Reviewerly/exHarmony huggingface dataset:
A virtual platform for real-time performance analysis of electromagnetic tracking systems for surgical navigation

&lt;p class="Abstract"&gt;Electromagnetic Tracking Systems (EMTSs) are widely used in surgical navigation, allowing to improve the outcome of diagnosis and surgical interventions, by providing the surgeon with real-time position of surgical instruments during medical procedures. However, particular effort was dedicated to the development of efficient and robust algorithms, to obtain an accurate estimation of the instrument position for distances from the magnetic field generator beyond 0.5 m. Indeed, the main goal is to improve the limited range of current commercial systems, which strongly affects the freedom of movement of the medical team. Studies are currently being conducted to optimize the magnetic field generator configuration (both geometrical arrangements and electrical properties) since it affects tracking accuracy. In this paper, we propose a virtual platform for assessing the performance of EMTSs for surgical navigation, providing real-time results and statistics, and allowing to track instruments both in real and simulated environments. Simulations and experimental tests are performed to validate the proposed virtual platform, by employing it to assess the performance of a real EMTS. The platform offers a real-time tool to analyze EMTS components and field generator configurations, for a deeper understanding of EMTS technology, thus supporting engineers during system design and characterization.&lt;/p&gt;
Universal hardware-efficient topological measurement-based quantum computation via color-code-based cluster states

Topological measurement-based quantum computation (MBQC) enables one to carry out universal fault-tolerant quantum computation via single-qubit measurements with a family of large entangled states called cluster states as resources. Raussendorf's three-dimensional cluster states (RTCSs) based on the surface codes are mainly considered for topological MBQC. In such schemes, however, the logical Hadamard, phase $({Z}^{1/2})$, and $T$ $({Z}^{1/4})$ gates which are essential for building up arbitrary logical gates are not implemented natively without using state distillation or lattice dislocations, to the best of our knowledge. In particular, state distillation generally consumes many ancillary logical qubits; thus it is a severe obstacle against practical quantum computing. To solve this problem, we suggest an MBQC scheme via a family of cluster states called color-code-based cluster states (CCCSs) based on the two-dimensional color codes instead of the surface codes. We define logical qubits, construct elementary logical gates, and describe error correction schemes. We show that all logical Clifford gates generated by the cnot, Hadamard, and phase gates can be implemented natively in a fault-tolerant manner, although the $T$ gate still requires state distillation to be fault-tolerant. The error thresholds of MBQC via CCCSs for logical-$Z$ errors are calculated to be 2.7%--2.8%, which are comparable to the values for RTCSs, assuming a simple error model where physical qubits have nontrivial errors independently with the same probability. We analyze and compare the resource overheads of both the schemes. In particular, we show that the number of physical qubits required for implementing a phase gate with CCCSs is at least about 26 times smaller than with RTCSs using state distillation, for the same code distance.
Improving Dense FAQ Retrieval with Synthetic Training

Frequently Asked Question (F AQ) retrieval is a valuable task which aims to find the most relevant question-answer pair from a FAQ dataset given a user query. Currently, most works implement F AQ retrieval considering the similarity between the query and the question as well as the relevance between the query and the answer. However, the query-answer relevance is difficult to model effectively due to the heterogeneity of query-answer pairs in terms of syntax and semantics. To alleviate this issue and improve retrieval performance, we propose a novel approach to consider answer information into F AQ retrieval by question generation, which provides high-quality synthetic positive training examples for dense retriever. Experiment results indicate that our method outperforms term-based BM25 and pretrained dense retriever significantly on two recently published COVID-19 F AQ datasets.
Identifikasi Citra Tanaman Obat Jenis Rimpang dengan Euclidean Distance Berdasarkan Ciri Bentuk dan Tekstur

In the midst of the Covid-19 pandemic, increasing the body's immunity is very important. Some experts suggest consuming medicinal plants or herbs to boost immunity. In addition to being used as a cooking spice, this rhizome type plant turns out to have properties and benefits for health, especially to increase immunity. However, many people do not know and it is difficult to distinguish the type of rhizome plant. This type of rhizome plant can be identified based on the characteristics seen from the shape and texture. However, most people judge the type of rhizome has a shape that is difficult to distinguish. This study aims to determine the type of medicinal plant rhizome with Euclidean distance and extraction of shape and texture. Extraction of shape features using metric and eccentricity parameters. This parameter is considered to be able to recognize shape objects and can distinguish them from other objects. Meanwhile, texture feature extraction uses Gray Level Co-occurence Matrix (GLCM) with contrast, correlation, energy, and homogeneity parameters. For the identification process, Euclidean distance is used which serves to represent the level of two images that consider the distance value from Euclidean. From the results of the evaluation using a confusion matrix by calculating precision, recall and accuracy, it gets a precision value of 83%, recal 87% and an accuracy of 85%. These results indicate that the Euclidean distance and extraction of shape and texture features can identify the object image of medicinal plants with rhizome types well
Library Service Brand and Mark: General and Special

