Reviewerly/exHarmony huggingface dataset:
Unmasking overestimation: a re-evaluation of deep anomaly detection in spacecraft telemetry

Authors:
raw_author_name: Marie Bieber
raw_affiliation_string: Faculty of Aerospace Engineering, Delft University of Technology, Kluyverweg 1, 2629 HS, Delft, Zuid-Holland, The Netherlands
raw_author_name: Wim J. C. Verhagen
raw_affiliation_string: Aerospace Engineering and Aviation, RMIT University, Carlton, VIC, 3053, Australia
raw_author_name: Fabrice Cosson
raw_affiliation_string: European Space Research & Technology Centre, ESA, Keplerlaan 1, 2200 AG, Noordwijk, Zuid-Holland, The Netherlands
raw_author_name: Bruno F. Santos
raw_affiliation_string: Faculty of Aerospace Engineering, Delft University of Technology, Kluyverweg 1, 2629 HS, Delft, Zuid-Holland, The Netherlands

Abstract: Abstract As the volume of telemetry data generated by satellites and other complex systems continues to grow, there is a pressing need for more efficient and accurate anomaly detection methods. Current techniques often rely on human analysis and preset criteria, presenting several challenges including the necessity for expert interpretation and continual updates to match the dynamic mission environment. This paper critically examines the use of deep anomaly detection (DAD) methods in addressing these challenges, evaluating their efficacy on real-world spacecraft telemetry data. It exposes limitations in current DAD research, highlighting the tendency for performance results to be overestimated and suggesting that simpler methods can sometimes outperform more complex DAD algorithms. By comparing established metrics for anomaly detection with newly proposed ones, this paper aims to improve the evaluation of DAD algorithms. It underscores the importance of using less accuracy-inflating metrics and offers a comprehensive comparison of DAD methods on popular benchmark datasets and real-life satellite telemetry data. Among the DAD methods examined, the LSTM algorithm demonstrates considerable promise. However, the paper also reveals the potential limitations of this approach, particularly in complex systems that lack a single, clear predictive failure channel. The paper concludes with a series of recommendations for future research, including the adoption of best practices, the need for high-quality, pre-split datasets, and the investigation of other prediction error methods. Through these insights, this paper contributes to the improved understanding and application of DAD methods, ultimately enhancing the reliability and effectiveness of anomaly detection in real-world scenarios.
Cybersecurity Challenges In The Era Of Digital Transformation A Comprehensive Analysis Of Information Systems

Authors:
raw_author_name: None Adi Ahmad
raw_affiliation_string: STMIK Indonesia Banda Aceh
raw_author_name: None Riyan Maulana
raw_affiliation_string: STMIK Indonesia Banda Aceh

Abstract: Increased digitalization in business transformation has provided significant benefits, but has also created new challenges for information security. This research aims to analyze the cyber security challenges faced in the era of digital transformation, with a focus on Information Systems. The research method used is a comprehensive analysis of literature and case studies to gain an in-depth understanding of these issues. In this research, we found that there are a number of significant challenges related to cyber security in implementing digital transformation, including malware attacks, phishing, insufficient network security, and privacy violations. Causal analysis concluded that insufficient training and security awareness, as well as weak design in information systems, were the main risk factors. The implication of this research is the need for proactive steps in implementing strong security practices in information systems, including adequate training for employees, careful security monitoring, and early warning of security threats. In addition, the importance of collaboration between organizations, governments and related entities in facing increasingly global threats was also highlighted.
Impact of age and comorbid heart failure on the utility of smart voice-assistant devices

Authors:
raw_author_name: Guang Zhang
raw_affiliation_string: DREAM-CV Lab, Centre for Outcomes Research, McGill University , Montreal, Quebec , Canada; Division of Cardiology, McGill University Health Centre, McGill University , Montreal, Quebec , Canada
raw_author_name: Amir Razaghizad
raw_affiliation_string: Division of Cardiology, McGill University Health Centre, McGill University , Montreal, Quebec , Canada
raw_author_name: Robert Avram
raw_affiliation_string: Heartwise AI Lab, Montreal Heart Institute Research Center , Quebec , Canada; Montreal Heart Institute, University of Montreal , Quebec , Canada
raw_author_name: Abhinav Sharma
raw_affiliation_string: DREAM-CV Lab, Centre for Outcomes Research, McGill University , Montreal, Quebec , Canada; Division of Cardiology, McGill University Health Centre, McGill University , Montreal, Quebec , Canada

