Miracle-dz/UniFAR-FTUs huggingface dataset:
Leakage-resilient lattice-based partially blind signatures

Blind signature schemes (BSS) play a pivotal role in privacy-oriented cryptography. However, with BSS, the signed message remains unintelligible to the signer, giving them no guarantee that the blinded message he signed actually contained valid information. Partially BSS (PBSS) were introduced to address precisely this problem. In this study, the authors present the first leakage-resilient, lattice-based PBSS in the literature. The proposed construction is provably secure in the random oracle model and offers quasi-linear complexity w.r. t. key/signature sizes and signing speed. In addition, it offers statistical partial blindness and its unforgeability is based on the computational hardness of worst-case ideal lattice problems for approximation factors in O ~ ( n 4 ) in dimension n. The proposed scheme benefits from the subexponential hardness of ideal lattice problems and remains secure even if a ( 1 − o ( 1 ) ) fraction of the signer's secret key leaks to an adversary via arbitrary side-channels. Several extensions of the security model, such as honest-user unforgeability and selective failure blindness, are also considered and concrete parameters for instantiation are proposed.
Organizational Learning Through Disruptive Digital Innovation. A Blockchain Implementation

Organizational Learning Through Disruptive Digital Innovation. A Blockchain Implementation by Veneetia Smith Johnson August 2019 Chair: Balasubramaniam Ramesh Major Academic Unit: Executive Doctorate in Business Organizational learning and management are at a transition point because of the shift in disruptive digital innovations (DDI). Organizing axioms are challenged or fundamentally changed by the nature of innovation (Nambisan, Lyytinen, Majchrzak, & Song, 2017). There is widespread recognition that investing in organizational learning drives change and innovation (Linares, 2017). The early research examined DDI and the factors that enable or inhibit it. However, there is a limited amount of research on the relationship between DDI and organizational learning. More specifically, research that is conducted to understand the theoretical relationship between organizational learning and DDI is needed. The phenomenon has been studied in the rich context of information technology (IT) and supply chain management (SCM). In this research, a single case study approach is used to examine singleand double-loop learning. IT organizations use DDI to remain practical in a dynamic environment. In the present study, the DDI framework is used to illustrate how organizational learning is facilitated. Recommendations are offered on how IT organizations could enhance organizational learning to improve project implementation and delivery related to disruptive digital innovation. INDEX WORDS: disruptive digital innovation, organizational learning, blockchain, organizational inertia
Pedagogy-informed Design of Conversational Learning at Scale

This paper examines how an explicit theory of learning as conversation has informed design of the FutureLearn MOOC platform. We describe a process of pedagogy-informed systems design and show how Conversation Theory has provided a framework for design that combines learning as conversation with instruction through structured content. The paper compares performance metrics across three MOOC platforms. Results show higher levels of social engagement, with comparable completion rates, for the FutureLearn platform.
Policy compliance and deterrence mechanism in the sharing economy

Purpose In a sharing economy, economically inactive members can serve as providers owing to the low start-up costs. However, such providers may operate without sufficient knowledge of the market and policies, causing significant problems. To prevent illegal sharing, governments encourage providers to register their businesses after meeting certain requirements, but most providers still operate unregistered businesses. The purpose of this paper is to explore the causes of policy non-compliance and suggest measures that can induce compliance. Design/methodology/approach Based on the rational choice and deterrence theories, this study combines qualitative and quantitative research. The former is used to investigate the antecedent factors affecting compliance. Using the latter, this study assumes that the existence of platform operators can resolve information asymmetries. The qualitative findings provide the variables that can lead to policy compliance, while the quantitative research verifies the causal relationships. Findings Business registration by providers in the sharing economy arises from their subjective cost-benefit calculations of policy compliance. According to the qualitative research, they believe there is a low risk of detection of policy non-compliance by the government. The quantitative research suggests that interventions by platform operators could resolve information asymmetries between the government and providers. Originality/value This study designed a mechanism to guide providers toward policy compliance. To reduce friction with the existing market and ensure efficient growth, it is necessary to cooperate with sharing economy participants. The results suggest that the role of platform operators and the government is important.
Improving Deep Learning for Face Verification Using Color Histogram Equalization Data Augmentation

- This paper proposes a new method of improving face verification learning using color histogram equalization by incorporating the results of deep convolutional neural networks (CNNs). The entire process of face verification using deep learning and color histogram equalization is described in detail. This research uses advanced deep learning methods for face verification tasks. Because the CNN achieves the best results for larger datasets, the main challenge is to increase the smaller dataset enhancements and validate in environments different from the training datasets. This paper presents a new training enhancement method. When the face-image datasets were small, we could use our enhanced method to expand the dataset and improve the accuracy of face verification to adopt different environments. Consequently, the accuracy reached 99.7996%, i.e., approximately 7% higher than the result trained on the original dataset.
Co-evolutionary design processes applied to building spatial design optimization

