Reviewerly/exHarmony huggingface dataset:
Container Technologies for ARM Architecture: A Comprehensive Survey of the State-of-the-Art

Container technology is becoming increasingly popular as an alternative to traditional virtual machines because it provides a faster, lighter, and more portable runtime environment for the applications. A container bundles the application and its binary code, libraries, and configuration files together while sharing the host operating system image. Accordingly, containers efficiently share resources and operate small micro-services, software programs, and even more extensive applications with less overhead than virtual machines. There are many container technologies available with Docker being the most popular and many technologies support multiple architectures, including the ARM architecture. Due to its energy efficiency and high-performance, which are crucial parameters in containerization, ARM architecture is becoming prevalent in container technologies. In this paper, we explore various container technologies that support ARM architecture and investigate the pros and cons of each technology. Moreover, we provide a comparative analysis of both container orchestrators and container runtimes that are most prominent competitors of Docker. We also consider security of container technologies with particular focus on the image scanning tools that supports ARM architecture. Our survey reveals that ARM technology is gaining popularity in containerization and almost all recent technologies support ARM architecture.
Polyp-Mixer: An Efficient Context-Aware MLP-Based Paradigm for Polyp Segmentation

Precise and efficient polyp segmentation plays a crucial role in colonoscopy, which is important for the prevention of colorectal cancer. Despite CNN-based methods have achieved great progress in the polyp segmentation task, they are incapable of modeling long-range dependencies. Transformer-based models utilize self-attention mechanism to overcome this problem while suffering from heavy computing cost. Benefiting from simple structures, MLP-based models seem to be an alternative. However, they struggle with dealing with flexible input scales and modeling long-term dependencies. Both of these two factors are important for image segmentation, which could explain why the MLP architecture performs poorly compared to the Transformer. To remedy this issue, we propose a novel Polyp-Mixer, which utilizes MLP-based structures in both encoder and decoder. In particular, we use CycleMLP as the encoder to overcome the fixed input scale issue. Besides, we propose a Multi-head Mixer by converting the current CycleMLP into a Multi-head fashion, allowing our model to explore rich context information from various subspaces. In addition, we build a powerful Contextual Bridger Module between the encoder and decoder, which can capture semantics from larger receptive fields and combine them with various decoder layers. Experiments demonstrate the proposed method with fewer parameters ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sim 16\text{M}$ </tex-math></inline-formula> ) achieves SOTA on 4 public benchmarks. Our code will be released at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/shijinghuihub/Polyp-Mixer</uri>
Minimally overfitted learners: A general framework for ensemble learning

The combination of Machine Learning (ML) algorithms is a solution for constructing stronger predictors than a single one. However, some approximations suggest that combining unstable algorithms provides better results than combining stable algorithms. For instance, Generative ensembles, based on re-sampling techniques, have demonstrated high performance by fusing the information of unstable base learners. Random Forest and Gradient Boosting are two well-known examples, both combining Decision Trees and providing better predictions than those obtained using a single tree. However, such successful results have not been achieved by assembling stable algorithms. This paper introduces the notion of limited learner and a new ensemble general framework called Minimally Overfitted Ensemble (MOE), a re-sampling-based ensemble approach that constructs slightly overfitted-based learners. The proposed framework works well with stable and unstable base algorithms, thanks to a Weighted RAndom Bootstrap (WRAB) sampling that provides the necessary diversity for the stable base algorithms. A hyperparameter analysis of the proposal is carried out on artificial data. Besides, its performance is evaluated on real datasets against well-known ML methods. The results confirm that the MOE framework works successfully using stable and unstable base algorithms, improving in most cases the predictive ability of single ML models and other ensemble methods.
EXTR: Click-Through Rate Prediction with Externalities in E-Commerce Sponsored Search

Click-Through Rate (CTR) prediction, estimating the probability of a user clicking on items, plays a key fundamental role in sponsored search. E-commerce platforms display organic search results and advertisements (ads), collectively called items, together as a mixed list. The items displayed around the predicted ad, i.e. external items, may affect the user clicking on the predicted. Previous CTR models assume the user click only relies on the ad itself, which overlooks the effects of external items, referred to as external effects, or externalities. During the advertising prediction, the organic results have been generated by the organic system, while the final displayed ads on multiple ad slots have not been figured out, which leads to two challenges: 1) the predicted (target) ad may win any ad slot, bringing about diverse externalities. 2) external ads are undetermined, resulting in incomplete externalities. Facing the above challenges, inspired by the Transformer, we propose EXternality TRansformer (EXTR) which regards target ad with all slots as query and external items as key&value to model externalities in all exposure situations in parallel. Furthermore, we design a Potential Allocation Generator (PAG) for EXTR, to learn the allocation of potential external ads to complete the externalities. Extensive experimental results on Alibaba datasets demonstrate the effectiveness of externalities in the task of CTR prediction and illustrate that our proposed approach can bring significant profits to the real-world e-commerce platform. EXTR now has been successfully deployed in the online search advertising system in Alibaba, serving the main traffic.
Transfer from spatial education to verbal reasoning and prediction of transfer from learning-related neural change