The problem of forming the library image as an active social marketing tool in the context of globalization of the information space is becoming more urgent and relevant and requires new approaches, both in the theoretical and practical spheres of the library sector. In modern realities, libraries realize the importance of their own image as a socio-cultural phenomenon, echoing such a complex and multifaceted concept of “brand”. In the library space, the brand is used as a tool for updating the image and developing social marketing. The purpose of the article is to determine the features of the service mark as a key element of the library brand and the mechanisms of its civil-legal protection. The issues related to the use of the service mark as one of the elements of the library brand represent the scientific novelty and, in particular, specify the rules of its registration. The author notes that the term in question does not have a legal status, but it can be considered in relation to the institution of civil law close to it — means of individualization — a service mark (trademark). The article discusses the procedure for registering the service mark, as well as determines the stages of the libraryʼs brand policy. Despite the fact that the term “brand” has become widespread in scientific, professional and everyday life, it does not have a legal status, i. e. there is no legal definition in the legislation of the Russian Federation. Therefore, the necessary legal procedure aimed at the legal protection of the library brand as an element of the library branding system is the registration of a service mark — the designation that serves to individualize the work performed or services provided. Basing on the analysis of jurisprudence practice, the article determines the possibility of preferential use of the term “service mark” in the designation of library and information products and services. The analysis of registered service marks (trademarks) of libraries, relevant as of June 2021, demonstrates the awareness by libraries of the importance of their own brand. However, the professional library community is far from always aware of the advantages of using the service mark, which leads to the expiration of the exclusive right to it without its extension in the manner prescribed by law. Registration of the service mark contributes to strengthening the status of the library as an active marketing component of the information market and the formation of reputation capital. It can be considered as a specific incentive for the development of library branding.
Reading order detection on handwritten documents

Abstract Recent advances in Handwritten Text Recognition and Document Layout Analysis have made it possible to convert digital images of manuscripts into electronic text. However, providing this text with the correct structure and context is still an open problem that needs to be solved to actually enable extracting the relevant information conveyed by the text. The most important structure needed for a set of text elements is their reading order. Most of the studies on the reading order problem are rule-based approaches and focus on printed documents. Much less attention has been paid so far to handwritten text documents, where the problem becomes particularly important—and challenging. In this work, we propose a new approach to automatically determine the reading order of text regions and lines in handwritten text documents. The task is approached as a sorting problem where the order-relation operator is automatically learned from examples. We experimentally demonstrate the effectiveness of our method on three different datasets at different hierarchical levels.
OptABC: an Optimal Hyperparameter Tuning Approach for Machine Learning Algorithms

Hyperparameter tuning in machine learning algorithms is a computationally challenging task due to the large-scale nature of the problem. In order to develop an efficient strategy for hyper-parameter tuning, one promising solution is to use swarm intelligence algorithms. Artificial Bee Colony (ABC) optimization lends itself as a promising and efficient optimization algorithm for this purpose. However, in some cases, ABC can suffer from a slow convergence rate or execution time due to the poor initial population of solutions and expensive objective functions. To address these concerns, a novel algorithm, OptABC, is proposed to help ABC algorithm in faster convergence toward a near-optimum solution. OptABC integrates artificial bee colony algorithm, K-Means clustering, greedy algorithm, and opposition-based learning strategy for tuning the hyper-parameters of different machine learning models. OptABC employs these techniques in an attempt to diversify the initial population, and hence enhance the convergence ability without significantly decreasing the accuracy. In order to validate the performance of the proposed method, we compare the results with previous state-of-the-art approaches. Experimental results demonstrate the effectiveness of the OptABC compared to existing approaches in the literature.
k-Center Clustering with Outliers in Sliding Windows

