Reviewerly/exHarmony huggingface dataset:
Multi-National Cyber Security Exercise, Case Flagship 2

Cyber security is not merely about securing devices and focusing on software and hardware. Staff members with skills and know-how are among the most valuable assets in the context of cyber security. Globally, there is a lack of competent cyber security experts available and cyber security skills should be educated more widely. One of the most effective practices for training cyber security experts is a cyber security exercise. During a cyber security exercise, the learning audience train their skills with a realistic scenario depicting a hectic and stressful cyber incident or cyber attack. In order to successfully implement a cyber security exercise, there must be sufficient technical infrastructure mimicking required systems and networks. The infrastructure should allow the use of realistic threat actors with realistic attack vectors and real malware without compromising any production environments. Facilities offering such infrastructure are widely known as the cyber ranges. There are two special requirements raised by modern cyber range exercises: (i) cyber range collaboration, including capabilities for sharing and pooling cyber range services, and (ii) on-line cyber security exercises without restrictions of being on-site on the exercise premises. The requirement of implementing on-line exercises has increased especially after the spread of COVID-19 pandemic. In this study, we introduce Flagship 2, a multinational state-of-the-art on-line cyber security exercise based on cyber range federation. We analyse the technical implementation of the cyber range federation and the learning outcomes of the exercise event based on a participant survey and relevant theories. The analysed results are explained with identified future research topics.
Blockchain Technology and Its Application in Higher Education

Blockchain technology is considered to be one of the most promising technologies for reconstructing education in the 21st century. Compared with the rapid development of online education, traditional institutions show disadvantages in school management, recording students’ learning process and certification. This paper presents the combination of blockchain technology and education, that makes the results and processes of education real and credible, and the decentralized education platform also helps to form a new teaching evaluation model and an open sharing system of digital teaching resources. So as to realize the learning achievement certification and credit mutual recognition of learners in different educational institutions, at different times and in different places, and eventually, stimulate learner to achieve lifelong learning goals.
A Deep Learning-Driven Approach to Personalized English Online Learning Resource Recommendation

The deep learning driver can help to realize a personalized online learning resource recommendation system for English, which is conducive to the efficiency and quality of online English learning. Therefore, this paper will carry out relevant research. This study first introduces the basic concept of deep learning and its driving mechanism, and then puts forward the implementation method of English personalized online learning resource recommendation system in the way of system design. Through the research, the personalized online learning resource recommendation system for English is implemented by relying on deep learning. The system recommendation accuracy is above 96%, which can provide help for the majority of online English learners.
The Importance of a Quality Assessment for Educational Applications

Educational applications (learning apps) are becoming increasingly common in the education sector and many students ask themselves which app can assist in their learning process, promote their academic success, and improve their performance. However, there is an incredibly large selection of apps, and their quality and content are often questionable. To sift through and evaluate the entire learning apps, the Austrian Ministry for Education, Research, and Science tries to certify suitable learning apps with a quality seal in a uniform certification process. This paper provides an overview of the Austrian certification process of learning apps and points out its advantages for being a possible role model. Concerning the importance of a general quality seal for all learning apps, two non-certified ones that are widely used by students and teachers in the German-speaking area are compared and analyzed with the Austrian evaluation grid used in the certification process. In addition, after being introduced to the two learning apps in school, 244 students were asked whether they or their teachers would continue to use them for their learning process. To get the opinions of the students, an online questionnaire with open-ended and closed-ended questions was created and evaluated. According to the opinion of the students, most of the respondents would continue to use the two learning apps for studying and think that their teachers would use them in class. However, the findings also show that some students are hesitant about the utilization of learning apps for their studies, because of the apps’ false content or advertisements. Some of the mentioned concerns are exclusion criteria for the Austrian seal of quality. Therefore, after comparison with the evaluation grid, executed by three teachers, and based on the student’s perceptions, possible disadvantages of non-certified applications are pointed out. It can be assumed that certification criteria for learning apps are necessary. Moreover, this information is important not only for students but also for parents, teachers, and app developers. Further studies comparing certified and non-certified applications will take place in 2023.
Clinical Concept-Based Radiology Reports Classification Pipeline for Lung Carcinoma

