Secure and Private Cloud Storage Systems with Random Linear Fountain Codes

An information theoretic approach to security and privacy called Secure And Private Information Retrieval (SAPIR) is introduced. SAPIR is applied to distributed data storage systems. In this approach, random combinations of all contents are stored across the network. Our coding approach is based on Random Linear Fountain (RLF) codes. To retrieve a content, a group of servers collaborate with each other to form a Reconstruction Group (RG). SAPIR achieves asymptotic perfect secrecy if at least one of the servers within an RG is not compromised. Further, a Private Information Retrieval (PIR) scheme based on random queries is proposed. The PIR approach ensures the users privately download their desired contents without the servers knowing about the requested contents indices. The proposed scheme is adaptive and can provide privacy against a significant number of colluding servers.Comment: 8 pages, 2 figure

Prediction, Recommendation and Group Analytics Models in the domain of Mashup Services and Cyber-Argumentation Platform

Mashup application development is becoming a widespread software development practice due to its appeal for a shorter application development period. Application developers usually use web APIs from different sources to create a new streamlined service and provide various features to end-users. This kind of practice saves time, ensures reliability, accuracy, and security in the developed applications. Mashup application developers integrate these available APIs into their applications. Still, they have to go through thousands of available web APIs and chose only a few appropriate ones for their application. Recommending relevant web APIs might help application developers in this situation. However, very low API invocation from mashup applications creates a sparse mashup-web API dataset for the recommendation models to learn about the mashups and their web API invocation pattern. One research aims to analyze these mashup-specific critical issues, look for supplemental information in the mashup domain, and develop web API recommendation models for mashup applications. The developed recommendation model generates useful and accurate web APIs to reduce the impact of low API invocations in mashup application development. Cyber-Argumentation platform also faces a similarly challenging issue. In large-scale cyber argumentation platforms, participants express their opinions, engage with one another, and respond to feedback and criticism from others in discussing important issues online. Argumentation analysis tools capture the collective intelligence of the participants and reveal hidden insights from the underlying discussions. However, such analysis requires that the issues have been thoroughly discussed and participant’s opinions are clearly expressed and understood. Participants typically focus only on a few ideas and leave others unacknowledged and underdiscussed. This generates a limited dataset to work with, resulting in an incomplete analysis of issues in the discussion. One solution to this problem would be to develop an opinion prediction model for cyber-argumentation. This model would predict participant’s opinions on different ideas that they have not explicitly engaged. In cyber-argumentation, individuals interact with each other without any group coordination. However, the implicit group interaction can impact the participating user\u27s opinion, attitude, and discussion outcome. One of the objectives of this research work is to analyze different group analytics in the cyber-argumentation environment. The objective is to design an experiment to inspect whether the critical concepts of the Social Identity Model of Deindividuation Effects (SIDE) are valid in our argumentation platform. This experiment can help us understand whether anonymity and group sense impact user\u27s behavior in our platform. Another section is about developing group interaction models to help us understand different aspects of group interactions in the cyber-argumentation platform. These research works can help develop web API recommendation models tailored for mashup-specific domains and opinion prediction models for the cyber-argumentation specific area. Primarily these models utilize domain-specific knowledge and integrate them with traditional prediction and recommendation approaches. Our work on group analytic can be seen as the initial steps to understand these group interactions

Exploring the Opportunity of Augmented Reality (AR) in Supporting Older Adults Explore and Learn Smartphone Applications

The global aging trend compels older adults to navigate the evolving digital landscape, presenting a substantial challenge in mastering smartphone applications. While Augmented Reality (AR) holds promise for enhancing learning and user experience, its role in aiding older adults' smartphone app exploration remains insufficiently explored. Therefore, we conducted a two-phase study: (1) a workshop with 18 older adults to identify app exploration challenges and potential AR interventions, and (2) tech-probe participatory design sessions with 15 participants to co-create AR support tools. Our research highlights AR's effectiveness in reducing physical and cognitive strain among older adults during app exploration, especially during multi-app usage and the trial-and-error learning process. We also examined their interactional experiences with AR, yielding design considerations on tailoring AR tools for smartphone app exploration. Ultimately, our study unveils the prospective landscape of AR in supporting the older demographic, both presently and in future scenarios

Innovating with Artificial Intelligence: Capturing the Constructive Functional Capabilities of Deep Generative Learning

