Reviewerly/exHarmony huggingface dataset:
SEVA: A Smart Electronic Voting Application Using Blockchain Technology

The development of electronic voting applications remains an active area of research and this has led to the proposal and implementation of many models based on blockchains. However, most of the proposed models are partially decentralized solutions, in which the blockchain is used as a storage media for votes while the application is written in programming tools such as HTML, CSS, and JavaScript. This makes them vulnerable to attacks such as Denial of Service (DoS) attacks, Single Point of Failure (SPF), and fraudulent record modification. In this paper, we propose a fully decentralized electronic voting application, SEVA, in which we placed the whole application (code and data) in Ethereum to protect the application from vulnerabilities. Additionally, we propose a new consensus algorithm, Proof of Smart Vote (PoSV) for SEVA, as a viable energy-saving alternative to the energy-intensive Proof of Work (PoW). We implemented and evaluated SEVA with PoSV and compared it with a partially decentralized model of the application.
Selection-based Per-Instance Heuristic Generation for Protein Structure Prediction of 2D HP Model

The present study aims at generating heuristics for Protein Structure Prediction represented in the 2D HP model. Protein Structure Prediction is about determining the 3-dimensional form of a protein from a given amino acid sequence. The resulting structure directly relates to the functionalities of the protein. There are a wide range of algorithms to address Protein Structure Prediction as an optimization problem. Being said that there is no an ultimate algorithm that can effectively solve PSP under varying experimental settings. Hyper-heuristics can offer a solution as high-level, problem-independent search and optimization strategies. Selection Hyper-heuristics operate on given heuristic sets that directly work on the solution space. One group of Selection Hyper-heuristics focus on automatically specify the best heuristics on-the-fly. Yet, the candidate heuristics tend to be decided, preferably a domain expert. Generation Hyper-heuristics approach differently as aiming to generate such heuristics automatically. This work introduces a automated heuristic generation strategy supporting Selection Hyper-heuristics. The generation task is formulated as a selection problem, disclosing the best expected heuristic specifically f or a given problem instance. The heuristic generation process is established as a parameter configuration problem. T he corresponding system is devised by initially generating a training data alongside with a set of basic features characterizing the Protein Structure Prediction problem instances. The data is generated discretizing the parameter configuration space o f a single heuristic. The resulting data is used to predict the best configuration of a specific heuristic used in a heuristic set under Selection Hyper-heuristics. The prediction is performed separately for each instance rather than using one setting for all the instances. The empirical analysis showed that the proposed idea offers both better and robust performance on 22 PSP instances compared to the one-for-all heuristic sets. Additional analysis linked to the selection method, ALORS, revealed insights on what makes the PSP instances hard / easy while providing dis/-similarity analysis between the candidate configurations.
Suggestive annotation of brain MR images with gradient-guided sampling

Machine learning has been widely adopted for medical image analysis in recent years given its promising performance in image segmentation and classification tasks. The success of machine learning, in particular supervised learning, depends on the availability of manually annotated datasets. For medical imaging applications, such annotated datasets are not easy to acquire, it takes a substantial amount of time and resource to curate an annotated medical image set. In this paper, we propose an efficient annotation framework for brain MR images that can suggest informative sample images for human experts to annotate. We evaluate the framework on two different brain image analysis tasks, namely brain tumour segmentation and whole brain segmentation. Experiments show that for brain tumour segmentation task on the BraTS 2019 dataset, training a segmentation model with only 7% suggestively annotated image samples can achieve a performance comparable to that of training on the full dataset. For whole brain segmentation on the MALC dataset, training with 42% suggestively annotated image samples can achieve a comparable performance to training on the full dataset. The proposed framework demonstrates a promising way to save manual annotation cost and improve data efficiency in medical imaging applications.
On genuine entanglement for tripartite systems

In this paper, we investigate the genuine entanglement in tripartite systems based on partial transposition and the norm of correlation tensors of the density matrices. We first derive an analytical sufficient criterion to detect genuine entanglement of tripartite qubit states combining with the partial transposition of the density matrices. Then, we use the norm of correlation tensors to study genuine entanglement for tripartite qudit quantum states and obtain a genuine entanglement criterion by constructing certain matrices. With detailed examples, our results are seen to be able to detect more genuine tripartite entangled states than previous studies.
DistMILE: A Distributed Multi-Level Framework for Scalable Graph Embedding