Metric k-center clustering is a fundamental unsupervised learning primitive. Although widely used, this primitive is heavily affected by noise in the data, so a more sensible variant seeks for the best solution that disregards a given number z of points of the dataset, which are called outliers. We provide efficient algorithms for this important variant in the streaming model under the sliding window setting, where, at each time step, the dataset to be clustered is the window W of the most recent data items. For general metric spaces, our algorithms achieve O1 approximation and, remarkably, require a working memory linear in k+z and only logarithmic in |W|. For spaces of bounded doubling dimension, the approximation can be made arbitrarily close to 3. For these latter spaces, we show, as a by-product, how to estimate the effective diameter of the window W, which is a measure of the spread of the window points, disregarding a given fraction of noisy distances. We also provide experimental evidence of the practical viability of the improved clustering and diameter estimation algorithms.
Fixed-Point Code Synthesis for Neural Networks

Over the last few years, neural networks have started penetrating safety critical systems to take decisions in robots, rockets, autonomous driving car, etc. A problem is that these critical systems often have limited computing resources. Often, they use the fixed-point arithmetic for its many advantages (rapidity, compatibility with small memory devices.) In this article, a new technique is introduced to tune the formats (precision) of already trained neural networks using fixed-point arithmetic, which can be implemented using integer operations only. The new optimized neural network computes the output with fixed-point numbers without modifying the accuracy up to a threshold fixed by the user. A fixed-point code is synthesized for the new optimized neural network ensuring the respect of the threshold for any input vector belonging the range [xmin, xmax] determined during the analysis. From a technical point of view, we do a preliminary analysis of our floating neural network to determine the worst cases, then we generate a system of linear constraints among integer variables that we can solve by linear programming. The solution of this system is the new fixed-point format of each neuron. The experimental results obtained show the efficiency of our method which can ensure that the new fixed-point neural network has the same behavior as the initial floating-point neural network.
A Review Of E-Voting System Based on Blockchain Technology

Voting is arguably the most important as well as elementary right in democracy that has existed for the past hundreds of years; it has taken place in the context of a large- and small-scale community. The process transitioned from paper ballots to electronic voting machines (EVM) in the late 20th century, but even with all the advancement, transparency in the election process remained the same. Blockchain technology came in existence in 2008 with the introduction of Bitcoin by Satoshi Nakamoto, and in the last decade we saw enormous growth in development and execution of this technology in various fields e-voting is one of them, blockchain-based e-voting system has potential to improve the election process if utilized to its potential. Blockchain can eliminate the need to print ballot paper as it is secure, immutable and convenient to voters and can make elections more transparent.
Assessing and Proposing Countermeasures for Cyber-Security Attacks

Cyber-attacks on IT domain infrastructure directly affect the security of businesses' operational processes, potentially leading to system failure.Some industries have a high risk than others due to the sensitivity of their data, including the transportation industry, which has recently moved from traditional data management to digitalization.This study aims to identify the main cyber threats in the transportation sector by analyzing related works and highlighting the main countermeasures used to respond to such threats as well as enhance overall cybersecurity.This paper presents a comprehensive cybersecurity risk assessment for the transportation companies, identifying the most common attacks and proposing methods to minimize risk as much as possible.A risk assessment analysis was prepared by industry experts that included previous cyberattack scenarios.The results of our paper identified the most critical attacks on the transportation company's booking system and recommended suitable countermeasures to minimize the risk of those attacks.
A Semantic Annotation Pipeline towards the Generation of Knowledge Graphs in Tribology

Within the domain of tribology, enterprises and research institutions are constantly working on new concepts, materials, lubricants, or surface technologies for a wide range of applications. This is also reflected in the continuously growing number of publications, which in turn serve as guidance and benchmark for researchers and developers. Due to the lack of suited data and knowledge bases, knowledge acquisition and aggregation is still a manual process involving the time-consuming review of literature. Therefore, semantic annotation and natural language processing (NLP) techniques can decrease this manual effort by providing a semi-automatic support in knowledge acquisition. The generation of knowledge graphs as a structured information format from textual sources promises improved reuse and retrieval of information acquired from scientific literature. Motivated by this, the contribution introduces a novel semantic annotation pipeline for generating knowledge in the domain of tribology. The pipeline is built on Bidirectional Encoder Representations from Transformers (BERT)—a state-of-the-art language model—and involves classic NLP tasks like information extraction, named entity recognition and question answering. Within this contribution, the three modules of the pipeline for document extraction, annotation, and analysis are introduced. Based on a comparison with a manual annotation of publications on tribological model testing, satisfactory performance is verified.
Mobile Control Plane Design for Quantum Satellite Backbones

