Reviewerly/exHarmony huggingface dataset:
QoE-Aware Resource Allocation for Semantic Communication Networks

With the aim of accomplishing intelligence tasks, semantic communications transmit task-related information only, yielding significant performance gains over conventional communications. To guarantee user requirements for different tasks, we study the semantic-aware resource allocation in a multi-cell multi-task network in this paper. Specifically, an approximate measure of semantic entropy is first developed to quantify the semantic information for different tasks, based on which a novel quality-of-experience (QoE) model is proposed. We formulate the QoE-aware resource allocation in terms of the number of transmitted semantic symbols, channel assignment, and power allocation. To solve this problem, we first decouple it into two independent subproblems. The first one is to optimize the number of transmitted semantic symbols with given channel assignment and power allocation, which is solved by the exhaustive search method. The second one is the channel assignment and power allocation subproblem, which is modeled as a many-to-one matching game and solved by the proposed low-complexity matching algorithm. Simulation results demonstrate the effectiveness and superiority of the proposed method on the overall QoE.
BERT-based Ensemble Approaches for Hate Speech Detection

With the freedom of communication provided in online social media, hate speech has increasingly generated. This leads to cyber conflicts affecting social life at the individual and national levels. As a result, hateful content classification is becoming increasingly demanded for filtering hate content before being sent to the social networks. This paper focuses on classifying hate speech in social media using multiple deep models that are implemented by integrating recent transformer-based language models such as BERT, and neural networks. To improve the classification performances, we evaluated with several ensemble techniques, including soft voting, maximum value, hard voting and stacking. We used three publicly available Twitter datasets (Davidson, HatEval2019, OLID) that are generated to identify offensive languages. We fused all these datasets to generate a single dataset (DHO dataset), which is more balanced across different labels, to perform multi-label classification. Our experiments have been held on Davidson dataset and the DHO corpora. The later gave the best overall results, especially F1 macro score, even it required more resources (time execution and memory). The experiments have shown good results especially the ensemble models, where stacking gave F1 score of 97% on Davidson dataset and aggregating ensembles 77% on the DHO dataset.
CAST-YOLO: An Improved YOLO Based on a Cross-Attention Strategy Transformer for Foggy Weather Adaptive Detection

Both transformer and one-stage detectors have shown promising object detection results and have attracted increasing attention. However, the developments in effective domain adaptive techniques in transformer and one-stage detectors still have not been widely used. In this paper, we investigate this issue and propose a novel improved You Only Look Once (YOLO) model based on a cross-attention strategy transformer, called CAST-YOLO. This detector is a Teacher–Student knowledge transfer-based detector. We design a transformer encoder layer (TE-Layer) and a convolutional block attention module (CBAM) to capture global and rich contextual information. Then, the detector implements cross-domain object detection through the knowledge distillation method. Specifically, we propose a cross-attention strategy transformer to align domain-invariant features between the source and target domains. This strategy consists of three transformers with shared weights, identified as the source branch, target branch, and cross branch. The feature alignment uses knowledge distillation, to address better knowledge transfer from the source domain to the target domain. The above strategy provides better robustness for a model with noisy input. Extensive experiments show that our method outperforms the existing methods in foggy weather adaptive detection, significantly improving the detection results.
Cyber insurance: state of the art, trends and future directions

Society has become increasingly dependent on IT infrastructure and services. Additionally, the pandemic of COVID-19 forced the transition of the traditional way of working (i.e., physical presence) into a more modern and flexible one (i.e., working remotely). This has led to an increase of cyberattacks, as a direct consequence of the increase of the attack surface but subsequently also led to an increased necessity for the protection of information systems. Toward the protection of information systems, cyber insurance is considered as a strategy for risk management, where necessary. Cyber insurance is emerging as an important tool to protect organizations against cyberattack-related losses. In this work, we extensively examine the relevant literature on cybersecurity insurance, research and practice, in order to draft the current landscape and present the trends.
The Purpose of Cybersecurity Governance in the Digital Transformation of Public Services and Protecting the Digital Environment

As it tries to incorporate computer-based technology into the public services provided by businesses or organisations, the digital transformation process is currently regarded one of the most prominent topics in circulation. Establishing fundamentals and points is necessary for digital transformation, as is relying on a specific set of employee talents and actively incorporating customers in the process's evolution. Cybersecurity has become an increasingly important issue for governments and businesses worldwide, and this shift towards digital services is a top priority for all of them. Accelerating the digital transformation process and the usage of its services are the vulnerabilities to cyberspace, the advancement of technology and gadgets, and the employment of artificial intelligence in the development of current apps. In order to build cybersecurity governance that can be relied upon and is useful in accomplishing duties without hacking and tampering with data and information, it is necessary to implement simple programmes and tactics. This article emphasises the significance of cybersecurity governance in delivering secure and efficient technical means that can meet all risks and challenges and protect persons' data across all industries.
Illumina faces short-read rivals

