Reviewerly/exHarmony huggingface dataset:
A Cross-Domain Data Security Sharing Approach for Edge Computing based on CP-ABE

Cloud computing is a unified management and scheduling model of computing resources. To satisfy multiple resource requirements for various application, edge computing has been proposed. One challenge of edge computing is cross-domain data security sharing problem. Ciphertext policy attribute-based encryption (CP-ABE) is an effective way to ensure data security sharing. However, many existing schemes focus on could computing, and do not consider the features of edge computing. In order to address this issue, we propose a cross-domain data security sharing approach for edge computing based on CP-ABE. Besides data user attributes, we also consider access control from edge nodes to user data. Our scheme first calculates public-secret key peer of each edge node based on its attributes, and then uses it to encrypt secret key of data ciphertext to ensure data security. In addition, our scheme can add non-user access control attributes such as time, location, frequency according to the different demands. In this paper we take time as example. Finally, the simulation experiments and analysis exhibit the feasibility and effectiveness of our approach.
Forensic Similarity for Source Camera Model Comparison

In the article "Forensic Similarity for Digital Images", O. Mayer and M. C. Stamm introduce the forensic similarity approach, which aims at determining whether two image patches contain the same forensic traces or not.The proposed method is based on a feed-forward neural network which consists of two modules: a feature extraction module using a pair of CNNs in a siamese configuration, and a three-layer neural network that maps the extracted features into a similarity score.In this article, we explore the use of the forensic similarity score for source camera model comparison, as one of the possible applications of such an approach suggested by Mayer and Stamm.
‘I’m Always Telling You My Honest Opinion’: Influencers and Gendered Authenticity Strategies on Instagram

The author addresses the world of Instagram influencers but offers a very different perspective by focusing on the gendering of claims to authenticity on social media. The author observes how high-profile male and female Czech influencers are required to navigate an authenticity bind (Duffy & Hund, 2019) through two key strategies: the opinion-leader strategy and the community-building strategy. Men rely predominantly on the opinion-leader strategy while women position themselves as community builders. By focusing on the communicative strategies of the influencers, the author offers an analysis of authenticity as performative process, rather than a static concept, that gets negotiated in everyday interactions between influencers and their followers.
Distinction of Scrambled Linear Block Codes Based on Extraction of Correlation Features

Aiming to solve the problem of the distinction of scrambled linear block codes, a method for identifying the scrambling types of linear block codes by combining correlation features and convolution long short-term memory neural networks is proposed in this paper. First, the cross-correlation characteristics of the scrambling sequence symbols are deduced, the partial autocorrelation function is constructed, the superiority of the partial autocorrelation function is determined by derivation, and the two are combined as the input correlation characteristics. A shallow network combining a convolutional neural network and LSTM is constructed; finally, the linear block code scrambled dataset is input into the network model, and the training and recognition test of the network is completed. The simulation results show that, compared with the traditional algorithm based on a multi-fractal spectrum, the proposed method can identify a synchronous scrambler, and the recognition accuracy is higher under a high bit error rate. Moreover, the method is suitable for classification under noise. The proposed method lays a foundation for future improvements in scrambler parameter identification.
A multiplicity-preserving crossover operator on graphs

Evolutionary algorithms usually explore a search space of solutions by means of crossover and mutation. While a mutation consists of a small, local modification of a solution, crossover mixes the genetic information of two solutions to compute a new one. For model-driven optimization (MDO), where models directly serve as possible solutions (instead of first transforming them into another representation), only recently a generic crossover operator has been developed. Using graphs as a formal foundation for models, we further refine this operator in such a way that additional well-formedness constraints are preserved: We prove that, given two models that satisfy a given set of multiplicity constraints as input, our refined crossover operator computes two new models as output that also satisfy the set of constraints.
Discussion of the Purpose and Role of the Undergraduates’ Extra-curricular: Focused on Expert Delphi Survey

The purpose of this paper is to establlish a theoretical system for the extra-curriculum by clarifying the purpose and role through the definition. To this end, three round Delphi Survey were conducted on the definition, purpose, and characteristics of the extra-curriculum from June 22 to August 17, 2022. Content validity(CVR), agreement, and convergence were analyze using the collected data. As a result of the study, first, the expert panel agreed on all programs and activities provided by universities except for regular curriculums for students’ self-development, supporting various experiences, and strengthening core competencies. Second, the purpose of the extra-curriculum can be divided into a university perspective and a student perspective, and the former aims to cultivate talent through core competencies and overall development of student, and achieve university educational goals. The latter aims to adapt to college life, prepare for career exploration, and social advancement, and develop competencies necessary for life. Third, the nature of the extra-curriculum agreed by the expert panel can be summarized in terms of curriculum operation (easy to open and abolish new program, wide selection of topics, various instructors) and student aspects(self-reliance, choice, individually, acceptance of various needs). Based on theses research results, the extra-curriculum of universities was reconceptualized, and proposals for the design and follow-up research of extra-curriculum of individual universities were discussed.
SimpLex: a lexical text simplification architecture

