Reviewerly/exHarmony huggingface dataset:
Augmented intelligence facilitates concept mapping across different electronic health records

With the advent of artificial intelligence, the secondary use of routinely collected medical data from electronic healthcare records (EHR) has become increasingly popular. However, different EHR systems typically use different names for the same medical concepts. This obviously hampers scalable model development and subsequent clinical implementation for decision support. Therefore, converting original parameter names to a so-called ontology, a standardized set of predefined concepts, is necessary but time-consuming and labor-intensive. We therefore propose an augmented intelligence approach to facilitate ontology alignment by predicting correct concepts based on parameter names from raw electronic health record data exports.We used the manually mapped parameter names from the multicenter "Dutch ICU data warehouse against COVID-19" sourced from three types of EHR systems to train machine learning models for concept mapping. Data from 29 intensive care units on 38,824 parameters mapped to 1,679 relevant and unique concepts and 38,069 parameters labeled as irrelevant were used for model development and validation. We used the Natural Language Toolkit (NLTK) to preprocess the parameter names based on WordNet cognitive synonyms transformed by term-frequency inverse document frequency (TF-IDF), yielding numeric features. We then trained linear classifiers using stochastic gradient descent for multi-class prediction. Finally, we fine-tuned these predictions using information on distributions of the data associated with each parameter name through similarity score and skewness comparisons.The initial model, trained using data from one hospital organization for each of three EHR systems, scored an overall top 1 precision of 0.744, recall of 0.771, and F1-score of 0.737 on a total of 58,804 parameters. Leave-one-hospital-out analysis returned an average top 1 recall of 0.680 for relevant parameters, which increased to 0.905 for the top 5 predictions. When reducing the training dataset to only include relevant parameters, top 1 recall was 0.811 and top 5 recall was 0.914 for relevant parameters. Performance improvement based on similarity score or skewness comparisons affected at most 5.23% of numeric parameters.Augmented intelligence is a promising method to improve concept mapping of parameter names from raw electronic health record data exports. We propose a robust method for mapping data across various domains, facilitating the integration of diverse data sources. However, recall is not perfect, and therefore manual validation of mapping remains essential.
Recovery policies for safe exploration of lunar permanently shadowed regions by a solar-powered rover

The success of a multi-kilometre drive by a solar-powered rover at the lunar south pole depends upon careful planning in space and time due to highly dynamic solar illumination conditions. An additional challenge is that the rover may be subject to random faults that can temporarily delay long-range traverses. The majority of existing global spatiotemporal planners assume a deterministic rover-environment model and do not account for random faults. In this paper, we consider a random fault profile with a known, average spatial fault rate. We introduce a methodology to compute recovery policies that maximize the probability of survival of a solar-powered rover from different start states. A recovery policy defines a set of recourse actions to reach a safe location with sufficient battery energy remaining, given the local solar illumination conditions. We solve a stochastic reach-avoid problem using dynamic programming to find an optimal recovery policy. Our focus, in part, is on the implications of state space discretization, which is required in practical implementations. We propose a modified dynamic programming algorithm that conservatively accounts for approximation errors. To demonstrate the benefits of our approach, we compare against existing methods in scenarios where a solar-powered rover seeks to safely exit from permanently shadowed regions in the Cabeus area at the lunar south pole. We also highlight the relevance of our methodology for mission formulation and trade safety analysis by comparing different rover mobility models in simulated recovery drives from the LCROSS impact region.
Semantic-Summarizer: Semantics-based text summarizer for English language text

<h2>Abstract</h2> Text summarization is a process that condenses text documents for efficient information consumption. It offers numerous benefits, including time savings, reduced information overload, informed decision-making, and even content generation. In this paper, we present <i>SemanticSum</i>, an advanced text document summarizer that not only achieves compression but also preserves the underlying semantics of the original text. By leveraging semantic analysis, our summarizer intelligently identifies and removes redundant sentences that express the same meaning in different forms. Our experimental evaluation demonstrates that <i>SemanticSum</i> outperforms several state-of-the-art open-source summarizers regarding summary quality. The results validate the effectiveness of our approach, which capitalizes on semantics to produce more accurate and contextually meaningful summaries.
Designing Virtual Interactive Objects to Enhance Visitors' Experience in Cultural Exhibits

In recent years, digital reality technologies such as virtual reality and augmented reality have been extensively employed in museums and other cultural exhibitions to enhance visitor interest and understanding. However, recent studies have shown a potential risk of technology overpowering the exhibition, leading to exclusive attention and distraction rather than serving as a supportive tool for visitors. To address this issue, this paper introduces virtual interactive objects designed to integrate seamlessly into the exhibition without overshadowing a specific object, allowing visitors to fulfill their need for interaction and acquire information through multimodal means while maintaining a clear distinction from the real exhibition. This enables a continuous and harmonious visitor experience between the virtual and real worlds, benefiting the entire cultural space. This work presents an implementation of these objects.
Network approaches in blockchain-based systems: Applications, challenges, and future directions

