Reviewerly/exHarmony huggingface dataset:
Deformable image registration with attention-guided fusion of multi-scale deformation fields

Abstract Deformable medical image registration plays a crucial role in theoretical research and clinical application. Traditional methods suffer from low registration accuracy and efficiency. Recent deep learning-based methods have made significant progresses, especially those weakly supervised by anatomical segmentations. However, the performance still needs further improvement, especially for images with large deformations. This work proposes a novel deformable image registration method based on an attention-guided fusion of multi-scale deformation fields. Specifically, we adopt a separately trained segmentation network to segment the regions of interest to remove the interference from the uninterested areas. Then, we construct a novel dense registration network to predict the deformation fields of multiple scales and combine them for final registration through an attention-weighted field fusion process. The proposed contour loss and image structural similarity index (SSIM) based loss further enhance the model training through regularization. Compared to the state-of-the-art methods on three benchmark datasets, our method has achieved significant performance improvement in terms of the average Dice similarity score (DSC), Hausdorff distance (HD), Average symmetric surface distance (ASSD), and Jacobian coefficient (JAC). For example, the improvements on the SHEN dataset are 0.014, 5.134, 0.559, and 359.936, respectively.
Response of the European Law Institute to the Public Consultation on Civil Liability – Adapting Liability Rules to the Digital Age and Artificial Intelligence

Article Response of the European Law Institute to the Public Consultation on Civil Liability – Adapting Liability Rules to the Digital Age and Artificial Intelligence was published on April 1, 2022 in the journal Journal of European Tort Law (volume 13, issue 1).
Reasoning About Vectors Using an SMT Theory of Sequences

Abstract Dynamic arrays, also referred to as vectors, are fundamental data structures used in many programs. Modeling their semantics efficiently is crucial when reasoning about such programs. The theory of arrays is widely supported but is not ideal, because the number of elements is fixed (determined by its index sort) and cannot be adjusted, which is a problem, given that the length of vectors often plays an important role when reasoning about vector programs. In this paper, we propose reasoning about vectors using a theory of sequences. We introduce the theory, propose a basic calculus adapted from one for the theory of strings, and extend it to efficiently handle common vector operations. We prove that our calculus is sound and show how to construct a model when it terminates with a saturated configuration. Finally, we describe an implementation of the calculus in cvc5 and demonstrate its efficacy by evaluating it on verification conditions for smart contracts and benchmarks derived from existing array benchmarks.
A Social Virtual Reality Mobile Application for Learning and Practicing English

Many studies promise that virtual reality (VR) applications will be one of the most used tools in foreign language learning. This is because VR keeps learners engaged, motivated, and prepared with better communication skills due to the simulation of real-life environments. As a result of the COVID-19 pandemic and social distancing, it became difficult to use and practice newly learned language skills with real people directly. This paper proposes a social VR mobile application that can be used anytime anywhere to offer a set of different VR environments that simulate real-life situations in order to learn and practice the English language. The application aims to improve speaking skills by offering a private session with a native speaker tutor or a community of learners to practice speaking. It combines the benefits of mobile learning, social networks, and VR. Initial usability testing results showed that the application is simple and easy to use, with some problems discovered and resolved. The application demonstrates that applying VR tools could contribute positively to learning English among the language learners using it.
Astronomia: breve história, principais conceitos e campos de atuação / Astronomy: brief history, main concepts and fields of activity

Astronomy and its aspects are presented in this paper, as well as its applications and importance with regard to astronomical phenomena.The basic and fundamental concepts of astronomy and a very brief history are presented.The fields of activity of astronomy are discussed, where it is inserted and how it contributes to the continuous construction of human scientific knowledge.The available technological tools are presented, such as: telescopes, programming languages, engineering, space stations, etc.This research is characterized as "exploratory", as it seeks to explain and familiarize the reader with the main concepts of astronomy, and "bibliographic", as it is based solely on previously published scientific materials and then presents itself in a logical, organized and
Low-area and high-speed hardware architectures of LBlock cipher for Internet of Things image encryption