Scalable graph embedding on large networks is challenging because of the complexity of graph structures and limited computing resources. Recent research shows that the multi-level framework can enhance the scalability of graph embedding methods with little loss of quality. In general, methods using this framework first coarsen the original graph into a series of smaller graphs then learn the representations of the original graph from them in an efficient manner. However, to the best of our knowledge, most multi-level based methods do not have a parallel implementation. Meanwhile, the emergence of high-performance computing for machine learning provides an opportunity to boost graph embedding by distributed computing. In this paper, we propose a Distributed MultI -Level Embedding (DistMILE <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> Our code is available at https://github.com/heyuntian/DistMILE) framework to further improve the scalability of graph embedding. DistMILE leverages a novel shared-memory parallel algorithm for graph coarsening and a distributed training paradigm for embedding refinement. With the advantage of high-performance computing techniques, Dist-MILE can smoothly scale different base embedding methods over large networks. Our experiments demonstrate that DistMILE learns representations of similar quality with respect to other baselines, while reduces the time of learning embeddings on large-scale networks to hours. Results show that DistMILE can achieve up to 28 x speedup compared with a popular multi-level embedding framework MILE and expedite existing embedding methods with 40 x speedup.
A Self-Parametrization Framework for Meta-Heuristics

Even while the scientific community has shown great interest in the analysis of meta-heuristics, the analysis of their parameterization has received little attention. It is the parameterization that will adapt a meta-heuristic to a problem, but it is still performed, mostly, empirically. There are multiple parameterization techniques; however, they are time-consuming, requiring considerable computational effort and they do not take advantage of the meta-heuristics that they parameterize. In order to approach the parameterization of meta-heuristics, in this paper, a self-parameterization framework is proposed. It will automatize the parameterization as an optimization problem, precluding the user from spending too much time on parameterization. The model will automate the parameterization through two meta-heuristics: A meta-heuristic of the solution space and one of the parameter space. To analyze the performance of the framework, a self-parameterization prototype was implemented. The prototype was compared and analyzed in a SP (scheduling problem) and in the TSP (traveling salesman problem). In the SP, the prototype found better solutions than those of the manually parameterized meta-heuristics, although the differences were not statistically significant. In the TSP, the self-parameterization prototype was more effective than the manually parameterized meta-heuristics, this time with statistically significant differences.
Face Morphing Attack Detection Methods

Abstract Morphing attacks pose a serious threat to face recognition systems, especially in the border control scenario. In order to guarantee a secure operation of face recognition algorithms in the future, it is necessary to be able to reliably detect morphed facial images and thus be able to reject them during enrolment or verification. This chapter provides an overview of morphing attack detection algorithms and metrics to measure and compare their performance. Different concepts of morphing attack detection are introduced and state-of-the-art detection methods are evaluated in a comprehensive cross-database experiments considering various realistic image post-processings.
On the Search for Feedback in Reinforcement Learning

The problem of Reinforcement Learning (RL) in an unknown nonlinear dynamical system is equivalent to the search for an optimal feedback law utilizing the simulations/ rollouts of the unknown dynamical system. Most RL techniques search over a complex global nonlinear feedback parametrization making them suffer from high training times as well as variance. Instead, we advocate searching over a local feedback representation consisting of an open-loop sequence, and an associated optimal linear feedback law completely determined by the open-loop. We show that this alternate approach results in highly efficient training, the answers obtained are repeatable and hence reliable, and the resulting closed performance is superior to global state of the art RL techniques. Finally, if we replan, whenever required, which is feasible due to the fast and reliable local solution, allows us to recover global optimality of the resulting feedback law.
Mathematical Model of Rational Location of Augmented Reality Objects in User's Environment

The growth in the number of applications using the technology of tagless augmented reality makes us think about the approaches and methods of developing AR applications in general. This work considers an example of a mathematical model that allows calculating the optimal location of a virtual object in the user's environment. The side data obtained when considering a mathematical model can be used to visualize hints in the user interface and control the placement of an augmented reality scene. The main idea of the algorithm for the rational location of a virtual object is to constantly evaluate the scanned space and compare its parameters to the pre-laid properties of the virtual object. Based on this comparison, it is possible to make recommendations on the placement of a virtual reality object in such a way that its location does not complicate interaction with the object and physical reality.
Robust Collaborative Fraudulent Transaction Detection using Federated Learning