Blockchain technology represents a transformative innovation that facilitates the storage, dissemination, and access of vital information within a distributed database framework. While not an inherent part of blockchain technology, peer-to-peer networks are frequently employed in its implementation due to their ability to enhance connection throughput and minimize connection overhead. These networks, known for their high bandwidth, low connection overhead, and robust security standards, are increasingly viewed as influential elements in many contemporary blockchain systems. The core of blockchain technology, however, lies in its distributed ledger that records and verifies transactions. Over recent years, a variety of network approaches incorporating blockchain-based systems have been proposed across numerous domains, including the Internet of Things (IoT), intelligent vehicle networks, and medical data access control, among others. This paper aims to explore and evaluate a selection of these network approaches, providing a comprehensive overview of current applications, along with the inherent advantages and disadvantages of such systems. Furthermore, we present a systematic introduction to future developments in network approaches incorporating blockchain-based systems, setting the stage for a deeper understanding of this rapidly evolving field.
Use of XR technologies for fostering visitors' experience and inclusion at industrial museums

The study presents the research conducted and the solutions adopted to integrate Extended Reality applications in situ and online to enhance visitors' experience of the Tomato Industrial Museum "D. Nomikos" with emphasis on the needs of people with hearing impairments. This paper outlines the methodological, technological, and museological approaches undertaken to integrate XR in ways that will foster the engagement with the narratives of people who worked at the premises and describe the production process. Storytelling and narration are key elements of the museums’ museological design (Sylaiou and Dafiotis, 2020) and therefore including visitors with hearing impairments through XR technologies that allow users to perceive audio tours and verbal accounts through real-time visualization of spoken language by an especially adapted speech to text functionality. Moreover, the paper explores the challenges faced throughout the effort to engage audiences and help them relate with the industrial heritage through exploring the exhibition by generating interest through multimodal resources accessible to all. To engage viewers, we developed interactive applications so that visitors will undertake to fulfill workers' tasks as in role-playing. The result of our approach is a balance between coming to close contact through digital narratives with the people of the time, ensuring inclusion of people with hearing impairments, while offering interactive visualizations of the exhibited machinery functions.
Motion capture method for college basketball training based on AR/VR technology

Abstract To improve the effectiveness of basketball running training, this paper proposes an AR/VR technology-based motion capture method for college basketball sports training. This paper first describes the method steps of virtual reality motion capture technology, data fusion and skeletal data normalization of skeletal data, and calibration to obtain the rotation matrix and displacement vector of each Kinect sensor to integrate the skeleton data. Then the data features are extracted, 3D joint position, joint velocity, joint angle and angular velocity are extracted from the fused skeleton information of each frame, and then the LSTM algorithm is used to obtain the timing information in the action sequence and to classify the action for recognition. Finally, the method’s performance is evaluated in terms of accuracy, recall, and response time. Regarding accuracy, the recognition rates of “shooting” and “defense” were around 85%, while the recognition rates of other actions were 93% and above. In terms of recognition time, the recognition time of common equipment is about 350ms, while the recognition time of virtual reality equipment is about 210ms, which is 100ms less than that of traditional equipment, demonstrating the effectiveness and feasibility of this method.
Simultaneous BPSK classical communication and continuous variable quantum key distribution with a locally local oscillator regenerated by optical injection phase locked loop

This paper proposes a scheme for simultaneous classical communication and continuous variable quantum key distribution with a true local oscillator. In this scheme, the emitter’s laser, after binary phase-shift keying (BPSK) modulation, is multiplexed in polarization with the quantum signal and sent to the receiver. After BPSK demodulation and correction, this signal is used for local oscillator regeneration by an optical injection phase-locked loop method. Comparing the effective noise sources in this scheme with typical local local oscillator schemes revealed that continuous variable quantum key distribution (CV-QKD) with a true local oscillator based on the optical injection phase-locked loop encounters lower levels of noise in comparison to the pre-existing genuine local oscillator CV-QKDs.
“Do this instead” – Robots that Adequately Respond to Corrected Instructions

