Reviewerly/exHarmony huggingface dataset:
Split Compilation for Security of Quantum Circuits

An efficient quantum circuit (program) compiler aims to minimize the gate-count - through efficient instruction translation, routing, gate, and cancellation - to improve run-time and noise. Therefore, a high-efficiency compiler is paramount to enable the game-changing promises of quantum computers. To date, the quantum computing hardware providers are offering a software stack supporting their hardware. However, several third-party software toolchains, including compilers, are emerging. They support hardware from different vendors and potentially offer better efficiency. As the quantum computing ecosystem becomes more popular and practical, it is only prudent to assume that more companies will start offering software-as-a-service for quantum computers, including high-performance compilers. With the emergence of third-party compilers, the security and privacy issues of quantum intellectual properties (IPs) will follow. A quantum circuit can include sensitive information such as critical financial analysis and proprietary algorithms. Therefore, submitting quantum circuits to untrusted compilers creates opportunities for adversaries to steal IPs. In this paper, we present a split compilation methodology to secure IPs from untrusted compilers while taking advantage of their optimizations. In this methodology, a quantum circuit is split into multiple parts that are sent to a single compiler at different times or to multiple compilers. In this way, the adversary has access to partial information. With analysis of over 152 circuits on three IBM hardware architectures, we demonstrate the split compilation methodology can completely secure IPs (when multiple compilers are used) or can introduce factorial time reconstruction complexity while incurring a modest overhead (~ 3% to ~ 6% on average).
Large scale multi-node simulations of ℤ<sub>2</sub> gauge theory quantum circuits using Google Cloud Platform

Simulating quantum field theories on a quantum computer is one of the most exciting fundamental physics applications of quantum information science. Dynamical time evolution of quantum fields is a challenge that is beyond the capabilities of classical computing, but it can teach us important lessons about the fundamental fabric of space and time. Whether we may answer scientific questions of interest using near-term quantum computing hardware is an open question that requires a detailed simulation study of quantum noise. Here we present a large scale simulation study powered by a multi-node implementation of qsim using the Google Cloud Platform. We additionally employ newly-developed GPU capabilities in qsim and show how Tensor Processing Units — Application-specific Integrated Circuits (ASICs) specialized for Machine Learning — may be used to dramatically speed up the simulation of large quantum circuits. We demonstrate the use of high performance cloud computing for simulating ℤ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> quantum field theories on system sizes up to 36 qubits. We find this lattice size is not able to simulate our problem and observable combination with sufficient accuracy, implying more challenging observables of interest for this theory are likely beyond the reach of classical computation using exact circuit simulation.
Smart Glasses for Visually Impaired Using Image Processing Techniques

In this world, approximately 300 million are visually impaired, smart glasses helps the people who are Visually Impaired to enhance their life. With the help of this device they can navigate independently, feel convenient in the society, and also feel secure. They have many problems in their life, some of them are they cannot navigate independently, cheated in terms of money, Bus or Train number identification etc. In this system two features are implemented they are Currency Recognition and Traffic Light Detection. A Machine Learning (ML) Model is configured which consists of Convolution Neutral Network (CNN), image processing techniques, and Object detection model. Currency recognition extracts both visible and hided marks on the money for effective classification. Traffic light detection uses an image processing algorithm with standard object recognition methods. This system helps the Visually Impaired to cross the road without other's help. For the first feature i.e. Currency recognition, the output is the value of the currency that person has in his/her hand and for the second feature i.e. Traffic light detection, the output is whether the traffic light is red or green, if it is green then the number of seconds that person has to cross the road.
DistFL

Federated learning (FL) has emerged as an effective solution to decentralized and privacy-preserving machine learning for mobile clients. While traditional FL has demonstrated its superiority, it ignores the non-iid (independently identically distributed) situation, which widely exists in mobile scenarios. Failing to handle non-iid situations could cause problems such as performance decreasing and possible attacks. Previous studies focus on the "symptoms" directly, as they try to improve the accuracy or detect possible attacks by adding extra steps to conventional FL models. However, previous techniques overlook the root causes for the "symptoms": blindly aggregating models with the non-iid distributions. In this paper, we try to fundamentally address the issue by decomposing the overall non-iid situation into several iid clusters and conducting aggregation in each cluster. Specifically, we propose DistFL, a novel framework to achieve automated and accurate Distribution-aware Federated Learning in a cost-efficient way. DistFL achieves clustering via extracting and comparing the distribution knowledge from the uploaded models. With this framework, we are able to generate multiple personalized models with distinctive distributions and assign them to the corresponding clients. Extensive experiments on mobile scenarios with popular model architectures have demonstrated the effectiveness of DistFL.
<i>TreeAndLeaf</i>: an R/Bioconductor package for graphs and trees with focus on the leaves