New sequencers are poised to disrupt Illumina's dominance. Users will need to navigate the opportunities and challenges on offer in an increasingly crowded market.
What is “technology integration” and how is it measured in K-12 education? A systematic review of survey instruments from 2010 to 2021

This systematic review provides an overview and analysis of the survey instruments measuring technology integration (TI) in educational settings from 2010 to 2021. Given the importance of addressing aspects related to the quality of TI (e.g., the use of technology to support pedagogical goals), we paid particular attention to them. Search results from the PsycINFO, ERIC, Web of Science, and Scopus databases yielded 695 records. Thirty-five different survey instruments used in 36 studies met our eligibility criteria and were then analyzed by applying content analysis. Our results indicate that the diversity of operationalizations is very high and that several instruments have no explicit conceptual or theoretical underpinnings. Most of the instruments measure aspects related to classroom practices and measure TI from the teachers' point of view. The most frequently measured pedagogical aspects of TI in instructional practices include the use of technology to (1) enhance students’ cognitive engagement, (2) promote collaboration between students, and (3) allow students to conduct research online. The study concludes with some perspectives for future research, an attempt to formulate a definition of TI, and some more general recommendations to ensure terminological unambiguity in TI research.
Exponential data encoding for quantum supervised learning

Reliable quantum supervised learning of a multivariate function mapping depends on the expressivity of the corresponding quantum circuit and measurement resources. We introduce exponential-data-encoding strategies that are hardware-efficient and optimal among all nonentangling Pauli-encoded schemes, which is sufficient for a quantum circuit to express general functions having very broad Fourier frequency spectra using only exponentially few encoding gates. We show that such an encoding strategy not only reduces the quantum resources, but also exhibits practical resource advantage during training in contrast with known efficient classical strategies when polynomial-depth training circuits are also employed. When computation resources are constrained, we numerically demonstrate that even exponential-data-encoding circuits with single-layer training modules can generally express functions that lie outside the classically expressible region, thereby supporting the practical benefits of such a resource advantage. Finally, we illustrate the performance of exponential encoding in learning the potential-energy surface of the ethanol molecule and California's housing prices.
Impact of the covid-19 pandemic on the use of ICT tools in science and technology education

This article analyzes the self-concept about digital competence in university professors of Sciences, Health Sciences and Engineering and the impact that the COVID-19 pandemic has had on the Information and Communication Technologies (ICT) use habits of professors in these areas. For this purpose, a survey designed by the authors was completed by 340 university professors in the aforementioned areas. Based on the answers obtained from this survey, a descriptive quantitative analysis of the assessments of the self-concept of digital competence and training of the participants, of the didactic use of ICT and the frequency of their use before and after the pandemic has been carried out. The results showed that the digital competence of the professors is intermediate, but their training is valued as low, especially in Sciences and Health Sciences. The assessment of ICT is very good. The pandemic has caused a generalized increase in the use of ICT, mainly in Health Sciences, which is the area in which university students were most reluctant to use them. In addition, a gender gap which did not exist before the pandemic has been generated favoring females in the use of ICT in Science and Engineering. An age-based digital gap that existed before the pandemic has also been corrected in Health Sciences.
A Survey on Evaluation Metrics for Machine Translation

The success of Transformer architecture has seen increased interest in machine translation (MT). The translation quality of neural network-based MT transcends that of translations derived using statistical methods. This growth in MT research has entailed the development of accurate automatic evaluation metrics that allow us to track the performance of MT. However, automatically evaluating and comparing MT systems is a challenging task. Several studies have shown that traditional metrics (e.g., BLEU, TER) show poor performance in capturing semantic similarity between MT outputs and human reference translations. To date, to improve performance, various evaluation metrics have been proposed using the Transformer architecture. However, a systematic and comprehensive literature review on these metrics is still missing. Therefore, it is necessary to survey the existing automatic evaluation metrics of MT to enable both established and new researchers to quickly understand the trend of MT evaluation over the past few years. In this survey, we present the trend of automatic evaluation metrics. To better understand the developments in the field, we provide the taxonomy of the automatic evaluation metrics. Then, we explain the key contributions and shortcomings of the metrics. In addition, we select the representative metrics from the taxonomy, and conduct experiments to analyze related problems. Finally, we discuss the limitation of the current automatic metric studies through the experimentation and our suggestions for further research to improve the automatic evaluation metrics.
Perspective Chapter: Museums and the Metaverse – Emerging Technologies to Promote Inclusivity and Engagement