Natural language instructions are effective at tasking autonomous robots and for teaching them new knowledge quickly. Yet, human instructors are not perfect and are likely to make mistakes at times, and will correct themselves when they notice errors in their own instructions. In this paper, we introduce a complete system for robot behaviors to handle such corrections, during both task instruction and action execution. We then demonstrate its operation in an integrated cognitive robotic architecture through spoken language in two tasks: a navigation and retrieval task and a meal assembly task. Verbal corrections occur before, during, and after verbally taught sequences of tasks, demonstrating that the proposed methods enable fast corrections not only of the semantics generated from the instructions, but also of overt robot behavior in a manner shown to be reasonable when compared to human behavior and expectations.
Exploring the role of augmented reality in purchase intention: Through flow and immersive experience

Augmented Reality has emanated as a service for customers' engaged in shopping through highly immersive experiences. New interactive technologies have helped shopping become more engaging. The current study explains the flow dimension by building-and-broadening its definition and posits its applicability as an antecedent to highly immersive experiences. The study constructively probed and established the immersion-psychological ownership-comfort decision gap. Finally, the study adds to the explanation of WoW feeling/Awe experience and purchase intentions for customers' engaged in AR shopping. Partial Least Square Structural Equation Modelling was administered in order to analyze 352 samples for AR shoppers. The study used Importance Performance Matrix Analysis to provide a rendition to the managerial implications. As a cardinal element of shopping, AR is an indispensable tool for attaining highly immersive experiences and the study motivates scholars in theoretical advancements. The findings will help businesses capitalize on AR as a Service (ARaaS) and harbor increased profitability with high customer satisfaction.
A DDPG-Based USV Path-Planning Algorithm

Path planning is crucial in the automatic navigation of USVs (unmanned underwater vehicles), which directly affects the operational efficiency and safety of USVs. In this paper, we propose a path-planning algorithm based on DDPG (Deep Deterministic Policy Gradient) and make a detailed comparison with the traditional A-Star algorithm and the recent Actor–Critical algorithm. Through a series of simulation experiments, it can be observed that the optimal path for USVs found by the DDPG-based path planning algorithm is faster and more accurate than that found by the other two methods. The experimental results show that the DDPG algorithm has a significant advantage in processing time and better performance in terms of path quality and safety. These results provide a strong reference for future research on automatic navigation for USVs and demonstrate the potential of DDPG-based path planning for USVs.
An effective framework to improve the managerial activities in global software development

Abstract Global Software Development (GSD) is a contemporary approach to software development that offers numerous advantages, including enhanced cost-effectiveness and timely delivery. It enables access to a vast pool of skilled developers and facilitates the exchange of best practices and innovative ideas within the software industry. However, effective project management plays a vital role in ensuring successful product development. Organizations that achieve project success consistently adhere to well-defined project management methodologies, resulting in desired outcomes within predefined time frames and allocated resources. The success rate of software projects significantly increases with diligent software management efforts. Nevertheless, the distributed nature of GSD presents significant challenges related to collaboration, information dissemination, and process control, which ultimately impede effective development and compromise software quality. In this study, we identify various challenges associated with the GSD process and propose strategies to overcome obstacles to effective project management. Additionally, we introduce a comprehensive framework designed to enhance managerial activities in GSD.
Advancements in e-Governance Initiatives: Digitalizing Healthcare in India

In order to improve the quality of service delivery to the public, to encourage interactive communications between government and citizens or government and business, and to address development challenges in any given society, information and electronic governance is the sophisticated fusion of a wide range of information and communication technologies with non-technological measures and resources. Digital technology advancements over the past ten years have made it possible to quickly advance data gathering, analysis, display, and application for bettering health outcomes. Digital health is the study and practice of all facets of using digital technologies to improve one’s health, from conception through implementation. Digital health strategies seek to improve the data that is already accessible and encourage its usage in decision-making. Digital patient records that are updated in real-time are known as electronic health records (EHRs). An electronic health record (EHR) is a detailed account of someone’s general health. Electronic health records (EHRs) make it easier to make better healthcare decisions, track a patient’s clinical development, and deliver evidence-based care. This concept paper is based on secondary data that was collected from a variety of national and international periodicals, official records, and public and private websites. This paper presents a review of advancements for scaling digital health within India’s overall preparedness for pandemics and the use of contact tracing applications in measuring response efforts to counter the impact of the pandemic. The paper provides information about the government of India’s EHR implementation and initiatives taken toward the establishment of a system of e-governance. The document also covers the advantages of keeping EHR for improved outreach and health care. Further, this paper discusses in depth the effectiveness of using contact tracing applications in enhancing digital health.
Short Paper: Static and Microarchitectural ML-Based Approaches For Detecting Spectre Vulnerabilities and Attacks

