Reviewerly/exHarmony huggingface dataset:
Robust encoder-decoder learning framework for offline handwritten mathematical expression recognition based on a multi-scale deep neural network

Offline handwritten mathematical expression recognition is a challenging task, because handwritten mathematical expressions mainly have two problems in the process of recognition. On one hand, it is how to correctly recognize different mathematical symbols. On the other hand, it is how to correctly recognize the two-dimensional structure existing in mathematical expressions. Inspired by recent work in deep learning, a new neural network model that combines a Multi-Scale convolutional neural network (CNN) with an Attention recurrent neural network (RNN) is proposed to identify two-dimensional handwritten mathematical expressions as one-dimensional LaTeX sequences. As a result, the model proposed in the present work has achieved a WER error of 25.715% and ExpRate of 28.216%.
Blockchain for IoT Enabled Supply Chain Management - A Systematic Review

Blockchain will increase supply chains' productivity and accountability, and have a positive effect on anything from warehousing to distribution to payment. To bridge the supply chain visibility gap, blockchain is being deployed because of its security features like immutability, tamper-resistant and hash proof. Blockchain integration with IoT increases the traceability and verifiability of the supply chain management and drastically eradicates the fraudulent activities including bribery, money laundering, forged checks, sanction violations, misrepresentation of goods and services. Blockchain can help to cross-check the verification, identification and authenticity of IoT devices to reduce the frequency and ramifications of fraud in supply chain management. The epidemic outbreak of SARS-CoV-2 has disrupted many global supply chains. The Geneva-based World Economic Forum declared that SARS-CoV-2 exposed supply chain failures can be tackled by blockchain technology. This paper explores the modern methodologies of supply chain management with integration of blockchain and IoT.
A Closer Look at Generalisation in RAVEN

Humans have a remarkable capacity to draw parallels between concepts, generalising their experience to new domains. This skill is essential to solving the visual problems featured in the RAVEN and PGM datasets, yet, previous papers have scarcely tested how well models generalise across tasks. Additionally, we encounter a critical issue that allows existing models to inadvertently ‘cheat’ problems in RAVEN. We therefore propose a simple workaround to resolve this issue, and focus the conversation on generalisation performance, as this was severely affected in the process. We revise the existing evaluation, and introduce two relational models, Rel-Base and Rel-AIR, that significantly improve this performance. To our knowledge, Rel-AIR is the first method to employ unsupervised scene decomposition in solving abstract visual reasoning problems, and along with Rel-Base, sets states-of-the-art for image-only reasoning and generalisation across both RAVEN and PGM.
Multiparameter quantum estimation theory in quantum Gaussian states

Multiparameter quantum estimation theory aims to determine simultaneously the ultimate precision of all parameters contained in the state of a given quantum system. Determining this ultimate precision depends on the quantum Fisher information matrix (QFIM) which is essential to obtaining the quantum Cram\'er-Rao bound. This is the main motivation of this work which concerns the computation of the analytical expression of the QFIM. Inspired by the results reported in J. Phys. A 52, 035304 (2019), the general formalism of the multiparameter quantum estimation theory of quantum Gaussian states in terms of their first and second moments are given. We give the analytical formulas of right logarithmic derivative (RLD) and symmetric logarithmic derivative (SLD) operators. Then we derive the general expressions of the corresponding quantum Fisher information matrices. We also derive an explicit expression of the condition which ensures the saturation of the quantum Cram\'er-Rao bound in estimating several parameters. Finally, we examine some examples to clarify the use of our results
Augmented Reality in Maintenance Training for Military Equipment

Abstract After more than 20 years of development, augmented reality technology has shown a strong development momentum, successfully applied in civil and military fields. And augmented reality maintenance system is one of the most typical application. This paper introduces the present situation of augmented reality maintenance system, illustrates the structure of a typical system, discuss key technologies of augmented reality maintenance, and investigates the insufficiencies of it in military equipment maintenance according to the contemporary condition. Eventually, this paper indicates the potential development tendency of the augmented reality military equipment maintenance system in the future.
A Framework for Systematic Classification of Assets for Security Testing

Over the last decade, a significant increase has been observed in the use of web-based Information systems that process sensitive information, e.g., personal, financial, medical.With this increased use, the security of such systems became a crucial aspect to ensure safety, integrity and authenticity of the data.To achieve the objectives of data safety, security testing is performed.However, with growth and diversity of information systems, it is challenging to apply security testing for each and every system.Therefore, it is important to classify the assets based on their required level of security using an appropriate technique.In this paper, we propose an asset security classification technique to classify the System Under Test (SUT) based on various factors such as system exposure, data criticality and security requirements.We perform an extensive evaluation of our technique on a sample of 451 information systems.Further, we use security testing on a sample extracted from the resulting prioritized systems to investigate the presence of vulnerabilities.Our technique achieved promising results of successfully assigning security levels to various assets in the tested environments and also found several vulnerabilities in them.
Minimum Snap Trajectory Tracking for a Quadrotor UAV using Nonlinear Model Predictive Control

