Unsupervised by any other name: Hidden layers of knowledge production in artificial intelligence on social media
بدون نظارت با هر نام دیگری: لایه های پنهان تولید دانش در هوش مصنوعی در رسانه های اجتماعی-2019
Artificial Intelligence (AI) in the form of different machine learning models is applied to Big Data as a way to turn data into valuable knowledge. The rhetoric is that ensuing predictions work well—with a high degree of autonomy and automation. We argue that we need to analyze the process of applying machine learning in depth and highlight at what point human knowledge production takes place in seemingly autonomous work. This article reintroduces classification theory as an important framework for understanding such seemingly invisible knowledge production in the machine learning development and design processes. We suggest a framework for studying such classification closely tied to different steps in the work process and exemplify the framework on two experiments with machine learning applied to Facebook data from one of our labs. By doing so we demonstrate ways in which classification and potential discrimination take place in even seemingly unsupervised and autonomous models. Moving away from concepts of non-supervision and autonomy enable us to understand the underlying classificatory dispositifs in the work process and that this form of analysis constitutes a first step towards governance of artificial intelligence.
Keywords: Artificial intelligence | machine learning | classification | social media| Facebook | discrimination | bias
Friction, snake oil, and weird countries: Cybersecurity systems could deepen global inequality through regional blocking
اصطکاک، روغن مار، و کشورهای عجیب و غریب: سیستم های امنیت سایبری می تواند نابرابری جهانی را از طریق مسدود سازی منطقه ای تقویت کند-2019
In this moment of rising nationalism worldwide, governments, civil society groups, transnational companies, and web users all complain of increasing regional fragmentation online. While prior work in this area has primarily focused on issues of government censorship and regulatory compliance, we use an inductive and qualitative approach to examine targeted blocking by corporate entities of entire regions motivated by concerns about fraud, abuse, and theft. Through participant-observation at relevant events and intensive interviews with experts, we document the quest by professionals tasked with preserving online security to use new machine-learning based techniques to develop a ‘‘fairer’’ system to determine patterns of ‘‘good’’ and ‘‘bad’’ usage. However, we argue that without understanding the systematic social and political conditions that produce differential behaviors online, these systems may continue to embed unequal treatments, and troublingly may further disguise such discrimination behind more complex and less transparent automated assessment. In order to support this claim, we analyze how current forms of regional blocking incentivize users in blocked regions to behave in ways that are commonly flagged as problematic by dominant security and identification systems. To realize truly global, non-Eurocentric cybersecurity techniques would mean incorporating the ecosystems of service utilization developed by marginalized users rather than reasserting norms of an imagined (Western) user that casts aberrations as suspect.
Keywords: Regional blocking | machine learning | classification | inequality | discrimination | security
تحلیل احساسات مبتنی بر یادگیری عمیق در متن رومی اردو
سال انتشار: 2019 - تعداد صفحات فایل pdf انگلیسی: 5 - تعداد صفحات فایل doc فارسی: 9
آنالیز احساسات با توجه به رویکرد همه جانبه در آنالیز احساسات کاربران شبکه های اجتماعی مختلف، انجمن ها، سایت های بازاریابی الکترونیکی و وبلاگ ها، اهمیت زیادی دارد. داده های مربوط به احساسات در وب اهمیت زیادی دارد و بر مشتریان، خوانندگان و شرکت های تجاری تأثیر می گذارد. شبکه عصبی مکرر به طور گسترده ای در انجام وظایف پردازش زبان طبیعی مورد استفاده قرار گرفته است، زیرا برای مدل سازی داده های متوالی به صورت موثر طراحی شده است.
در این مقاله از مدل عصبی عمیق حافظه کوتاه-طولانی مدت (LSTM) استفاده شده است. توانایی فوق العاده ای در ضبط اطلاعات دور برد و حل مشکل کاهش گرادیان و همچنین ارائه اطلاعات متنی آتی، معناشناسی توالی لغات با شکوه دارد. این مقاله پایه و اساس تطبیق روش های یادگیری عمیق در آنالیز رومن اردو است. نتایج تجربی نشان داد که مدل ما دقت قابل توجهی دارد و دقت بیشتری از روش های یادگیری ماشین دارد.
