Reviewerly/exHarmony huggingface dataset:
Efficient PSoC Implementation of Modular Multiplication and Exponentiation Based on Serial-Parallel Combination

This paper presents an FPGA implementation of the most critical operations of Public Key Cryptography (PKC), namely the Modular Exponentiation (ME) and the Modular Multiplication (MM). Both operations are integrated as Programmable System on Chip (PSoC) where the processor Microblaze of Xilinx is used for flexibility. Our objective is to achieve a best trade-off between time execution, occupied area and flexibility. The implementation of these operations on such environment requires taking into account several criteria. Indeed, the Hardware (HW) architectures data bus should be smaller than the input data length. The design must be scalable to support different security levels. The implementation achieves optimums execution time and HW resources number. In order to satisfy these constraints, Montgomery Power Ladder (MPL) and Montgomery Modular Multiplication (MMM) algorithms are utilized for the ME and the MM implementations as HW accelerators, respectively. Our implementation approach is based on the digit-serial method for performing the basic arithmetic operations. Efficient parallel and pipeline strategies are developed at the digit level for the optimization of the execution times. The application for 1024-bits data length shows that the MMM run in 6.24[Formula: see text][Formula: see text]s and requires 647 slices. The ME is executed in 6.75[Formula: see text]ms using 2881 slices.
Building Community

We discuss the outcomes of the 2nd Workshop of the International and Interdisciplinary Perspectives on Children &amp; Recommender Systems (KidRec 2018), co-located with the 2018 ACM Interaction Design and Children (IDC) Conference, which took place June 19-22 in Trondheim, Norway. The goal for this workshop was to explore research and industry efforts focused directly on or facilitating the algorithmic recommendation process for children. The diversity of attendees including K-12 teachers, educators, researchers in various areas of computer science, and industry participants-made it possible to continue to build community around this important topic and further discuss and outline the salient research questions and the next steps to promote more research in this area.
An Empirical Study on Distributed Bayesian Approximation Inference of Piecewise Sparse Linear Models

The importance of interpretability of machine learning models has been increasing due to emerging enterprise predictive analytics. Piecewise linear models have been actively studied to achieve both accuracy and interpretability. They often produce competitive accuracy against state-of-the-art non-linear methods. In addition, their representations (i.e., rule-based segmentation plus sparse linear formula) are often preferred by domain experts. A disadvantage of such models, however, is high computational cost for simultaneous determinations of the number of “pieces” and cardinality of each linear predictor, which has restricted their applicability to middle-scale data sets. This paper discusses an empirical study on the derivation of a distributed factorized asymptotic Bayesian (FAB) inference of learning piece-wise sparse linear models on distributed memory architectures from the original FAB inference algorithm. The distributed FAB inference solves the simultaneous model selection issue without communicating O(N) data where N is the number of training samples and achieves linear scale-out against the number of CPU cores. Experimental results demonstrate that the distributed FAB inference achieves high prediction accuracy and performance scalability with both synthetic and public benchmark data.
Robust digital image watermarking Scheme based on discrete fractional fourier transform

In order to protect images from unauthentic distortions, the watermarking methods provide a noteworthy solution to such problems. This paper proposes a new image watermarking method in Discrete Fractional Fourier Transform (DFrFT) domain. In this scheme, the original cover image is transformed into the frequency components using DFrFT. The binary watermark is embedded in various blocks of such components using a quantization based approach. However, the extraction of watermark bits is performed using adaptive thresholding methodology. The experimental analysis of the proposed scheme is performed over various standard images. The observed results are found satisfactory fulfilling the requirements of robustness and imperceptibility.
Diagnosing the Health of a Plant in a Click

In today's world of social media, not a single person or region is untouched by it. Even in rural regions with poor Internet connectivity and inadequate electricity supply, people are using smartphones with active social media accounts. Social media has made them educated enough to click photos and share them on sites. This learning can be used to introduce new technologies to tech novices. Using a similar approach, an app is designed using an artificial intelligence-based image recognition technique for predicting the health of a plant by analyzing its photograph. Farmers have to take snapshots using a mobile camera and upload them in the app, and in real time, they will get the diagnosis about the health of the plant. This paper provides insight into the design research process and outcome of the design of a mobile app for plant disease identification. The paper deals with creating a product that replicates existing physical systems to avoid dedicated user training. The research also aims to empower farmers by reducing their dependence on third parties for disease diagnosis by bringing the diagnosis to their smartphones.
Analysing Effectiveness of Multi-Label Classification in Clinical Coding

