Reviewerly/exHarmony huggingface dataset:
Comparative evaluation of uncertainty estimation and decomposition methods on liver segmentation

Abstract Purpose Deep neural networks need to be able to indicate error likelihood via reliable estimates of their predictive uncertainty when used in high-risk scenarios, such as medical decision support. This work contributes a systematic overview of state-of-the-art approaches for decomposing predictive uncertainty into aleatoric and epistemic components, and a comprehensive comparison for Bayesian neural networks (BNNs) between mutual information decomposition and the explicit modelling of both uncertainty types via an additional loss-attenuating neuron. Methods Experiments are performed in the context of liver segmentation in CT scans. The quality of the uncertainty decomposition in the resulting uncertainty maps is qualitatively evaluated, and quantitative behaviour of decomposed uncertainties is systematically compared for different experiment settings with varying training set sizes, label noise, and distribution shifts. Results Our results show the mutual information decomposition to robustly yield meaningful aleatoric and epistemic uncertainty estimates, while the activation of the loss-attenuating neuron appears noisier with non-trivial convergence properties. We found that the addition of a heteroscedastic neuron does not significantly improve segmentation performance or calibration, while slightly improving the quality of uncertainty estimates. Conclusions Mutual information decomposition is simple to implement, has mathematically pleasing properties, and yields meaningful uncertainty estimates that behave as expected under controlled changes to our data set. The additional extension of BNNs with loss-attenuating neurons provides no improvement in terms of segmentation performance or calibration in our setting, but marginal benefits regarding the quality of decomposed uncertainties.
Integrating Virtual Reality into IOSH Safety Training

The construction industry has high rate of accidents and poor safety records around the world. In addition to the injuries and fatalities, accidents affect both time and cost of the projects, thereby leading to time delays and additional costs. Previous research studies indicate that poor training contributes to the majority of accidents in the construction sector. Traditional training methods are inefficient in inducing the desired learning outcomes due to the passivity of knowledge receivers. Accordingly, new experimental and proactive learning approaches have emerged, out of which the safest and most ethical is the virtual reality (VR) construction safety training. VR safety training enables users to simulate reality in 360 degrees at the full visual capacity and to react accordingly improving understanding and critical response to stimuli. The research stream in this area is still relatively not fully explored, and previous work mainly focuses on semi-immersive VR technologies. The goal of this research is to develop and test a fully immersive and interactive VR model for safety training in accordance with the Institution of Occupational Safety and Health (IOSH). The focus is on advancing hazard recognition and mitigation “skills” rather than just providing “information”. The VR model was developed and deployed using state-of-the-art technologies, with a focus on falls, struck-by, slips, and general site safety. A testing phase was then initiated throughout interviewing professional safety inspectors to validate the model capabilities and to give their insight into approaches for further development. After that, the model was tested on senior university construction engineering students, and their performance was compared to students who didn’t take the VR training. Interviews indicated that the developed model would be of great importance for entry level workers and for general safety inductions; in addition, it can be more developed to target professionals on training centers. Experimental testing indicated statistical significance in the effectiveness of the developed model in enhancing students’ understanding and visualization. In addition, when compared to other available VR models, the developed model performed better in terms of visualization, immersion, realism, and ability to enhance the desired hazard identification and mitigation skills.
Design and Implementation of a Cloud-based Collaborative Signature System for Two Parties Based on SM2

In applying the collaborative signature protocol to intelligent mobile terminals, problems include low efficiency, lack of optimization based on national cryptographic algorithms, and imperfect keymanagement systems. A cloud-based collaborative signature system based on SM2 is proposed to address this. The system comprises a cloud-side cryptographic module and a mobile intelligent terminal cryptographic module. It ensures the security of sensitive data during the signature process and the legality of the signature result by optimizing the private key splitting scheme and designing secure management techniques for private keys on intelligent mobile terminals. The collaborative signature efficiency is improved through independently developed data preprocessing techniques and SM2 fast signature methods. In addition, a keyrecovery plan is proposed to enhance the system's robustness in the key management system. Experimental results show that the collaborative signature system is superior to similar systems in security and is not compromised in efficiency. The system has already met the level 2 requirements of the “GM/T 0028–2014 Cryptographic Module Security Requirements” and has great potential for market application.
An Instruction Extension Based SHA-3 Algorithm Co-Processor Design Scheme

