An explainable assistant for multiuser privacy

Multiuser Privacy (MP) concerns the protection of personal information in situations where such information is co-owned by multiple users. MP is particularly problematic in collaborative platforms such as online social networks (OSN). In fact, too often OSN users experience privacy violations due to conflicts generated by other users sharing content that involves them without their permission. Previous studies show that in most cases MP conflicts could be avoided, and are mainly due to the difficulty for the uploader to select appropriate sharing policies. For this reason, we present ELVIRA, the first fully explainable personal assistant that collaborates with other ELVIRA agents to identify the optimal sharing policy for a collectively owned content. An extensive evaluation of this agent through software simulations and two user studies suggests that ELVIRA, thanks to its properties of being role-agnostic, adaptive, explainable and both utility- and value-driven, would be more successful at supporting MP than other approaches presented in the literature in terms of (i) trade-off between generated utility and promotion of moral values, and (ii) users’ satisfaction of the explained recommended output.

Resolving User Conflicts in Multi-user Context-aware Home Environment

Context-awareness is an enabling technology of pervasive computing that allows applications to adapt themselves in responseto contexts (e.g. activity, location, temperature level, etc.). However, an issue of user conflicts in context-aware applicationsmay arise when multiple users want to access the same application. Our research focuses on this issue and proposes aconflict resolution approach that resolves the conflicts in context-aware home applications. The proposed approach takes intoconsideration the users’ special case contexts (e.g. illness of user) along with their priorities and preferences. The proposedapproach is also useful in cases where multiple users with multiple special cases try to access one application or service.To show the usefulness of the proposed approach, we have integrated the proposed conflict manager with the UbiREAL, asimulated context-aware home environment. The conflict manager utilizes different strategies and different approaches toresolve user conflicts according to the involved situation to suit the need of the family. The prototype evaluation shows thatthe users are satisfied with the proposed system and suggests that the use of users’ special case contexts in detecting andresolving the user conflicts is essential and necessary in the context-aware smart home environments.

A Novel PM-OFDM-CDMA System for 5G Wireless Communication with High Capacity and Low Energy Consumption

Phase Modulation Orthogonal Frequency Division Multiplexing (PM-OFDM) and Code Division Multiple Access (CDMA) have distinctive advantages. On one hand, PM-OFDM is an attractive technique to combat multipath fading and reduce high PAPR in conventional OFDM. On the other hand, CDMA has the ability to serve multiple users at the same time and/or frequency. In this article, a new PM-OFDM-CDMA system that combines PM-OFDM and CDMA is proposed. The idea behind the proposed technique is to combine the advantages of these techniques in order to enhance the performance of the 5G system by serving multiple users simultaneously. The performance of the proposed system is analysed in terms of BER under different channel conditions. A Minimum Mean Square Equaliser (MMSE) scheme is used in order to avoid the effect of multipath effect and noise simultaneously. From the simulation results, we conclude that the proposed system has good performance that can improve 5G wireless communication networks, especially for battery-powered mobile devices. This amounts to the constant envelope of the proposed waveform which generates constant instantaneous power and therefore reduced PAPR.

Control of WPT Transmitter Coils for Power Distribution to Two Receiver Coils without Feedback

This paper proposes the algorithm to control the current ratio of the transmitting (Tx) coils for proper power distribution to the two receiving (Rx) coils in wireless power transfer (WPT) system. The proposed algorithm assumes that each Rx coil appears at different times to consider the situation where multiple users request power transmission as practically as possible. That is, suppose the second Rx coil enters the charging space later than the first Rx coil. When each coil enters the charging space, only the Tx coil is used to obtain the value required for calculation. Using the obtained result, the optimized Tx coil current is calculated by the proposed algorithm and proper power distribution to both Rx coils is achieved. Three Tx coils and two Rx coils are constructed using the ANSYS MAXWELL simulation tool. As a result of applying the proposed algorithm, it was confirmed that a similar level of power was transmitted between 40∼60%, respectively. The sum of the power transmitted to the two Rx coils also appeared as more than 75%.