Building spatial design is in practice a co-evolutionary design process. To optimize a building spatial design, an evolutionary algorithm can be used, but the search space is large and complex. In simulations of co-evolutionary design processes the size and complexity are not hindering the search. Such simulations are in the presented work proposed for finding search spaces that are small but still contain high quality solutions.
Deep learning–based fifth-generation millimeter-wave communication channel tracking for unmanned aerial vehicle Internet of things networks

Using unmanned aerial vehicle as movable base stations is a promising approach to enhance network coverage. Moreover, movable unmanned aerial vehicle–base stations can dynamically move to the target devices to expand the communication range as relays in the scenario of the Internet of things. In this article, we consider a communication system with movable unmanned aerial vehicle–base stations in millimeter-Wave. The movable unmanned aerial vehicle–base stations are equipped with antennas and multiple sensors for channel tracking. The cylindrical array antenna is mounted on the movable unmanned aerial vehicle–movable base stations, making the beam omnidirectional. Furthermore, the attitude estimation method using the deep neural network can replace the traditional attitude estimation method. The estimated unmanned aerial vehicle attitude information is combined with beamforming technology to realize a reliable communication link. Simulation experiments have been performed, and the results have verified the effectiveness of the proposed method.
The Use of Bluetooth Low Energy for Continuous Monitoring of Body Sensor Networks

Author(s): Ayoub, Michael Atef Mikhail | Advisor(s): Eltawil, Ahmed | Abstract: Wireless communications enable remote monitoring and controlling of Body Sensor Networks (BSN), thus playing a key role in the development of numerous medical and fitness applications and providing various advantages in terms of cost and user’s convenience. However, several issues have been brought to the surface regarding the sensor’s size and lifetime.Bluetooth Low Energy (BLE) is a booming wireless technology that targets low-power, low-complexity and low-throughput applications and thus an excellent candidate for BSN connectivity. Additionally, BLE-powered smartphones can act as user controllers and internet gateways for BSN devices, at no extra cost of network deployment.This thesis conducts a study on the use of BLE technology in continuous monitoring of BSNs in terms of the required throughput, power consumption and latency. We compare the performance of different versions of the Bluetooth core specification using a theoretical model and an experimental setup based on nRF52840 chip by Nordic Semiconductor. We focus on Electrocardiography (EKG) and give the current consumption and battery lifetime estimation of an EKG gateway and an EKG node for different BLE versions and configurations.
Protecting Cloud Virtual Machines from Hypervisor and Host Operating System Exploits

Hypervisors are widely deployed by cloud computing providers to support virtual machines, but their growing complexity poses a security risk as large codebases contain many vulnerabilities. We have created HypSec, a new hypervisor design for retrofitting an existing commodity hypervisor using microkernel principles to reduce its trusted computing base while protecting the confidentiality and integrity of virtual machines. HypSec partitions the hypervisor into an untrusted host that performs most complex hypervisor functionality without access to virtual machine data, and a trusted core that provides access control to virtual machine data and performs basic CPU and memory virtualization. Hardware virtualization support is used to isolate and protect the trusted core and execute it at a higher privilege level so it can mediate virtual machine exceptions and protect VM data in CPU and memory. HypSec takes an end-to-end approach to securing I/O to simplify its design, with applications increasingly using secure network connections in the cloud. We have used HypSec to retrofit KVM, showing how our approach can support a widely-used full-featured hypervisor integrated with a commodity operating system. The implementation has a trusted computing base of only a few thousand lines of code, many orders of magnitude less than KVM. We show that HypSec protects the confidentiality and integrity of virtual machines running unmodified guest operating systems while only incurring modest performance overhead for real application workloads.
Designing and Prototyping Record System for Machine Usage Time

Machine cost is one of the components of production cost. Accurate record of machine usage time is thus crucial to obtain accurate production cost calculation. Besides, machine usage time can be used as parameter to assess the performance of the machine operator. Furthermore, the data can be used as input to the machine’s maintenance and reparation plan. The author is motivated by the current problems found at Akademi Teknik Mesin Industri (ATMI) Cikarang, where it is still possible to use one machine simultaneously by more than one operator. This causes inaccuracy in calculation of machine usage time and production cost. In this paper, the author proposed a design of device to record detailed data of shop drawing and machine usage time. By the use of this device, a certain machine will be activated and deactivated by using RFID technology, with the whole system controlled by Arduino Mega 2560 microcontroller. Furthermore, the shop drawing and machine usage time will be able to be accessed through a Windows-based interface application, implemented by using Visual Studio. A database is also made available by using Microsoft Access. The use of the proposed device makes possible of accurate measurement of production time efficiency and machine operator performance. Through this improved accuracy, further analysis can be made in order to achieve production improvements in planning, process, and maintenance.
Profiling symmetric encryption algorithms for implantable medical devices