Security is becoming an essential field of research as network technology is changing at a rapid pace. Digital images are transmitted in many modes of communication. This transmission must be secure, particularly in applications that demand a high level of security, such as military applications, surveillance, radars, and biometrics. Two hardware solutions of the lightweight block cipher LBlock are provided to secure image data. These proposed architectures are implemented on a Xilinx field programmable gate arrays (FPGA) platform, and the implementation results are compared with existing state-of-the-art designs. The proposed designs took 56 and 64 slices operating at a frequency of 492.441 and 681.663 MHz, respectively, on a Virtex-5 FPGA platform. In addition, the proposed round-based and pipelined designs showed improvement in hardware efficiency over state-of-the-art designs. The proposed hardware architectures are capable of securing different sizes of gray-scale and RGB color images. These hardware solutions were verified by different analyses, including correlation coefficient, mean square error (MSE), peak signal-to-noise ratio (PSNR), histogram, entropy, number of pixel changing rate (NPCR), and unified average changing intensity (UACI). The existing solutions were compared with correlation coefficients, NPCR, UACI, MSE, PSNR, and entropy values and provide better solutions than the state-of-the-art designs.
Learning-Based Stereoscopic View Synthesis with Cascaded Deep Neural Networks

Depth image-based rendering (DIBR) is an important technique in the 2D to 3D conversion process, which renders virtual views with a texture image and the associated depth map. However, certain problems, such as disocclusion, still exist in current DIBR systems. In this study, a new learning-based framework that models conventional DIBR synthesis pipelines is proposed to solve these problems. The proposed model adopts a coarse-to-fine approach to realize virtual view prediction and disocclusion region refinement sequentially in a unified deep learning framework that includes two cascaded joint filter block-based convolutional neural networks (CNNs) and one residual learning-based generative adversarial network (GAN). An edge-guided global looping optimization strategy is adopted to progressively reconstruct the scene structures on the novel view, and a novel directional discounted reconstruction loss is proposed for better training. In this way, our framework performs well in terms of virtual view quality and is more suitable for 2D to 3D conversion applications. The experimental results demonstrate that the proposed method can generate visually satisfactory results.
Robust coverless steganography using limited mapping images

Coverless image steganography (CIS) has attracted significant attention because it can fundamentally resist steganalysis tools. Available CIS schemes are mainly divided into synthesis-based and mapping-based schemes. Compared with the former, mapping-based methods ensure lossless information extraction and stronger attack robustness. However, these methods still face the challenges of incomplete secret information mapping, trade-off between robustness and distinguishability of mapping features, and demand for huge numbers of mapping images. To tackle these issues, a novel robust coverless steganography using limited mapping images is proposed in this paper. In our scheme, we extract ring statistics to ensure both the distinguishability and robustness of mapping features. Moreover, different from conventional CIS schemes, we further design a chaotic system for scrambling image features to map secret information. Using this system, a single image is mapped to multiple secret information instances using different scrambled features, which reduces the demand on the numbers of images and avoids incomplete mapping. Furthermore, the scrambled features also enhance the security of secret information. Experimental results demonstrate that our scheme has superior performance in terms of distinguishability and robustness against geometrical attacks, the required number of mapping images, and mapping completeness.
Temporally ultralong biphotons with a linewidth of 50 kHz

We report the generation of biphotons, with a temporal full width at the half maximum (FWHM) of 13.4 ± 0.3 µs and a spectral FWHM of 50 ± 1 kHz, via the process of spontaneous four-wave mixing with laser-cooled atoms. The temporal width is the longest, and the spectral linewidth is the narrowest to date. This is also the first biphoton result that obtains a linewidth below 100 kHz, reaching a new milestone. The very long biphoton wave packet has a signal-to-background ratio of 3.4, which violates the Cauchy–Schwarz inequality for classical light by 4.8 folds. Furthermore, we demonstrated a highly tunable-linewidth biphoton source and showed that while the biphoton source’s temporal and spectral width were controllably varied by about 24 folds, its generation rate only changed by less than 15%. A spectral brightness or generation rate per pump power per linewidth of 1.2× 106 pairs/(s mW MHz) was achieved at the temporal width of 13.4 µs. The above results were made possible by the low decoherence rate and high optical depth of the experimental system, as well as a novel scheme of classical fields’ and biphotons’ propagation directions in the experiment. This work has demonstrated a high-efficiency ultranarrow-linewidth biphoton source and has made substantial advancements in quantum technology utilizing heralded single photons.
Detecting Mineral Resources and Suggesting a Physical Concentration Flowsheet for Economic Minerals at the Northern Border Region of Saudi Arabia