Rising incidence and mortality of cancer have led to an incremental amount of research in the field. To learn from preexisting data, it has become important to capture maximum information related to disease type, stage, treatment, and outcomes. Medical imaging reports are rich in this kind of information but are only present as free text. The extraction of information from such unstructured text reports is labor-intensive. The use of Natural Language Processing (NLP) tools to extract information from radiology reports can make it less time-consuming as well as more effective. In this study, we have developed and compared different models for the classification of lung carcinoma reports using clinical concepts. This study was approved by the institutional ethics committee as a retrospective study with a waiver of informed consent. A clinical concept-based classification pipeline for lung carcinoma radiology reports was developed using rule-based as well as machine learning models and compared. The machine learning models used were XGBoost and two more deep learning model architectures with bidirectional long short-term neural networks. A corpus consisting of 1700 radiology reports including computed tomography (CT) and positron emission tomography/computed tomography (PET/CT) reports were used for development and testing. Five hundred one radiology reports from MIMIC-III Clinical Database version 1.4 was used for external validation. The pipeline achieved an overall F1 score of 0.94 on the internal set and 0.74 on external validation with the rule-based algorithm using expert input giving the best performance. Among the machine learning models, the Bi-LSTM_dropout model performed better than the ML model using XGBoost and the Bi-LSTM_simple model on internal set, whereas on external validation, the Bi-LSTM_simple model performed relatively better than other 2. This pipeline can be used for clinical concept-based classification of radiology reports related to lung carcinoma from a huge corpus and also for automated annotation of these reports.
A cognitive emotion model enhanced sequential method for social emotion cause identification

Social emotion refers to the emotion evoked to the reader by a textual document. In contrast to the emotion cause extraction task which analyzes the cause of the author's sentiments based on the expressions in text, identifying the causes of social emotion evoked to the reader from text has not been explored previously. Social emotion mining and its cause analysis is not only an important research topic in Web-based social media analytics and text mining but also has a number of applications in multiple domains. As the focus of social emotion cause identification is on analyzing the causes of the reader's emotions elicited by a text that are not explicitly or implicitly expressed, it is a challenging task fundamentally different from the previous research. To tackle this, it also needs a deeper level understanding of the cognitive process underlying the inference of social emotion and its cause analysis. In this paper, we propose the new task of social emotion cause identification (SECI). Inspired by the cognitive structure of emotions (OCC) theory, we present a Cognitive Emotion model Enhanced Sequential (CogEES) method for SECI. Specifically, based on the implications of the OCC model, our method first establishes the correspondence between words/phrases in text and emotional dimensions identified in OCC and builds the emotional dimension lexicons with 1,676 distinct words/phrases. Then, our method utilizes lexicons information and discourse coherence for the semantic segmentation of document and the enhancement of clause representation learning. Finally, our method combines text segmentation and clause representation into a sequential model for cause clause prediction. We construct the SECI dataset for this new task and conduct experiments to evaluate CogEES. Our method outperforms the baselines and achieves over 10% F1 improvement on average, with better interpretability of the prediction results.
Modern impressions: Getting to know your library website users

Academic library websites need to support a variety of users (student, faculty, staff, administrator, alumni, etc.) with intellectual pursuits related to research, teaching, and learning, in addition to being user-friendly, valuable and informative. Although the CUNY Queens College Library (QCL) website has been redesigned multiple times, there has not been a formal user research study of the website and its users to inform the redesign. Taking a user-centered approach, specifically the user-centered design (UCD) methodology, this study explores users of an urban academic library website in the United States through the use of triangulation, a mixed-methods approach that includes user survey, user interviews, and website analysis. The findings reveal that the majority of users like the QCL website and find the content valuable for teaching and research, but users also thought the website design was dated (not a modern design), some of the content was inconsistent, and access of all types emerged as a major theme. The results provide recommendations for the library website redesign and are important in helping librarians of similar libraries use triangulation of data to gain a multidimensional understanding of their users in order to inform the design and development of library websites.
The cost of privacy on blockchain: A study on sealed-bid auctions

In order to preserve privacy in a blockchain ecosystem, the main objective is to keep a transaction's data private, such as the sender, the receiver, and the amount transferred. The current work studies the cryptographic tools commonly used to achieve this type of privacy, primarily focusing on the Ethereum blockchain. Such tools usually require many computational and storage resources, leading to additional fees. An anonymous auction protocol was developed as a case study to explore these costs, where hiding the identity and the amount of the bids utilizes a variety of cryptographic primitives. The proposed implementation was compared against three sealed-bid auction protocols, which utilize similar cryptographic tools for preserving privacy throughout the auction process. The results show that providing an additional level of anonymity, such as hiding someone's identity, can increase the gas cost significantly, up to 2.5 times, depending on the choice of the cryptographic tools, which determine the usage of the blockchain's storage and computational resources. By adjusting the level of decentralization on the application level by moving some operations off-chain and maintaining the role of the auctioneer, we show that we can maintain anonymity while reducing the gas cost by 40%.
One-generator quasi-cyclic codes and their dual codes