Over the past two decades, museums have increasingly sought to build connections with the community and increase inclusivity of visitors. At the same time, emerging technologies, such as extended reality (XR) and virtual museums (VM) are increasingly adopted to engage with different generational expectations but also for the purposes of supporting inclusivity and neurodiverse populations. First such technologies were adopted to augment exhibitions in the physical museum space for edutainment. Since then, XR has expanded from room-size environments (CAVEs) and augmented exhibitions to the creation of entire virtual museums, such as The Museum of Pure Form and The Virtual Museum of Sculpture. Digital twins of museums are increasingly common, along with UNESCO World Heritage Sites. Such virtual experiences can be leveraged to prepare neurodiverse visitors prior to visiting a museum. This chapter will outline how existing approaches to social stories and sensory maps may be combined with XR experiences to support neurodiverse visitors and their families. While onsite, immersive technologies can be used both for engagement and to provide accommodations for greater inclusivity and diversity.
Effective Techniques for Multimodal Data Fusion: A Comparative Analysis

Data processing in robotics is currently challenged by the effective building of multimodal and common representations. Tremendous volumes of raw data are available and their smart management is the core concept of multimodal learning in a new paradigm for data fusion. Although several techniques for building multimodal representations have been proven successful, they have not yet been analyzed and compared in a given production setting. This paper explored three of the most common techniques, (1) the late fusion, (2) the early fusion, and (3) the sketch, and compared them in classification tasks. Our paper explored different types of data (modalities) that could be gathered by sensors serving a wide range of sensor applications. Our experiments were conducted on Amazon Reviews, MovieLens25M, and Movie-Lens1M datasets. Their outcomes allowed us to confirm that the choice of fusion technique for building multimodal representation is crucial to obtain the highest possible model performance resulting from the proper modality combination. Consequently, we designed criteria for choosing this optimal data fusion technique.
What Is the Draw of the Metaverse? Personality Correlates of Zepeto Use Motives and Their Associations With Psychological Well-Being

Despite the hype surrounding the metaverse, there is scant empirical research that examines who uses the service, for what specific purposes, and with what consequences. Based on a survey of current Zepeto users (N = 200), a popular metaverse application that enables people to create avatars and socialize while exploring the virtual spaces, we investigated (a) the key motives of Zepeto use, (b) how Big Five personality traits predict specific motives of Zepeto use, and (c) how specific motives of Zepeto use are associated with users' psychological well-being. Overall, users were largely driven by the desire to explore the virtual world and enjoy unique experiences, but such a tendency was stronger among those higher on openness and agreeableness. Extroverts were more likely to use Zepeto for functional purposes, while those higher on neuroticism turned to Zepeto to escape from reality. As for psychological consequences, while those using Zepeto for functional and escaping purposes reported higher levels of loneliness, those who used Zepeto for social and experiential goals were less lonely. The experiential and escape motives predicted perceived social support in the opposite directions. Moreover, by comparing Zepeto users' responses with those of non-users (N = 200), we found that (a) non-users overestimated users' motives of Zepeto use, especially social and escape motives, (b) Zepeto users were higher on extraversion and openness than non-users, and (c) users reported higher levels of loneliness than non-users with no significant difference in perceived social support. Implications of the findings and future directions are discussed.
Semi-supervised Visual Tracking of Marine Animals Using Autonomous Underwater Vehicles

In-situ visual observations of marine organisms is crucial to developing behavioural understandings and their relations to their surrounding ecosystem. Typically, these observations are collected via divers, tags, and remotely-operated or human-piloted vehicles. Recently, however, autonomous underwater vehicles equipped with cameras and embedded computers with GPU capabilities are being developed for a variety of applications, and in particular, can be used to supplement these existing data collection mechanisms where human operation or tags are more difficult. Existing approaches have focused on using fully-supervised tracking methods, but labelled data for many underwater species are severely lacking. Semi-supervised trackers may offer alternative tracking solutions because they require less data than fully-supervised counterparts. However, because there are not existing realistic underwater tracking datasets, the performance of semi-supervised tracking algorithms in the marine domain is not well understood. To better evaluate their performance and utility, in this paper we provide (1) a novel dataset specific to marine animals located at http://warp.whoi.edu/vmat/, (2) an evaluation of state-of-the-art semi-supervised algorithms in the context of underwater animal tracking, and (3) an evaluation of real-world performance through demonstrations using a semi-supervised algorithm on-board an autonomous underwater vehicle to track marine animals in the wild.
Microwave-to-optical transduction with erbium ions coupled to planar photonic and superconducting resonators