The amount of Implantable Medical Devices (IMD) keeps booming in recent years. However, not many of them have encryption and decryption methods to protect their data communication. The purpose of this thesis work is to profile various popular symmetric encryption algorithms and select the best candidates as the benchmark for generic IMD. Moreover, this thesis gives suggestions for design digital CPU for generic IMD based on lab work simulation output. This thesis work will focus on profiling and analyzing several metrics: average power consumption, peak power consumption and energy consumption of all candidates because those metrics are the crucial constraint of IMD. Also the background information of all candidates will be introduced and all relevant metrics such as security level, encryption rates of all candidates will be discussed in this thesis work.
Improving Accuracy and Efficiency with Concurrent Use of Artificial Intelligence for Digital Breast Tomosynthesis.

Purpose To evaluate the use of artificial intelligence (AI) to shorten digital breast tomosynthesis (DBT) reading time while maintaining or improving accuracy. Materials and Methods A deep learning AI system was developed to identify suspicious soft-tissue and calcified lesions in DBT images. A reader study compared the performance of 24 radiologists (13 of whom were breast subspecialists) reading 260 DBT examinations (including 65 cancer cases) both with and without AI. Readings occurred in two sessions separated by at least 4 weeks. Area under the receiver operating characteristic curve (AUC), reading time, sensitivity, specificity, and recall rate were evaluated with statistical methods for multireader, multicase studies. Results Radiologist performance for the detection of malignant lesions, measured by mean AUC, increased 0.057 with the use of AI (95% confidence interval [CI]: 0.028, 0.087; P < .01), from 0.795 without AI to 0.852 with AI. Reading time decreased 52.7% (95% CI: 41.8%, 61.5%; P < .01), from 64.1 seconds without to 30.4 seconds with AI. Sensitivity increased from 77.0% without AI to 85.0% with AI (8.0%; 95% CI: 2.6%, 13.4%; P < .01), specificity increased from 62.7% without to 69.6% with AI (6.9%; 95% CI: 3.0%, 10.8%; noninferiority P < .01), and recall rate for noncancers decreased from 38.0% without to 30.9% with AI (7.2%; 95% CI: 3.1%, 11.2%; noninferiority P < .01). Conclusion The concurrent use of an accurate DBT AI system was found to improve cancer detection efficacy in a reader study that demonstrated increases in AUC, sensitivity, and specificity and a reduction in recall rate and reading time. © RSNA, 2019See also the commentary by Hsu and Hoyt in this issue.
Robust Label Prediction via Label Propagation and Geodesic k-Nearest Neighbor in Online Semi-Supervised Learning

SUMMARY This paper proposes a computationally e ﬃ cient o ﬄ ine semi-supervised algorithm that yields a more accurate prediction than the label propagation algorithm, which is commonly used in online graph-based semi-supervised learning (SSL). Our proposed method is an o ﬄ ine method that is intended to assist online graph-based SSL algorithms. The e ﬃ cacy of the tool in creating new learning algorithms of this type is demonstrated in numerical experiments.
Framework for creating performance model of AI algorithms for early architecture exploration

— We are now living in an age of AI enabled smart homes, autonomous vehicles, and chatbots. Researchers are coming up with complex AI algorithms that are useful in various areas like speech and image recognition, even surpassing human accuracy. Increased complexity, growing competition and splurge in demand of AI enabled devices constantly put pressure on vendors to improve the chip performance and deliver the product as early as possible. System designers tend to use simulation techniques to quickly analyze and improve their model before implementing it in silicon. But till now these simulation techniques have not been leveraged for the AI domain. This paper proposes a novel framework to leverage Virtual Prototyping for early design exploration of power and performance targeting AI centric designs. We have used this framework to model a known Convolutional Neural Network, resnet-18 and simulated it on NVDLA hardware model. In our case study, we were able to determine key bottlenecks in the hardware design and were successful in our goal of reducing the inference latency to 5ms and minimizing the total energy and average power consumption.
Gene Signatures Research Involved in Cancer Using Machine Learning