کليدواژه: شبکه عصبی مکرر (RNN)| حافظه کوتاه-بلند مدت (LSTM) | آنالیز معنایی رومن اردو | تعبیه لغت
|مقاله ترجمه شده|
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
Role of Big Data and Machine Learning in Diagnostic Decision Support in Radiology
نقش داده های بزرگ و یادگیری ماشین در پشتیبانی از تصمیم گیری تشخیصی در رادیولوژی-2018
The field of diagnostic decision support in radiology is undergoing rapid transformation with the availability of large amounts of patient data and the development of new artificial intelligence methods of machine learning such as deep learning. They hold the promise of providing imaging specialists with tools for improving the accuracy and efficiency of diagnosis and treatment. In this article, we will describe the growth of this field for radiology and outline general trends highlighting progress in the field of diagnostic decision support from the early days of rule-based expert systems to cognitive assistants of the modern era.
Key Words: Diagnostic decision support, artificial intelligence, deep learning, machine learning, cognitive assistants, medical image analysis, knowledge and reasoning
Applications of Big Social Media Data analysis: An Overview
برنامه های تحلیل داده های اجتماعی رسانه هابزرگ : یک مرور کلی-2018
Over the last few years, online communication has moved toward user-driven technologies, such as online social networks (OSNs), blogs, online virtual communities, and online sharing platforms. These social technologies have ushered in a revolution in user-generated data, online global communities, and rich human behavior-related content. Human-generated data and human mobility patterns have become important steps toward developing smart applications in many areas. Understanding human preferences is important to the development of smart applications and services to enable such applications to understand the thoughts and emotions of humans, and then act smartly based on learning from social media data. This paper discusses the role of social media data in comprehending online human data and in consequently different real applications of SM data for smart services are executed.
Keywords: Online social networks (OSN), social media (SM), big social data, machine learning, smart society
Fault-diagnosis for reciprocating compressors using big data and machine learning
تشخیص گسل برای کمپرسورهای مجاور با استفاده از داده های بزرگ و یادگیری ماشین-2018
Reciprocating compressors are widely used in petroleum industry. A small fault in recipro cating compressor may cause serious issues in operation. Traditional regular maintenance and fault diagnosis solutions cannot efficiently detect potential faults in reciprocating com pressors. This paper proposes a fault-diagnosis system for reciprocating compressors. It applies machine-learning techniques to data analysis and fault diagnosis. The raw data is denoised first. Then the denoised data is sparse coded to train a dictionary. Based on the learned dictionary, potential faults are finally recognized and classified by support vector machine (SVM). The system is evaluated by using 5-year operation data collected from an offshore oil corporation in a cloud environment. The collected data is evenly divided into two halves. One half is used for training, and the other half is used for testing. The results demonstrate that the proposed system can efficiently diagnose potential faults in com pressors with more than 80% accuracy, which represents a better result than the current practice.
Keywords: Reciprocating compressor، Big data ، Cloud computing ، Deep learning ، RPCA ، SVM
Semi-Supervised Learning Based Big Data-Driven Anomaly Detection in Mobile Wireless Networks
تشخیص ناهنجاری های رانده شده با داده های نیمه نظارت بر اساس داده ها در شبکه های بی سیم سیار-2018
With rising capacity demand in mobile networks, the infrastructure is also becoming increasingly denser and complex. This results in collection of larger amount of raw data (big data) that is generated at different levels of network architecture and is typically underutilized. To unleash its full value, innovative machine learning algorithms need to be utilized in order to extract valuable insights which can be used for improving the overall network’s performance. Additionally, a major challenge for network operators is to cope up with increasing number of complete (or partial) cell outages and to simultaneously reduce operational expenditure. This paper contributes towards the aforementioned problems by exploiting big data generated from the core network of 4G LTE-A to detect network’s anomalous behavior. We present a semi-supervised statistical-based anomaly detection technique to identify in time: first, unusually low user activity region depicting sleeping cell, which is a special case of cell outage; and second, unusually high user traffic area corresponding to a situation where special action such as additional resource allocation, fault avoidance solution etc. may be needed. Achieved results demonstrate that the proposed method can be used for timely and reliable anomaly detection in current and future cellular networks.
