A basic framework for privacy protection in personalized information retrieval

Personalized information retrieval is an effective tool to solve the problem of information overload. Along with the rapid development of emerging network technologies such as cloud computing, however, network servers are becoming more and more untrusted, resulting in a serious threat to user privacy of personalized information retrieval. In this paper, we propose a basic framework for the comprehensive protection of all kinds of user privacy in personalized information retrieval. Its basic idea is to construct and submit a group of well-designed dummy requests together with each user request to the server, to mix up the user requests and then cover up the user privacy behind the requests. Also, the framework includes a privacy model and its implementation algorithm. Finally, theoretical analysis and experimental evaluation demonstrate that the framework can comprehensively improve the security of all kinds of user privacy, without compromising the availability of personalized information retrieval.

Personalized Healthy Anti-COVID Menu Generator Chatbot Based on Prolog

It is a healthy diet that creates conditions for human life, ensuring the optimal functioning of all processes in the body. Of course, a healthy diet cannot be a protection against the penetration of infection into the body, but it is the balanced and full-fledged nature of the diet that creates the conditions for the formation of a timely and adequate immune response. In order to help consumers in getting more balanced and healthy diet, we created a personalized Healthy Menu Generator Chatbot, based on Prolog Knowledge base. The user request is constructed by user in a subset of English Language by using Request Fragments from a list. Cross-translation of the user request and its execution in the Prolog Execution Environment is extensively covered in the paper.

BER Illusion Methodology: A Novel, Open Sourced and Scalable Approach to Troubleshooting High Radix Photonics Interconnects in a Modern Hyperscale Datacenter

Facebook Datacenter consists of a large number of servers that run diverse Facebook services aggregated to serve any given user request. To allow this aggregation, servers have to interact with each other via different traffic flows which are managed by networking fabric. The underlying connection powering this fabric consists of a large number of pluggable optical interconnects and On Board Optical (OBO) modules carrying production data. This connectivity at scale requires fast and reliable detection of the link failures to ensure resolution. In the first generation of the deployments, detection of the link failure was sequential and a slow process. The troubleshoot process was equally tedious as the available tools required characterizing one optical transceiver at a time. Further, the failure analysis also presented a majority of resolution with no failed optics as a root cause resulting in high No Trouble Found (NTF) rate. In this paper we introduce a novel link failure detection and resolution method that improves on the previous method across three dimensions: faster resolution, reliable troubleshooting and scalable implementation. We introduce BER Illusion Methodology (BIM) that is a highly scalable and resource efficient solution that significantly reduces the time taken to troubleshoot pluggable optical interconnects. This is also scalable to next-gen OBO modules at Facebook datacenters aiming to lower the NTF rate and optimally utilizing the available resources. BIM, which is based on Open Compute Platform (OCP) network switches, can be used to troubleshoot 128 QSFP28, 64 QSFP56 or 32 OBO modules simultaneously in under 30 minutes. The tool is easy to implement and capable of also reporting diagnostics on the transceiver such as Transmitter Power, Transmitter Bias Current, Receiver Power, Case Temperature, Bit Error Rate result per channel, Vendor information and Manufacturing part number. This additional test data report along with true failure indication helps optic suppliers gain confidence and build customer credibility. The open-source nature and the universal applicability of this tool offers possibility for other users to adopt and further customize it for their networking needs.

A Comparative Analysis of Content based Image Retrieval

With the development of multimedia technology, the rapid increasing usage of large image database becomes possible. To carry out its management and retrieval, Content-Based Image Retrieval (CBIR) is an effective method. It will be very difficult to manage this database of images stored at the remote servers. The right tool will be required which can process these images for different operations. These operations include searching etc. It will be difficult to classify the images into groups and then search each class for providing the image as the information against the user request query. The content based image retrieval is the most suitable way to identify the image from the large repository. It will search the image from the large set of images based on contents rather than the image name. It will be having less time to search the image from the large repository when the image is retrieved using content based. In the current research the hybrid approach for content based image retrieval is performed. This proposed procedure will be in the first step perform the classification of the image into multiple classes. The classes are prepared based on the attributes values.

Equilibrium analysis of cloud user request based on the Markov queue with variable vacation and vacation interruption

This paper considers the equilibrium balking behavior of customers in a single-server Markovian queue with variable vacation and vacation interruption, where the server can switch across four states: vacation, working vacation, idle period, and busy period. Once the queue becomes empty, the server commences a working vacation and slows down its service rate. However, this period may be interrupted anytime by the vacation interruption. Upon the completion of a working vacation, the server takes a vacation in a probability-based manner and stops service if the system is empty. The system stays idle after a vacation until a new customer arrives. The comparisons between the equilibrium balking strategy of customers and the optimal expected social benefit per time unit for each type of queue are elucidated and the inconsistency between the individual optimization and the social optimization is revealed. Moreover, the sensitivity of the expected social benefit and the equilibrium threshold with respect to the several parameters as well as diverse precision levels is illustrated through numerical examples in a competitive cloud environment.

