Reviewerly/exHarmony huggingface dataset:
Benchmarking quantum error-correcting codes on quasi-linear and central-spin processors

Abstract We evaluate the performance of small error-correcting codes, which we tailor to hardware platforms of very different connectivity and coherence: on a superconducting processor based on transmon qubits and a spintronic quantum register consisting of a nitrogen-vacancy center in diamond. Taking the hardware-specific errors and connectivity into account, we investigate the dependence of the resulting logical error rate on the platform features such as the native gates, native connectivity, gate times, and coherence times. Using a standard error model parameterized for the given hardware, we simulate the performance and benchmark these predictions with experimental results when running the code on the superconducting quantum device. The results indicate that for small codes, the quasi-linear layout of the superconducting device is advantageous. Yet, for codes involving multi-qubit controlled operations, the central-spin connectivity of the color centers enables lower error rates.
Entering hyper-reality: “Resonance-In-Sight,” a mixed-reality art installation

Enhanced virtual communicative technologies extend human perception in extraordinary ways that junction in detaching or augmenting its operator from physical reality. Prospects of the metaverse are progressing towards a possibly fully immersive cyberspace in which users will be entirely disconnected from their analogue physical surroundings. As we advance towards such an alternative reality, the practice-based research project discussed in this paper, titled “Resonance-In-Sight,” foresees and demonstrates the near-future of the metaverse to be a blended hyper-reality in which fiction and reality seamlessly blend into a “Mixed Reality” (MR)-based, co-urban design environment. As digital content in such an environment creatively coexists with our physical cityscape, the research question posed by this project is how such media environment can be used as a proactive cultural educational tool to engage with and inform user audiences of otherwise challenging art-historical content. Through this demonstrator project, the article discusses and reflects on applied methods for user experience (UX) design and the development of MR mobile applications for effective mixed-reality installations. It further reports on employed “Simultaneous Localisation And Mapping” (SLAM) techniques, which are essential for merging co-virtual design content in a hybrid metaverse, and concludes with an assessment of the project’s community impact from an analysis of available user data.
Realistic and general model for quantum key distribution with entangled-photon sources

Entanglement-based quantum key distribution (EB-QKD) has several advantages over prepare-and-measure protocols, such as eliminating the need for decoy states and active modulators. At present, entanglement sources based on the spontaneous parametric down-conversion process have been widely used in EB-QKD experiments. Most of the existing models regard the photon-number distribution of pulse-pumped entanglement sources as thermal. However, doing so ignores any effects of spectral modes, which will lead to the overestimation of the secure key rate. In this paper, we analyze the correlation between the photon-number distribution and the joint spectral distribution and rewrite the quantum state of the photon pairs. Then we apply the results to EB-QKD and present a general model which can be used to calculate the secure key rate not only for any pulse-pumped systems but even for cw-pumped systems.
Design of ultracompact broadband focusing spectrometers based on diffractive optical networks

We propose the inverse design of ultracompact, broadband focusing spectrometers based on adaptive deep diffractive neural networks (a-D$^2$NNs). Specifically, we introduce and characterize two-layer diffractive devices with engineered angular dispersion that focus and steer broadband incident radiation along predefined focal trajectories with desired bandwidth and $5$ nm spectral resolution. Moreover, we systematically study the focusing efficiency of two-layer devices with side length $L=100~\mu\mathrm{m}$ and focal length $f=300~\,\mu\mathrm{m}$ across the visible spectrum and we demonstrate accurate reconstruction of the emission spectrum from a commercial superluminescent diode. The proposed a-D$^2$NNs design method extends the capabilities of efficient multi-focal diffractive optical devices to include single-shot focusing spectrometers with customized focal trajectories for applications to ultracompact multispectral imaging and lensless microscopy.
Monitoring the security of audio biomedical signals communications in wearable IoT healthcare

The COVID-19 pandemic has imposed new challenges on the healthcare industry as hospital staff are exposed to a massive coronavirus load when registering new patients, taking temperatures, and providing care. The Ebola epidemic of 2014 is another example of a pandemic which a hospital in New York decided to use an audio-based communication system to protect nurses. This idea quickly turned into an Internet of Things (IoT) healthcare solution to help to communicate with patients remotely. However, it has grabbed the attention of criminals who use this medium as a cover for secret communication. The merging of signal processing and machine-learning techniques has led to the development of steganalyzers with very higher efficiencies, but since the statistical properties of normal audio files differ from those of purely speech audio files, the current steganalysis practices are not efficient enough for this type of content. This research considers the Percent of Equal Adjacent Samples (PEAS) feature for speech steganalysis. This feature efficiently discriminates the least significant bit stego speech samples from clean ones with a single analysis dimension. A sensitivity of 99.82% was achieved for the steganalysis of 50% embedded stego instances using a classifier based on the Gaussian membership function.
XSpace: An Augmented Reality Toolkit for Enabling Spatially-Aware Distributed Collaboration