Optical quantum networks can connect distant quantum processors to enable secure quantum communication and distributed quantum computing. Superconducting qubits are a leading technology for quantum information processing but cannot couple to long-distance optical networks without an efficient, coherent, and low noise interface between microwave and optical photons. Here, we demonstrate a microwave-to-optical transducer using an ensemble of erbium ions that is simultaneously coupled to a superconducting microwave resonator and a nanophotonic optical resonator. The coherent atomic transitions of the ions mediate the frequency conversion from microwave photons to optical photons and using photon counting we observed device conversion efficiency approaching 10-7. With pulsed operation at a low duty cycle, the device maintained a spin temperature below 100 mK and microwave resonator heating of less than 0.15 quanta.
Practical continuous-variable quantum key distribution with feasible optimization parameters

Continuous-variable quantum key distribution (CV-QKD) offers an approach to achieve a potential high secret key rate (SKR) in metropolitan areas. There are several challenges in developing a practical CV-QKD system from the laboratory to the real world. One of the most significant points is that it is really hard to adapt different practical optical fiber conditions for CV-QKD systems with unified hardware. Thus, how to improve the performance of practical CV-QKD systems in the field without modification of the hardware is very important. Here, a systematic optimization method, combining the modulation variance and error correction matrix optimization, is proposed to improve the performance of a practical CV-QKD system with a restricted capacity of postprocessing. The effect of restricted postprocessing capacity on the SKR is modeled as a nonlinear programming problem with modulation variance as an optimization parameter, and the selection of an optimal error correction matrix is studied under the same scheme. The results show that the SKR of a CV-QKD system can be improved by 24% and 200% compared with previous frequently used optimization methods theoretically with a transmission distance of 50 km. Furthermore, the experimental results verify the feasibility and robustness of the proposed method, where the achieved optimal SKR achieved practically deviates < 1.6% from the theoretical optimal value. Our results pave the way to deploy high-performance CV-QKD in the real world.
Improving the robustness and stability of a machine learning model for breast cancer prognosis through the use of multi-modal classifiers

Breast cancer is a deadly disease with a high mortality rate among PAN cancers. The advancements in biomedical information retrieval techniques have been beneficial in developing early prognosis and diagnosis systems for cancer patients. These systems provide the oncologist with plenty of information from several modalities to make the correct and feasible treatment plan for breast cancer patients and protect them from unnecessary therapies and their toxic side effects. The cancer patient's related information can be collected using various modalities like clinical, copy number variation, DNA-methylation, microRNA sequencing, gene expression, and histopathological whole slide images. High dimensionality and heterogeneity in these modalities demand the development of some intelligent systems to understand related features to the prognosis and diagnosis of diseases and make correct predictions. In this work, we have studied some end-to-end systems having two main components : (a) dimensionality reduction techniques applied to original features from different modalities and (b) classification techniques applied to the fusion of reduced feature vectors from different modalities for automatic predictions of breast cancer patients into two categories: short-time and long-time survivors. Principal component analysis (PCA) and variational auto-encoders (VAEs) are used as the dimensionality reduction techniques, followed by support vector machines (SVM) or random forest as the machine learning classifiers. The study utilizes raw, PCA, and VAE extracted features of the TCGA-BRCA dataset from six different modalities as input to the machine learning classifiers. We conclude this study by suggesting that adding more modalities to the classifiers provides complementary information to the classifier and increases the stability and robustness of the classifiers. In this study, the multimodal classifiers have not been validated on primary data prospectively.
Speed limits and locality in many-body quantum dynamics

We review the mathematical speed limits on quantum information processing in many-body systems. After the proof of the Lieb-Robinson Theorem in 1972, the past two decades have seen substantial developments in its application to other questions, such as the simulatability of quantum systems on classical or quantum computers, the generation of entanglement, and even the properties of ground states of gapped systems. Moreover, Lieb-Robinson bounds have been extended in non-trivial ways, to demonstrate speed limits in systems with power-law interactions or interacting bosons, and even to prove notions of locality that arise in cartoon models for quantum gravity with all-to-all interactions. We overview the progress which has occurred, highlight the most promising results and techniques, and discuss some central outstanding questions which remain open. To help bring newcomers to the field up to speed, we provide self-contained proofs of the field's most essential results.