Text simplification (TS) is the process of generating easy-to-understand sentences from a given sentence or piece of text. The aim of TS is to reduce both the lexical (which refers to vocabulary complexity and meaning) and syntactic (which refers to the sentence structure) complexity of a given text or sentence without the loss of meaning or nuance. In this paper, we present SimpLex, a novel simplification architecture for generating simplified English sentences. To generate a simplified sentence, the proposed architecture uses either word embeddings (i.e., Word2Vec) and perplexity, or sentence transformers (i.e., BERT, RoBERTa, and GPT2) and cosine similarity. The solution is incorporated into a user-friendly and simple-to-use software. We evaluate our system using two metrics, i.e., SARI and Perplexity Decrease. Experimentally, we observe that the transformer models outperform the other models in terms of the SARI score. However, in terms of perplexity, the word embedding-based models achieve the biggest decrease. Thus, the main contributions of this paper are: (1) We propose a new word embedding and transformer-based algorithm for text simplification; (2) we design SimpLex—a modular novel text simplification system—that can provide a baseline for further research; and (3) we perform an in-depth analysis of our solution and compare our results with two state-of-the-art models, i.e., LightLS as reported by Glavaš and Štajner (in: Proceedings of the 53rd annual meeting of the association for computational linguistics and the 7th international joint conference on natural language processing, 2015) and NTS-w2v as reported by Nisioi et al. (in: Proceedings of the 55th annual meeting of the association for computational linguistics, 2017). We also make the code publicly available online.
Crane Cloud: A resilient multi-cloud service abstraction layer for resource-constrained settings

Developers and users situated in low-resource settings are faced with unique contextual and infrastructure challenges when accessing and consuming cloud-based services. In low-resource settings, access to cloud services and platforms is usually characterised by low-end computing devices and often unreliable and slow mobile broadband Internet connections. In this paper, we discuss key challenges for developing for and accessing cloud services in resource constrained settings, namely, (1) Frequent Internet partitions and bandwidth constraints, (2) Data jurisdiction restrictions, (3) Vendor lock-in, and (4) Poor quality of service. Inspired by these challenges, we propose a set of important design considerations and properties for a resilient multi-cloud service layer, that includes: (1) Containerisation and orchestration of applications, (2) Application placement and replication, (3) Portability and multi-cloud migration, (4) Resilience to network partitions and bandwidth constraints, (5) Automated service discovery and load balancing, (6) Localised image registry, and (7) Support for platform monitoring and management. We present an implementation and validation case study, Crane Cloud, an open source multi-cloud service abstraction layer built on-top of Kubernetes that is designed with inherent support for resilience to network partitions, microservice orchestration (deployment, scaling and management of containerized applications), a localized image registry, support for migration of services between private and public clouds to avoid vendor lock-in issues and platform monitoring. We evaluate the performance and user experience of Crane Cloud by implementing and deploying a computational and bandwidth intensive machine learning system. The results show lower response times of the system on Crane Cloud compared with hosting on other public clouds. The Crane Cloud platform is serving as a cloud-service for students and developers in low-resource settings and also as an education platform for cloud computing. .
Dynamic Cross-Task Representation Adaptation for Clinical Targets Co-Segmentation in CT Image-Guided Post-Prostatectomy Radiotherapy

Adjuvant and salvage radiotherapy after radical prostatectomy requires precise delineations of prostate bed (PB), i.e., the clinical target volume, and surrounding organs at risk (OARs) to optimize radiotherapy planning. Segmenting PB is particularly challenging even for clinicians, e.g., from the planning computed tomography (CT) images, as it is an invisible/virtual target after the operative removal of the cancerous prostate gland. Very recently, a few deep learning-based methods have been proposed to automatically contour non-contrast PB by leveraging its spatial reliance on adjacent OARs (i.e., the bladder and rectum) with much more clear boundaries, mimicking the clinical workflow of experienced clinicians. Although achieving state-of-the-art results from both the clinical and technical aspects, these existing methods improperly ignore the gap between the hierarchical feature representations needed for segmenting those fundamentally different clinical targets (i.e., PB and OARs), which in turn limits their delineation accuracy. This paper proposes an asymmetric multi-task network integrating dynamic cross-task representation adaptation (i.e., DyAdapt) for accurate and efficient co-segmentation of PB and OARs in one-pass from CT images. In the learning-to-learn framework, the DyAdapt modules adaptively transfer the hierarchical feature representations from the source task of OARs segmentation to match up with the target (and more challenging) task of PB segmentation, conditioned on the dynamic inter-task associations learned from the learning states of the feed-forward path. On a real-patient dataset, our method led to state-of-the-art results of PB and OARs co-segmentation. Code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/ladderlab-xjtu/DyAdapt</uri> .
Method for Determining the Similarity of Text Documents for the Kazakh language, Taking Into Account Synonyms: Extension to TF-IDF