Augmented Reality (AR) has the potential to leverage environmental information to better facilitate distributed collaboration, however, such applications are difficult to develop. We present XSpace, a toolkit for creating spatially-aware AR applications for distributed collaboration. Based on a review of existing applications and developer tools, we design XSpace to support three methods for creating shared virtual spaces, each emphasizing a different aspect: shared objects, user perspectives, and environmental meshes. XSpace implements these methods in a developer toolkit, and also provides a set of complimentary visual authoring tools to allow developers to preview a variety of configurations for a shared virtual space. We present five example applications to illustrate that XSpace can support the development of a rich set of collaborative AR experiences that are difficult to produce with current solutions. Through XSpace, we discuss implications for future application design, including user space customization and privacy and safety concerns when sharing users' environments.
The decoupling principle

The three decade struggle to ensure Internet data confidentiality---a key aspect of communications privacy---is finally behind us. Encryption is fast, secure, and standard in all browsers, modern transports, and major protocols. Yet it has long seemed that network privacy is not unified by core principles but a grab bag of techniques and ideas applied to an equally wide range of applications, contexts, layers of infrastructure, and software stacks.
Rethinking data-driven networking with foundation models

Foundational models have caused a paradigm shift in the way artificial intelligence (AI) systems are built. They have had a major impact in natural language processing (NLP), and several other domains, not only reducing the amount of required labeled data or even eliminating the need for it, but also significantly improving performance on a wide range of tasks. We argue foundation models can have a similar profound impact on network traffic analysis, and management. More specifically, we show that network data shares several of the properties that are behind the success of foundational models in linguistics. For example, network data contains rich semantic content, and several of the networking tasks (e.g., traffic classification, generation of protocol implementations from specification text, anomaly detection) can find similar counterparts in NLP (e.g., sentiment analysis, translation from natural language to code, out-of-distribution). However, network settings also present unique characteristics and challenges that must be overcome. Our contribution is in highlighting the opportunities and challenges at the intersection of foundation models and networking.
Online Hate Speech and Immigration Acceptance: A Study of Spanish Provinces

Online hate speech against migrants and refugees poses a grave challenge for coexistence and democracy. However, it also offers an opportunity to measure social acceptance towards this group. Using the Intergroup Contact and the Mediated Intergroup Contact Theory, and an already validated methodology, this article seeks to validate whether the use of hate speech as a predictor of social acceptance is valid at a provincial level in Spain. Contrasting 97,710 tweets about migrants and refugees with secondary data from public Spanish institutions about acceptance of immigration and foreign population, no correlation was observed, rejecting the main hypotheses, and hinting that the application of this approach might not be recommended for smaller entities, such as provinces (NUTS 3). However, the study offers descriptive data about racist hate speech spread on Twitter in Spain, and also discusses the need for more studies using big data to increase knowledge about online hate speech against migrants and refugees.
Justifying Arabic Text Sentiment Analysis Using Explainable AI (XAI): LASIK Surgeries Case Study

With the increasing use of machine learning across various fields to address several aims and goals, the complexity of the ML and Deep Learning (DL) approaches used to provide solutions has also increased. In the last few years, Explainable AI (XAI) methods to further justify and interpret deep learning models have been introduced across several domains and fields. While most papers have applied XAI to English and other Latin-based languages, this paper aims to explain attention-based long short-term memory (LSTM) results across Arabic Sentiment Analysis (ASA), which is considered an uncharted area in previous research. With the use of Local Interpretable Model-agnostic Explanation (LIME), we intend to further justify and demonstrate how the LSTM leads to the prediction of sentiment polarity within ASA in domain-specific Arabic texts regarding medical insights on LASIK surgery across Twitter users. In our research, the LSTM reached an accuracy of 79.1% on the proposed data set. Throughout the representation of sentiments using LIME, it demonstrated accurate results regarding how specific words contributed to the overall sentiment polarity classification. Furthermore, we compared the word count with the probability weights given across the examples, in order to further validate the LIME results in the context of ASA.
A Scalable Montgomery Modular Multiplication Architecture with Low Area-Time Product Based on Redundant Binary Representation