Fraudulent transaction detection is a difficult problem for an individual bank, since the number of fraudulent transactions within a single bank’s records is significantly less compared to the day-to-day regular transactions it processes. Hence, due to this extreme data imbalance, training a classifier is difficult. Also, the model will not be able to learn from different types of fraudulent transactions, which a single bank’s database lacks. Collaboration between banks is the only way to achieve a generalized model, but banks will not share their data with each other due to competition and regulatory restrictions. Federated Learning can be leveraged here to solve this problem. However, in a cross-silo setting like this, the data held by different banks will be different in terms of distribution and hence follows a non-IID scenario across the participants’ datasets. Moreover, we are considering that a minority of the banks could be malicious and will try to disrupt this federated learning process. Hence the problem is to perform federated learning in a non-IID setting with active adversaries involved, which is a new research area under fraud detection. We perform non-IID partitioning of the transaction dataset to simulate 10 banks or silos. Then, for benchmark, we perform federated averaging with a subset of the banks set as malicious. Furthermore, we propose a novel algorithm - Epsilon Cluster Selection, a filter-based aggregation technique to recognize and prevent malicious nodes from contributing to the global model being trained. We apply this algorithm to the same setting with malicious banks and compare the results.
Learning Safe Cooperative Policies in Autonomous Multi-UAV Navigation

The deployment of multiple Unmanned Aerial Vehicles (UAV) in constrained environments has various challenges concerning trajectory optimization with the target(s) reachability and collisions. In this paper, we formulate multi-UAV navigation in constrained environments as a multi-agent learning problem. Further, we propose a reinforcement learning based Safe-MADDPG method to learn safe and cooperative multi-UAV navigation policies in a constrained environment. The safety constraints to handle inter-UAV collisions during navigation are modeled through action corrections of the learned autonomous navigation policies using an additional safety layer. We have implemented our proposed approach on the Webots Simulator and provided a detailed analysis of the proposed solution. The results demonstrate that the proposed Safe-MADDPG approach is effective in learning safe actions for multi-UAV navigation in constrained environments.
Neural Network Verification using Polynomial Optimisation

The desire to provide robust guarantees on neural networks has never been more important, as their prevalence in society is increasing. One popular method that has seen a large amount of success is to use bounds on the activation functions within these networks to provide such guarantees. However, due to the large number of possible ways to bound the activation functions, there is a trade-off between conservativeness and complexity. We approach the problem from a different perspective, using polynomial optimisation and real algebraic geometry (the Positivstellensatz) to assert the emptiness of a semi-algebraic set. We show that by using the Positivstellen- satz, bounds on the robustness guarantees can be tightened significantly over other popular methods, at the expense of computational resource. We demonstrate the effectiveness of this approach on networks that use the ReLU, sigmoid and tanh activation functions. This method can be extended to more activation functions, and combined with recent sparsity- exploiting methods can result in a computationally acceptable method for verifying neural networks.
A contrast enhancement framework under uncontrolled environments based on just noticeable difference

Image contrast enhancement refers to an operation of remapping the pixels’ values of an image to emphasize desired information in the image. In this work, we propose a novel pixel-based (local) contrast enhancement algorithm, based on the human visual perception. First, we make an observation that pixels with lower regional contrast should be amplified for the purpose of enhancing the contrast and pixels with higher regional contrast should be suppressed to avoid undesired over-enhancement. To determine the quality of the regional contrast in the image (either lower or higher), a reference image will be created using a proposed global based contrast enhancement method (termed as Mean Brightness Bidirectional Histogram Equalization in the paper) for fast computation reason. To quantify the abovementioned regional contrast, we propose a method based on human visual perception taking Just Noticeable Difference (JND) into account. In short, our proposed algorithm is able to limit the enhancement of well-contrasted regions and enhance the poor contrast regions in an image. Both objective quality and subjective quality experimental results suggested that the proposed algorithm enhances images consistently across images with different dynamic range. We conclude that the proposed algorithm exhibits excellent consistency in producing satisfactory result for different type of images. It is important to note that the algorithm can be directly implemented in color space and not limited only to grayscale. The proposed algorithm can be obtained from the following GitHub link: https://github.com/UTARSL1/CHE.
Smart Surveillance and Tracking System using Resnet and Tesseract-OCR