In this article, we study 1-generator quasi-cyclic (QC) codes over fields. QC codes of index l can be viewed as linear codes of length l. We give a description of the dual codes of 1-generator QC codes of index 2. Moreover, we obtain examples of quantum error-correcting codes (QECCs) from this study. They have better parameters than those from the online database. We consider self orthogonality conditions on such codes. We also construct some self orthogonal best known linear codes (BKLCs) linear codes, and using them we compute quantum codes. Using our symplectic study of QC codes, we are presenting three quantum codes with record-breaking parameters which improve the current record.
Dispensing of quantum information beyond no-broadcasting theorem—is it possible to broadcast anything genuinely quantum?

No-broadcasting theorem is one of the most fundamental results in quantum information theory; it guarantees that the simplest attacks on any quantum protocol, based on eavesdropping and copying of quantum information, are impossible. Due to the fundamental importance of the no-broadcasting theorem, it is essential to understand the exact boundaries of this limitation. We generalize the standard definition of broadcasting by restricting the set of states which we want to broadcast and restricting the sets of measurements which we use to test the broadcasting. We show that in some of the investigated cases broadcasting is equivalent to commutativity, while in other cases commutativity is not necessary.
A novel chaotic DICOM image encryption algorithm based on pseudo-random polyline and rapid diffusion-scrambling

Abstract This paper proposes a novel chaotic DICOM image encryption algorithm based on pseudo-random polyline and rapid diffusion-scrambling. This paper proposes to preprocess the image twice in the encryption process to ensure that any type of image can be encrypted, including images with negative pixel values, which increases the practicability of the algorithm. Two pseudo-random polylines are generated to segment the image during the encryption process. The pseudo-random polylines are generated from the information of the plaintext image, which ensures that each image produces different pseudo-random polylines and increases the security of the algorithm. In the encryption process, rapid diffusion-scrambling is used to avoid the waste of extra space. And piecewise diffusion and non-adjacent matrix reading are adopted to make the encryption more unpredictable. The scheme is analyzed from the performance indicators such as pixel correlation and key space, and the results demonstrated strong security and performance of the algorithm.
Complementary Feature Pyramid Network for Object Detection

The way of constructing a robust feature pyramid is crucial for object detection. However, existing feature pyramid methods, which aggregate multi-level features by using element-wise sum or concatenation, are inefficient to construct a robust feature pyramid. The reason is that these methods cannot be effective in discriminating the relevant semantics of objects. In this article, we propose a Complementary Feature Pyramid Network (CFPN) to aggregate multi-level features selectively and efficiently by exploring complementary information between multi-level features. Specifically, a Spatial Complementary Module (SCM) and a Channel Complementary Module (CCM) are designed and embedded in CFPN to enhance useful information and suppress irrelevant information during feature fusions along spatial and channel dimensions, respectively. CFPN is a generic feature extractor, as evidenced by its seamless integration into single-stage, two-stage, and end-to-end object detectors. Experiments conducted on the COCO and Pascal VOC datasets demonstrate that integrating our CFPN into RetinaNet, Faster RCNN, Cascade RCNN, and Sparse RCNN obtains consistent performance improvements with negligible overheads. Code and models are available at: https://github.com/VIPLab-CQU/CFPN .
Objective classification of high‐resolution geophysical data: Empowering the next generation of mineral exploration in Sierra Leone

Abstract In 2019, a nationwide airborne geophysical survey of Sierra Leone was flown at 150 m nominal line spacing and 50 m nominal terrain clearance. Contractual deliverables included magnetic, radiometric and supporting data streams. The primary aim of the geophysical survey was to provide a national geoscientific benchmark for resource management by the National Minerals Agency and to encourage foreign investment in the rich mining potential of Sierra Leone. Geophysical interpretation is often highly subjective. This can be helpful when an interpreter is skilled in the project area and exploration targets being sought but can be constraining and even dangerous when exploring new areas, and for new commodities. Objective classification tools can challenge interpretive bias and enhance geological understanding, both locally and regionally. We present self‐organizing maps and neural network products generated from the Sierra Leone geophysical dataset which both confirm and challenge the validity of conventional, subjective interpretation workflows. We demonstrate the usefulness of self‐organizing maps as a tool for inferring complementary geological information and show some initial results of convolution neural network classification of a reduced‐to‐equator grid to locate magnetic lineaments in plan and in depth. Moreover, we show initial results of self‐organizing map clustering as a tool for joint analysis of multiphysics datasets and construction of a pseudolithological cluster map.
Out-of-Distribution Detection Without Class Labels