Spectre intrusions exploit speculative execution design vulnerabilities in modern processors. The attacks violate the principles of isolation in programs to gain unauthorized private user information. Current state-of-the-art detection techniques utilize micro-architectural features or vulnerable speculative code to detect these threats. However, these techniques are insufficient as Spectre attacks have proven to be more stealthy with recently discovered variants that bypass current mitigation mechanisms. Side-channels generate distinct patterns in processor cache, and sensitive information leakage is dependent on source code vulnerable to Spectre attacks, where an adversary uses these vulnerabilities, such as branch prediction, which causes a data breach. Previous studies predominantly approach the detection of Spectre attacks using the microarchitectural analysis, a reactive approach. Hence, in this paper, we present the first comprehensive evaluation of static and microarchitectural analysis-assisted machine learning approaches to detect Spectre vulnerable code snippets (preventive) and Spectre attacks (reactive). We evaluate the performance trade-offs in employing classifiers for detecting Spectre vulnerabilities and attacks.
ALARM: Active LeArning of Rowhammer Mitigations

Rowhammer is a serious security problem of contemporary dynamic random-access memory (DRAM) where reads or writes of bits can flip other bits. DRAM manufacturers add mitigations, but don’t disclose details, making it difficult for customers to evaluate their efficacy. We present a tool, based on active learning, that automatically infers parameter of Rowhammer mitigations against synthetic models of modern DRAM.
Side-Channel Analysis and Countermeasure Design for Implementation of Curve448 on Cortex-M4

The highly secure Curve448 cryptographic algorithm has been recently recommended by NIST. While this algorithm provides 224-bit security over elliptic curve cryptography, its implementation may still be vulnerable to physical side-channel attacks. In this paper, we present a speed-optimized implementation on a 32-bit ARM Cortex-M4 platform achieving more than 40% improvement compared to the best previous work. Our design can perform 43 scalar multiplications per second on an STM32F4 working at 168 MHz. At 24 MHz, our proposed implementation takes only 3,740k clock cycles. On the other hand, the security of Curve448 is thoroughly evaluated to have a trade-off between performance and required protection. We apply different effective countermeasures to prevent a subset of side-channel and fault injection attacks at the cost of 8%-22% overhead.
A New Idea for RSA Backdoors

This article proposes a new method to inject backdoors in RSA (the public-key cryptosystem invented by Rivest, Shamir, and Adleman) and other cryptographic primitives based on the integer factorization problem for balanced semi-primes. The method relies on mathematical congruences among the factors of the semi-primes based on a large prime number, which acts as a “designer key” or “escrow key”. In particular, two different backdoors are proposed, one targeting a single semi-prime and the other one a pair of semi-primes. This article also describes the results of tests performed on a SageMath implementation of the backdoors.
Evolution of Diagnostic and Forensic Microbiology in the Era of Artificial Intelligence

Diagnostic microbiology plays a vital role in managing infectious diseases, combating antimicrobial resistance, and containment of outbreaks. During the fourth industrial revolution, when artificial intelligence (AI) became an essential part of our day-to-day lives, its integration into healthcare would further revolutionize our knowledge and potential. Although in the budding stage, AI with machine learning is being increasingly utilized in various aspects of diagnostic microbiology. It can handle large datasets that are difficult to analyze manually. Researchers have developed and demonstrated several machine-learning algorithms for interpreting bacterial cultures, conducting image analysis for microbial detection, and predicting antimicrobial susceptibility patterns. Thus, AI may most likely be the ultimate solution to the ever-increasing demand for improved results with shorter turnaround times. AI can also assist forensic microbiologists in crime scene investigations, as it can guide individual identification, cause and time since death, and manner of death. This review summarizes the application of AI in diagnostic microbiology for performing diverse sets of microbial investigations and is an essential aid in forensic microbiology.
Fast Expansion into Harmonics on the Disk: A Steerable Basis with Fast Radial Convolutions

.We present a fast and numerically accurate method for expanding digitized \(L \times L\) images representing functions on \([-1,1]^2\) supported on the disk \(\{x \in \mathbb{R}^2 : |x| \lt 1\}\) in the harmonics (Dirichlet Laplacian eigenfunctions) on the disk. Our method, which we refer to as the Fast Disk Harmonics Transform (FDHT), runs in \(\mathcal{O}(L^2 \log L)\) operations. This basis is also known as the Fourier–Bessel basis, and it has several computational advantages: it is orthogonal, ordered by frequency, and steerable in the sense that images expanded in the basis can be rotated by applying a diagonal transform to the coefficients. Moreover, we show that convolution with radial functions can also be efficiently computed by applying a diagonal transform to the coefficients.KeywordsLaplacian eigenfunctionssteerable basisFourier–Bessel basisMSC codes65R1065D1842-0433C10