Abstract: Abstract Aims The accuracy of voice-assisted technologies, such as Amazon Alexa, to collect data in patients who are older or have heart failure (HF) is unknown. The aim of this study is to analyze the impact of increasing age and comorbid HF, when compared with younger participants and caregivers, and how these different subgroups classify their experience using a voice-assistant device, for screening purposes. Methods and Results Subgroup analysis (HF vs Caregivers and younger vs older participants) of the VOICE-COVID-II trial, a randomized controlled study where participants were assigned with subsequent crossover to receive a SARS-CoV2 screening questionnaire by Amazon Alexa or a healthcare personnel. Overall concordance between the two methods was compared using unweighted kappa scores and percentage of agreement. From the 52 participants included, median age was 51 (34-65) years and 21 (40%) were HF patients. The HF subgroup showed a significant lower percentage of agreement compared with caregivers (95% vs 99%, p=0.03), and both the HF and older subgroup tended to have lower unweighted kappa scores than their counterparts. In a post-screening survey, both the HF and older subgroups were less acquainted and found the voice-assistant device more difficult to use comparing with caregivers and younger individuals. Conclusions This subgroup analysis highlights important differences in the performance of a voice-assistant based technology in an older and comorbid HF population. Younger individuals and caregivers, serving as facilitators, have the potential to bridge the gap and enhance the integration of these technologies into clinical practice. Trial registration number: NCT04508972.
Beyond Quantum Threats: Bridging Fully Homomorphic Encryption Schemes

Authors:
raw_author_name: Mugurel Barcau
raw_affiliation_string: Institute of Mathematics of the Romanian Academy “Simion Stoilow”, Romania; certSign - R&D Department, Bucharest, Romania
raw_author_name: George Gugulea
raw_affiliation_string: certSign - R&D Department, Bucharest, Romania
raw_author_name: Cristian Lupascu
raw_affiliation_string: certSign - R&D Department, Bucharest, Romania
raw_author_name: Vicentiu Pasol
raw_affiliation_string: Institute of Mathematics of the Romanian Academy “Simion Stoilow”, Romania; certSign - R&D Department, Bucharest, Romania
raw_author_name: Mihai Togan
raw_affiliation_string: Military Technical Academy “Ferdinand I”, Bucharest, Romania; certSign - R&D Department, Bucharest, Romania
raw_author_name: George Turcas
raw_affiliation_string: Babes-Bolyai University, Cluj-Napoca, Romania; certSign - R&D Department, Bucharest, Romania

Abstract: In the advent of quantum computing, fully homomorphic encryption (FHE), whose security is based on the hardness of lattice problems, gains significant importance due to its quantum-resistant nature. This work provides a comprehensive survey of recent advancements in bridges between FHE schemes, with an emphasis on practical applications.
VELIE: A Vehicle-Based Efficient Low-Light Image Enhancement Method for Intelligent Vehicles

Authors:
raw_author_name: Linwei Ye
raw_affiliation_string: Department of Electrical and Electronic Engineering, University of Liverpool, Liverpool L69 3BX, UK; School of Advance Technology, Xi’an Jiaotong-Liverpool University, 111 Ren’ai Road, Suzhou 215123, China
raw_author_name: Dong Wang
raw_affiliation_string: Department of Electrical and Electronic Engineering, University of Liverpool, Liverpool L69 3BX, UK; School of Advance Technology, Xi’an Jiaotong-Liverpool University, 111 Ren’ai Road, Suzhou 215123, China
raw_author_name: Zhiyuan Ma
raw_affiliation_string: Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX 78712, USA
raw_author_name: Quan Zhang
raw_affiliation_string: Department of Electrical and Electronic Engineering, University of Liverpool, Liverpool L69 3BX, UK; School of Advance Technology, Xi’an Jiaotong-Liverpool University, 111 Ren’ai Road, Suzhou 215123, China