Dendrogram is a classical diagram for visualizing binary trees. Although efficient to represent hierarchical relations, it provides limited space for displaying information on the leaf elements, especially for large trees.Here, we present TreeAndLeaf, an R/Bioconductor package that implements a hybrid layout strategy to represent tree diagrams with focus on the leaves. The TreeAndLeaf package combines force-directed graph and tree layout algorithms using a single visualization system, allowing projection of multiple layers of information onto a graph-tree diagram. The Supplementary Information provides two case studies that use breast cancer data from epidemiological and experimental studies.TreeAndLeaf is written in the R language, and is available from the Bioconductor project at http://bioconductor.org/packages/TreeAndLeaf/ (version≥1.4.2).Supplementary data are available at Bioinformatics online.
Blockchain Integrated Crowdfunding Platform for Enhanced Secure Transactions

Building a project starts with innovation and idea but funding is a part that decides the evolution of the project from idea to product. During the last decade funding campaign and project via crowdfunding has become a common theme. With the COVID situation, it has become a necessity for NGOs, campaigns, projects, start-ups to consider the concept of crowdfunding and build seed funds through it, therefore organizations are trying to create a safe, secure, and fraud-proof gateway for people to get funds and as well as provide funds, which is hard in the current time of pandemic and technical advancements. It has become quite easy to fall into a trap and lose your hard-earned funds with cybercrimes as identity fraud, theft of financial data, and internet fraud. This work is an attempt to create a secure, efficient, and viable tool for crowdfunding. The solution proposed has Blockchain integrated to build trust among the funders and those raising these funds, with its characteristics as decentralized, irrefutable, distributed ledgers, consensus, and faster settlement. The proposed model has been built on the smart contract protocol, created for crowdfunding transactions, campaigns for the proposed model was implemented on remix ide, this will create a campaign for those in need of funds and for donors to donate funds to these campaigns. The campaign master has the right to reject or accept requests thus creating fraud and a tamper-proof environment. The model has been subjected to positive & negative unit and integration tests on mocha, the efficiency of the model obtained is at par with existing solutions with an added edge on security via smart contract protocols.
Embedded LUKS (E-LUKS): A Hardware Solution to IoT Security

The Internet of Things (IoT) security is one of the most important issues developers have to face. Data tampering must be prevented in IoT devices and some or all of the confidentiality, integrity, and authenticity of sensible data files must be assured in most practical IoT applications, especially when data are stored in removable devices such as microSD cards, which is very common. Software solutions are usually applied, but their effectiveness is limited due to the reduced resources available in IoT systems. This paper introduces a hardware-based security framework for IoT devices (Embedded LUKS) similar to the Linux Unified Key Setup (LUKS) solution used in Linux systems to encrypt data partitions. Embedded LUKS (E-LUKS) extends the LUKS capabilities by adding integrity and authentication methods, in addition to the confidentiality already provided by LUKS. E-LUKS uses state-of-the-art encryption and hash algorithms such as PRESENT and SPONGENT. Both are recognized as adequate solutions for IoT devices being PRESENT incorporated in the ISO/IEC 29192-2:2019 for lightweight block ciphers. E-LUKS has been implemented in modern XC7Z020 FPGA chips, resulting in a smaller hardware footprint compared to previous LUKS hardware implementations, a footprint of about a 10% of these LUKS implementations, making E-LUKS a great alternative to provide Full Disk Encryption (FDE) alongside authentication to a wide range of IoT devices.
Multi-agent Reinforcement Learning in a Large Scale Environment via Supervisory Network and Curriculum Learning

Multi-agent reinforcement learning is essential for learning optimal policy for collaboration and competition environments. However, as the action space of the agent increases, the number of state-action pairs which have to be explored increases exponentially. As a result, increasing search space causes difficulty to converge the learning. To solve this problem, we propose a supervisory network. To achieve the global goal, the supervisory network creates a sub-goal and assigns the goals to the agents so that the agents can effectively learn the optimal policy with a small action space. In addition, we adapt the curriculum learning method to learn a large-scale environment. As a consequence, the agent can explore the environment in which the complexity increases gradually. Although a baseline network was learned in the same environment to compare with our model, the baseline fails to learn an optimal policy while our model successes to learn in the large-scale environment.
The Blockchain Quadrilemma: When Also Computational Effectiveness Matters