We present an indoor autonomous quadrotor flight that incorporates motion planning, trajectory generation, and trajectory tracking. The asymptotically optimal Rapidly-exploring Random Tree* (RRT*) algorithm is used to generate a set of obstacle-free waypoints. In highly cluttered settings, effective deviations of the attitude should be permitted allowing a greater range for roll and pitch angles hence an exact nonlinear model was derived using Newton and Euler formulations. The minimum snap cubic spline algorithm is used to generate a dynamically feasible optimal trajectory passing through the waypoints then a nonlinear model predictive control (NMPC) is implemented to track this trajectory. Simulations are carried out in both two and three-dimensional obstacle cluttered environments and the results are discussed.
A New Lightweight AES Using a Combination of Chaotic Systems

In current times, the Internet of Things (IoT) applications have been widely used in various life fields, such as smart homes, health care, and other applications designed to help people. The data passing through IoT devices need to be protected against any unauthorized access. So, lightweight encryption algorithms are of high demand. In this paper, a new Lightweight Advanced Encryption Standard (LAES) based on a combination of chaotic systems is introduced to protect sensing data. It was designed to have the ability to reduce encryption/decryption time. A combination of logistic maps with different dimensions is adapted to construct the main processes in AES, such as S-Box, initial permutation (IP), shifting values, and encryption keys. The Shift rows stage replaced by IP while the MixColumns replaced by dynamic ShiftRows. One of the essential operations in AES is the S-Box; a new S-Box is generated from a ID chaotic logistic map system to enhance the security level. The analysis results show that the new LAES can encrypt text file of size 5MB in only 1.987 sec. It also passes SBox tests and NIST. The result of each of NIST tests was near to one. So, LAES can be used for IoT security.
Integration of Discrete Wavelet Transform, DBSCAN, and Classifiers for Efficient Content Based Image Retrieval

In the domain of computer vision, the efficient representation of an image feature vector for the retrieval of images remains a significant problem. Extensive research has been undertaken on Content-Based Image Retrieval (CBIR) using various descriptors, and machine learning algorithms with certain descriptors have significantly improved the performance of these systems. In this proposed research, a new scheme for CBIR was implemented to address the semantic gap issue and to form an efficient feature vector. This technique was based on the histogram formation of query and dataset images. The auto-correlogram of the images was computed w.r.t RGB format, followed by a moment’s extraction. To form efficient feature vectors, Discrete Wavelet Transform (DWT) in a multi-resolution framework was applied. A codebook was formed using a density-based clustering approach known as Density-Based Spatial Clustering of Applications with Noise (DBSCAN). The similarity index was computed using the Euclidean distance between the feature vector of the query image and the dataset images. Different classifiers, like Support Vector (SVM), K-Nearest Neighbor (KNN), and Decision Tree, were used for the classification of images. The set experiment was performed on three publicly available datasets, and the performance of the proposed framework was compared with another state of the proposed frameworks which have had a positive performance in terms of accuracy.
Ship Detection in Multispectral Remote Sensing Images via Saliency Analysis

Despite the increasing visible optical remote sensing cameras equipped with panchromatic and four-band multispectral sensors, the application of multispectral data is still rarely used in the field of remote sensing ship target detection with the openly recognized challenge to improve detection precision. Towards this end, a ship target saliency detection method is proposed in the paper, which combines weighted least squares (WLS) with maximum symmetric surround (MSS), based on RGB-NIR four-band multispectral remote sensing images to locate the candidate area of ship targets quickly and accurately. The high frequency information of the NIR band image is extracted through the WLS filter and integrated into the RGB band image, and then the saliency of the image is analyzed. The detection results are combined with AIS to achieve complementary information for ship recognition. Some experiments show that the proposed method can effectively suppress the complex background information of clutter interference such as cloud waves and sea waves, highlight the ship target in low contrast scenes, and also increase recall and precision effectively.
Biometric Blockchain: A Better Solution for the Security and Trust of Food Logistics