Abstract: In Advanced Driving Assistance Systems (ADAS), Automated Driving Systems (ADS), and Driver Assistance Systems (DAS), RGB camera sensors are extensively utilized for object detection, semantic segmentation, and object tracking. Despite their popularity due to low costs, RGB cameras exhibit weak robustness in complex environments, particularly underperforming in low-light conditions, which raises a significant concern. To address these challenges, multi-sensor fusion systems or specialized low-light cameras have been proposed, but their high costs render them unsuitable for widespread deployment. On the other hand, improvements in post-processing algorithms offer a more economical and effective solution. However, current research in low-light image enhancement still shows substantial gaps in detail enhancement on nighttime driving datasets and is characterized by high deployment costs, failing to achieve real-time inference and edge deployment. Therefore, this paper leverages the Swin Vision Transformer combined with a gamma transformation integrated U-Net for the decoupled enhancement of initial low-light inputs, proposing a deep learning enhancement network named Vehicle-based Efficient Low-light Image Enhancement (VELIE). VELIE achieves state-of-the-art performance on various driving datasets with a processing time of only 0.19 s, significantly enhancing high-dimensional environmental perception tasks in low-light conditions.
Comparison and improvement of some attention mechanisms in the video field

Authors:
raw_author_name: Jianguang Zhao
raw_affiliation_string: Hebei Univ. of Architecture (China)
raw_author_name: Jingjing Fan
raw_affiliation_string: Hebei Univ. of Architecture (China)
raw_author_name: Junqiu Zhang
raw_affiliation_string: Hebei Univ. of Architecture (China)

Abstract: This paper compares the effects of SE-Net and the improved CBAM attention mechanism, and proposes a CTSA (Channel and Temporal and Spatial Attention) attention mechanism that adds temporal attention for temporal features, and further proposes a TSA (Time-domain and Spatial Attention) attention mechanism that focuses on time-domain on the basis of comparing the effects of multiple attention mechanisms. The experimental results show that in the field of behaviour recognition, the TSA attention mechanism designed in this paper can achieve better recognition results when using a lighter weight network structure.
The Use of Digital and Disruptive Technologies in the Artificial Intelligence Era

Authors:
raw_author_name: José G. Vargas-Hernandez
raw_affiliation_string: Tecnológico Nacional de México, Mexico & Budapest Centre for Long-Term Sustainability, Hungary
raw_author_name: Selene Castañeda-Burciaga
raw_affiliation_string: Universidad Politécnica de Zacatecas, Mexico
raw_author_name: Omar Alejandro Guirette-Barbosa
raw_affiliation_string: Universidad Politécnica de Zacatecas, Mexico

Abstract: This study aims to analyze the emergent and disruptive digital technologies in an era of artificial intelligence (IA). It departs from the assumption that the emerging technologies create both opportunities and challenges with an impact on individuals, organizations, institutions, and society at large in terms of bias, surveillance, hacking, etc. The method employed is the critical analysis based on the recent developments reported in scientific literature. It is concluded that control over the ethical disruption of digital technologies and more specifically artificial strategy must require a digital conversation and leadership about acceptable ethical behaviors under the assumption that digital transformation cannot be interrupted and must be guided by humans.
PQC and QKD Are Both Required to Enable a Quantum-Safe Future

Authors:
raw_author_name: Noel Farrugia
raw_affiliation_string: Merqury Cybersecurity Limited, Malta
raw_author_name: André Xuereb
raw_affiliation_string: Department of Physics, University of Malta, Msida MSD 2080, Malta; Merqury Cybersecurity Limited, Malta

Abstract: Quantum Key Distribution (QKD) and Post Quantum Cryptography (PQC) are often incorrectly assumed to be competitors of one another. In reality, they are complementary technologies aimed at solving different problems, with each having strengths and weaknesses that the other does not have. In this article, we will briefly summarise the current state of QKD and PQC, highlighting the advantages, disadvantages, and limitations of each technology stack. Finally, we explain our take on how both these technologies can be combined to provide better solutions that are better than any solution that uses solely PQC or QKD.
An Inductive Heterogeneous Graph Recommendation Model for High-Scoring Items Applied to Business Intelligence

