Reviewerly/exHarmony huggingface dataset:
LitCovid ensemble learning for COVID-19 multi-label classification

The Coronavirus Disease 2019 (COVID-19) pandemic has shifted the focus of research worldwide, and more than 10 000 new articles per month have concentrated on COVID-19-related topics. Considering this rapidly growing literature, the efficient and precise extraction of the main topics of COVID-19-relevant articles is of great importance. The manual curation of this information for biomedical literature is labor-intensive and time-consuming, and as such the procedure is insufficient and difficult to maintain. In response to these complications, the BioCreative VII community has proposed a challenging task, LitCovid Track, calling for a global effort to automatically extract semantic topics for COVID-19 literature. This article describes our work on the BioCreative VII LitCovid Track. We proposed the LitCovid Ensemble Learning (LCEL) method for the tasks and integrated multiple biomedical pretrained models to address the COVID-19 multi-label classification problem. Specifically, seven different transformer-based pretrained models were ensembled for the initialization and fine-tuning processes independently. To enhance the representation abilities of the deep neural models, diverse additional biomedical knowledge was utilized to facilitate the fruitfulness of the semantic expressions. Simple yet effective data augmentation was also leveraged to address the learning deficiency during the training phase. In addition, given the imbalanced label distribution of the challenging task, a novel asymmetric loss function was applied to the LCEL model, which explicitly adjusted the negative-positive importance by assigning different exponential decay factors and helped the model focus on the positive samples. After the training phase, an ensemble bagging strategy was adopted to merge the outputs from each model for final predictions. The experimental results show the effectiveness of our proposed approach, as LCEL obtains the state-of-the-art performance on the LitCovid dataset. Database URL: https://github.com/JHnlp/LCEL.
MASK-MORPH: Does Morphing of Custom 3D Face Masks Threatens the Face Recognition Systems?

Face Recognition Systems (FRS) are vulnerable to morphing attacks that are targeted towards highly secured applications, including Automatic Border Control (ABC) gates. In this paper, we investigate a 3D-face custom silicone mask as the source for generating face morphing attacks for the first time. We present a systematic study to benchmark the attack potential of mask morphing (digital) attacks on both commercial and academic FRS. To this extent, a new dataset is constructed using eight custom 3D silicone face masks and corresponding bona fide face images captured using three different smartphones. The mask morphing is carried out using a landmark-based method, and the newly constructed dataset comprises 635 bona fide, 1034 face masks and 613 mask morphing face images. Extensive experiments are carried out to benchmark the attack potential and detection of mask morphing attacks on FRS.
Effects of design features of train electronic guidance interface on visual search, behavior, and usability

In urban rail trains, traditional printed guide information interfaces are being replaced by digital interfaces. An electronic guide screen is an important device for passengers to obtain information about the ride because the train is running underground. In particular, when the train is crowded, passengers must obtain information in advance and make appropriate decisions. Therefore, it is important to efficiently search for guidance interfaces. In this study, use scenarios of the guidance interface were simulated. The typical information layout and display mode in the current electronic guidance interface were the experimental variables. Through eye tracking and subjective evaluation, the effects of design features on user search performance, visual behavior, and usability satisfaction were discussed. The experimental results showed that the information layout and display mode of the guidance interface had an interactive effect on the task completion time, fixation counts, and usability score; they also had a significant effect on the recall accuracy and saccade ratio. Task completion time had a strong positive correlation with usability satisfaction and was correlated with visual behavior. The study confirmed that the split-screen display mode combined with interlaced layout could improve search performance and subjective satisfaction. Printed guide signs in subway cars are being replaced by electronic guide screens of different styles. Passengers must search for the required information within a limited period. In this study, the effects of different interface layouts and display modes on search performance, visual behavior, and usability were studied. This had a positive significance in improving user experience.
Undergraduate Research and Education in Quantum Machine Learning

This Work-In-Progress paper describes a program in quantum machine learning launched in the academic year of 2021-22. The program engaged undergraduate students from STEM areas with faculty and industry mentors. Because of the COVID-19 conditions, this undergraduate engagement was offered in a virtual format. In 2022, some face-to-face meetings with presentations were also held. The program included: a) training in machine learning with quantum simulators, b) weekly presentations, and c) semester end presentations. The assessment of the program included surveys, interviews, and presentation observations. Challenges and opportunities from virtual engagement were also part of the assessment.
On business adoption and use of reproducible builds for open and closed source software