Ethereum's founder Buterin raised the challenge of solving the blockchain Trilemma towards a decentralized computer that could together achieve high degrees of security, scalability, and decentralization. Later on, Algorand claimed to have resolved Buterin's blockchain Trilemma and is nowadays increasingly adopted by designers of decentralized computations. Motivated by the need of selecting a blockchain to run some decentralized computations, we observe the limitations of using the Trilemma as benchmark, and we propose as alternative a Quadrilemma that takes into account also computational effectiveness, namely the capability of running non-trivial decentralized computations at affordable costs. For concreteness, motivated by the current trends of using blockchains for the management of non-fungible tokens (NFTs) related to highly desired items (i.e., NFTs for art), we consider the use case of decentralized auctions in various scenarios that mainly differ on the desired degree of confidentiality. Our contribution gives the following insights. Except very limited cases where also Bitcoin can be taken into account (i.e., when latency is not a big deal and only notarization is required), Algorand can often be the right choice as long as decentralized computations consist of basic operations only. Instead, Ethereum is advisable when more sophisticated computations are required, in particular when ad-hoc cryptographic tasks are essential, and one can afford the involved costs and latency. Focusing on those three blockchains, the state of affairs about resolving the blockchain Quadrilemma is somewhat unsatisfying. Even in natural cases where computations and storage requirements for a smart contract are low (e.g., public-key encryption), none of those decentralized computers achieves simultaneously low cost and fast transaction confirmations.
Conceptual Design of a Creative Artificial Intelligence System Based on the Neurocognitive Bases of Human Creativity in the Brain

The main objective of this paper is the conceptual design of a computational AI system that emulates human creativity. To do this, extensive research has been done on recent discoveries about the neural bases of human creativity. As a result, eleven neurocognitive factors have been identified on which the tremendous creative capacity of the human brain is based. The integration of these factors has made it possible to create a neurocognitive model of human creativity. The analysis of each of creative neurocognitive structures separately suggests certain characteristics that must be present in the structure of a creative computational system to emulate brain creativity. Properly integrating these characteristics, a general architecture for a creative multi-agent system has been proposed, as well as the guidelines for the correct representation of knowledge. Based on this general architecture a creative computational system can be implemented for a specific field of knowledge, or general purpose.
A bibliometric analysis of off-line handwritten document analysis literature (1990–2020)

Providing computers with the ability to process handwriting is both important and challenging, since many difficulties (e.g., different writing styles, alphabets, languages, etc.) need to be overcome for addressing a variety of problems (text recognition, signature verification, writer identification, word spotting, etc.). This paper reviews the growing literature on off-line handwritten document analysis over the last thirty years. A sample of 5389 articles is examined using bibliometric techniques. Using bibliometric techniques, this paper identifies (i) the most influential articles in the area, (ii) the most productive authors and their collaboration networks, (iii) the countries and institutions that have led research on the topic, (iv) the journals and conferences that have published most papers, and (v) the most relevant research topics (and their related tasks and methodologies) and their evolution over the years.
Laplacian Filter in Reconstruction of Images using Gradient-Based Algorithm

In this paper, we present a procedure for the reconstruction of images using a gradient-based algorithm, combined with the Laplacian filter as a noise-detection tool. Despite being commonly considered as an edge detection tool in the digital image processing, owing to its extensive noise sensitivity, the Laplacian can be efficiently used in the detection of noisy pixels. This paper deals with the noise corruption within the pixel intensity range. The reconstruction of detected pixels is performed using a gradient-based algorithm. The effectiveness of the method is verified through examples.
Mixing Activations and Labels in Distributed Training for Split Learning

Split Learning (SL) is a distributed machine learning setting that allows several nodes to train neural networks based on model parallelism. Since SL avoids sharing raw data among training nodes, it can protect data privacy by nature. However, recent studies show that, raw data may be reconstructed from activations in training, which may cause data privacy leakage. Besides raw data, label sharing in SL may also cause privacy problems. In order to address these issues, we propose a novel mechanism called multiple activations and labels mix (MALM). By taking advantage of the diversity of sample categories, MALM generates mixed activations that preserve a low distance correlation with the raw data so as to reduce the risk of reconstruction attacks. To protect label information, MALM creates obfuscated labels associated with the raw data so as to prevent adversaries from inferring ground-truth labels. Since clients with few sample categories may not effectively generate mixed activations and obfuscated labels, we propose a bipartite graph based assistant client match technique for MALM, which lets clients with a large number of categories provide mixed activations and obfuscated labels for clients with few categories. Those clients with few categories can mix the obtained mixed activations and obfuscated labels with their own activations and labels. Experimental results show that, compared with baselines, MALM can reduce the risk of raw data and label information leakage with lower cost, while achieving comparable even better model performance.
Energy-aware task scheduling in data centers using an application signature