In Australia, hospital discharge summaries created at the end of an episode of care contain the patient's medical information based on which clinical codes are assigned. A patient can have multiple diseases and interventions carried out during their stay in the hospital. In this paper, we have done multi-label diseases and interventions classification using Binary Relevance, Label Power-set, and Multi-Layer k-Nearest Neighbor classifier. Our experimental work is divided into three tasks: Random Selection, User Selected, and Repetitive Task. Repetitive task gave better performance in comparison to the other two task.
The Mechanized Marriage of Effects and Monads with Applications to High-assurance Hardware

Constructing high-assurance, secure hardware remains a challenge, because to do so relies on both a verifiable means of hardware description and implementation. However, production hardware description languages (HDL) lack the formal underpinnings required by formal methods in security. Still, there is no such thing as high-assurance systems without high-assurance hardware. We present a core calculus of secure hardware description with its formal semantics, security type system, and mechanization in Coq. This calculus is the core of the functional HDL, ReWire, shown in previous work to have useful applications in reconfigurable computing. This work supports a full-fledged, formal methodology for producing high-assurance hardware.
Cryptoanalysis on ‘A round-optimal lattice-based blind signature scheme for cloud services’

In this note, we review the article published by Zhu et al. in Future Generation Computer Systems in 2017. We show that their construction of a blind signature does not hold the correctness requirement or the blindness requirement.
Multiple Instance Learning with Bag-Level Randomized Trees

Knowledge discovery in databases with a flexible structure poses a great challenge to machine learning community. Multiple Instance Learning (MIL) aims at learning from samples (called bags) represented by multiple feature vectors (called instances) as opposed to single feature vectors characteristic for the traditional data representation. This relaxation turns out to be useful in formulating many machine learning problems including classification of molecules, cancer detection from tissue images or identification of malicious network communications. However, despite the recent progress in this area, the current set of MIL tools still seems to be very application specific and/or burdened with many tuning parameters or processing steps. In this paper, we propose a simple, yet effective tree-based algorithm for solving MIL classification problems. Empirical evaluation against 28 classifiers on 29 publicly available benchmark datasets shows a high level performance of the proposed solution even with its default parameter settings. Data related to this paper are available at: https://github.com/komartom/MIDatasets.jl . Code related to this paper is available at: https://github.com/komartom/BLRT.jl .
Universally Composable Key Bootstrapping and Secure Communication Protocols for the Energy Internet

The Energy Internet is an advanced smart grid solution to increase energy efficiency by jointly operating multiple energy resources via the Internet. However, such an increasing integration of energy resources requires secure and efficient communication in the Energy Internet. To address such a requirement, we propose a new secure key bootstrapping protocol to support the integration and operation of energy resources. By using a universal composability model that provides a strong security notion for designing and analyzing cryptographic protocols, we define an ideal functionality that supports several cryptographic primitives used in this paper. Furthermore, we provide an ideal functionality for key bootstrapping and secure communication, which allows exchanged session keys to be used for secure communication in an ideal manner. We propose the first secure key bootstrapping protocol that enables a user to verify the identities of other users before key bootstrapping. We also present a secure communication protocol for unicast and multicast communications. The ideal functionalities help in the design and analysis of the proposed protocols. We perform some experiments to validate the performance of our protocols, and the results show that our protocols are superior to the existing related protocols and are suitable for the Energy Internet. As a proof of concept, we apply our functionalities to a practical key bootstrapping protocol, namely generic bootstrapping architecture.
Using Visualization to Illustrate Machine Learning Models for Genomic Data

Massive amounts of genomic data are created for the advent of Next Generation Sequencing technologies. Visualizing these complex genomic data requires not only simply plotting of data but should also invite a decision or a choice. Machine learning has the ability to make prediction and aid in decision-making. Machine learning and visualization are both effective ways to deal with big data but focus on different purposes. Machine learning applies statistical learning techniques to automatically identify patterns in data to make highly accurate predictions while visualization can leverage the human perceptual system to interpret and uncover hidden patterns in big data. Clinicians, experts and researchers intend to use both visualization and machine learning to analyze their complex genomic data, but it is a serious challenge for them to understand and trust machine learning models in the medical industry. This paper overcomes this problem by combining intelligent and interactive visualization with machine learning models. Our prototype not only visualizes the complex genomics data in a meaningful 3D similarity space, but also illustrates the machine learning models and the real-time prediction results. Interactions and connections between the machine learning model and the 3D scatter plot are also developed and illustrated.
InterCriteria Analysis of Different Variants of ACO Algorithm for Wireless Sensor Network Positioning