Authors:
raw_author_name: Songlin Tian
raw_affiliation_string: School of Computer, Electronics and Information, Guangxi University, Nanning 530004, China
raw_author_name: Ying Yang
raw_affiliation_string: School of Computer, Electronics and Information, Guangxi University, Nanning 530004, China
raw_author_name: Lei Yang
raw_affiliation_string: Guangxi Academy of Sciences, Nanning 530007, China

Abstract: Business intelligence (BI), as a system for business data integration, processing, and analysis, is receiving increasing attention from enterprises. Data visualization is an important feature of BI, which allows users to visually observe the distribution and direction of data and assists them in making correct decisions. The core of this feature is visual analysis charts, which need to be pre-created and integrated into the dashboard by the chart makers, so there are situations where user needs cannot be accurately grasped. At the same time, there may be omissions in the work of users, and a method is needed to remind them. Introducing recommendation models into data visualization is a good solution; therefore, this paper proposes a recommendation model suitable for this type of scenario, which recommends high-scoring items (charts) to users. This model consists of a inductive heterogeneous graph recommendation algorithm with user preferences and a slow-acting collaborative filtering method. The experimental results in two datasets showed an improvement of 0.020/0.045, 0.083/0.019 and 0.076/0.023 in Hit, F-score, and NDCG compared to baselines, which proves that it is more suitable for data visualization requirements and other similar scenarios that require inductive recommendations based on user preferences.
Adaptive variational ground state preparation for spin-1 models on qubit-based architectures

Authors:
raw_author_name: João C. Getelina
raw_affiliation_string: Ames National Laboratory, Ames, Iowa 50011, USA
raw_author_name: Cai-Zhuang Wang
raw_affiliation_string: Ames National Laboratory, Ames, Iowa 50011, USA; Department of Physics and Astronomy, Iowa State University, Ames, Iowa 50011, USA
raw_author_name: Thomas Iadecola
raw_affiliation_string: Ames National Laboratory, Ames, Iowa 50011, USA; Department of Physics and Astronomy, Iowa State University, Ames, Iowa 50011, USA
raw_author_name: Yong-Xin Yao
raw_affiliation_string: Ames National Laboratory, Ames, Iowa 50011, USA; Department of Physics and Astronomy, Iowa State University, Ames, Iowa 50011, USA
raw_author_name: Peter P. Orth
raw_affiliation_string: Ames National Laboratory, Ames, Iowa 50011, USA; Department of Physics and Astronomy, Iowa State University, Ames, Iowa 50011, USA; Department of Physics, Saarland University, 66123 Saarbrücken, Germany

Abstract: We apply the adaptive variational quantum imaginary time evolution (AVQITE) method to prepare ground states of one-dimensional spin $S=1$ models. We compare different spin-to-qubit encodings (standard binary, Gray, unary, and multiplet) with regard to the performance and quantum resource cost of the algorithm. Using state-vector simulations, we study two well-known spin-1 models: the Blume-Capel model of transverse-field Ising spins with single-ion anisotropy, and the XXZ model with single-ion anisotropy. We consider system sizes of up to 20 qubits, which corresponds to spin-1 chains up to length 10. We determine the dependence of the number of cnot gates in the AVQITE state preparation circuit on the encoding, the initial state, and the choice of operator pool in the adaptive method. Independent of the choice of encoding, we find that the cnot gate count scales cubically with the number of spins for the Blume-Capel model and quartically for the anisotropic XXZ model. However, the multiplet and Gray encodings present smaller prefactors in the scaling relations. These results provide useful insights for the implementation of AVQITE on quantum hardware.
Unlabeled learning algorithms and operations: overview and future trends in defense sector

Authors:
raw_author_name: Eduardo e Oliveira
raw_author_name: António M. Lopes
raw_author_name: Ivana Ilic Mestric