Keywords: 5G; 4G LTE-A; anomaly detec tion; call detail record; machine learning; big data analytics; network behavior analysis; sleeping cell
Big Data Analytics, Machine Learning and Artificial Intelligence in Next-Generation Wireless Networks
تحلیل داده های بزرگ، یادگیری ماشین و هوش مصنوعی در شبکه های بی سیم نسل بعدی-2018
The next-generation wireless networks are evolving into very complex systems because of the very diversified service requirements, heterogeneity in applications, devices, and networks. The network operators need to make the best use of the available resources, for example, power, spectrum, as well as infrastructures. Traditional networking approaches, i.e., reactive, centrally-managed, one-size-fits-all approaches and conventional data analysis tools that have limited capability (space and time) are not competent anymore and cannot satisfy and serve that future complex networks regarding operation and optimization cost-effectively. A novel paradigm of proactive, self-aware, selfadaptive and predictive networking is much needed. The network operators have access to large amounts of data, especially from the network and the subscribers. Systematic exploitation of the big data dramatically helps in making the system smart, intelligent, and facilitates efficient as well as cost-effective operation and optimization. We envision data-driven next-generation wireless networks, where the network operators employ advanced data analytics, machine learning and artificial intelligence. We discuss the data sources and strong drivers for the adoption of the data analytics, and the role of machine learning, artificial intelligence in making the system intelligent regarding being self-aware, selfadaptive, proactive and prescriptive. A set of network design and optimization schemes are presented concerning data analytics. The paper concludes with a discussion of challenges and benefits of adopting big data analytics, machine learning, and artificial intelligence in the next-generation communication systems.
Index Terms: Big data analytics, Machine learning, Artificial intelligence, Next-generation wireless.
A survey towards an integration of big data analytics to big insights for value-creation
مروری به سوی تجمیع تحلیل داده های بزرگ به بینشی بزرگ برای ایجاد ارزش-2018
Big Data Analytics (BDA) is increasingly becoming a trending practice that generates an en ormous amount of data and provides a new opportunity that is helpful in relevant decision making. The developments in Big Data Analytics provide a new paradigm and solutions for big data sources, storage, and advanced analytics. The BDA provide a nuanced view of big data development, and insights on how it can truly create value for firm and customer. This article presents a comprehensive, well-informed examination, and realistic analysis of deploying big data analytics successfully in companies. It provides an overview of the architecture of BDA including six components, namely: (i) data generation, (ii) data acquisition, (iii) data storage, (iv) advanced data analytics, (v) data visualization, and (vi) decision-making for value-creation. In this paper, seven Vs characteristics of BDA namely Volume, Velocity, Variety, Valence, Veracity, Variability, and Value are explored. The various big data analytics tools, techniques and tech nologies have been described. Furthermore, it presents a methodical analysis for the usage of Big Data Analytics in various applications such as agriculture, healthcare, cyber security, and smart city. This paper also highlights the previous research, challenges, current status, and future di rections of big data analytics for various application platforms. This overview highlights three issues, namely (i) concepts, characteristics and processing paradigms of Big Data Analytics; (ii) the state-of-the-art framework for decision-making in BDA for companies to insight value-crea tion; and (iii) the current challenges of Big Data Analytics as well as possible future directions.
Keywords: Big data ، Data analytics ، Machine learning ، Big data visualization ، Decision-making ، Smart agriculture ، Smart city application ، Value- reation ، Value-discover ، Value-realization