There is a limited number of studies on sand deposit resources of Saudi Arabia, which cover nearly the one-third of the area of the country, whereas most of these studies deal with the environmental rather than the mineralogical or mining aspects. In this paper, and in the effort to detect the mineral resources of the Northern Border Region, the surficial Wadi sediments along the Ar'ar-Sakaka road are studied. The deposits of several Wadies (Al Aqra, Shiban al Hanzaliyat, and Arar) are mixed. The sediments of the collected samples are investigated to determine definite areas characterized by a relatively higher content of heavy minerals and a relatively lower content of carbonate minerals that are also friable enough to be investigated by some of the available physical concentration techniques. A large quantity of the surficial deposits, weighing 4.69 tons was collected from the stretch at the investigated area which is 3km long and 1.5km wide. Evaluation of the heavy minerals content, their types, and their ability for concentration and separation, was conducted. A suggested physical concentration flowsheet was concluded for concentrating and separating the contained economic minerals. The average heavy mineral content is 1.55 wt% and the identified economic minerals are magnetite, ilmenite, hematite, goethite, zircon, rutile, anatase, monazite, and xenotime. The other contained heavy minerals include monoclinic pyroxenes (diopside, and augite), monoclinic amphibole (winchite), and muscovite mica. Dolomite and calcite carbonate are also contained. The concluding results ensure that magnetite, zircon, TiO2 minerals, and monazite are mineable for separation in individual mineral concentrates. Most of the detected economic minerals are recorded in the area for the first time. Monazite, xenotime, and zircon are responsible for some recorded radioactivity in the area.
Use of Social Media Data to Diagnose and Monitor Psychotic Disorders: Systematic Review

Schizophrenia is a disease associated with high burden, and improvement in care is necessary. Artificial intelligence (AI) has been used to diagnose several medical conditions as well as psychiatric disorders. However, this technology requires large amounts of data to be efficient. Social media data could be used to improve diagnostic capabilities.The objective of our study is to analyze the current capabilities of AI to use social media data as a diagnostic tool for psychotic disorders.A systematic review of the literature was conducted using several databases (PubMed, Embase, Cochrane, PsycInfo, and IEEE Xplore) using relevant keywords to search for articles published as of November 12, 2021. We used the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) criteria to identify, select, and critically assess the quality of the relevant studies while minimizing bias. We critically analyzed the methodology of the studies to detect any bias and presented the results.Among the 93 studies identified, 7 studies were included for analyses. The included studies presented encouraging results. Social media data could be used in several ways to care for patients with schizophrenia, including the monitoring of patients after the first episode of psychosis. We identified several limitations in the included studies, mainly lack of access to clinical diagnostic data, small sample size, and heterogeneity in study quality. We recommend using state-of-the-art natural language processing neural networks, called language models, to model social media activity. Combined with the synthetic minority oversampling technique, language models can tackle the imbalanced data set limitation, which is a necessary constraint to train unbiased classifiers. Furthermore, language models can be easily adapted to the classification task with a procedure called "fine-tuning."The use of social media data for the diagnosis of psychotic disorders is promising. However, most of the included studies had significant biases; we therefore could not draw conclusions about accuracy in clinical situations. Future studies need to use more accurate methodologies to obtain unbiased results.
Mako: a low-pause, high-throughput evacuating collector for memory-disaggregated datacenters

Resource disaggregation has gained much traction as an emerging datacenter architecture, as it improves resource utilization and simplifies hardware adoption. Under resource disaggregation, different types of resources (memory, CPUs, etc.) are disaggregated into dedicated servers connected by high-speed network fabrics. Memory disaggregation brings efficiency challenges to concurrent garbage collection (GC), which is widely used for latency-sensitive cloud applications, because GC and mutator threads simultaneously run and constantly compete for memory and swap resources.
Quantifying Multisensory Immersive Experiences using Wearables: Is (Stimulating) More (Senses) Always Merrier?

Virtual reality applications are on the rise and touching numerous domains, including healthcare, training, and gaming, to name a few. Existing experiences, however, are not fully immersive, as only two senses (audio-visual) are stimulated. To overcome this limitation, olfactory and haptic devices are emerging, thus making multisensory immersive experiences a reality. To date, however, little is known about the impact that each stimulated sense has on the overall experience, as well as on the user’s sense of e.g., realism, immersion, and engagement. In this pilot, we aim to answer this question. Using a multisensory pod, sixteen participants were immersed in a 2.5-minute virtual world, where smells, vibroacoustic, and somatosensory stimuli (i.e., wind and heat) were presented, in addition to 360-degree video and surround sound. Using two wearable devices, we kept track of the user’s heart rate, breathing rate, skin temperature, blood volume pulse, and electrodermal activity while they were immersed. In this paper, we report the impact that stimulating different senses had on the users’ overall experience, sense of presence, immersion, realism, flow, cybersickness, and emotional states, both subjectively, as well as objectively using features extracted from the wearable devices.
“So What? What's That to Do With Me?” Expectations of People With Visual Impairments for Image Descriptions in Their Personal Photo Activities