Blockchain has been emerging as a promising technology that could totally change the landscape of data security in the coming years, particularly for data access over Internet-of-Things and cloud servers. However, blockchain itself, though secured by its protocol, does not identify who owns the data and who uses the data. Other than simply encrypting data into keys, in this paper, we proposed a protocol called Biometric Blockchain (BBC) that explicitly incorporate the biometric cues of individuals to unambiguously identify the creators and users in a blockchain-based system, particularly to address the increasing needs to secure the food logistics, following the recently widely reported incident on wrongly labelled foods that caused the death of a customer on a flight. The advantage of using BBC in the food logistics is clear: it can not only identify if the data or labels are authentic, but also clearly record who is responsible for the secured data or labels. As a result, such a BBC-based solution can great ease the difficulty to control the risks accompanying the food logistics, such as faked foods or wrong gradient labels.
Re-thinking health through design: collaborations in research, education and practice

Current health-care systems are confronted with progressively complex demands: ageing populations, growing drug ineffectiveness, health mis/disinformation and access to comprehensive services are just a few of the challenges faced today. Design offers methods, practices and processes to help address these challenges. While design and health have a long history of working together, much of this work has been limited. In this article, we make the case for further opportunities for design and health to work together in deep, innovative and human ways. We start by reviewing the transforming space(s) of design, moving on to a discussion of the similarities between healthcare and design. Next, we present three case studies that employ design methods and processes within healthcare settings, exploring new opportunities for synergy. We conclude by summarizing the areas of opportunity uncovered through these case studies, articulating future possibilities for design and healthcare to collaborate.
Loss-tolerant teleportation on large stabilizer states

We present a general method for finding loss-tolerant teleportation on large, entangled stabilizer states using only single-qubit measurements, known as stabilizer pathfinding (SPF). For heralded loss, SPF is shown to generate optimally loss-tolerant measurement patterns on any given stabilizer state. Furthermore, SPF also provides highly loss-tolerant teleportation strategies when qubit loss is unheralded. We provide a fast algorithm for SPF that updates continuously as a state is generated and measured, which is therefore suitable for real-time implementation on a quantum-computing device. When compared to simulations of previous heuristics for loss-tolerant teleportation on graph states, SPF provides considerable gains in tolerance to both heralded and unheralded loss, achieving a near-perfect teleportation rate (>95%) in the regime of low qubit loss (<10%) on various graph state lattices. Using these results we also present evidence that points towards the existence of loss-tolerant thresholds on such states, which in turn indicates that the loss-tolerant behaviour we have found also applies as the number of qubits tends to infinity. Our results represent a significant advance towards the realistic implementation of teleportation in both large-scale and near-future quantum architectures that are susceptible to qubit loss, such as linear optical quantum computation and quantum communication networks.
A ROS-based Control Application for a Robotic Platform Using the Gazebo 3D Simulator

This paper presents a ROS-based control application for a robotic platform, using the Gazebo 3D simulator that will allow a user to see and understand the simulation and moving of a mobile robot through a predefined map before being used in the real world. Usually, developers in the field of robotics need to test, verify and validate the dynamics of the robot in specific environments. Nowadays, the availability of virtual tools allows developers to create virtual working environments, and to do a targeted simulation of robots' dynamics. This approach highly reduces the times necessary for preparing the robot-specific content (design, deployment, environment), and afterwards the robot dynamics debugging. In our paper, we will detail these steps while considering the particulars of the mobile robot that is under development. For this, we created a replica of our robot simulated in Gazebo, a map of the robot's working environment, and, we have provided support for the robot's main applications: navigation, exploration, and interaction with sensors. All these elements are essential to simulate and visualize the robot's activity within a virtual working environment before the robot will be used in its target actual environment.
Side-Channel Propagation Measurements and Modeling for Hardware Security in IoT Devices

The ubiquitous interconnectivity of electronic devices offered by Internet-of-Things (IoT) networks has been increasingly embraced in a wide range of applications. In IoT networks, threats to hardware security are often not perceived as serious, with the assumption that an attack could only be carried out at close proximity. However, in this article, we show that through electromagnetic (EM) side-channel signal leakage, operational information and program activities of IoT devices and field-programmable gate array (FPGA) modules can be garnered from approximately 200 m away in an outdoor line-of-sight (LOS) environment. We describe an extensive measurement campaign conducted to investigate the aforementioned leakage and provide propagation models that can be used to predict the power (and corresponding variation i.e., shadowing gain) of the EM side-channel signal emanation at various distances, scenarios, and environments. With a circularly polarized receiver antenna, our results show that the received power of the emanated EM side-channel (carrier) signal varies from about -61 dBm at 1 m to about -112 dBm at 200 m in the outdoor LOS environment. Furthermore, a received signal power of about -73 dBm was observed at 1 m and -88 dBm was recorded at 10 m in an indoor LOS environment. Power variation (shadowing gain) of about 3.6 and 2.0 dB was observed in the outdoor and indoor environments, respectively. This work is relevant for EM side-channel leakage countermeasure development and provides pertinent information to embedded systems and wireless network security engineers.
Greenberger-Horne-Zeilinger-based quantum private comparison protocol with bit-flipping