Out-of-distribution detection seeks to identify novelties, samples that deviate from the norm. The task has been found to be quite challenging, particularly in the case where the normal data distribution consist of multiple semantic classes (e.g. multiple object categories). To overcome this challenge, current approaches require manual labeling of the normal images provided during training. In this work, we tackle multi-class novelty detection without class labels. Our simple but effective solution consists of two stages: we first discover "pseudo-class" labels using unsupervised clustering. Then using these pseudo-class labels, we are able to use standard supervised out-of-distribution detection methods. We verify the performance of our method by favorable comparison to the state-of-the-art, and provide extensive analysis and ablations.
Toward a taxonomy of trust for probabilistic machine learning

Probabilistic machine learning increasingly informs critical decisions in medicine, economics, politics, and beyond. To aid the development of trust in these decisions, we develop a taxonomy delineating where trust in an analysis can break down: (i) in the translation of real-world goals to goals on a particular set of training data, (ii) in the translation of abstract goals on the training data to a concrete mathematical problem, (iii) in the use of an algorithm to solve the stated mathematical problem, and (iv) in the use of a particular code implementation of the chosen algorithm. We detail how trust can fail at each step and illustrate our taxonomy with two case studies. Finally, we describe a wide variety of methods that can be used to increase trust at each step of our taxonomy. The use of our taxonomy highlights not only steps where existing research work on trust tends to concentrate and but also steps where building trust is particularly challenging.
EESRNet: A Network for Energy Efficient Super-Resolution

Recently, various high-performance video super-resolution methods have been proposed. However, deployment on mobile phones is cumbersome due to the limitations of mobile phones' power consumption and computing power. We find methods that exploit temporal information in videos (e.g. optical flow) require huge energy consumption. Therefore, we use hidden features to preserve temporal information. Besides, the energy-efficient super-resolution network (EESRNet) is obtained by removing the residual connections in the Anchor-Based Plain Network (ABPN) [8]. Combining the two, we propose a Temporal Energy Efficient Super-Resolution Network (TEESRNet), which can efficiently utilize video spatio-temporal information with low energy consumption. Experiments show that for EESRNet, compared with ABPN, the latency is reduced by more than $$40\%$$ , while performance decreases slightly. Furthermore, for TEESRNet, the PSNR is improved by 0.24 dB and 1.19 dB compared to EESRNet and RRN [19] respectively, while still maintaining real-time (<30 ms).
Real-Time Under-Display Cameras Image Restoration and HDR on Mobile Devices

The new trend of full-screen devices implies positioning the camera behind the screen to bring a larger display-to-body ratio, enhance eye contact, and provide a notch-free viewing experience on smartphones, TV or tablets. On the other hand, the images captured by under-display cameras (UDCs) are degraded by the screen in front of them. Deep learning methods for image restoration can significantly reduce the degradation of captured images, providing satisfying results for the human eyes. However, most proposed solutions are unreliable or efficient enough to be used in real-time on mobile devices. In this paper, we aim to solve this image restoration problem using efficient deep learning methods capable of processing FHD images in real-time on commercial smartphones while providing high-quality results. We propose a lightweight model for blind UDC Image Restoration and HDR, and we also provide a benchmark comparing the performance and runtime of different methods on smartphones. Our models are competitive on UDC benchmarks while using $$\times 4$$ less operations than others. To the best of our knowledge, we are the first work to approach and analyze this real-world single image restoration problem from the efficiency and production point of view.
CIDBNet: A Consecutively-Interactive Dual-Branch Network for JPEG Compressed Image Super-Resolution

Compressed image super-resolution (SR) task is useful in practical scenarios, such as mobile communication and the internet, where images are usually downsampled and compressed due to limited bandwidth and storage capacity. However, a combination of compression and downsampling degradations makes the SR problem more challenging. To restore high-quality and high-resolution images, local context and long-range dependency modeling are both crucial. In this paper, for JPEG compressed image SR, we propose a consecutively-interactive dual-branch network (CIDBNet) to take advantage of both convolution and transformer operations, which are good at extracting local features and global interactions, respectively. To better aggregate the two-branch information, we newly introduce an adaptive cross-branch fusion module (ACFM), which adopts a cross-attention scheme to enhance the two-branch features and then fuses them weighted by a content-adaptive map. Experiments show the effectiveness of CIDBNet, and in particular, CIDBNet achieves higher performance than a larger variant of HAT (HAT-L).