Abstract: Abstract In the defense sector, artificial intelligence (AI) and machine learning (ML) have been used to analyse and decipher massive volumes of data, namely for target recognition, surveillance, threat detection and cybersecurity, autonomous vehicles and drones guidance, and language translation. However, there are key points that have been identified as barriers or challenges, especially related to data curation. For this reason, and also due to the need for quick response, the defense sector is looking for AI technologies capable of successfully processing and extracting results from huge amounts of unlabelled or very poorly labelled data. This paper presents an in-depth review of AI/ML algorithms for unsupervised or poorly supervised data, and machine learning operations (MLOps) techniques that are suitable for the defense industry. The algorithms are divided according to their nature, meaning that they either focus on techniques, or on applications. Techniques can belong to the supervision spectrum, or focus on explainability. Applications are either focused on text processing or computer vision. MLOps techniques, tools and practices are then discussed, revealing approaches and reporting experiences with the objective of declaring how to make the operationalization of ML integrated systems more efficient. Despite many contributions from several researchers and industry, further efforts are required to construct substantially robust and reliable models and supporting infrastructures for AI systems, which are reliable and suitable for the defense sector. This review brings up-to-date information regarding AI algorithms and MLOps that will be helpful for future research in the field.
PNER: Applying the Pipeline Method to Resolve Nested Issues in Named Entity Recognition

Authors:
raw_author_name: Qinghao Zhang
raw_affiliation_string: School of Computer Science and Engineering, Pusan National University, Busan 46241, Republic of Korea
raw_author_name: Hyuk-Chul Kwon
raw_affiliation_string: School of Computer Science and Engineering, Pusan National University, Busan 46241, Republic of Korea

Abstract: Named entity recognition (NER) in natural language processing encompasses three primary types: flat, nested, and discontinuous. While the flat type often garners attention from researchers, nested NER poses a significant challenge. Current approaches to addressing nested NER involve sequence labeling methods with merged label layers, cascaded models, and those rooted in reading comprehension. Among these, sequence labeling with merged label layers stands out for its simplicity and ease of implementation. Yet, highlighted issues persist within this method, prompting our aim to enhance its efficacy. In this study, we propose augmentations to the sequence labeling approach by employing a pipeline model bifurcated into sequence labeling and text classification tasks. Departing from annotating specific entity categories, we amalgamated types into main and sub-categories for a unified treatment. These categories were subsequently embedded as identifiers in the recognition text for the text categorization task. Our choice of resolution involved BERT+BiLSTM+CRF for sequence labeling and the BERT model for text classification. Experiments were conducted across three nested NER datasets: GENIA, CMeEE, and GermEval 2014, featuring annotations varying from four to two levels. Before model training, we conducted separate statistical analyses on nested entities within the medical dataset CMeEE and the everyday life dataset GermEval 2014. Our research unveiled a consistent dominance of a particular entity category within nested entities across both datasets. This observation suggests the potential utility of labeling primary and subsidiary entities for effective category recognition. Model performance was evaluated based on F1 scores, considering correct recognition only when both the complete entity name and category were identified. Results showcased substantial performance enhancement after our proposed modifications compared to the original method. Additionally, our improved model exhibited strong competitiveness against existing models. F1 scores on the GENIA, CMeEE, and GermEval 2014 datasets reached 79.21, 66.71, and 87.81, respectively. Our research highlights that, while preserving the original method’s simplicity and implementation ease, our enhanced model achieves heightened performance and competitive prowess compared to other methodologies.
Using QoE Metric as a Decision Criterion in Multimedia Heterogeneous Network Optimization: Challenges and Research Perspectives

Authors:
raw_author_name: M. Hamidou Harouna Omar
raw_affiliation_string: Aube Nouvelle University, Ouagadougou, Burkina Faso

Abstract: This article explores the growing importance of the QoE (Quality of Experience) metric as a fundamental criterion in the optimization of heterogeneous multimedia networks. We explore the benefits of using QoE, such as improving user experience, efficient resource management, and adaptation to network conditions. However, this paradigm presents technical challenges, including accurately measuring QoE and managing the complexity of heterogeneous networks. To address these challenges, we highlight promising research prospects, such as the development of advanced algorithms, real-time measurement of QoE, and the integration of machine learning. Ultimately, the use of QoE in the optimization of heterogeneous multimedia networks contributes to a substantial improvement in the quality of multimedia services offered to end users.