Secure Selections on Encrypted Multi-writer Streams

Performing searches over encrypted data is a very current and active area. Several efficient solutions have been provided for the single-writer scenario in which all sensitive data originate with one party (the Data Owner ) that encrypts and uploads the data to a public repository. Subsequently, the Data Owner accesses the encrypted data through a Query Processor , which has direct access to the public encrypted repository. Motivated by the recent trend in pervasive data collection, we depart from this model and consider a multi-writer scenario in which the data originate with several and mutually untrusted parties, the Data Sources . In this new scenario, the Data Owner provides public parameters so that each Data Source can add encrypted items to the public encrypted stream; moreover, the Data Owner keeps some related secret information needed to generate tokens so that different Query Sources can decrypt different subsets of the encrypted stream, as specified by corresponding access policies. We propose security model for this problem that we call Secure Selective Stream ( SSS ) and give a secure construction for it based on hard problems in Pairing-Based Cryptography. The cryptographic core of our construction is a new primitive, Amortized Orthogonality Encryption , that is crucial for the efficiency of the proposed implementation for SSS .

Applying Parallel and Distributed Models on Bio-Inspired Algorithms via a Clustering Method

In the world of optimization, especially concerning metaheuristics, solving complex problems represented by applying big data and constraint instances can be difficult. This is mainly due to the difficulty of implementing efficient solutions that can solve complex optimization problems in adequate time, which do exist in different industries. Big data has demonstrated its efficiency in solving different concerns in information management. In this paper, an approach based on multiprocessing is proposed wherein clusterization and parallelism are used together to improve the search process of metaheuristics when solving large instances of complex optimization problems, incorporating collaborative elements that enhance the quality of the solution. The proposal deals with machine learning algorithms to improve the segmentation of the search space. Particularly, two different clustering methods belonging to automatic learning techniques, are implemented on bio-inspired algorithms to smartly initialize their solution population, and then organize the resolution from the beginning of the search. The results show that this approach is competitive with other techniques in solving a large set of cases of a well-known NP-hard problem without incorporating too much additional complexity into the metaheuristic algorithms.

Radiant Conditioning Retrofitting for Residential Buildings

In order to achieve Australia’s greenhouse gas emissions reduction targets, a majority of the existing residential building stock in Australia will require retrofitting in favour of energy-efficient solutions. This paper considers retrofitting for conditioning to be one of the most straightforward and offers the greatest potential to deliver significant comfort and energy-saving results. Radiant conditioning systems are not new, yet some game-changing innovations have taken place over the last decade that may require an entire paradigm shift in the manner we condition our buildings. The reiteration of the principle ‘thermally active systems’ suggests that our buildings need to accommodate these systems into the fabric of building components. However, extremely few products and/or innovative solutions for doing such seem to be provided by the industry. We seem incompetent with solutions that are not costing the Earth, insulating, lightweight, and offering an instant response time to conditioning. We still have the concept embedded in our minds that radiative systems consist of heavy ‘combat’ construction with time lags of a day or two and that they are very costly to implement, especially if we are to retrofit a project. The purpose of this paper is to rectify and change our understanding of radiant systems, namely through a review of the existing technology and its recent advancements. It intends to introduce the fact that radiant systems can become highly reactive, responsive, and thermally dynamic conditioning systems. Lightweight radiant systems can be 40% more energy-efficient than common air conditioners and can respond in less than 15 min rather than in the hours required of heavy radiant systems. Thus, an insulated, lightweight radiant system is ideal for retrofitting residential buildings. Furthermore, this paper supports and introduces various systems suited to retrofitting a residential building with hydronic radiant systems.

An Efficient Metaheuristic-Based Clustering with Routing Protocol for Underwater Wireless Sensor Networks

In recent years, the underwater wireless sensor network (UWSN) has received a significant interest among research communities for several applications, such as disaster management, water quality prediction, environmental observance, underwater navigation, etc. The UWSN comprises a massive number of sensors placed in rivers and oceans for observing the underwater environment. However, the underwater sensors are restricted to energy and it is tedious to recharge/replace batteries, resulting in energy efficiency being a major challenge. Clustering and multi-hop routing protocols are considered energy-efficient solutions for UWSN. However, the cluster-based routing protocols for traditional wireless networks could not be feasible for UWSN owing to the underwater current, low bandwidth, high water pressure, propagation delay, and error probability. To resolve these issues and achieve energy efficiency in UWSN, this study focuses on designing the metaheuristics-based clustering with a routing protocol for UWSN, named MCR-UWSN. The goal of the MCR-UWSN technique is to elect an efficient set of cluster heads (CHs) and route to destination. The MCR-UWSN technique involves the designing of cultural emperor penguin optimizer-based clustering (CEPOC) techniques to construct clusters. Besides, the multi-hop routing technique, alongside the grasshopper optimization (MHR-GOA) technique, is derived using multiple input parameters. The performance of the MCR-UWSN technique was validated, and the results are inspected in terms of different measures. The experimental results highlighted an enhanced performance of the MCR-UWSN technique over the recent state-of-art techniques.

