Reviewerly/exHarmony huggingface dataset:
Challenging 21st-Century Competencies for STEM Students: Companies’ Vision in Slovenia and Norway in the Light of Global Initiatives for Competencies Development

Authors:
raw_author_name: Alenka Temeljotov Salaj
raw_affiliation_string: Department of Civil and Environmental Engineering, Faculty of Engineering, Norwegian University of Science and Technology, 7491 Trondheim, Norway
raw_author_name: Bojan Cestnik
raw_affiliation_string: Jožef Stefan Institute, Jamova cesta 39, 1000 Ljubljana, Slovenia; Temida d.o.o., Dunajska cesta 51, 1000 Ljubljana, Slovenia

Abstract: Rapid technological innovations are changing the jobs that Science, Technology, Engineering, and Mathematics (STEM) students are employed in after completing their education. Therefore, employers are looking for applicants with skills beyond the specialised professional competencies that can be acquired in the chosen subjects during formal education. Higher education institutions (HEIs) have an important role to play in this process, as they endeavour to equip graduates with the skills and knowledge for future jobs. Our study aims to identify the key competencies that Slovenian and Norwegian companies expect from graduates of selected STEM degree programmes in both countries. Based on these findings, the participating HEIs can tailor their degree programmes to the employability requirements of companies. This study first analysed European and international recommendations and initiatives relevant to skills development in STEM employment and education. We selected 30 key competencies and included them in the questionnaires in which companies’ human resources managers (HRMs) defined their attitudes towards employability skills in the future. We focused on companies employing graduates of the Ecotechnologies and Civil and Environmental Engineering programmes of the participating HEIs. The analysis of the results showed that companies in the two countries have different attitudes towards skills. Four competencies were identified as more important in both countries: (i) management and design of processes, technologies, and people’s work, (ii) adaptation to changes, (iii) ability to manage uncertainty, and (iv) innovation. Based on the analysis, we have developed a framework of ten employability skills for STEM students. In addition, this study provides the basis for operating a decision support system (DSS) to monitor the development of students’ competencies. The DSS is being developed as part of the RESPO-VI project supported by the Norway Grants to better match the skills of STEM students as future job seekers with the needs of potential employers. The study’s results will also help other formal and non-formal education adapt their curricula and thus reduce the employability skill gaps between current STEM education and future labour market needs.
Fast Multi-User Searchable Encryption with Forward and Backward Private Access Control

Authors:
raw_author_name: Zaid Ameen Abduljabbar
raw_affiliation_string: Department of Computer Science, College of Education for Pure Sciences, University of Basrah, Basrah 61004, Iraq
raw_author_name: Vincent Omollo Nyangaresi
raw_affiliation_string: Department of Computer Science and Software Engineering, Jaramogi Oginga Odinga University of Science & Technology, Bondo 40601, Kenya
raw_author_name: Junchao Ma
raw_affiliation_string: College of Big Data and Internet, Shenzhen Technology University, Shenzhen 518118, China
raw_author_name: Abdulla J. Y. Aldarwish
raw_affiliation_string: Department of Computer Science, College of Education for Pure Sciences, University of Basrah, Basrah 61004, Iraq