The Montgomery modular multiplication is an integral operation unit in the public key cryptographic algorithm system. Previous work achieved good performance at low input widths by combining Redundant Binary Representation (RBR) with Montgomery modular multiplication, but it is difficult to strike a good balance between area and time as input bit widths increase. To solve this problem, based on the redundant Montgomery modular multiplication, in this paper, we propose a flexible and pipeline hardware implementation of the Montgomery modular multiplication. Our proposed structure guarantees a single-cycle delay between two-stage pipeline units and reduces the length of the critical path by redistributing the data paths between the pipelines and preprocessing the input in the loop. By analyzing the structure and comparing the related work in this paper, our structure ensures a lower area-time product while achieving a controllable and small area consumption. The comprehensive results under different Taiwan Semiconductor Manufacturing Company (TSMC) processes demonstrate the advantages of our structure in terms of flexibility and area-time product.
Knowledge Mapping Visualization Analysis of Research on Blockchain in Management and Economics

As an emerging technology enabling economic, social, and environmental sustainability, blockchain has drawn considerable attention from the academic community in management and economics. It is essential to clarify this field’s research status, hotspots, and evolution trends. This study took 642 pieces of literature on blockchain in management and economics from the Web of Science (WoS) as data sources. It combined the bibliometric knowledge mapping visualization and statistical analysis methods to conduct a systematic analysis. It is found that the United States and China are the primary core strengths, and highly influential research organizations and authors have emerged in this field. The research of blockchain in management and economics has the property of being interdisciplinary. Research hotspots are mainly distributed in the combination and application of blockchain technology and emerging technologies and the research on blockchain in supply chain management (SCM), supply chain finance, intellectual property, digital currency, and transaction costs. The evolution trends of research hotspots show a rule of overlapping commonness and individuality. This study systematically revealed the overall research development situation on blockchain in management and economics to provide some reference and inspiration for relevant researchers.
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Visual perception is one of the key technologies for smart ships to achieve intelligent navigation in various complex water scenes, which adopts the object detection algorithm based on visible image to detect obstacles. Affected by the rolling and high-speed motion of ships and bad weather, motion blur and haze appear in the images of vision sensor, which seriously interfere with the obstacle detection. To ensure the navigation safety of smart ships, we propose the integrated multi-task deblurring, dehazing and object detection convolutional neural network (D3-Net). Firstly, the proposed method is able to complete deblurring, dehazing and object detection within a single network, which is more suitable to be applied in intelligent navigation. Secondly, different from traditional methods that reconstruct enhanced images first and then perform object detection, D3-Net combines image reconstruction and detection feature extraction to realize the integration of image enhancement and detection procedure. Finally, to improve the deblurring and dehazing performance of the network, we propose classification attention-based detection feature loss (CADF-Loss), which uses the image classification features to guide the network perform the detection feature enhancement more specifically. The experimental results in real navigation scenes show that D3-Net not only improves the detection accuracy of blurred and hazy images by 42%, but also has excellent detection efficiency, which is helpful to improve the navigation safety performance of smart ships.
Toward Holographic Video Communications: A Promising AI-Driven Solution

Real-time holographic video communications enable immersive experiences for next-generation video services in the future metaverse era. However, high-fidelity holographic videos require high bandwidth and significant computation resources, which exceed the transferring and computing capacity of 5G networks. This article reviews state-of-the-art holographic point cloud video transmission techniques and highlights the critical challenges of delivering such immersive services. We further implement a preliminary prototype of an AI-driven holographic video communication system and present critical experimental results to evaluate its performance. Finally, we identify future research directions and discuss potential solutions for providing real-time and high-quality holographic experiences.
Robot path planner based on deep reinforcement learning and the seeker optimization algorithm