Wireless sensor networks are formed by spatially distributed sensors, which communicate in a wireless way. This network can monitor various kinds of environment and physical conditions like movement, noise, light, humidity, images, chemical substances etc. A given area needs to be fully covered with minimal number of sensors and the energy consumption of the network needs to be minimal too. We propose several algorithms, based on Ant Colony Optimization, to solve the problem. We study the algorithms behaviour when the number of ants varies from 1 to 10. We apply InterCriteria analysis to study relations between proposed algorithms and number of ants and analyse correlation between them.
Transfer to Rank for Top-N Recommendation

In this paper, we study top-N recommendation by exploiting users' explicit feedback such as 5-star numerical ratings, which has been overlooked to some extent in the past decade. As a response, we design a novel and generic transfer learning based recommendation framework coarse-to-fine transfer to rank (CoFiToR), which is a significant extension of a very recent work called transfer to rank (ToR). The key idea of our solution is modeling users' behaviors by simulating users' shopping processes. Therefore, we convert the studied ranking problem to three subtasks corresponding to three specific questions, including (i) whether an item will be examined by a user, (ii) how an item will be scored by a user, and (iii) whether an item will finally be purchased by a user. Based on this new conversion, we then develop a three-staged solution that progressively models users' preferences from a coarse granularity to a fine granularity. In each stage, we adopt an appropriate recommendation algorithm with pointwise or pairwise preference assumption to answer each question in order to seek an effective and efficient overall solution. Empirical studies on two large and public datasets showcase the merits of our solution in comparison with the state-of-the-art methods.
Efficient FPGA implementation of local binary convolutional neural network

Binarized Neural Networks (BNN) has shown a capability of performing various classification tasks while taking advantage of computational simplicity and memory saving. The problem with BNN, however, is a low accuracy on large convolutional neural networks (CNN). Local Binary Convolutional Neural Network (LBCNN) compensates accuracy loss of BNN by using standard convolutional layer together with binary convolutional layer and can achieve as high accuracy as standard AlexNet CNN. For the first time we propose FPGA hardware design architecture of LBCNN and address its unique challenges. We present performance and resource usage predictor along with design space exploration framework. Our architecture on LBCNN AlexNet shows 76.6% higher performance in terms of GOPS, 2.6X and 2.7X higher performance density in terms of GOPS/Slice, and GOPS/DSP compared to previous FPGA implementation of standard AlexNet CNN.
On using collaborative economy for test cost reduction in high fragmented environments

The grown adoption of mobile devices makes the development of applications a very attractive market. On top of it, run tests is a crucial activity and a big challenge due to the high fragmentation on Android ecosystem. In this paper, we discuss how a new platform based on Collaborative Economy could be used to create a new alternative to software testing. We present an analysis of using this platform and we confirm its advantages over existing cloud solutions, from a scalability and cost viewpoints. Our solution can provide an average cost reduction upper to 85% and a potential increasing in scalability.
Quantum Phase Transition in Fully Connected Quantum Wajnflasz–Pick Model

We construct a quantum Wajnflasz-Pick model that is a generalized quantum Ising model, and investigate a nature of quantum phase transitions of the model with infinite-range interactions. Quantum phase transition phenomena have drawn attention in the field of quantum computing as well as condensed matter physics, since the phenomena are closely related to the performance of quantum annealing (QA) and adiabatic quantum computation (AQC). We add a quantum driver Hamiltonian to the Hamiltonian of classical Wajnflasz-Pick model. The classical Wajnflasz-Pick model consists of two-level systems as with the usual Ising model. Unlike the usual Ising spin, each of the upper and the lower levels of the system can be degenerate. The states in the upper level and the lower level are referred to as upper states and lower states, respectively. The quantum driver Hamiltonian we introduced causes spin flip between the upper and the lower states and state transitions within each of the upper and the lower states. Numerical analysis showed that the model undergoes first-order phase transitions whereas a corresponding quantum Ising model, quantum Curie-Weiss model, does not undergo first-order phase transitions. In particular, we observed an anomalous phenomenon that the system undergoes successive first-order phase transitions under certain conditions. The obtained results indicate that the performance of QA and AQC by using degenerate two-level systems can be controlled by the parameters in the systems.
A Laser Curtain for Detecting Heterogeneous Lane-less Traffic