With the cheapening of mass sequencing techniques and the rise of computer technologies, capable of analyzing a huge amount of data, it is necessary nowadays that both branches mutually benefit. Transcriptomics, in this case, is a branch of biology focused on the study of mRNA molecules, among others. The quantification of these molecules gives us information about the expression that a gene is having at a given moment. Having information on the expression of the approximately 20,000 genes harbored by human beings is a really useful source of information for the study of certain conditions and/or pathologies. In this work, patient expression -omic data data have been used to offer a new analysis methodology through Machine Learning. The results of this methodology were compared with a conventional methodology to observe how they differed and how they resembled each other. These techniques, therefore, offer a new mechanism for the search of genetic signatures involved, in this case, with cancer.
H∞mixed-sensitivity design with fixed structure controller through Putinar positivstellensatz

In this paper we describe a novel approach for $H$∞mixed-sensitivity design with fixed structure controller. We introduce the feasible controller set as the set of controllers, within a given class, which guarantees fulfillment of requirements on robust stability and nominal performances for the closed loop system. We formulate the $H$∞problem as a non convex optimization problem. Results on Putinar positivstellensatz and convex relaxation techniques are exploited to check the feasibility of the problem, thus the existence of a suitable controller, and to extract the desired parameters from the feasible controller set.
An inheritance-based lexical approach to sentiment analysis

Sentiment analysis is defined as a computational study of people’s beliefs and opinions regarding entities and events, and their attributes, as expressed in a text. The two main approaches to sentiment analysis are machine learning and lexicon-based. The machine learning approach builds a model by learning from observed data to analyse the sentiment of a text, whereas the lexicon-based approach associates sentiment scores with individual words and calculates an overall sentiment score for the document. Each approach has strengths and weaknesses: the lexicon-based approach can capture specific lexical sentiment behaviour very precisely, but relies on expert development of lexicons which is expensive and does not scale easily; the machine learning approach can exploit broader linguistic context and so is more robust, but it is less precise and requires large-scale training data. This project introduced a novel ’extended’ lexical approach which uses inheritance-based techniques to represent both lexical behaviour and broader linguistic context derived from corpus-based learning. This approach used lexical items not just in isolation, but in context, which allowed the study to take into account more complex linguistic constructions. The corpusbased learning technique was then used to refine this model with examples derived from corpus data. This was done by using a non-monotonic, inheritance-based architecture to represent both the lexical algorithmic component and the example-based refinements. This thesis introduced a sentiment modelling system called Galadriel , based on the inheritance mechanisms of the lexical knowledge description representation language DATR. The Galadriel system handles sentiment phrases and supports exceptions to general rules using corpus-based learning methodology. However, I did not aim to explore automatic acquisition for sentiment analysis using machine learning methods in this thesis. More specifically, this project developed a final system (Galadriel ) to address different levels of sentiment analysis related to the current research area: documentlevel, sentence-level and aspect-level. The main properties of the Galadriel system involve the calculation of sentiment magnitude and the polarity of a text. A cali-
A Hybrid Many-Objective Evolutionary Algorithm With Region Preference for Decision Makers

In many-objective optimization, maintaining a good balance between convergence and diversity have turned out to be a considerable challenge for classical evolutionary algorithms. The large scale solution set required to describe the entire Pareto optimal front hinder the decision makers from finding the most satisfactory solution, whereas he/she is only interested in a limited part of the objective space. The dilemma can be handled by incorporating the preference information in the search process. In this paper, an evolutionary algorithm based on region preference is proposed for many-objective optimization. The preference model is constructed in combination of the target region and reference points. To focus the search on the preference region while maintaining well convergence and diversity within the region, a tri-level ranking criterion is introduced into the proposed algorithm, and different rank works at different phase of the search process. A fuzzy theory-based interactive approach is proposed to guide more individuals to further search into the objective space with higher preference degree and help the decision maker choose the most interested solution. The proposed algorithm has been extensively compared with other state-of-the-art preference-based algorithms on DTLZ1~ DTLZ4 test problems having 3–10 objectives. The experimental results indicate that the proposed algorithm can achieve competitive and better performance. Moreover, we extend the algorithm to handle multiple target regions.
Consumer and provider: roles to understand continued use behavior in virtual communities

The increasing use of virtual communities makes research in this technology vital to understand the dynamics that users establish around them. In this sense, the virtual communities’ literature recognizes that an individual has at least two behaviors in a virtual community: consumer and provider. The present research proposes a conceptual model where continued usage – in the context of virtual communities, specifically of social networks – is conceptualized as a multidimensional construct formed by the continued consumption and the continued information sharing. It also considers that these dimensions are distinguishable from each other and are due to different factors. This approach is expected to provide a more complete perspective on the roles of users within a virtual community. Likewise, it is expected that it will be able to provide bases for strategies for the continued existence of virtual communities.