Path planning is one of the key technologies for mobile robot applications. However, the traditional robot path planner has a slow planning response, which leads to a long navigation completion time. In this paper, we propose a novel robot path planner (SOA+A2C) that produces global and local path planners with the seeker optimization algorithm (SOA) and the advantage actor-critic (A2C) algorithm, respectively. In addition, to solve the problems of poor convergence performance when training deep reinforcement learning (DRL) agents in complex path planning tasks and path redundancy when metaheuristic algorithms, such as SOA, are used for path planning, we propose the incremental map training method and path de-redundancy method. Simulation results show that first, the incremental map training method can improve the convergence performance of the DRL agent in complex path planning tasks. Second, the path de-redundancy method can effectively alleviate path redundancy without sacrificing the search capability of the metaheuristic algorithm. Third, the SOA+A2C path planner is superior to the Dijkstra & dynamic window approach (Dijkstra+DWA) and the Dijkstra & timed elastic band (Dijkstra+TEB) path planners provided by the robot operating system (ROS) in terms of path length, path planning response time, and navigation completion time. Therefore, the developed SOA+A2C path planner can serve as an effective tool for mobile robot path planning.
M-CBN: Manifold constrained joint image dehazing and super-resolution based on chord boosting network

This paper proposes a Manifold Constrained Chord Boosting Network (M-CBN), which incorporates the super-resolution principle to achieve image dehazing. M-CBN is a task-specific image restoration network that explicitly learns the mapping from Low-resolution (LR) hazy images to High-resolution (HR) haze-free images. Hence, we design a preliminary image degradation to imitate super-resolution training on hazy images. In M-CBN, a plug-and-play Cross-linked Dual Projection Module (CDPM) for skip connections is developed. In CDPM, back-projections for HR encoder features and LR decoder features are cross-linked for better recovery of spatial information, and a cross-resolution spatial attention is designed to enhance fusion features. Then, to boost the generation of image details and textures, we propose a Chord Residual Module (CRM), which can separately process High-frequency (HF) and Low-frequency (LF) features by progressive inner-frequency updating and dense inter-frequency cross-collaboration to enhance decoding features. Finally, a manifold constraint dual discriminator is established. The static discriminator explicitly constrains dehazed images in the expected manifold to unify the joint learning of image dehazing and super-resolution. And the dynamic discriminator implicitly optimizes the network by adversarial training. Extensive experiments on general, dense and non-homogeneous haze datasets and cross-domain dehazing tasks show the proposed M-CBN presents high-quality dehazed results with natural colors and clear details.
A comprehensive analysis of acknowledgement texts in Web of Science: a case study on four scientific domains

Analysis of acknowledgments is particularly interesting as acknowledgments may give information not only about funding, but they are also able to reveal hidden contributions to authorship and the researcher's collaboration patterns, context in which research was conducted, and specific aspects of the academic work. The focus of the present research is the analysis of a large sample of acknowledgement texts indexed in the Web of Science (WoS) Core Collection. Record types 'article' and 'review' from four different scientific domains, namely social sciences, economics, oceanography and computer science, published from 2014 to 2019 in a scientific journal in English were considered. Six types of acknowledged entities, i.e., funding agency, grant number, individuals, university, corporation and miscellaneous, were extracted from the acknowledgement texts using a Named Entity Recognition (NER) tagger and subsequently examined. A general analysis of the acknowledgement texts showed that indexing of funding information in WoS is incomplete. The analysis of the automatically extracted entities revealed differences and distinct patterns in the distribution of acknowledged entities of different types between different scientific domains. A strong association was found between acknowledged entity and scientific domain and acknowledged entity and entity type. Only negligible correlation was found between the number of citations and the number of acknowledged entities. Generally, the number of words in the acknowledgement texts positively correlates with the number of acknowledged funding organizations, universities, individuals and miscellaneous entities. At the same time, acknowledgement texts with the larger number of sentences have more acknowledged individuals and miscellaneous categories.
Bi-Directional CNN-RNN Architecture with Group-Wise Enhancement and Attention Mechanisms for Cryptocurrency Sentiment Analysis

As the cryptocurrency trading market has grown significantly in recent years, the number of comments related to cryptocurrency has increased tremendously in social media platforms. Due to this, sentiment analysis of the cryptocurrency-related comments has become highly desirable to give a comprehensive picture of peoples’ opinions about the trend of the market. In this regard, we perform cryptocurrency-related text sentiment classification using tweets based on positive and negative sentiments. For increasing the efficacy of the sentiment analysis, we introduce a novel deep neural network hybrid architecture which is composed of an embedding layer, a convolution layer, a group-wise enhancement mechanism, a bidirectional layer, an attention mechanism, and a fully connected layer. Local features are derived using a convolution layer, and weight values associated with intuitive features are developed using the group-wise enhancement mechanism. After feeding the improved context vector to the bidirectional layer to grab global features, the attention mechanism and the fully connected layer have been employed. The experimental findings indicate that the proposed architecture outperforms the state-of-the-art architectures with an accuracy value of 93.77%.