Current debate surrounds the promise of neuroscience for education, including whether learning-related neural changes can predict learning transfer better than traditional performance-based learning assessments. Longstanding debate in philosophy and psychology concerns the proposition that spatial processes underlie seemingly nonspatial/verbal reasoning (mental model theory). If so, education that fosters spatial cognition might improve verbal reasoning. Here, in a quasi-experimental design in real-world STEM classrooms, a curriculum devised to foster spatial cognition yielded transfer to improved verbal reasoning. Further indicating a spatial basis for verbal transfer, students’ spatial cognition gains predicted and mediated their reasoning improvement. Longitudinal fMRI detected learning-related changes in neural activity, connectivity, and representational similarity in spatial cognition–implicated regions. Neural changes predicted and mediated learning transfer. Ensemble modeling demonstrated better prediction of transfer from neural change than from traditional measures (tests and grades). Results support in-school “spatial education” and suggest that neural change can inform future development of transferable curricula.
FedAttack: Effective and Covert Poisoning Attack on Federated Recommendation via Hard Sampling

Federated learning (FL) is a feasible technique to learn personalized recommendation models from decentralized user data. Unfortunately, federated recommender systems are vulnerable to poisoning attacks by malicious clients. Existing recommender system poisoning methods mainly focus on promoting the recommendation chances of target items due to financial incentives. In fact, in real-world scenarios, the attacker may also attempt to degrade the overall performance of recommender systems. However, existing general FL poisoning methods for degrading model performance are either ineffective or not concealed in poisoning federated recommender systems. In this paper, we propose a simple yet effective and covert poisoning attack method on federated recommendation, named FedAttack. Its core idea is using globally hardest samples to subvert model training. More specifically, the malicious clients first infer user embeddings based on local user profiles. Next, they choose the candidate items that are most relevant to the user embeddings as hardest negative samples, and find the candidates farthest from the user embeddings as hardest positive samples. The model gradients inferred from these poisoned samples are then uploaded for aggregation. Extensive experiments on two benchmark datasets show that FedAttack can effectively degrade the performance of various federated recommender systems, meanwhile cannot be effectively detected nor defended by many existing methods.
COKG-QA: Multi-hop Question Answering over COVID-19 Knowledge Graphs

Abstract COVID-19 evolves rapidly and an enormous number of people worldwide desire instant access to COVID-19 information such as the overview, clinic knowledge, vaccine, prevention measures, and COVID-19 mutation. Question answering (QA) has become the mainstream interaction way for users to consume the ever-growing information by posing natural language questions. Therefore, it is urgent and necessary to develop a QA system to offer consulting services all the time to relieve the stress of health services. In particular, people increasingly pay more attention to complex multi-hop questions rather than simple ones during the lasting pandemic, but the existing COVID-19 QA systems fail to meet their complex information needs. In this paper, we introduce a novel multi-hop QA system called COKG-QA, which reasons over multiple relations over large-scale COVID-19 Knowledge Graphs to return answers given a question. In the field of question answering over knowledge graph, current methods usually represent entities and schemas based on some knowledge embedding models and represent questions using pre-trained models. While it is convenient to represent different knowledge (i.e., entities and questions) based on specified embeddings, an issue raises that these separate representations come from heterogeneous vector spaces. We align question embeddings with knowledge embeddings in a common semantic space by a simple but effective embedding projection mechanism. Furthermore, we propose combining entity embeddings with their corresponding schema embeddings which served as important prior knowledge, to help search for the correct answer entity of specified types. In addition, we derive a large multi-hop Chinese COVID-19 dataset (called COKG-DATA for remembering) for COKG-QA based on the linked knowledge graph OpenKG-COVID19 launched by OpenKG①, including comprehensive and representative information about COVID-19. COKG-QA achieves quite competitive performance in the 1-hop and 2-hop data while obtaining the best result with significant improvements in the 3-hop. And it is more efficient to be used in the QA system for users. Moreover, the user study shows that the system not only provides accurate and interpretable answers but also is easy to use and comes with smart tips and suggestions.
FlyMon