Abstract: Untrusted servers are servers or storage entities lacking complete trust from the data owner or users. This characterization implies that the server hosting encrypted data may not enjoy full trust from data owners or users, stemming from apprehensions related to potential security breaches, unauthorized access, or other security risks. The security of searchable encryption has been put into question by several recent attacks. Currently, users can search for encrypted documents on untrusted cloud servers using searchable symmetric encryption (SSE). This study delves deeply into two pivotal concepts of privacy within dynamic searchable symmetric encryption (DSSE) schemes: forward privacy and backward privacy. The former serves as a safeguard against the linkage of recently added documents to previously conducted search queries, whereas the latter guarantees the irretrievability of deleted documents in subsequent search inquiries. However, the provision of fine-grained access control is complex in existing multi-user SSE schemes. SSE schemes may also incur high computation costs due to the need for fine-grained access control, and it is essential to support document updates and forward privacy. In response to these issues, this paper suggests a searchable encryption scheme that uses simple primitive tools. We present a multi-user SSE scheme that efficiently controls access to dynamically encrypted documents to resolve these issues, using an innovative approach that readily enhances previous findings. Rather than employing asymmetric encryption as in comparable systems, we harness low-complexity primitive encryption tools and inverted index-based DSSE to handle retrieving encrypted files, resulting in a notably faster system. Furthermore, we ensure heightened security by refreshing the encryption key after each search, meaning that users are unable to conduct subsequent searches with the same key and must obtain a fresh key from the data owner. An experimental evaluation shows that our scheme achieves forward and Type II backward privacy and has much faster search performance than other schemes. Our scheme can be considered secure, as proven in a random oracle model.
Diffractive Deep-Neural-Network-Based Classifier for Holographic Memory

Authors:
raw_author_name: Tomoyoshi Ito
raw_affiliation_string: Graduate School of Engineering, Chiba University, 1-33, Yayoi-cho, Inage-ku, Chiba-shi 263-8522, Chiba, Japan
raw_author_name: Tomoyoshi Shimobaba
raw_affiliation_string: Graduate School of Engineering, Chiba University, 1-33, Yayoi-cho, Inage-ku, Chiba-shi 263-8522, Chiba, Japan

Abstract: Holographic memory offers high-capacity optical storage with rapid data readout and long-term durability. Recently, read data pages have been classified using digital deep neural networks (DNNs). This approach is highly accurate, but the prediction time hinders the data readout throughput. This study presents a diffractive DNN (D2NN)-based classifier for holographic memory. D2NNs have so far attracted a great deal of attention for object identification and image transformation at the speed of light. A D2NN, consisting of trainable diffractive layers and devoid of electronic devices, facilitates high-speed data readout. Furthermore, we numerically investigated the classification performance of a D2NN-based classifier. The classification accuracy of the D2NN was 99.7% on 4-bit symbols, exceeding that of the hard decision method.
A Secure Blockchain-Enabled Remote Healthcare Monitoring System for Home Isolation

Authors:
raw_author_name: May Thu
raw_affiliation_string: Faculty of Engineering, Cambodia University of Technology and Science, CamTech Street, Sangkat Prek Tasek, Khan Chroy Chongvar Phnom Penh, Phnom Penh 121003, Cambodia
raw_author_name: Chanankorn Jandaeng
raw_affiliation_string: Informatics Innovative Center of Excellence (IICE), School of Informatics, Walailak University, Thasala, Nakhon Si Thammarat 80160, Thailand

Abstract: This article presents a secure framework for remote healthcare monitoring in the context of home isolation, thereby addressing the concerns related to untrustworthy client connections to a hospital information system (HIS) within a secure network. Our proposed solution leverages a public blockchain network as a secure distributed database to buffer and transmit patient vital signs. The framework integrates an algorithm for the secure gathering and transmission of vital signs to the Ethereum network. Additionally, we introduce a publish/subscribe paradigm, thus enhancing security using the TLS channel to connect to the blockchain network. An analysis of the maintenance cost of the distributed database underscores the cost-effectiveness of our approach. In conclusion, our framework provides a highly secure and economical solution for remote healthcare monitoring in home isolation scenarios.
Error Analysis of Pretrained Language Models (PLMs) in English-to-Arabic Machine Translation

Authors:
raw_author_name: Hend Al-Khalifa
raw_affiliation_string: Information Technology Department, King Saud University, Riyadh, Saudi Arabia

