences is an important aspect of business success. Having information on hand about most favorite food is a key success for everyone who takes apart in the culinary business. Exact sales data on certain food is hardly available to the public. Restaurant owner tends to keep their data for their own business strategy. Therefore, generating a food trend in a certain community is hardly possible using food sales data. This paper discussed extracting food general trend from social media, with the case study on Twitter data with a certain regional area of interest. Social media provides a tremendous amount of data including people choice of food when they visit the certain place. However, the available data is unstructured in human language. The challenge is twofold: to grasp the meaning and extract the relevant information to the food trends. We proposed a bag of words technique to gather relevant information in the Indonesian language for feature extracting purpose. While K-mean Clustering and Simple Additive Weighting (SAW) algorithm are proposed to draw up the food rank. In order to measure the accuracy, we compare our result with the sales data of some restaurants in Yogyakarta. We test the algorithm using 4 weeks of data, the result is compared against the available data and an accuracy of 72.75 % is achieved
Keywords: social media; food trend; big data; bag of words; K mean clustering; simple additive weighting
When are stakeholder pressures effective? An extension of slack resources theory
چه موقع فشارهای سهامدار موثر می باشد؟ یک بسطی از نظریه منابع سست-2018
There has been an intense debate on when stakeholder pressures are effective in driving firms to contribute to sustainable development. Drawing upon institutional theory and slack resources theory, we theorize that country-level sustainability performance interacts with slack resources in shaping corporate responsiveness to stakeholder pressures. Empirical results based on the data from 6th International Manufacturing Strategy Survey and secondary data of the Human Development Index and the Environmental Performance Index support our hypotheses. As hypothesized, in countries with low level sustainability performance, firms with considerable slack resources are more responsive to stakeholder pressures than their peers with limited slack resources. In contrast, in countries with high levels of sustainability performance, there are no significant differences between firms with and without considerable slack resources in their responsiveness to stakeholder pressures. This study contributes to a better understanding of organizational responses to stakeholder pressures. Moreover, it suggests that stakeholders, depending on country-level sustainability performance, should adopt different strategies to stimulate firms to participate in sustainable development.
keywords: Environmental issues |Social responsibility |Stakeholder pressures |Survey methods |Hierarchical linear modelling
Hotel location when competitors may react: A game-theoretic gravitational model
مکان هتل وقتی که رقبا ممکن است واکنش نشان دهند: یک مدل نظری گرانشی بازی-2018
This paper presents a hotel location model that incorporates concepts from both game theory and gravitational site location models. We consider a hotel chain intending to build new hotels in a given region. Customers travel to the region to visit some specific points, termed “attractions”, and they choose a hotel according to room price, location and hotel attractiveness. Competitor hotels react to the new hotels by changing prices, in order to maximize their own profits, so the final set of prices will be a Nash equilibrium. We propose an iterative procedure for finding the equilibrium prices and a genetic algorithm-based procedure for finding the optimal strategy, in terms of new hotels to be built and respective typologies. Using a mini case, we illustrate and analyse the influence of several parameters. Then, we present computational experiments, concluding that the proposed procedures are effective in finding good solutions for the model.
keywords: Tourism site location| Game theory| Genetic algorithms| Spatial interaction models
Simulation methodology and performance analysis of network coding based transport protocol in wireless big data networks
روش شبیه سازی و تجزیه و تحلیل کارایی پروتکل انتقال مبتنی بر کدگذاری شبکه در شبکه های داده های بزرگ بی سیم-2018
The Multi-Path, Multi-Hop (MPMH) communications have been extensively used in wire less network. It is especially suitable to big data transmissions due to its high throughput. To provide congestion and end-to-end reliability control, two types of transport layer pro tocols have been proposed in the literature: the TCP-based protocols and the rateless cod ing based protocols. However, the former is too conservative to explore the capacity of the MPMH networks, and the latter is too aggressive in filling up the communication capac ity and performs poorly when dealing with congestions. To overcome their drawbacks, a novel network coding scheme, namely, Adjustable Batching Coding (ABC), was proposed by us, which uses redundancy coding to overcome random loss and uses retransmissions and window size shrink to relieve congestion. The stratified congestion control strategy makes the ABC scheme especially suitable for big data transmissions. However, there is no simu lation platform built so far that can accurately test the performance of the network coding based transport protocols. We have built a modular, easy-to-customize simulation system based on an event-based programming method, which can simulate the ABC-based MPMH transport layer behaviors. Using the proposed simulator, the optimal parameters of the protocol can be fine-tuned, and the performance is superior to other transport layer pro tocols under the same settings. Furthermore, the proposed simulation methodology can be easily extended to other variants of MPMH communication systems by adjusting the ABC parameters.