The task of determining the similarity of text documents has received considerable attention in many areas such as Information Retrieval, Text Mining, Natural Language Processing (NLP) and Computational Linguistics. Transferring data to numeric vectors is a complex task where algorithms such as tokenization, stopword filtering, stemming, and weighting of terms are used. The term frequency - inverse document frequency (TF-IDF) is the most widely used term weighting method to facilitate the search for relevant documents. To improve the weighting of terms, a large number of TF-IDF extensions are made. In this paper, another extension of the TF-IDF method is proposed where synonyms are taken into account. The effectiveness of the method is confirmed by experiments on functions such as Cosine, Dice and Jaccard to measure the similarity of text documents for the Kazakh language.
INTERDISCIPLINARITY FOR FUTURE-ORIENTED EDUCATION: INSIGHT FROM EDUCATIONAL RESEARCH IN ENGLAND

Appears in: ICERI2022 Proceedings Publication year: 2022Page: 6509 (abstract only)ISBN: 978-84-09-45476-1ISSN: 2340-1095doi: 10.21125/iceri.2022.1629Conference name: 15th annual International Conference of Education, Research and InnovationDates: 7-9 November, 2022Location: Seville, Spain
ASSESSMENT OF ALGORITHMIC AND LOGICAL THINKING OF FIRST- AND SECOND-YEAR COMPUTER SCIENCE STUDENTS AT J. SELYE UNIVERSITY IN ACADEMIC YEARS 2019/20 AND 2021/22

ASSESSMENT OF ALGORITHMIC AND LOGICAL THINKING OF FIRST- AND SECOND-YEAR COMPUTER SCIENCE STUDENTS AT J. SELYE UNIVERSITY IN ACADEMIC YEARS 2019/20 AND 2021/22
An Automated Essay Scoring Based on Neural Networks to Predict and Classify Competence of Examinees in Community Academy

AES has been widely used in assessing student learning outcomes. However, few studies use Automated Essay Scoring (AES) to simultaneously determine the community academy's competency test scores and levels. This study aims to apply AES to assess essays on the competency certification test. The AES can predict the examinees' scores and classify examinees' competency levels. The method used to build AES uses Back Propagation Neural Networks (BPNN). BPNN was chosen because of its simplicity and ease in building the model. The results showed that the AES for predicting the examinee's competency value showed the MAE value is 0.061621 and the accuracy value is = 97.9665 %. The results of the classification of student competency levels show Accuracy= 0.9063, Precision= 0.9167, Recall= 0.8888, and F1 Score= 0.8857.
Asymptotically Tight Simulation of Universal Quantum Circuits.

but still asserting outstanding performance and tractable interface to higher-level quantum programming environments, such as IBM Qiskit, Microsoft Q\# and Google Cirq. Circumventing the disability of polymorphism in GPUs and leveraging the device-initiated one-sided communication, SV-Sim can process dynamically synthesized quantum circuit in a single GPU/CPU kernel without the need of expensive JIT or runtime branching, significantly improving the performance and simplifying the programming complexity for the emerging variational quantum algorithms. Evaluations on NVIDIA A100-DGX-1, V100-DGX-2, AMD MI100, ALCF Theta, and OLCF Summit HPCs show that SV-Sim can delivery scalable performance on various state-of-the-art HPC platforms, offering a useful tool for quantum algorithm validation and verification.
Challenging Mitosis Detection Algorithms: Global Labels Allow Centroid Localization

Mitotic activity is a crucial proliferation biomarker for the diagnosis and prognosis of different types of cancers. Nevertheless, mitosis counting is a cumbersome process for pathologists, prone to low reproducibility, due to the large size of augmented biopsy slides, the low density of mitotic cells, and pattern heterogeneity. To improve reproducibility, deep learning methods have been proposed in the last years using convolutional neural networks. However, these methods have been hindered by the process of data labelling, which usually solely consist of the mitosis centroids. Therefore, current literature proposes complex algorithms with multiple stages to refine the labels at pixel level, and to reduce the number of false positives. In this work, we propose to avoid complex scenarios, and we perform the localization task in a weakly supervised manner, using only image-level labels on patches. The results obtained on the publicly available TUPAC16 dataset are competitive with state-of-the-art methods, using only one training phase. Our method achieves an F1-score of 0.729 and challenges the efficiency of previous methods, which required multiple stages and strong mitosis location information.
3D path planning of underwater robot based on sparrow search algorithm with potential field heuristic