Solving Problems of Simple Structural Mechanics

Solve problems in elementary structural mechanics thoughtfully and efficiently with this self-contained volume. Covers the basics of structural mechanics and focuses on simple structures, truss frameworks, beams and frames, design choices, and deformity. Carefully interrogates underlying assumptions for efficiencies in working out whilst expounding fundamental principles for a consistent understanding. Heavily connects the practical world of indeterminate structures to their analysis, to underline benefits they impart to the latter: that certain analytical methods provide a wealth of efficient solutions for problems of indeterminate structures compared to determinate ones. Celebrates the beauty of analytical indeterminacy and its relationship to practical structures. Perfect for students invested in structural mechanics, and aims to complement their learning and understanding.

Multimodal Optimization with the Local Optimum Ranking 2 Algorithm

In optimization tasks, it is interesting to achieve a set of efficient solutions instead of one single output, in the case the best solution is not suitable. Many niching methods offer a diversified response, yet some important problems are common: (1) The most interesting solutions of each local optimum are not identified. Thus, the output is the overall population of solutions, which increases the work of the designer in verifying which solution is the most interesting. (2) Existing niching algorithms tend to distribute the solutions on the most promising regions, over-populating some local optima and sub-populating others, which leads to poor optimization.To solve these challenges, a novel niching method is presented, named local optimum ranking 2 (LOR2). This sorting methodology favors the exploration of a defined number of local optima and ranks each local population by objective value within each local optimum. Thus, is performed a multi-focus exploration, with an equalized number of solutions on each local optimum, while identifying which solutions are the local apices. To exemplify its application, the LOR2 algorithm is applied in the design optimization of a metallic cantilever beam. It achieves a set of efficient and diverse design configurations, offering both performance and diversity for structural design challenges.In addition, a second experiment describes how the algorithm can be applied to segment the domain of any function, into a mesh of similar sized or custom-sized elements. Thus, it can significantly simplify metamodels and reduce their computation time.

Intelligent Techniques for Analysis of Big Data About Healthcare and Medical Records

To define huge datasets, the term of big data is used. The considered “4 V” datasets imply volume, variety, velocity and value for many areas especially in medical images, electronic medical records (EMR) and biometrics data. To process and manage such datasets at storage, analysis and visualization states are challenging processes. Recent improvements in communication and transmission technologies provide efficient solutions. Big data solutions should be multithreaded and data access approaches should be tailored to big amounts of semi-structured/unstructured data. Software programming frameworks with a distributed file system (DFS) that owns more units compared with the disk blocks in an operating system to multithread computing task are utilized to cope with these difficulties. Huge datasets in data storage and analysis of healthcare industry need new solutions because old fashioned and traditional analytic tools become useless.

iWeek experience: the innovation challenges of digital transformation in industry

Information Technology, communication, and innovation require specific, essential competencies that employers look for in engineers. Responding to this, Tecnologico de Monterrey has been implementing the Tec21 Educational model to foster students' competencies by involving them in challenge-based learning (CBL). The iWeek is one of the first implementations of this model, where students experience immersive learning for a whole week. This study presents the iWeek Innovation Challenge to improve students' innovation and information technology and communication skills through a CBL didactic technique. During this iWeek, a group of students developed efficient solutions with Microsoft Power Apps to solve real challenges confronting a global company. The results proved that students could quickly learn and apply knowledge and develop practical, innovative solutions to real problems in Industry. It was a revelation to the stakeholders to notice how fast students can become familiar with new information technology tools to propose solutions that positively impact the company. Strong partnerships between academia and industry are crucial to developing student disciplinary and transversal competencies by challenging them to solve real-life problems in real-world environments. This work presents a roadmap for planning and designing a CBL iWeek with an educational partner from Industry. It includes the implementation details, assessment instruments, and results analysis. Finally, we also highlight the significant contributions of iWeek to explain the value of this immersive experience in the teaching–learning process.

Optimality and duality for complex multi-objective programming

<p style='text-indent:20px;'>We consider a complex multi-objective programming problem (CMP). In order to establish the optimality conditions of problem (CMP), we introduce several properties of optimal efficient solutions and scalarization techniques. Furthermore, a certain parametric dual model is discussed, and their duality theorems are proved.</p>