Popularity-Aware In-Network Caching for Edge Named Data Network

The traditional centralized network architecture can lead to a bandwidth bottleneck in the core network. In contrast, in the information-centric network, decentralized in-network caching can alleviate the traffic flow pressure from the network center to the edge. In this paper, a popularity-aware in-network caching policy, namely, Pop, is proposed to achieve an optimal caching of network contents in the resource-constrained edge networks. Specifically, Pop senses content popularity and distributes content caching without adding additional hardware and traffic overhead. We conduct extensive performance evaluation experiments by using ndnSIM. The experiments showed that the Pop policy achieves 54.39% cloud service hit reduction ratio and 22.76% user request average hop reduction ratio and outperforms other policies including Leave Copy Everywhere, Leave Copy Down, Probabilistic Caching, and Random choice caching. In addition, we proposed an ideal caching policy (Ideal) as a baseline whose popularity is known in advance; the gap of Pop and Ideal in cloud service hit reduction ratio is 4.36%, and the gap in user request average hop reduction ratio is only 1.47%. More simulation results further show the accuracy of Pop in perceiving popularity of contents, and Pop has good robustness in different request scenarios.

An Approach to Modeling and Analyzing Reliability for Microservice-Oriented Cloud Applications

Microservice architecture is a cloud-native architectural style, which has attracted extensive attention from the scientific research and industry communities to benefit independent development and deployment. However, due to the complexity of cloud-based platforms, the design of fault-tolerant strategies for microservice-oriented cloud applications becomes challenging. In order to improve the quality of service, it is essential to focus on the microservice with more criticality and maximize the reliability of the entire cloud application. This paper studies the modeling and analysis of service reliability in the cloud environment. Firstly, a formal description language is defined to model microservice, user request, and container accurately. Secondly, the reliability analysis is conducted to measure a critical microservice’s fluctuation and vibration attributes within a period, and the related properties of the constructed model are analyzed. Thirdly, a fault-tolerant strategy with redundancy operation has been proposed to optimize cloud application reliability. Finally, the effectiveness of the method is verified by experiments. The simulation results show that the algorithm obtains the maximum benefits and has high performance through several experiments.

Digital bibliography: The libraries in search of innovative bibliographic tools

The author discusses application of innovative digital technologies in library bibliographic services. The digitalization cannot be limited to collection digitization, development of digital collections and provision of digital access to these collections. Its essential task is to find integrated solutions in bibliographic activities based on innovative system comprised primarily of the big data technology, machine learning and artificial intellect. The author examines the potential of artificial intellect systems implemented in many foreign libraries. Their successful experience is very promising. The artificial intellect is used for retrieving relevant and reliable information, mining bibliographic metadata, creating standard bibliographic records and reference lists, designing chat-bots, automatic in formation distribution on user request, selecting key values from document array. With digital technology advances, the bibliographers will have to be directly in volved in designing systems, services, programs and apps to provide bibliographic and information products and services so that traditional bibliographical principles, values and ethics lay the foundation for and are preserved in innovative artificial intellect technologies.

Measuring the Accuracy of Aggregates Computed from a Statistical Register

The Italian National Statistical Institute (Istat) is currently engaged in a modernization programme that foresees a significant revision of the methods traditionally used for the production of official statistics. The main concept behind this transformation is the use of the Integrated System Statistical Registers, created by a massive integration of administrative archives and survey data. In this article, we focus on how to measure the accuracy of register estimates of a population total from measurements calculated at the unit level. We propose the global mean squared error (GMSE) as a statistical quantity suitable for measuring accuracy in the context of the production of official statistics. It can be defined to explicitly consider the main sources of uncertainty that may affect registers. The article suggests a feasible calculation strategy for the GMSE that allows National Statistical Institutes to build algorithms that can promptly be applied for each user request, thus improving the relevance, transparency and confidence of official statistics. Through a simulation study, we verified the efficacy of the proposed strategy.

CLOUD STORAGE DENGAN TEKNOLOGI KUBERNETES UNTUK PLATFORM COLLABORATIVE RESEARCH

Memberikan model alternatif untuk penerapan penelitian kolaboratif melalui layanan teknologi komputasi awan yang diim- plementasikan di lingkungan komputer lokal atau untuk menampung penyimpanan file di luar lokasi, penggunaan berbagi file cloud sangat bermanfaat dan mudah. Penggunaan teknologi ini harus diimplementasikan dan diuji keandalannya dalam skala yang baik sehingga jika akan dikembangkan lebih lanjut dapat segera diimplementasikan dan sesuai dengan lingkungan dan sumber daya jaringan yang ada. Penelitian ini berfokus pada implementasi dan pengujian performa kecepatan dan user request yang menghasilkan nilai A pada uji kecepatan pada GTMetrix dan load peak tertinggi connection time 0-5ms. Solusi terhadap penurunan performa dapat disolusikan dengan menggunakan model scaling dikombinasikan dengan proxy, dan load balancing.