Keywords: Network simulator ، Wireless big data networks ، Multi-path multi-hop communications ، Transport layer ، Network coding
Which governance structures drive economic, environmental, and social upgrading? A quantitative analysis in the assembly industries
کدام ساختارهای نظارتی، به روزرسانی اقتصادی، محیطی و اجتماعی را تحریک می کنند؟ یک تحلیل کمّی در صنعتهای مونتاژ-2018
As industries are becoming increasingly global, researchers and practitioners are compelled to look at supply chains (SCs) from a global perspective. In this respect, the Global Value Chain (GVC) framework is particularly useful in understanding global dynamics because it relates the nature of relationships between firms (governance) to the possibilities for firms to move toward higher value-added activities (upgrading). Whereas the literature to date has explored these issues via qualitative approaches, this paper explores the effect that different forms of governance with suppliers and customers have on economic (product, process, functional), environmental and social upgrading based on an analysis of the International Manufacturing Strategy Survey (IMSS) data. The results show that participating to GVCs supports only some forms of upgrading and only under specific governance structures.
keywords: Supply chain management |Global value chain |Governance |IMSS |Upgrading
Performance outcomes of offshoring, backshoring and staying at home manufacturing
خروجی های عملکرد دور از ساحل، پشت ساحل، و ماندن در ساخت و تولید خانه-2018
The objective of this paper is to advance the understanding of performance outcomes of companies pursuing different strategies in moving manufacturing abroad and moving it back again. The study is based on a large-scale survey of perceptual data from 233 senior managers in manufacturing companies. Furthermore, secondary performance data of return on capital employed is included in the analysis. Companies that have an explicit corporate manufacturing strategy report better operational performance in terms of product quality, lead time and flexibility than companies that do not have such a strategy. The analysis does not reveal any differences in productivity among companies that have offshored, backshored, or maintained manufacturing at home. Companies that have offshored manufacturing report lower unit costs than companies that have applied a staying at home strategy. No significant level of difference in unit costs was found when comparing companies that have backshored manufacturing and companies that have maintained manufacturing at home. Companies that have offshored manufacturing report that they can extract detailed component, product and process data from their cost-accounting systems to a higher degree. The paper stresses the importance of access to, and the quality of, cost-accounting data to make informed strategic manufacturing decisions, and how such decisions may affect operational and cost performance. The paper provides novel empirical insights on cost performance, operational performance and cost accounting data among manufacturers pursuing different strategies of moving manufacturing abroad and back to home destinations.
keywords: Offshoring |Backshoring |Cost performance |Operational performance |Cost accounting capabilities
SERAC3: Smart and economical resource allocation for big data clusters in community clouds
SERAC3: تخصیص منابع هوشمند و اقتصادی برای خوشه های داده بزرگ در ابرهای جامعه-2018
Big data analysis jobs on clouds are gaining more and more popularity in recent years. It is critical but challenging to pick the right configuration for an incoming job, since the configuration space is too large, and the relationship between allocated resources and job performance is not deterministic. In this paper, we propose SERAC3 to allocate resources smartly and economically for big data clusters in community clouds. SERAC3 is a system that can automatically extract representative workloads from incoming big data analysis jobs, smartly decide an optimal configuration for each job, and adjust its assigning strategy in a quasi-realtime mode. With experiments on a community cloud built on OpenStack, we show that on average, SERAC3 can smartly select a configuration within 2.2% of the exact optimal one, while saving about 80.1% search cost compared to the exhaustive search.
Keywords: Resource allocation ، Big data clusters ، Representative workloads ، Community clouds