Abstract Reproducible builds (R-Bs) are software engineering practices that reliably create bit-for-bit identical binary executable files from specified source code. R-Bs are applied in some open source software (OSS) projects and distributions to allow verification that the distributed binary has been built from the released source code. The use of R-Bs has been advocated in software maintenance and R-Bs are applied in the development of some OSS security applications. Nonetheless, industry application of R-Bs appears limited, and we seek to understand whether awareness is low or if significant technical and business reasons prevent wider adoption. Through interviews with software practitioners and business managers, this study explores the utility of applying R-Bs in businesses in the primary and secondary software sectors and the business and technical reasons supporting their adoption. We find businesses use R-Bs in the safety-critical and security domains, and R-Bs are valuable for traceability and support collaborative software development. We also found that R-Bs are valued as engineering processes and are seen as a badge of software quality, but without a tangible value proposition. There are good engineering reasons to use R-Bs in industrial software development, and the principle of establishing correspondence between source code and binary offers opportunities for the development of further applications.
Towards Quantum Control with Advanced Quantum Computing: A Perspective

We propose the combination of digital quantum simulation and variational quantum algorithms as an alternative approach to numerical methods for solving quantum control problems. As a hybrid quantum-classical framework, it provides an efficient simulation of quantum dynamics compared to classical algorithms, exploiting the previous achievements in digital quantum simulation. We analyze the trainability and the performance of such algorithms based on our preliminary works. We show that specific quantum control problems, e.g., finding the switching time for bang-bang control or the digital quantum annealing schedule, can already be studied in the noisy intermediate-scale quantum era. We foresee that these algorithms will contribute even more to quantum control of high precision if the hardware for experimental implementation is developed to the next level.
Modified Symmetric Image Encryption Approach Based on Mixed Column and Substitution Box

Internet a network of interconnected systems which is used to transmit different kinds of sensitive data is made secure by encrypting this data during transmission and protecting it against any kind of violation. This paper presents a symmetric data encryption algorithm developed in Python to encrypt images, making the transmission secure. The major computations performed in the algorithm are Mix Columns, Key dependent ex-or operation with upper and lower triangular sections of the matrix, Shift Rows, and Customized Sub Bytes transformation which provides diffusion and confusion. Results of various analysis performed on the algorithm prove its authenticity, confidentiality, and integrity.
Quantum Dynamic Optimization Algorithm for Neural Architecture Search on Image Classification

Deep neural networks have proven to be effective in solving computer vision and natural language processing problems. To fully leverage its power, manually designed network templates, i.e., Residual Networks, are introduced to deal with various vision and natural language tasks. These hand-crafted neural networks rely on a large number of parameters, which are both data-dependent and laborious. On the other hand, architectures suitable for specific tasks have also grown exponentially with their size and topology, which prohibits brute force search. To address these challenges, this paper proposes a quantum dynamic optimization algorithm to find the optimal structure for a candidate network using Quantum Dynamic Neural Architecture Search (QDNAS). Specifically, the proposed quantum dynamics optimization algorithm is used to search for meaningful architectures for vision tasks and dedicated rules to express and explore the search space. The proposed quantum dynamics optimization algorithm treats the iterative evolution process of the optimization over time as a quantum dynamic process. The tunneling effect and potential barrier estimation in quantum mechanics can effectively promote the evolution of the optimization algorithm to the global optimum. Extensive experiments on four benchmarks demonstrate the effectiveness of QDNAS, which is consistently better than all baseline methods in image classification tasks. Furthermore, an in-depth analysis is conducted on the searchable networks that provide inspiration for the design of other image classification networks.
The Study of DeFi in Virtual Community and Financial Supervision

In recent years, when the world economic activity was affected by the epidemic, it was very active in the virtual world. Since Facebook announced the development of the Metaverse, its related applications and services have instantly become hot issues. Although it may take a few years for the Metaverse to become popular as a social platform today, its impact on future community development will be significant. On the other hand, since Ethereum pioneered smart contracts in the blockchain, the Ethereum Virtual Machine (EVM) can connect public nodes scattered throughout the network into a virtual machine to execute Turing-complete programs, and the issue of decentralization has gradually spread. Today, as the application of smart contracts becomes more and more extensive and innovative, such as the decentralized architecture and applications of NFT, DeFi, GameFi, DApp, DAO, DEX, its influence has gradually expanded from the virtual world to physical applications, and also has impacts on real financial applications. The recent burst of NFT and DeFi addressed this issue. There are some gaps between reality and hype, and it is a lesson just like e-commerce burst some 20 years ago. A well format regulation and supervision seem required. However, the challenge for financial supervision today is the decentralized application and anonymity. This paper discusses the innovations in communities that related to decentralized financial services in the blockchain. And the blockchain financial supervision issues that within the tolerable range of risk and impact to both virtual and real world.
Federated Deep Reinforcement Learning-Based Caching and Bitrate Adaptation for VR Panoramic Video in Clustered MEC Networks