As an emerging species of artificial intelligence, deep generative learning models can generate an unprecedented variety of new outputs. Examples include the creation of music, text-to-image translation, or the imputation of missing data. Similar to other AI models that already evoke significant changes in society and economy, there is a need for structuring the constructive functional capabilities of DGL. To derive and discuss them, we conducted an extensive and structured literature review. Our results reveal a substantial scope of six constructive functional capabilities demonstrating that DGL is not exclusively used to generate unseen outputs. Our paper further guides companies in capturing and evaluating DGL’s potential for innovation. Besides, our paper fosters an understanding of DGL and provides a conceptual basis for further research

ADC Emulation on FPGA

Analog-to-Digital Converters (ADCs) are devices that transform analog signals into digital signals and are used in various applications such as audio recording, data acquisition, and measurement systems [1]. Prior to the development of actual chip, there is a need for prototyping, testing and verifying the performance of ADCs in different scenarios. Analog macros cannot be tested on an FPGA. In order to ensure the macros function properly, the emulation of the ADC is done first. This is a digital module and can be designed in System Verilog. This paper demonstrates the design of the module on FPGA for Analog to Digital Converter (ADC) emulation. The emulation is done specific to the ADC macro which has programmable resolutions of 12, 10, 8 or 6 bits. To validate the simulation results, the designed module is tested on FPGA. The outputs and logic block utilization are analyzed. The number of LUTs utilized in the design is 38, the number of flip flops needed is 41 and the input output pin utilization is 20. The dynamic power utilization of the design is 0.543W and the static power utilization is 0.082W

ADC Emulation on FPGA

Analog-to-Digital Converters (ADCs) are devices that transform analog signals into digital signals and are used in various applications such as audio recording, data acquisition, and measurement systems [1]. Prior to the development of actual chip, there is a need for prototyping, testing and verifying the performance of ADCs in different scenarios. Analog macros cannot be tested on an FPGA. In order to ensure the macros function properly, the emulation of the ADC is done first. This is a digital module and can be designed in System Verilog. This paper demonstrates the design of the module on FPGA for Analog to Digital Converter (ADC) emulation. The emulation is done specific to the ADC macro which has programmable resolutions of 12, 10, 8 or 6 bits. To validate the simulation results, the designed module is tested on FPGA. The outputs and logic block utilization are analyzed. The number of LUTs utilized in the design is 38, the number of flip flops needed is 41 and the input output pin utilization is 20. The dynamic power utilization of the design is 0.543W and the static power utilization is 0.082W

ISBDD model for classification of hyperspectral remote sensing imagery

The diverse density (DD) algorithm was proposed to handle the problem of low classification accuracy when training samples contain interference such as mixed pixels. The DD algorithm can learn a feature vector from training bags, which comprise instances (pixels). However, the feature vector learned by the DD algorithm cannot always effectively represent one type of ground cover. To handle this problem, an instance space-based diverse density (ISBDD) model that employs a novel training strategy is proposed in this paper. In the ISBDD model, DD values of each pixel are computed instead of learning a feature vector, and as a result, the pixel can be classified according to its DD values. Airborne hyperspectral data collected by the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) sensor and the Push-broom Hyperspectral Imager (PHI) are applied to evaluate the performance of the proposed model. Results show that the overall classification accuracy of ISBDD model on the AVIRIS and PHI images is up to 97.65% and 89.02%, respectively, while the kappa coefficient is up to 0.97 and 0.88, respectively

Direct Acyclic Graph based Ledger for Internet of Things: Performance and Security Analysis

Direct Acyclic Graph (DAG)-based ledger and the corresponding consensus algorithm has been identified as a promising technology for Internet of Things (IoT). Compared with Proof-of-Work (PoW) and Proof-of-Stake (PoS) that have been widely used in blockchain, the consensus mechanism designed on DAG structure (simply called as DAG consensus) can overcome some shortcomings such as high resource consumption, high transaction fee, low transaction throughput and long confirmation delay. However, the theoretic analysis on the DAG consensus is an untapped venue to be explored. To this end, based on one of the most typical DAG consensuses, Tangle, we investigate the impact of network load on the performance and security of the DAG-based ledger. Considering unsteady network load, we first propose a Markov chain model to capture the behavior of DAG consensus process under dynamic load conditions. The key performance metrics, i.e., cumulative weight and confirmation delay are analysed based on the proposed model. Then, we leverage a stochastic model to analyse the probability of a successful double-spending attack in different network load regimes. The results can provide an insightful understanding of DAG consensus process, e.g., how the network load affects the confirmation delay and the probability of a successful attack. Meanwhile, we also demonstrate the trade-off between security level and confirmation delay, which can act as a guidance for practical deployment of DAG-based ledgers.Comment: accepted by IEEE Transactions on Networkin