Abstract By introducing a semi-honest third party (TP), this paper proposes a novel quantum private comparison (QPC) protocol using ( n + 1)-qubit ( n ≥ 2) Greenberger-Horne-Zeilinger (GHZ) states as information carriers. The parameter n not only determines the number of qubits contained in a GHZ state, but also determines the probability that TP can successfully steal the participants’ data and the qubit efficiency. In the proposed protocol, any other quantum technologies (e.g., entanglement swapping and unitary operation) except necessary technologies such as preparing quantum states and quantum measurements are employed, which can reduce the need for quantum devices. The proposed protocol uses the keys generated by quantum key distribution and bit-flipping for privacy protection, and decoy photons for eavesdropping checking, making both external and internal attacks invalid. Specifically, for external attacks, several well-known attack means (e.g., the intercept-resend attack and the measurement-resend attack) are taken as examples to show that the attackers outside the protocol can not steal the participants’ data successfully, in which the security proof of the protocol against the entanglement-measurement attack is provided. For internal attacks, it is shown that TP cannot steal the participants’ data and the participants cannot steal each other’s data. It is also shown that the existing attack means against QPC protocols are invalid for our protocol.
A Decision Trees-based knowledge mining approach for controlling a complex production system

In this study, we use decision trees constructed by learning-with-supervision techniques for representing the best policies found by a Reinforcement Learning algorithm applied to an optimization problem of a complex production system. Until now, the relative scientific literature includes studies that mainly propose dynamic programming-based approaches for treating such kind of combinatorial problems. Decision trees are used to approximate functions of multiple variables with discrete values. In this case the "leaves" of the tree correspond to the set of function values while the non-terminal nodes to its independent variables. In the present research, the parameters of the optimization problem and the corresponding optimal policies found by the Reinforcement Learning algorithm applied, will be used as the training data set. Representing the best found policies using decision trees will support more effective qualitative analysis and further understanding of their properties.
Fast High-Dimensional Kernel Filtering

The bilateral and nonlocal means filters are instances of kernel-based filters that are popularly used in image processing. It was recently shown that fast and accurate bilateral filtering of grayscale images can be performed using a low-rank approximation of the kernel matrix. More specifically, based on the eigendecomposition of the kernel matrix, the overall filtering was approximated using spatial convolutions, for which efficient algorithms are available. Unfortunately, this technique cannot be scaled to high-dimensional data such as color and hyperspectral images. This is simply because one needs to compute/store a large matrix and perform its eigendecomposition in this case. We show how this problem can be solved using the Nyström method, which is generally used for approximating the eigendecomposition of large matrices. The resulting algorithm can also be used for nonlocal means filtering. We demonstrate the effectiveness of our proposal for bilateral and nonlocal means filtering of color and hyperspectral images. In particular, our method is shown to be competitive with state-of-the-art fast algorithms, and moreover, it comes with a theoretical guarantee on the approximation error.
Incentives in Ethereum's hybrid Casper protocol

We present an overview of hybrid Casper the Friendly Finality Gadget (FFG): a Proof-of-Stake checkpointing protocol overlaid onto Ethereum's Proof-of-Work blockchain. We describe its core functionalities and reward scheme, and explore its properties. Our findings indicate that Casper's implemented incentives mechanism ensures liveness, while providing safety guarantees that improve over standard Proof-of-Work protocols. Based on a minimal-impact implementation of the protocol as a smart contract on the blockchain, we discuss additional issues related to parametrisation, funding, throughput and network overhead and detect potential limitations.
Cross-lingual Spoken Language Understanding with Regularized Representation Alignment

Despite the promising results of current crosslingual models for spoken language understanding systems, they still suffer from imperfect cross-lingual representation alignments between the source and target languages, which makes the performance sub-optimal.To cope with this issue, we propose a regularization approach to further align word-level and sentence-level representations across languages without any external resource.First, we regularize the representation of user utterances based on their corresponding labels.Second, we regularize the latent variable model (Liu et al., 2019a) by leveraging adversarial training to disentangle the latent variables.Experiments on the cross-lingual spoken language understanding task show that our model outperforms current state-of-the-art methods in both few-shot and zero-shot scenarios, and our model, trained on a few-shot setting with only 3% of the target language training data, achieves comparable performance to the supervised training with all the training data. 1