Virtual reality (VR) panoramic video is more expressive and experiential than traditional video. With the accelerated deployment of 5G networks, VR panoramic video has experienced explosive development. The large data volume and multi-viewport characteristics of VR panoramic videos make it more difficult to cache and transcode them in advance. Therefore, VR panoramic video services urgently need to provide powerful caching and computing power over the edge network. To address this problem, this paper establishes a hierarchical clustered mobile edge computing (MEC) network and develops a data perception-driven clustered-edge transmission model to meet the edge computing and caching capabilities required for VR panoramic video services. The joint optimization problem of caching and bitrate adaptation can be formulated as a Markov Decision Process (MDP). The federated deep reinforcement learning (FDRL) algorithm is proposed to solve the problem of caching and bitrate adaptation (called FDRL-CBA) for VR panoramic video services. The simulation results show that FDRL-CBA can outperform existing DRL-based methods in the same scenarios in terms of cache hit rate and quality of experience (QoE). In conclusion, this work developed a FDRL-CBA algorithm based on a data perception-driven clustered-edge transmission model, called Hierarchical Clustered MEC Networks. The proposed method can improve the performance of VR panoramic video services.
Hateful Memes Detection Based on Multi-Task Learning

With the popularity of posting memes on social platforms, the severe negative impact of hateful memes is growing. As existing detection models have lower detection accuracy than humans, hateful memes detection is still a challenge to statistical learning and artificial intelligence. This paper proposed a multi-task learning method consisting of a primary multimodal task and two unimodal auxiliary tasks to address this issue. We introduced a self-supervised generation strategy in auxiliary tasks to generate unimodal auxiliary labels automatically. Meanwhile, we used BERT and RESNET as the backbone for text and image classification, respectively, and then fusion them with a late fusion method. In the training phase, the backward guidance technique and the adaptive weight adjustment strategy were used to capture the consistency and variability between different modalities, numerically improving the hateful memes detection accuracy and the generalization and robustness of the model. The experiment conducted on the Facebook AI multimodal hateful memes dataset shows that the prediction accuracy of our model outperformed the comparing models.
A surrogate-assisted variable grouping algorithm for general large-scale global optimization problems

Problem decomposition plays an important role when applying cooperative coevolution (CC) to large-scale global optimization problems. However, most learning-based decomposition algorithms only apply to additively separable problems, while the others insensitive to problem type perform low decomposition accuracy and efficiency. Given this limitation, this study designs a general-separability-oriented detection criterion, and further proposes a novel decomposition algorithm called surrogate-assisted variable grouping (SVG). The new criterion detects the separability between a variable and some other variables by checking whether its optimum changes with the latter. Consistent with the definition of general separability, this criterion endows SVG with strong applicability and high accuracy. To reduce expensive fitness evaluations, SVG locates the optimum of a variable with the help of a surrogate model rather than the original high-dimensional model. Moreover, it converts the variable-grouping process into a search process in a binary tree by taking variable subsets as tree nodes. This facilitates the reutilization of historical separability information, thereby reducing separability detection times. Experimental results on a general benchmark suite indicate that compared with six state-of-the-art decomposition algorithms, SVG achieves higher accuracy and efficiency on both additively and nonadditively separable problems. Furthermore, it can significantly enhance the optimization performance of CC.
Interface critique at large

This article considers how the pursuit of problematization advocated by Agre’s concept of critical technical practice has been articulated in relation to the increasing proliferation of interfaces across everyday life. While the ethos of Agre’s work to bridge the split identity of critique and craft can readily be found in reflexive design or software art, these cases are not always situated within broader ecologies of practice that also grapple with the asymmetries and exploitative aspects of interface design. Drawing from software studies and media theoretical accounts of the interface as a fluid milieu, I provide a navigational matrix to contextualize modes of interface critique at large, namely specifying traps and enclosures, surfacing asymmetries and augmenting alternatives. I argue, finally, that these modes provide an invitation to develop new metacritical theories and common capacities, particularly through the possibilities of grappling with systems of domination otherwise built to prefigure our experiences of them.
Engaging in cybersecurity proactive behavior: awareness in COVID-19 age