Cryptography is the cornerstone of securing information, and the traditional way of deploying cryptographic algorithms has the problems of slow computation speed and low flexibility. In this paper, we first analyze the principle and computation process of SHA-3 hash algorithm, propose a cryptographic instruction extension method to solve the problems in the traditional cryptographic algorithm deployment method according to the characteristics of the hash algorithm, and develop a cryptographic instruction extension scheme combined with Hummingbird E203 processor, carry out the design of co-processor hardware unit, build a behavior-level simulation and FPGA board-level verification environment, and carry out The correctness of the coprocessor function is verified and the performance is analyzed and compared. The experimental results show that the designed cryptographic instruction scheme can reduce the number of instructions and cycles by more than 90% with less than 8% increase in hardware resources and 9mW power consumption, which achieves a more obvious performance improvement and is suitable for resource-constrained scenarios.
"Nip it in the Bud": Moderation Strategies in Open Source Software Projects and the Role of Bots

Much of our modern digital infrastructure relies critically upon open sourced software. The communities responsible for building this cyberinfrastructure require maintenance and moderation, which is often supported by volunteer efforts. Moderation, as a non-technical form of labor, is a necessary but often overlooked task that maintainers undertake to sustain the community around an OSS project. This study examines the various structures and norms that support community moderation, describes the strategies moderators use to mitigate conflicts, and assesses how bots can play a role in assisting these processes. We interviewed 14 practitioners to uncover existing moderation practices and ways that automation can provide assistance. Our main contributions include a characterization of moderated content in OSS projects, moderation techniques, as well as perceptions of and recommendations for improving the automation of moderation tasks. We hope that these findings will inform the implementation of more effective moderation practices in open source communities.
The effects of viewing by scrolling on a small screen on the encoding of objects into visual long-term memory

The perception of an image obtained by scrolling through a small screen can differ from the typical perception of a wide visual field in a stable environment. However, we do not fully understand image perception by scrolling on a small screen based on psychological knowledge of visual perception and cognition of images. This study investigated how screen size limitations and image shifts caused by scrolling affect image encoding in visual long-term memory. Participants explored the stimulus images under three conditions. Under the scrolling condition, they explored the image through a small screen. Under the moving-window condition, they explored the image by moving the screen over a masked image; this is similar to looking through a moving peephole. Under the no-window condition, participants were able to view the entire image simultaneously. Each stimulus comprised 12 objects. After 1 h, the samples were tested for object recognition. Consequently, the memory retention rate was higher in the scrolling and moving-window conditions than in the no-window condition, and no difference was observed between the scrolling and moving-window conditions. The time required by participants to explore the stimulus was shorter under the no-window condition. Thus, encoding efficiency (i.e., the rate of encoding information into memory in a unit of time) did not differ among the three conditions. An analysis of the scan trace of the scrolling and window movements in relation to the image revealed differences between the scrolling and moving-window conditions in terms of the scan’s dynamic features. Moreover, a negative correlation was observed between the memory retention rate and image-scrolling speed. We conclude that perceiving images by scrolling on a small screen enables better memory retention than that obtained through whole-image viewing if the viewing time is not limited. We suggest that viewing through a small screen is not necessarily disadvantageous for memory encoding efficiency depending on the presentation mode, and the results show that participants who scrolled fast tended to have worse memory retention. These findings can impact school education and thus suggest that the use of mobile devices in learning has some merit from the viewpoint of cognitive psychology.
Graph-Segmenter: graph transformer with boundary-aware attention for semantic segmentation

The transformer-based semantic segmentation approaches, which divide the image into different regions by sliding windows and model the relation inside each window, have achieved outstanding success. However, since the relation modeling between windows was not the primary emphasis of previous work, it was not fully utilized. To address this issue, we propose a Graph-Segmenter, including a graph transformer and a boundary-aware attention module, which is an effective network for simultaneously modeling the more profound relation between windows in a global view and various pixels inside each window as a local one, and for substantial low-cost boundary adjustment. Specifically, we treat every window and pixel inside the window as nodes to construct graphs for both views and devise the graph transformer. The introduced boundary-aware attention module optimizes the edge information of the target objects by modeling the relationship between the pixel on the object’s edge. Extensive experiments on three widely used semantic segmentation datasets (Cityscapes, ADE-20k and PASCAL Context) demonstrate that our proposed network, a Graph Transformer with Boundary-aware Attention, can achieve state-of-the-art segmentation performance.
Preface