Traffic management system plays a vital role in the present smart city applications. In developing countries, heterogeneous lane-less traffic management is a significant challenge. This paper proposes a dual-purpose laser-based sensor configuration for vehicle classification. It classifies heterogeneous and less-lane disciplined traffic based on vehicles width. This work also suggests a methodology for the vehicle to infrastructure (V2I) communication using visible light.
On characterizing integral zeros of Krawtchouk polynomials by quantum entanglement

We propose a new characterization of integral zeros of binary Krawtchouk polynomials in terms of the range criterion for entangled states in quantum information theory.
GAIUS

Despite increasing mobile Internet penetration in emerging regions, the growth of web page complexity combined with inadequate content delivery mechanisms and lack of relevant local content make the web experience poor for users in these regions. In this paper, we propose GAIUS, a content ecosystem that enables the efficient creation and dissemination of locally relevant web content. GAIUS aims at creating a community content exchange that enables users to easily create and consume relevant web content with the help of a mobile application. Based on an early deployment of GAIUS in various regions, we show that our ecosystem enables localized content creation and diffusion efficiently.
Correction: Solving path planning problems in urban environments based on a priori sensors availabilities and execution error propagation

This paper addresses safe path planning problem in urban environments under onboard sensor availability uncertainty. In this context, an approach based on Mixed-Observability Markov Decision Process (MOMDP) is presented. Such a model enables the planner to deal with a priori probabilistic sensor availability and path execution error propagation, the which depends on the navigation solution. Due to modelling particularities of this safe path planning problem, such as bounded hidden and fully observable state variables, discrete actions and particular transition function form, the belief state update function becomes a complex step that cannot be ignored during planning. Recent advances in Partially Observable Markov Decision Process (POMDP) solving have proposed a planning algorithm called POMCP, which is based on Monte-Carlo Tree Search method. It allows the planner to work on the history of the action-observation pairs without the need to compute belief state updates. Thereby, this paper proposes to apply a POMCP-like algorithm to solve the addressed MOMDP safe path planning problem. The obtained results show the feasibility of the approach and the impact of considering different a priori probabilistic sensor availability on the result policy.
Challenges in Agile Security Engineering

This chapter describes a case of a large ICT service provider building a secure identity management system for a government customer. Security concerns are a guiding factor in the design of software-intensive products and services. They also affect the processes of their development. In regulated environments, development of products requires special security for the development processes, product release, maintenance and hosting, and also require security-oriented management and governance. Integrating the security engineering processes into an agile development model is argued to have the effect of mitigating the agile methods' intended benefits. The project case was an effort of multi-team, multi-site, security engineering, and development work, executed using the Scrum framework and regulated by governmental security standards and guidelines. In this case research, the experiences in combining security engineering with agile development are reported, challenges discussed, and certain security enhancements to Scrum are proposed.
An Interactive Visualization Tool for Sensor-based Physical Activity Data Analysis

The paper proposes to apply an interactive visual tool to support analysis of human daily physical activities and sedentary behaviours. Current research of physical activity relies on data-driven methods such as deep learning while few of them adopts human-centric approaches. This research aims to highlight the user- centred exploration of physical activity data, and inspire comprehensive data interpretation by visualization. The design of the interactive visualization tool is derived from the parallel coordinates technique, which is capable of mapping high-dimensional datasets, and allows users to have an intuitive and global view of all the features. Additional visual extension such as brushing axes and selecting individual or multiple groups improves further detailed exploration of the dataset. A focus group evaluation is employed to assess the visualization tool qualitatively. According to observation, parallel coordinates plots effectively aid to distinguish physical activities and sedentary behaviours from patterns observation. Moreover, interactions with the visualization tool also enhance the user-centred visualization. Users are able to look into high-dimensional datasets, inspect data quality, and select effective features subsets by themselves with this interactive visualization tool.
Motion of animated streamlets appears to surpass their graphical alterations in human visual detection of vector field maxima

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