In recent times the field of computer vision and deep learning has seen many advancements that have helped in efficient and accurate face and object detection, resulting in many security and surveillance applications. Furthermore, with the increase in localized cameras monitoring every human action, every vehicle tracked it only seemed logical to use these camera's video feeds more efficiently and smartly. Present security systems include manual surveillance or a single smart camera setup. In this paper, we propose a smart multi-camera system using a Resnet-34 model for face recognition and Tesseract-based Optical Character Recognition for vehicle number plate recognition. The individuals and vehicles captured in the multiple cameras are traced out on a map which will graphically show when and where they were detected. This security and surveillance system will be beneficial in large organizations such as offices, banks, shopping malls, residential areas, etc.
Deep Learning based Semantic Ontology Alignment Process and Predictive Analysis of Depressive Disorder

The paper proposes deep learning based semantic ontology alignment process and predictive analysis methodology of DD as a use case. In the development of a semantic ontology alignment process, a mapping procedure has performed to find a WoO based semantic interoperable ontology of DD with respected to base ontology and three reference ontologies. And the semantic interoperable ontology developed in the first step is applied to perform a predictive analysis for characterization of the strong features to affect depression. In the experimentation, this paper shows its reasonable applicability through deep learning based analysis of DD case.
Quantum illumination with three-mode Gaussian state

Abstract The quantum illumination is examined by making use of the three-mode maximally entangled Gaussian state, which involves one signal and two idler beams. It is shown that the quantum Bhattacharyya bound between $$\rho $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>ρ</mml:mi> </mml:math> (state for target absence) and $$\sigma $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>σ</mml:mi> </mml:math> (state for target presence) is less than the previous result derived by two-mode Gaussian state when $$N_S$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>N</mml:mi> <mml:mi>S</mml:mi> </mml:msub> </mml:math> , average photon number per signal, is less than 0.295. This indicates that the quantum illumination with three-mode Gaussian state gives less error probability compared to that with two-mode Gaussian state when $$N_S &lt; 0.295$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:msub> <mml:mi>N</mml:mi> <mml:mi>S</mml:mi> </mml:msub> <mml:mo>&lt;</mml:mo> <mml:mn>0.295</mml:mn> </mml:mrow> </mml:math> .
The United Kingdom’s Cyber Security Degree Certification Program: A Cyber Security Body of Knowledge Case Study

The Cyber Security Body of Knowledge (CyBOK) aims to codify common and broadly accepted foundations for cybersecurity. We discuss how CyBOK has since been utilized to develop an updated program for certifying undergraduate and postgraduate degrees in the United Kingdom.
Enhancing risk assessment of manufacturing production process integrating failure modes and sequential fuzzy cognitive map

When a risk occurs in a stage of the production process, it can be due to the risks of the previous stages, or it is effective in causing the risks in the later stages. The current paper proposes an intelligent approach based on cause-and-effect relationships to assess and prioritize a manufacturing unit’s risks. Sequential multi-stage fuzzy cognitive maps (MSFCMs) are used for drawing the map of risks. Then, the learning algorithm is implemented for learning the MSFCM and finalizing the risks score. A case study on an auto-parts manufacturing unit is applied to demonstrate the capabilities of the proposed approach.
A novel temporal recommendation method based on user query topic evolution

Personalized product recommendation provides online customers with options in line with consumers’ interests and preferences to assist users in making purchasing decisions. Previous personalized recommendation methods identify user preferences by analyzing historical purchase data. As the user preferences reflected in the user–item matrix take only two values: like or dislike, the binary purchase data-based method is too constrained to capture individual diverse opinions and user preferences evolution. Despite the fact that online search queries explicitly express consumers’ content preferences for product attributes, very little research has focused on inferring user preferences from online queries. To this end, this study proposes a novel temporal recommendation method based on estimating consumers’ dynamic preferences from their search queries. Our approach involves: first, proposing a temporal topic model that utilizes a Markov process to predict users’ potential interests, and then integrating customer preferences and product attribute features into a unified product ranking model to obtain personalized and diversified product lists. Empirical analysis shows that the proposed approach can better capture the temporal dynamics of customer preferences drift and can more accurately predict consumer purchase intentions. Furthermore, products recommended by the proposed approach not only better match individual preferences, but also cover more diverse content topics.