Deep Reinforcement Learning for Computation Offloading and Resource Allocation in Unmanned-Aerial-Vehicle Assisted Edge Computing

Computation offloading technology extends cloud computing to the edge of the access network close to users, bringing many benefits to terminal devices with limited battery and computational resources. Nevertheless, the existing computation offloading approaches are challenging to apply to specific scenarios, such as the dense distribution of end-users and the sparse distribution of network infrastructure. The technological revolution in the unmanned aerial vehicle (UAV) and chip industry has granted UAVs more computing resources and promoted the emergence of UAV-assisted mobile edge computing (MEC) technology, which could be applied to those scenarios. However, in the MEC system with multiple users and multiple servers, making reasonable offloading decisions and allocating system resources is still a severe challenge. This paper studies the offloading decision and resource allocation problem in the UAV-assisted MEC environment with multiple users and servers. To ensure the quality of service for end-users, we set the weighted total cost of delay, energy consumption, and the size of discarded tasks as our optimization objective. We further formulate the joint optimization problem as a Markov decision process and apply the soft actor–critic (SAC) deep reinforcement learning algorithm to optimize the offloading policy. Numerical simulation results show that the offloading policy optimized by our proposed SAC-based dynamic computing offloading (SACDCO) algorithm effectively reduces the delay, energy consumption, and size of discarded tasks for the UAV-assisted MEC system. Compared with the fixed local-UAV scheme in the specific simulation setting, our proposed approach reduces system delay and energy consumption by approximately 50% and 200%, respectively.

Trycycler: consensus long-read assemblies for bacterial genomes

While long-read sequencing allows for the complete assembly of bacterial genomes, long-read assemblies contain a variety of errors. Here, we present Trycycler, a tool which produces a consensus assembly from multiple input assemblies of the same genome. Benchmarking showed that Trycycler assemblies contained fewer errors than assemblies constructed with a single tool. Post-assembly polishing further reduced errors and Trycycler+polishing assemblies were the most accurate genomes in our study. As Trycycler requires manual intervention, its output is not deterministic. However, we demonstrated that multiple users converge on similar assemblies that are consistently more accurate than those produced by automated assembly tools.

FPGA Remote Laboratory Using IoT Approaches

Field-Programmable Gate Arrays (FPGAs) are relatively high-end devices that are not easily shared between multiple users. In this work, we achieved a remotely accessible FPGA framework using accessible Internet of Things (IoT) approaches. We sought to develop a method for students to receive the same level of educational quality in a remote environment that they would receive in a typical, in-person course structure for a university-level digital design course. Keeping cost in mind, we are able to combine the functionality of an entry-level FPGA and a Raspberry Pi Zero to provide IoT access for laboratory work. Previous works in this field allow only one user to access an FPGA at a time, which requires students to schedule time slots. Our design is unique in that it gives multiple users the ability to interact simultaneously with one individual top-level design on an FPGA. This novel design has the benefit for classroom presentations, collaboration and debugging, and eliminates the need for restricting student access to a time slot for FPGA access. Further, our hardware wrapper is lightweight, utilizing less than 1% of tested FPGA chips, allowing it to be integrated with resource-heavy designs. The application is meant to scale with large designs; there is no difference between how many users can interact with the remote design, regardless of the complexity of the design. Further, the number of users who can interact with a single project is limited only by the bandwidth restrictions imposed by Google Fire Base, which is far beyond any practical number of users for simultaneous access.

Indoor Localization for Personalized Ambient Assisted Living of Multiple Users in Multi-Floor Smart Environments

This paper presents a multifunctional interdisciplinary framework that makes four scientific contributions towards the development of personalized ambient assisted living (AAL), with a specific focus to address the different and dynamic needs of the diverse aging population in the future of smart living environments. First, it presents a probabilistic reasoning-based mathematical approach to model all possible forms of user interactions for any activity arising from user diversity of multiple users in such environments. Second, it presents a system that uses this approach with a machine learning method to model individual user-profiles and user-specific user interactions for detecting the dynamic indoor location of each specific user. Third, to address the need to develop highly accurate indoor localization systems for increased trust, reliance, and seamless user acceptance, the framework introduces a novel methodology where two boosting approaches—Gradient Boosting and the AdaBoost algorithm are integrated and used on a decision tree-based learning model to perform indoor localization. Fourth, the framework introduces two novel functionalities to provide semantic context to indoor localization in terms of detecting each user’s floor-specific location as well as tracking whether a specific user was located inside or outside a given spatial region in a multi-floor-based indoor setting. These novel functionalities of the proposed framework were tested on a dataset of localization-related Big Data collected from 18 different users who navigated in 3 buildings consisting of 5 floors and 254 indoor spatial regions, with an to address the limitation in prior works in this field centered around the lack of training data from diverse users. The results show that this approach of indoor localization for personalized AAL that models each specific user always achieves higher accuracy as compared to the traditional approach of modeling an average user. The results further demonstrate that the proposed framework outperforms all prior works in this field in terms of functionalities, performance characteristics, and operational features.