Data centers are power hungry facilities. Energy-aware task scheduling approaches are of utmost importance to improve energy savings in data centers, although they need to know beforehand the energy consumption of the applications that will run in the servers. This is usually done through a full profiling of the applications, which is not feasible in long-running application scenarios due to the long execution times. In the present work we use an application signature that allows to estimate the energy without the need to execute the application completely. We use different scheduling approaches together with the information of the application signature to improve the makespan of the scheduling process and therefore improve the energy savings in data centers. We evaluate the accuracy of using the application signature by means of comparing against an oracle method obtaining an error below 1.5%, and Compression Ratios around 39.7 to 45.8.
Guided neighborhood affine subspace embedding for feature matching

Feature matching, which refers to determining reliable correspondences between two sets of feature points, is a fundamental component of numerous visual tasks. This paper proposes a novel method, termed as guided neighborhood affine subspace embedding (NASE), to eliminate false matches from the given tentative feature matches. Its essential philosophy is to preserve the underlying intrinsic manifold of potential true matches. Specifically, we aim to approximate the manifold of an inlier with an affine subspace fitted on its neighbors by imposing a motion-consistency constraint. Considering that the “corresponding manifold” of inliers may be biased by gross outliers, we introduce a density-based seed point selection strategy for neighborhood refinement. Based on the above two strategies, we further formulate the general feature matching problem into a mathematical optimization model and deduce a closed-form solution with linearithmic time complexity (i.e., O(NlogN)) for mismatch removal. Additionally, we devise a multi-scale strategy for neighborhood construction, making our method more robust to various degradations. Extensive experiments on general feature matching, fundamental matrix estimation, and loop closure detection demonstrate the clear superiority of NASE over the state-of-the-arts.
Towards Automated Semantic Explainability of Multimedia Feature Graphs

Multimedia feature graphs are employed to represent features of images, video, audio, or text. Various techniques exist to extract such features from multimedia objects. In this paper, we describe the extension of such a feature graph to represent the meaning of such multimedia features and introduce a formal context-free PS-grammar (Phrase Structure grammar) to automatically generate human-understandable natural language expressions based on such features. To achieve this, we define a semantic extension to syntactic multimedia feature graphs and introduce a set of production rules for phrases of natural language English expressions. This explainability, which is founded on a semantic model provides the opportunity to represent any multimedia feature in a human-readable and human-understandable form, which largely closes the gap between the technical representation of such features and their semantics. We show how this explainability can be formally defined and demonstrate the corresponding implementation based on our generic multimedia analysis framework. Furthermore, we show how this semantic extension can be employed to increase the effectiveness in precision and recall experiments.
Lyapunov stable learning laws for multilayer recurrent neural networks

This study aims to develop stable learning laws (in the sense of Lyapunov) for a general class of multilayer recurrent neural network (RNN) non-parametric identifier. The application of control Lyapunov functions in the discrete-time domain ensures the ultimate boundedness for the identification error enforcing it to reach a boundary set related to the power of parametric uncertainties and the modeling error. This work presents a general algorithm to design RNNs with n layers: one input layer, n-2 hidden layers, and one output layer. Numerical simulations support the theoretical results showing the advantages of increasing the number of layers in an RNN structure. A first example identifies the unknown states of the Van Der Pol oscillator. Then, a second example demonstrates the behavior of an RNN in a third-order system with high nonlinear dynamics describing an ozonation system of a single contaminant. For both cases, the application of the developed learning laws succeeds in estimating the uncertain dynamics. Moreover, the numerical simulations show how the identification error decreases as the number of layers increases.
Research on Intelligent Trash Can Garbage Classification Scheme Based on Improved YOLOv3 Target Detection Algorithm

In recent studies, YOLOv3, a deep learning-based target detection algorithm, becomes extensively used in object recognition, especially guiding the visually disabled. Current YOLOv3-based assistive technology for the disabled person can now achieve high-precision, real-time object recognition. Even though this algorithm has several flaws, including the failure to estimate distances and the difficulty of accurately recognizing points in fog or haze, it can perform well in waste management. Therefore, this study proposes an Intelligent Garbage Monitoring Scheme based on an improved YOLOv3 Target Detection Algorithm (IGMS-iYTDA) to classify the IoT’sgarbages (IoT) enabled trash can. The performance of the proposed scheme has been evaluated and illustrated for various classification evaluation metrics. The evaluation results show the highest classification accuracy of 99.9% compared to existing models for the proposed scheme.