People with visual impairments (PVI) access photos through image descriptions. Thus far, research has studied what PVI expect in these descriptions mostly regarding functional purposes (e.g., identifying an object) and when engaging with online, publicly available images. Extending this research, we interviewed 30 PVI to understand their expectations for image descriptions when viewing, taking, searching, and reminiscing with personal photos on their own devices. We show how their expectations varied across photo activities and often went well beyond identifying objects in photos. Based on our findings, we propose design opportunities for generating and providing image descriptions for personal photo use by PVI. The design opportunities for PVI also point to novel support for the sighted for using image descriptions to enrich their experience of photos.
Design of classical-quantum systems with UML

Abstract Developers of the many promising quantum computing applications that currently exist are urging companies in many different sectors seriously consider integrating this new technology into their business. For these applications to function, not only are quantum computers required, but quantum software also. Accordingly, quantum software engineering has become an important research field, in that it attempts to apply or adapt existing methods and techniques (or propose new ones) for the analysis, design, coding, and testing of quantum software, as well as playing a key role in ensuring quality in large-scale productions. The design of quantum software nevertheless poses two main challenges: the modelling of software quantum elements must be done in high-level modelling languages; and the need to further develop so-called “hybrid information systems”, which combine quantum and classical software. To address these challenges, we first propose a quantum UML profile for analysing and designing hybrid information systems; we then demonstrate its applicability through various structural and behavioural diagrams such as use case, class, sequence, activity, and deployment. In comparison to certain other quantum domain-specific languages, this UML profile ensures compliance with a well-known international standard that is supported by many tools and is followed by an extensive community.
Character gated recurrent neural networks for Arabic sentiment analysis

Sentiment analysis is a Natural Language Processing (NLP) task concerned with opinions, attitudes, emotions, and feelings. It applies NLP techniques for identifying and detecting personal information from opinionated text. Sentiment analysis deduces the author's perspective regarding a topic and classifies the attitude polarity as positive, negative, or neutral. In the meantime, deep architectures applied to NLP reported a noticeable breakthrough in performance compared to traditional approaches. The outstanding performance of deep architectures is related to their capability to disclose, differentiate and discriminate features captured from large datasets. Recurrent neural networks (RNNs) and their variants Long-Short Term Memory (LSTM), Gated Recurrent Unit (GRU), Bi-directional Long-Short Term Memory (Bi-LSTM), and Bi-directional Gated Recurrent Unit (Bi-GRU) architectures are robust at processing sequential data. They are commonly used for NLP applications as they-unlike RNNs-can combat vanishing and exploding gradients. Also, Convolution Neural Networks (CNNs) were efficiently applied for implicitly detecting features in NLP tasks. In the proposed work, different deep learning architectures composed of LSTM, GRU, Bi-LSTM, and Bi-GRU are used and compared for Arabic sentiment analysis performance improvement. The models are implemented and tested based on the character representation of opinion entries. Moreover, deep hybrid models that combine multiple layers of CNN with LSTM, GRU, Bi-LSTM, and Bi-GRU are also tested. Two datasets are used for the models implementation; the first is a hybrid combined dataset, and the second is the Book Review Arabic Dataset (BRAD). The proposed application proves that character representation can capture morphological and semantic features, and hence it can be employed for text representation in different Arabic language understanding and processing tasks.
Code Smell Prioritization with Business Process Mining and Static Code Analysis: A Case Study

One of the most significant impediments to the long-term maintainability of software applications is code smells. Keeping up with the best coding practices can be difficult for software developers, which might lead to performance throttling or code maintenance concerns. As a result, it is imperative that large applications be regularly monitored for performance issues and code smells, so that these issues can be corrected promptly. Resolving code smells in software systems can be done in a variety of ways, but doing so all at once would be prohibitively expensive and can be out of budget. Prioritizing these solutions are therefore critical. The majority of current research prioritizes code smells according to the type of smell they cause. This method, however, is not sufficient because of a lack of knowledge regarding the frequency of code usage and code changeability behavior. Even the most complex programs have some components that are more important than others. Maintaining the functionality of certain parts is essential since they are often used. Identifying and correcting code smells in places that are frequently utilized and subject to rapid change should take precedence over other code smells. A novel strategy is proposed for finding frequently used and change-prone areas in a codebase by combining business logic, heat map information, and commit history analysis in this study. It examines the codebase, commits, and log files of Java applications to identify business processes, heat map graphs, and severity levels of various types of code smells and their commit history. This is done in order to present a comprehensive, efficient, and resource-friendly technique for identifying and prioritizing performance throttling with also handling code maintenance concerns.