The interconnection of quantum computers through the so-called Quantum Internet is a very promising approach. The most critical issues concern the physical layer, considering that the creation of entanglement over long distances is still problematic. Given the difficulty that usually arises from fiber optics due to exponential losses, the introduction of intermediate quantum repeaters (QRs) allows mitigating the problem. A quantum satellite network based on QRs on satellites deployed over low Earth orbit could make it possible to overcome the constraints of terrestrial optical networks. Hence, the recent technological developments in terms of quantum satellite communications motivated our investigation on an ad hoc quantum satellite backbone design based on the software defined networking paradigm with a control plane directly integrated into the constellation itself. Our aim is to outline some guidelines by comparing several options. Specifically, the focus is to analyze different architectural solutions making some considerations on their feasibility, possible benefits, and costs. Finally, we performed some simulations on the architectures we considered the most promising, concluding that the integration of the control plane in the constellation itself is the most appropriate solution.
Infrared Image Enhancement Algorithm based on Weighted Guided Filtering

Aiming at the problems of blurred details and over enhancement in traditional infrared image enhancement algorithms, an infrared image enhancement method based on guided weighted image filtering is proposed. Firstly, the gradient difference between the pixels around the image is used to find the isolated noise and the median filter is used to filter it; Then, the weighted guided image filter is used to decompose the image to obtain the basic component and detail component. The Contrast Limited Adaptive Histogram Equalization (CLAHE) is used to equalize the stretched contrast in the basic layer, and the nonlinear function is used to enhance and suppress the noise in the detail layer; Finally, different levels of results are fused to obtain contrast and detail enhanced infrared images. This method is used to simulate many groups of infrared images of different scenes, and compared with many enhancement methods. The results show that the proposed method has better results in infrared image detail and contrast enhancement.
Learning to be a statistician

Estimating the number of distinct values (NDV) in a column is useful for many tasks in database systems, such as columnstore compression and data profiling. In this work, we focus on how to derive accurate NDV estimations from random (online/offline) samples. Such efficient estimation is critical for tasks where it is prohibitive to scan the data even once. Existing sample-based estimators typically rely on heuristics or assumptions and do not have robust performance across different datasets as the assumptions on data can easily break. On the other hand, deriving an estimator from a principled formulation such as maximum likelihood estimation is very challenging due to the complex structure of the formulation. We propose to formulate the NDV estimation task in a supervised learning framework, and aim to learn a model as the estimator. To this end, we need to answer several questions: i) how to make the learned model workload agnostic; ii) how to obtain training data; iii) how to perform model training. We derive conditions of the learning framework under which the learned model is workload agnostic , in the sense that the model/estimator can be trained with synthetically generated training data, and then deployed into any data warehouse simply as, e.g. , user-defined functions (UDFs), to offer efficient (within microseconds on CPU) and accurate NDV estimations for unseen tables and workloads. We compare the learned estimator with the state-of-the-art sample-based estimators on nine real-world datasets to demonstrate its superior estimation accuracy. We publish our code for training data generation, model training, and the learned estimator online for reproducibility.
Decoder Transformer for Temporally-Embedded Health Outcome Predictions

Deep learning models are increasingly being used to predict patients’ diagnoses by analyzing electronic health records. Medical records represent observations of a patient’s health over time. A commonly used approach to analyze health records is to encode them as a sequence of ordered diagnoses (diagnostic-level encoding). Transformer models then analyze the sequence of diagnoses to learn disease patterns. However, the elapsed time between medical visits is not considered when transformers are used to analyze health records. In this paper, we present DT-THRE: Decoder Transformer for Temporally-Embedded Health Records Encoding that predicts patients’ diagnoses by analyzing their medical histories. In DTTHRE, instead of diagnostic-level encoding, we propose an encoding representation for health records called THRE: Temporally-Embedded Health Records Encoding. THRE encodes patient histories as a sequence of medical events such as age, sex, and diagnostic embedding while incorporating the elapsed time between visits. We evaluate a proof-of-concept DTTHRE on a real-world medical dataset and compare our model’s performance to an existing diagnostic transformer model in the literature. DTTHRE was successful on a medical dataset to predict patients’ final diagnosis with improved predictive performance (78.54± 0.22%) compared to the existing model in the literature (40.51± 0.13%).
Applying Digital Forensics Methodology to Open Source Investigations in Counterterrorism

This research paper discusses the technical and legal aspects of open source intelligence (OSINT) as an investigation discipline in combating terrorism cases. Due to the proliferation of internet connectivity and the use of the Internet by terrorists, new mythologies and framework are needed to effectively identify, collect, preserve, and present the evidence related to such a serious crime. While there is no internationally agreed standard to apply on using OSINT for criminal investigations, the researcher proposes new framework that utilizes the application of digital forensics standards and methodologies in open source investigation to deliver admissible evidence in court of law.