Abstract: Abstract Advances in neural machine translation utilizing pretrained language models (PLMs) have shown promise in improving the translation quality between diverse languages. However, translation from English to languages with complex morphology, such as Arabic, remains challenging. This study investigated the prevailing error patterns of state-of-the-art PLMs when translating from English to Arabic across different text domains. Through empirical analysis using automatic metrics (chrF, BERTScore, COMET) and manual evaluation with the Multidimensional Quality Metrics (MQM) framework, we compared Google Translate and five PLMs (Helsinki, Marefa, Facebook, GPT-3.5-turbo, and GPT-4). Key findings provide valuable insights into current PLM limitations in handling aspects of Arabic grammar and vocabulary while also informing future improvements for advancing English–Arabic machine translation capabilities and accessibility.
Digital Transformation in Tunneling – A Project Report on TransIT

Authors:
raw_author_name: Stefan Klikovits
raw_affiliation_string: Johannes Kepler University of Linz
raw_author_name: Galina Paskaleva
raw_affiliation_string: TU Wien
raw_author_name: Manuel Wimmer
raw_affiliation_string: Johannes Kepler University of Linz

Abstract: The digital transformation has significantly affected many industries. Recently, it has gained momentum in the Architecture, Engineering, and Construction (AEC) sector in general and tunneling in particular. To support these developments, TransIT represents an inter-university and interdisciplinary research initiative with the goal of advancing digitalization in tunneling. This article discusses the challenges connected with the digital transformation of the tunneling sector. We examine this topic with different use cases demonstrating solutions to those challenges.
Encrypted Video Search with Single/Multiple Writers

Authors:
raw_author_name: Yu Zheng
raw_affiliation_string: Department of Information Engineering, Chinese Univ. of Hong Kong, Shatin, Hong Kong, China
raw_author_name: Wenchao Zhang
raw_affiliation_string: College of Computer Science and Technology, Xi'an University of Science and Technology, Xi'an, China
raw_author_name: Wei Song
raw_affiliation_string: School of Computer Science and Engineering, Northeastern University, Shenyang, China
raw_author_name: Xiuhua Wang
raw_affiliation_string: Hubei Key Laboratory of Distributed System Security, Hubei Engineering Research Center on Big Data Security, School of Cyber Science and Engineering, Huazhong University of Science and Technology, Wuhan, China
raw_author_name: Chong Fu
raw_affiliation_string: School of Computer Science and Engineering, Northeastern University; School of Computer Science and Engineering, Northeastern University; Engineering Research Center of Security Technology of Complex Network System, Ministry of Education, Shenyang, China

Abstract: Video-based services have become popular. Clients often outsource their videos to the cloud to relieve local maintenance. However, privacy has become a major concern since many videos contain sensitive information. Although retrieving (unencrypted) videos has been extensively investigated, retrieving encrypted multimedia has received relatively rare attention, at best in a limitation of image-based similarity searches. We initiate the study of scalable encrypted video search, enabling clients to query videos similar to an image search. Our modular framework leverages intrinsic attributes of videos, such as semantics and visuals, to effectively capture their contents. We propose a two-step approach whereby lightweight searchable encryption techniques are used for pre-screening, followed by an interactive approach for fine-grained search. Furthermore, we present three instantiations, including one centralized-writer instantiation and two distributed-writer instantiations, to effectively cater to varying needs and scenarios– 1) The centralized one employs forward and backward private searchable encryption [CCS 2017] over deep hashing [CVPR 2020]. 2) Motivated by distributed computing, the multi-writer instantiations building atop HSE [Usenix Security 2022] allows searching the relevant videos contributed by multiple intuitions collaboratively. Our experimental results illustrate their practical performance over multiple real-world datasets, whether in a centralized setting or distributed setting.
Modeling and Verification of Natural Language Requirements based on States and Modes

Authors:
raw_author_name: Jean-Michel Bruel
raw_affiliation_string: Smart Modeling for softw@re Research and Technology