Purpose Due to the lack of awareness and poor cybersecurity practices that pose cyber threats during COVID-19 time, this research aims to explore user's attitude toward engaging in proactive cybersecurity awareness behavior. Design/methodology/approach Based on the theory of planned behavior, the relationship between multiple factors and their influence on the attitude is explored. A survey-based approach was utilized to collect responses and a model was proposed and tested on 229 respondents from the University of Petra-Jordan. Findings The attitude was significantly influenced by peers' influence and the individuals' cybersecurity threats awareness, especially threats that emerged during the COVID-19 time. Research limitations/implications The research benefits decision makers in educational institutions who intend to develop cybersecurity awareness programs and helps them to assess user cybersecurity background weaknesses. Originality/value The research is the first to explore users' knowledge dimensions including organizational, information systems and social media as well as peers' influence on cybersecurity awareness. Also, it sheds light on the users’ perception of major cybersecurity hazards in COVID-19 time.
ThaiTC:Thai Transformer-based Image Captioning

For problems with image captioning is a technique that has been used for a long time. In the past, there was a way to use convolutional neural network (CNN) for feature extraction and recurrent neural network (RNN) for generating text, and especially in Thai language, It has to be developed further in the era of the popular use of transformers. This paper proposes an end-to-end image captioning with pretrained vision Transformers (ViT) and text transformers in Thai language models namely ThaiTC, Which leverages the transformer architecture both. We has experiment pretrained vision transformer and text transformer in Thai language that best for Thai image captioning and tested on 3 Thai image captioning datasets 1) Travel 2) Food 3) Flickr 30k(t <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$r$</tex> anslate) with different challenges. Includes freeze vision transformers weight training for image captioning dataset training with less image features, From the experiment, We found that ThaiTC performed much better in the Food and Flickr30k datasets than the Travel datasets, Which allowed us to automatically create subtitles about food and travel.
Artificial intelligence techniques for neuropathological diagnostics and research

Artificial intelligence (AI) research began in theoretical neurophysiology, and the resulting classical paper on the McCulloch-Pitts mathematical neuron was written in a psychiatry department almost 80 years ago. However, the application of AI in digital neuropathology is still in its infancy. Rapid progress is now being made, which prompted this article. Human brain diseases represent distinct system states that fall outside the normal spectrum. Many differ not only in functional but also in structural terms, and the morphology of abnormal nervous tissue forms the traditional basis of neuropathological disease classifications. However, only a few countries have the medical specialty of neuropathology, and, given the sheer number of newly developed histological tools that can be applied to the study of brain diseases, a tremendous shortage of qualified hands and eyes at the microscope is obvious. Similarly, in neuroanatomy, human observers no longer have the capacity to process the vast amounts of connectomics data. Therefore, it is reasonable to assume that advances in AI technology and, especially, whole-slide image (WSI) analysis will greatly aid neuropathological practice. In this paper, we discuss machine learning (ML) techniques that are important for understanding WSI analysis, such as traditional ML and deep learning, introduce a recently developed neuropathological AI termed PathoFusion, and present thoughts on some of the challenges that must be overcome before the full potential of AI in digital neuropathology can be realized.
Chinese short news text classification based on BERT and sparse autoencoder

Short news text classification plays an import role in natural language processing as the popularity of mobile phones. In this paper we propose a Chinese short news text classification method based on BERT and sparse autoencoder, regarding the overfitting caused by pretrained BERT. We use the BERT for text representation, the output vectors of BERT are dimension reduced through the sparse autoencoder, and then the Softmax classifier takes the reduced vectors as input to get the prediction of the input text. Experimental results show that our method mitigate the unbalance of the performance of different categories, raises the overall classification performance by six percentage, effectively alleviates the overfitting of text representation of BERT, and achieve a better Chinese short text classification performance than using naïve autoencoder and without autoencoder.
Incremental High Fuzzy Utility Itemset Mining

In data mining, frequent-pattern mining methods are used for handling binary databases. Utility mining addresses this limitation by considering the item utilities and quantities when discovering the high utility itemsets. In addition, to make these high utility patterns more human-explainable, we use the fuzzy-set theory to the utility mining algorithms. However, real-world databases are usually dynamic. That is, new transactions may be intermittently added, and the corresponding mined knowledge needs to be updated. This paper uses the famous incremental strategy, fast update (FUP), to modify high fuzzy utility itemsets from new coming data. We implement the FUP strategy on the Apriori-based approach for maintaining up-to-date high fuzzy utility itemsets.We also conducted experiments to demonstrate that the incremental algorithm significantly outperforms the Apriori-based batch mining method.