Extract Overview Many activities, including experiments, surveys, clinical trials, and fieldwork, generate data. These data provide insights—intuitions and conclusions that come from identifying patterns in data. Insights are critical for answering questions, solving problems, guiding decisions, and formulating strategy. But getting insights from data, and doing so efficiently, reliably, and confidently, does not come easily. Yet getting insights from data is a foundational skill for all scientists. Insights from Data with R is for life and environmental science undergraduates (though may also help anyone beginning in their learning about data analysis), and for their instructors to teach alongside. It is not about statistics per se, but about that initial transition from having collected data as part of a project to that first, and so satisfying, realization that there is a pattern in your data. It combines the elements of the successful undergraduate data analysis courses of Petchey at the University of Zürich and of Childs at the University of Sheffield, the ‘Introduction to R’ courses taught internationally for 15 years by all four authors, and the book Getting Started with R: An Introduction for Biologists, second edition, by Beckerman, Childs, and Petchey (2017), all using R with the RStudio platform.
Accelerating sparse convolutional neural networks with systolic arrays on FPGA

Convolutional Neural Networks (CNNs) are frequently used in a wide range of applications, including speech, image recognition and natural language processing. However, due to the computational complexity of CNNs, deploying these networks on resource-limited edge devices has become a significant challenge. Sparse CNNs use the sparsity in the weight matrices of the networks to minimize computations while maintaining accuracy. By storing only the nonzero values, the Compressed Sparse Row (CSR) format compresses the sparse matrix, lowering the memory requirement and computational complexity of the network. This work presents a novel approach for accelerating Sparse CNNs on Field-Programmable Gate Arrays (FPGAs) using the CSR format and systolic arrays. The proposed method takes advantage of systolic arrays' parallel processing capabilities to perform CSR-based sparse convolutions. Furthermore, an algorithm has been presented that optimizes the data layout to maximize data reuse and minimize data movement between different processing elements of the systolic array and external memory. The architecture is evaluated and compared to a state-of-the-art GPU implementation on several benchmark datasets. The proposed architecture outperformed the GPU-based implementation in terms of throughput and power efficiency by 1.42x and 22.4x, respectively. The presented approach provides a promising solution for accelerating Sparse CNNs on resource-constrained devices and enabling the deployment of these networks in a variety of applications.
Incorporating Social Tagging for Unified Probabilistic Matrix Factorization Recommendation

With the development of social network, the information in social network environment is increasing rapidly. It is a hot issue that how to recommend the resources to users according to their interests using big data. In order to improve the quality of recommendation, this paper proposes a unified probability matrix factorization recommendation model called TSRec which integrates social tags. The model uses the information of users, resources and tags of social network, and optimizes the parameters of the training model to provide the best recommendation effect.
Smart Contract-based Crowdsensing Framework for Internet of Vehicles

Crowdsensing of Internet of Vehicles (IoV) can make full of use of the mobility of vehicles to provide data collection services to individuals in deed. In this paper, we elaborate on the framework, operation procedures of both centralized and distributed crowdsensing frameworks in IoV, and analyze the system complexity, secure communication, data storage and other problems facing the current architecture. In order to solve these problems, this paper proposed a crowdsensing framework of IoV based on blockchain and smart contract on the basis of introducing vehicular edge computing; Secondly, a multi-level smart contracts interaction system were built which designed a detailed multi-module smart contract; Finally, a specific scheme for performing sensing tasks is presented on this framework, showing the communication between different entities. The simulation test and performance evaluation certify the availability, security and convenience of the framework.
Incorporation of Media Arts for Chilean Young Students With Special Learning Needs

The present informal study of Information and Communication Technology (ICT) in special education through media arts. There are deep inequalities in the access of new technological opportunities and adaptive user experiences for students with special learning needs. Our goal as teacher educators is to improve enhance autonomous learning using the complementary subject of media arts. This new educational proposal aims to solve the technology usability problems for students with disabilities. The personalized autonomy of students’ use technology in school learning is a key strategy for students with disabilities. For this study as with many schools, the technology resources and educational services to implement digital skills and new ICT teaching-learning strategies for the complementary subject of media arts is not available. Finally, implementing these digital skills is an essential part of the educational-labor system for adolescent students with disabilities.
Smart Contract Vulnerability Detection Based on Deep Learning and Multimodal Decision Fusion