Abstract: The relationship between states (status of a system) and modes (capabilities of a system) used to describe system requirements is often poorly defined. The unclear relationship could make systems of interest out of control because of the out of boundaries of the systems caused by the newly added modes. Formally modeling and verifying requirements can clarify the relationship, making the system safer. To this end, an innovative approach to analyzing requirements is proposed. The MoSt language (a Domain Specific Language implemented on the Xtext framework) is firstly designed for requirements modeling and a model validator is realized to check requirements statically. A code generator is then provided to realize the automatic model transformation from the MoSt model to a NuSMV model, laying the foundation for the dynamic checks of requirements through symbolic model checking. Next, a NuSMV runner is designed to connect the NuSMV with the validator to automate the whole dynamic checks. The grammar, the model validator, the code generator, and the NuSMV runner are finally integrated into a publicly available Eclipse-based tool. Two case studies have been employed to illustrate the feasibility of our approach. For each case study, we injected 14 errors. The results show that the static and dynamic checks can successfully detect all the errors.
Optimal inexactness schedules for tunable oracle-based methods

Authors:
raw_author_name: François Glineur
raw_affiliation_string: UCLouvain, ICTEAM (INMA), Louvain-la-Neuve, Belgium

Abstract: Several recent works address the impact of inexact oracles in the convergence analysis of modern first-order optimization techniques, e.g. Bregman Proximal Gradient and Prox-Linear methods as well as their accelerated variants, extending their field of applicability. In this paper, we consider situations where the oracle's inexactness can be chosen upon demand, more precision coming at a computational price counterpart. Our main motivations arise from oracles requiring the solving of auxiliary subproblems or the inexact computation of involved quantities, e.g. a mini-batch stochastic gradient as a full-gradient estimate. We propose optimal inexactness schedules according to presumed oracle cost models and patterns of worst-case guarantees, covering among others convergence results of the aforementioned methods under the presence of inexactness. Specifically, we detail how to choose the level of inexactness at each iteration to obtain the best trade-off between convergence and computational investments. Furthermore, we highlight the benefits one can expect by tuning those oracles' accuracy instead of keeping it constant throughout. Finally, we provide extensive numerical experiments that support the practical interest of our approach, both in offline and online settings, applied to the Fast Gradient algorithm.
Simplified Multi-head Mechanism for Few-Shot Remote Sensing Image Classification

Authors:
raw_author_name: Lei Xing
raw_affiliation_string: College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao, China
raw_author_name: Anxun Han
raw_affiliation_string: College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao, China
raw_author_name: Weifeng Liu
raw_affiliation_string: College of Control Science and Engineering, China University of Petroleum (East China), Qingdao, China
raw_author_name: Baodi Liu
raw_affiliation_string: College of Control Science and Engineering, China University of Petroleum (East China), Qingdao, China

Abstract: Abstract The study of few-shot remote sensing image classification has received significant attention. Although meta-learning-based algorithms have been the primary focus of recent examination, feature fusion methods stress feature extraction and representation. Nonetheless, current feature fusion methods, like the multi-head mechanism, are restricted by their complicated network structure and challenging training process. This manuscript presents a simplified multi-head mechanism for obtaining multiple feature representations from a single sample. Furthermore, we perform specific fundamental transformations on remote-sensing images to obtain more suitable features for information representation. Specifically, we reduce multiple feature extractors of the multi-head mechanism to a single one and add an image transformation module before the feature extractor. After transforming the image, the features are extracted resulting in multiple features for each sample. The feature fusion stage is integrated with the classification prediction stage, and multiple linear classifiers are combined for multi-decision fusion to complete feature fusion and classification. By combining image transformation with feature decision fusion, we compare our results with other methods through validation tests and demonstrate that our algorithm simplifies the multi-head mechanism while maintaining or improving classification performance.
Moderately supervised learning: definition, framework and generality

Authors:
raw_author_name: Yongquan Yang
raw_affiliation_string: Institute of Clinical Pathology, West China Hospital, Sichuan University, 37 Guo Xue Road, Chengdu, 610041, China