Aiming at the problem of large amount of calculation and difficulty in convergence of robot path planning in complex underwater 3D environments, the paper proposes a 3D path planning algorithm based on the sparrow search algorithm with potential field heuristic. First, establish an underwater 3D grid model, and use artificial potential field forces to generate an initial feasible path. Secondly, use the infinite folding chaotic map to expand the sparrow population and increase the diversity of the population.Then use the dynamic adjustment strategy to improve the location update formula of the sparrow search algorithm to adjust the search area range. Finally, the improved algorithm proposed is applied to solve the 3D path planning problem of underwater robots. The simulation results show that the optimization performance of the potential field sparrow search algorithm (PFSSA) proposed in this paper is better than the particle swarm optimization algorithm (PSO) and the sparrow search algorithm (SSA), and it can quickly obtain an optimal and safe feasible path, which proves the effectiveness of the method, and it has application value in the field of path planning of underwater robot.
Application of deblur technology for improving the clarity of digital subtractive angiography

Background Digital subtraction angiography (DSA) is most commonly used in vessel disease examinations and treatments. We aimed to develop a novel deep learning-based method to deblur the large focal spot DSA images, so as to obtain a clearer and sharper cerebrovascular DSA image. Methods The proposed network cascaded several residual dense blocks (RDBs), which contain dense connected layers and local residual learning. Several loss functions for image restoration were investigated. Our training set consisted of 52 paired images of angiography with more than 350,000 cropped patches. The testing set included 10 body phantoms and 80 clinical images of different types of diseases for subjective evaluation. All test images were acquired using a large focal spot, and phantom images were simultaneously acquired using a micro focal spot as ground-truth. Peak-to-noise ratio (PSNR) and structural similarity (SSIM) were determined for quantitative analysis. The deblurring results were compared with the original data, and the image quality was subjectively evaluated and graded by two clinicians. Results For quantitative analysis of phantom images, the average PSNR/SSIM based on the deep-learning approach (35.34/0.9566) was better than that of large focal spot images (30.64/0.9163). For subjective evaluation of 80 clinical patient images, image quality in all types of cerebrovascular diseases was also improved based on a deep-learning approach ( p &lt; 0.001). Conclusions Deep learning-based focal spot deblur algorithm can efficiently improve DSA image quality for better visualization of blood vessels and lesions in the image.
Searching for hidden objects in 3D environments

The vast majority of visual search paradigms tend to use targets that are directly visible. However, in natural conditions, the target could be stored inside some furniture. Does knowing that a target may be hidden inside another object (e.g., drawer, box, oven) affect search behavior? To answer that question we created 28 virtual scenes modeled with interactable furniture, so that, for instance, one could open a refrigerator or a cupboard to look at its content inside. Participants wore a virtual reality (VR) headset to look for objects in 3 blocks of 27 trials (40s max.). In the first block, targets were always visible, while in block 2 and 3 targets could be hidden inside objects as well as visible. Participants did not know they could use the VR controller to interact with the scene until the start of block 2. We hypothesized that knowing that objects could be hidden, would alter participants’ search strategies, for instance, slowing down search due to the increased search space. However, compared to trials in block 1, our analyses show that participants were no less efficient searching for visible targets when there was a possibility that they could be hidden. In blocks 1 and 2, we report a decrease in average saccade amplitudes for visible and hidden targets, which we ascribe to the exploration of containers. As expected, searching for actually hidden targets increased scanning times and scanpath lengths, but had no effect on search initiation, target verification times or search success. In sum, knowledge about hidden objects did not significantly alter search behavior for visible objects in 3D environments. This study is the first in a series of hidden object paradigms in VR. Future experiments will explicitly investigate the importance of scene grammar related to object search efficiency.
Input-driven chaotic dynamics in vortex spin-torque oscillator

A new research topic in spintronics relating to the operation principles of brain-inspired computing is input-driven magnetization dynamics in nanomagnet. In this paper, the magnetization dynamics in a vortex spin-torque oscillator driven by a series of random magnetic field are studied through a numerical simulation of the Thiele equation. It is found that input-driven synchronization occurs in the weak perturbation limit, as found recently. As well, chaotic behavior is newly found to occur in the vortex core dynamics for a wide range of parameters, where synchronized behavior is disrupted by an intermittency. Ordered and chaotic dynamical phases are examined by evaluating the Lyapunov exponent. The relation between the dynamical phase and the computational capability of physical reservoir computing is also studied.