Network measurement is important to data center operators. Most existing efforts focus on developing new implementation schemes for measurement tasks. Little attention is paid to on-the-fly task reconfiguration. Due to resource constraints, it is impossible to configure all needed tasks at start-up and dynamically turn on/of them. To support real-time reconfiguration of many different tasks, a key observation is that it is unnecessary to bind a task and its implementation at the compilation phase. We design FlyMon, the first sketch-based measurement system that can make on-the-fly reconfigurations on a large set of measurement tasks. FlyMon introduces the concept of Composable Measurement Units (CMUs), which are general operation units that support reconfigurable implementation for measurement tasks combined from different flow keys and flow attributes. FlyMon maps the design of CMUs to programmable switches' data planes so that the number of compacted CMUs can be maximized. FlyMon also provides dynamic memory management. We prototype FlyMon on Tofino and currently enable four frequently used flow attributes. Each CMU Group (with 3 CMUs) can concurrently perform up to 96 isolated measurement tasks with less than 8.3% hardware resources. The tasks can be deployed with configurable memory size at the millisecond level. By cross-stacking, FlyMon can deploy up to 27 CMUs in one pipeline of Tofino.
An Analysis of Signal Energy Impacts and Threats to Deep Learning Based SEI

Specific Emitter Identification (SEI) was conceived to detect, characterize, and identify radars using their transmitted signals. SEI’s success is linked to the imperfections of an emitter’s Radio Frequency (RF) front-end, which imparts unique "coloration" to the signal during its formation and transmission without impeding normal transceiver operations. Recent works propose Deep Learning (DL) based SEI due to its demonstrated successes in image and facial recognition, as well as its ability to learn radio-specific features directly from the sampled signals. This removes the needless, handcrafted feature engineering of traditional SEI. However, signal energy, its impacts, and its susceptibility to adversary mimicry has received little attention by DL-based SEI works. This work is the first to investigate the impacts and threats posed to DL-based SEI by the presence, lack, or manipulation of signal energy. Our work shows that Long Short-Term Memory (LSTM)-based SEI provides the highest average percent correct classification performance of 89.9% and the lowest rate, 0.68%, at which an adversary can circumvent the SEI process by manipulating the energy of its signals.
Before and Amid COVID-19 Pandemic, Self-Perception of Digital Skills in Saudi Arabia Higher Education: A Longitudinal Study

Compatible with global sustainable development report, 2016 edition, and vision 2030, Saudi Arabia recognized the importance of technology in achieving the Sustainable Development Goals (SDGs). This paper aims to measure the self-perception of digital skills among students in Saudi Arabia's higher education system to understand how they were influenced before and amid the COVID-19 pandemic. In 2019 before the COVID-19 pandemic, we started a project to study the self-perception of digital skills among Saudi Arabia university students (group A). A total of 469 students participated in this research. The validity and reliability of the employed scale were tested with first-order confirmatory factor analysis (CFA). The differences between the two groups (before and amid the pandemic) were tested through the Mann-Whitney U test. The results for group A (N = 232 students) showed a higher self-perception of their digital skills. In March 2020, amid the pandemic, Saudi Arabia closed and shifted to technology-based teaching like many other countries worldwide. After students' return to universities in 2021, an evaluation of how the students perceived their own digital skills was again conducted (group B). The results for group B (N = 237 students) demonstrated a lower level of confidence in their own digital abilities. Comparing two groups (A and B), after the educational course was administered, group A (prior to COVID-19) had greater self-perceptions of digital skills than group B (amid COVID-19). Students' perceptions of their own digital skills have been negatively impacted as a result of the pandemic situation caused by COVID-19. The collected evidence suggests that there is a difference, and that this difference is statistically significant. As a result of the substantial relationship between self-perception of digital skills and how students deal with reality based on their own self-perception, Saudi Arabia higher education ministry shifted teaching methods to be based on technology. Other significant findings and their implications for practice and theory were reported in this study. Finally, limitations and prospects for future research were also elaborated.
Identification and measurement of Requirements Technical Debt in software development: A systematic literature review