With the rapid development and widespread application of blockchain technology in recent years, smart contracts running on blockchains often face security vulnerability problems, resulting in significant economic losses. Unlike traditional programs, smart contracts cannot be modified once deployed, and vulnerabilities cannot be remedied. Therefore, the vulnerability detection of smart contracts has become a research focus. Most existing vulnerability detection methods are based on rules defined by experts, which are inefficient and have poor scalability. Although there have been studies using machine learning methods to extract contract features for vulnerability detection, the features considered are singular, and it is impossible to fully utilize smart contract information. In order to overcome the limitations of existing methods, this paper proposes a smart contract vulnerability detection method based on deep learning and multimodal decision fusion. This method also considers the code semantics and control structure information of smart contracts. It integrates the source code, operation code, and control-flow modes through the multimodal decision fusion method. The deep learning method extracts five features used to represent contracts and achieves high accuracy and recall rates. The experimental results show that the detection accuracy of our method for arithmetic vulnerability, re-entrant vulnerability, transaction order dependence, and Ethernet locking vulnerability can reach 91.6%, 90.9%, 94.8%, and 89.5%, respectively, and the detected AUC values can reach 0.834, 0.852, 0.886, and 0.825, respectively. This shows that our method has a good vulnerability detection effect. Furthermore, ablation experiments show that the multimodal decision fusion method contributes significantly to the fusion of different modalities.
Analysis of the Effect of the English Learning Method on Student’s Learning Interest in Madrasah Aliyah Laboratorium State Islamic University of North Sumatra Medan

In this research journal, the author analyzes the interest of Madrasah Aliyah Laboratorium State Islamic University of North Sumatra Medan students in learning English Language in class. The focus of this research is to find some defening factors that faced students in learning English Language. The study is done by taking students randomly from the Madrasah Aliyah Laboratorium State Islamic University of North Sumatra Medan. This study uses a classical and individual approach in collecting qualitative and quantitative data. The data obtained were obtained from the results of student learning exploration in the classroom and conducting interviews with students. The results show that the determinant of increasing or decreasing students interest in learning English in class is determined by the method used by the teacher in learning and the completeness of learning facilities. In connection with the research results obtained, it is suggested that teachers can use learning methods that are preferred by students so that students get a high sense of motivation to learn English in the classroom.
User’s Appreciation About Software Prototyping: A Case Study

One of the problems in the software development projects is the ambiguity of the requirements or the multiple interpretations of each user. Likewise, it is known that the cost of correcting an error is less if it is detected in the early phases such as analysis and design compared to detecting in later phases such as construction and maintenance. Software prototyping is an alternative solution to this problem, helping to minimize changes and rework in the final phases of the software process and thus avoiding costs and dissatisfaction. Despite this, prototyping is not widely used in the analysis, specification, and validation of requirements activities. This work evaluates the user’s appreciation regarding the practice of working with prototypes. A questionnaire was used to assess motivation, user experience, and learning ability. The results show a positive perception of the users who participated in this case study.
Modularity in Deep Learning: A Survey

Modularity is a general principle present in many fields. It offers attractive advantages, including, among others, ease of conceptualization, interpretability, scalability, module combinability, and module reusability. The deep learning community has long sought to take inspiration from the modularity principle, either implicitly or explicitly. This interest has been increasing over recent years. We review the notion of modularity in deep learning around three axes: data, task, and model, which characterize the life cycle of deep learning. Data modularity refers to the observation or creation of data groups for various purposes. Task modularity refers to the decomposition of tasks into sub-tasks. Model modularity means that the architecture of a neural network system can be decomposed into identifiable modules. We describe different instantiations of the modularity principle, and we contextualize their advantages in different deep learning sub-fields. Finally, we conclude the paper with a discussion of the definition of modularity and directions for future research.
SCI-3000: A Dataset for Figure, Table and Caption Extraction from Scientific PDFs

Extracting figures and similar visual elements from PDFs of scientific publications is important but non-trivial, and progress is impeded by a lack of datasets for evaluation and machine learning. In this work, we describe and publish the SCI-3000 dataset, containing 3 000 PDFs of scientific publications (34 791 pages) with annotations of figures, tables, and corresponding captions, from the fields of computer science, biomedicine, chemistry, physics, and technology. We demonstrate the use of the dataset to benchmark two figure, table, and caption extraction approaches from recent literature: one rule-based and one deep learning-based.
The Standpoint for the Legal Course Onward to the Protection of Deceased Biometric Data

Rather, what is shown to us is an imprint of light that requires a leap of faith for us to accept that these images depict something no one has in fact seen.