Abstract: Abstract Learning with supervision has achieved remarkable success in numerous artificial intelligence (AI) applications. In the current literature, by referring to the properties of the labels prepared for the training dataset, learning with supervision is categorized as supervised learning (SL) and weakly supervised learning (WSL). SL concerns the situation where the training dataset is assigned with ideal (complete, exact and accurate) labels, while WSL concerns the situation where the training dataset is assigned with non-ideal (incomplete, inexact or inaccurate) labels. However, various solutions for SL tasks under the era of deep learning have shown that the given labels are not always easy to learn, and the transformation from the given labels to easy-to-learn targets can significantly affect the performance of the final SL solutions. Without considering the properties of the transformation from the given labels to easy-to-learn targets, the definition of SL conceals some details that can be critical to building the appropriate solutions for specific SL tasks. Thus, for practitioners in various AI application fields, it is desirable to reveal these details systematically. This article attempts to achieve this goal by expanding the categorization of SL and investigating the sub-type that plays the central role in SL. More specifically, taking into consideration the properties of the transformation from the given labels to easy-to-learn targets, we firstly categorize SL into three narrower sub-types. Then we focus on the moderately supervised learning (MSL) sub-type that concerns the situation where the given labels are ideal, but due to the simplicity in annotation, careful designs are required to transform the given labels into easy-to-learn targets. From the perspectives of the definition, framework and generality, we conceptualize MSL to present a complete fundamental basis to systematically analyse MSL tasks. At meantime, revealing the relation between the conceptualization of MSL and the mathematicians’ vision, this article as well establishes a tutorial for AI application practitioners to refer to viewing a problem to be solved from the mathematicians’ vision.
Digital quantum simulation of non-perturbative dynamics of open systems with orthogonal polynomials

Authors:
raw_author_name: José D. Guimarães
raw_affiliation_string: Centro de Física das Universidades do Minho e do Porto, Braga 4710-057, Portugal; Institute of Theoretical Physics and IQST, Ulm University, Albert-Einstein-Allee 11, Ulm 89081, Germany; International Iberian Nanotechnology Laboratory, Av. Mestre José Veiga s/n, Braga 4715-330, Portugal
raw_author_name: Mikhail I. Vasilevskiy
raw_affiliation_string: Departamento de Física, Universidade do Minho, Braga 4710-057, Portugal; International Iberian Nanotechnology Laboratory, Av. Mestre José Veiga s/n, Braga 4715-330, Portugal; Laboratório de Física para Materiais e Tecnologias Emergentes (LaPMET), Universidade do Minho, Braga 4710-057, Portugal
raw_author_name: Luís S. Barbosa
raw_affiliation_string: INESC TEC, Departamento de Informática, Universidade do Minho, Braga 4710-057, Portugal; International Iberian Nanotechnology Laboratory, Av. Mestre José Veiga s/n, Braga 4715-330, Portugal

Abstract: Classical non-perturbative simulations of open quantum systems&amp;apos; dynamics face several scalability problems, namely, exponential scaling of the computational effort as a function of either the time length of the simulation or the size of the open system. In this work, we propose the use of the Time Evolving Density operator with Orthogonal Polynomials Algorithm (TEDOPA) on a quantum computer, which we term as Quantum TEDOPA (Q-TEDOPA), to simulate non-perturbative dynamics of open quantum systems linearly coupled to a bosonic environment (continuous phonon bath). By performing a change of basis of the Hamiltonian, the TEDOPA yields a chain of harmonic oscillators with only local nearest-neighbour interactions, making this algorithm suitable for implementation on quantum devices with limited qubit connectivity such as superconducting quantum processors. We analyse in detail the implementation of the TEDOPA on a quantum device and show that exponential scalings of computational resources can potentially be avoided for time-evolution simulations of the systems considered in this work. We applied the proposed method to the simulation of the exciton transport between two light-harvesting molecules in the regime of moderate coupling strength to a non-Markovian harmonic oscillator environment on an IBMQ device. Applications of the Q-TEDOPA span problems which can not be solved by perturbation techniques belonging to different areas, such as the dynamics of quantum biological systems and strongly correlated condensed matter systems.