Requirements Technical Debt are related to the distance between the ideal value of the specification and the actual implementation of the system, which are consequences of strategic decisions for immediate gains, or unintended changes in context. To ensure the evolution of the software, it is necessary to to manage TD. Identification and measurement are the first two stages of the management process; however, they are poorly explored in academic research in requirements engineering. We aimed to investigating which evidence helps to strengthen the TD requirements management process, including identification and measurement. We conducted a Systematic Literature Review through manual and automatic searches considering 7499 studies from 2010 to 2020, and including 66 primary studies. We identified some causes related to Technical Debt requirements, existing strategies to help in the identification and measurement, and metrics to support the measurement stage. The studies on Requirements Technical Debt are still preliminary, especially regarding management software. Yet, however, the interpersonal aspects that prove difficult in the implementation of such activities are not sufficiently addressed. Finally, the provision of metrics to help measure technical debt is part of the contribution of this search, providing insights into the application in its requirements context.
Digitalization of ancient fabric using virtual reality technology at the Wieng Yong House Museum: The FabricVR project

The FabricVR project (https://angkaew.com/virtual_museum/) is aimed at digitizing the ancient fabric of the Wieng Yong House Museum for visitors to visit the site and obtain the cultural heritage knowledge in the COVID-19 pandemic during which the museum was forced to close as a result of the global pandemic restrictions. The project's goal is to develop technology to visualize cultural resources on a digital platform. To provide a visitor experience of a virtual museum, the project consists of three systems: first,3D FabricVR is a 3D virtual reality application which was designed based on the gamification concept to allow for an immersive experience for visitors to obtain knowledge of digital preservation. Second, 360° FabricVR: a web-based application of 360 virtual reality that enables visitors to have a flexible and easy exploration of the virtual museum via web-based browser. Finally, the web-based digital collection provides the organised information access to the repository of ancient fabric in the museum by the standard meta of VRA core. We evaluated the project by quantitative feedback of museum visitors of the Wieng Yong House Museum. Finally we embedded the project (http://lis.human.cmu.ac.th/virtual_museum/) as an extension of the university website providing a virtual museum services website for collaborations and with the benefits of sharing museum collections online.
OAU-net: Outlined Attention U-net for biomedical image segmentation

In this paper, we propose an Outlined Attention U-network (OAU-net) with bypass branching strategy to solve biomedical image segmentation tasks, which is capable of sensing shallow and deep features. Unlike previous studies, we use residual convolution and res2convolution as encoders. In particular, the outline filter and attention module are embedded in the skip connection part, respectively. Shallow features will enhance the edge information after being processed by the outline filter. Meanwhile, in the depths of the network, to better realize feature fusion, our attention module will simultaneously emphasize the independence between feature map channels (channel attention module) and each position information (spatial attention module), that is, the hybrid domain attention module. Finally, we conducted ablation experiments and comparative experiments according to three public data sets (pulmonary CT lesions, Kaggle 2018 data science bowl, skin lesions), and analyzed them with classical evaluation indexes. Experimental results show that our proposed method improves segmentation accuracy effectively. Our code is public at https://github.com/YF-W/OAU-net.
The Business Opportunity of Blockchain Value Creation among the Internet of Value

Blockchain is a buzzword today, and its application is daily increasing. Recently, literature has proposed the new concept of the Internet of Value (IoV), which refers to a view of the Internet as a means where value is transferred as easily, cheaply and reliably as data is now transferred. The ever-growing interest in Blockchain research and practice has given rise to disorganized knowledge around its application, especially for business purposes. To qualitatively assess this gap, we conducted a literature review by investigating a final number of 169 original contributions produced in the last half-decade. The insights have been analysed and systematized considering the possible contribution of the Blockchain application to the Internet of Value, to shed light on Blockchain opportunities in business value creation regarding five basic topics: Blockchain exploitation in the energy sector; Blockchain exploitation in healthcare; Blockchain exploitation in supply chain management (SCM); Blockchain exploitation in industrial marketing; and Blockchain exploitation in data privacy protection.
A Survey on Adversarial Domain Adaptation

Abstract Having a lot of labeled data is always a problem in machine learning issues. Even by collecting lots of data hardly, shift in data distribution might emerge because of differences in source and target domains. The shift would make the model to face with problems in test step. Therefore, the necessity of using domain adaptation emerges. There are three techniques in the field of domain adaptation namely discrepancy based, adversarial based and reconstruction based methods. For domain adaptation, adversarial learning approaches showed state-of-the-art performance. Although there are some comprehensive surveys about domain adaptation, we technically focus on adversarial based domain adaptation methods. We examine each proposed method in detail with respect to their structures and objective functions. The common aspect of proposed methods besides domain adaptation is considering the target labels are predicted as accurately as possible. It can be represented by some methods such as metric learning and multi-adversarial discriminators as are used in some of the papers. Also, we address the negative transfer issue for dissimilar distributions and propose the addition of clustering heuristics to the underlying structures for future research.