Reviewerly/exHarmony huggingface dataset:
Sharing study materials in health and medical research

Making study materials available allows for a more comprehensive understanding of the scientific literature. Sharing can take many forms and include a wide variety of outputs including code and data. Biomedical research can benefit from increased transparency but faces unique challenges for sharing, for instance, confidentiality concerns around participants' medical data. Both general and specialised repositories exist to aid in sharing most study materials. Sharing may also require skills and resources to ensure that it is done safely and effectively. Educating researchers on how to best share their materials, and properly rewarding these practices, requires action from a variety of stakeholders including journals, funders and research institutions.
ConceptExplainer: Interactive Explanation for Deep Neural Networks from a Concept Perspective

Traditional deep learning interpretability methods which are suitable for model users cannot explain network behaviors at the global level and are inflexible at providing fine-grained explanations. As a solution, concept-based explanations are gaining attention due to their human intuitiveness and their flexibility to describe both global and local model behaviors. Concepts are groups of similarly meaningful pixels that express a notion, embedded within the network's latent space and have commonly been hand-generated, but have recently been discovered by automated approaches. Unfortunately, the magnitude and diversity of discovered concepts makes it difficult to navigate and make sense of the concept space. Visual analytics can serve a valuable role in bridging these gaps by enabling structured navigation and exploration of the concept space to provide concept-based insights of model behavior to users. To this end, we design, develop, and validate CONCEPTEXPLAINER, a visual analytics system that enables people to interactively probe and explore the concept space to explain model behavior at the instance/class/global level. The system was developed via iterative prototyping to address a number of design challenges that model users face in interpreting the behavior of deep learning models. Via a rigorous user study, we validate how CONCEPTEXPLAINER supports these challenges. Likewise, we conduct a series of usage scenarios to demonstrate how the system supports the interactive analysis of model behavior across a variety of tasks and explanation granularities, such as identifying concepts that are important to classification, identifying bias in training data, and understanding how concepts can be shared across diverse and seemingly dissimilar classes.
A Novel Prediction Model: ELM-ABC for Annual GDP in the Case of SCO Countries

With the development of economic and technologies, the trend of annual Gross Domestic Product (GDP) and carbon dioxide (CO2) emission changes with time passes. The relationship between economic growth and carbon dioxide emissions is considered as one of the most important empirical relationships. In this study, we focus on the member of Shanghai Cooperation Organization, including China, Russia, India, and Pakistan and collect CO2 emission and annual GDP from 1969 to 2014. The statistical methods and tests are used to find the relationship between annual GDP and CO2 emission in these countries. Based on relationship between annual and CO2 emission, a novel multi-step prediction algorithm called Extreme Learning Machine with Artificial Bee Colony (ELM-ABC) is proposed for forecasting annual GDP based on CO2 emission and historical GDP features. According to the experimental results, it proved that the proposed model had a super forecasting ability in GDP prediction and it could predict ten-year future annual GDP for the corresponding countries. Moreover, the forecasting results showed that the annual GDP of China and Pakistan will continue to grow but growth will slow after 2025. The annual GDP in India will exhibit unstable growth. The trend of Russia will follow the pattern between 2010 and 2016.
MreNet: A Vision Transformer Network for Estimating Room Layouts from a Single RGB Panorama

The major problem with 3D room layout reconstruction is estimating the 3D room layout from a single panoramic image. In practice, the boundaries between indoor objects are difficult to define, for example, the boundary position of a sofa and a table, and the boundary position of a picture frame and a wall. We propose MreNet, a novel neural network architecture for predicting 3D room layout, which outperforms previous state-of-the-art approaches. It can efficiently model the overall layout of indoor rooms through a global receptive field and sparse attention mechanism, while prior works tended to use CNNs to gradually increase the receptive field. Furthermore, the proposed feature connection mechanism can solve the problem of the gradient disappearing in the process of training, and feature maps of different granularity can be obtained in different layers. Experiments on both cuboid-shaped and general Manhattan layouts show that the proposed work outperforms recent algorithms in prediction accuracy.
Me, the Hill and My Browser – Investigating the Role of Time in Posthuman Interaction

Despite the growing concerns related to environmental disruptions, such as forest fires and floods, our interaction with nature has become increasingly distant. The belief is that this is due to human attunement to quick changes over short periods, while natural changes seem slow-paced over the long term. We argue that rather than the problem lying with the pace of time, humans should further attune to the various temporalities within nature. To unpack this topic, we follow a two-fold process: Research through Design and Autobiographical Design. The resulting prototype takes environmental sensor data and visualises the changes happening over a short time. We complement this IT-supported process with think-aloud walks, a practice that is embodied and devoid of technology, for a plural perspective on time. We offer insights into how noticing the various temporalities in nature was enabled, finally arguing for designs that support progressive adjustments that sustain human-nonhuman mutualism.
Electrocoagulation technique for removing Organic and Inorganic pollutants (COD) from the various industrial effluents: An overview

The treatment and reuse of industrial wastewater plays a foremost role in succeeding environmental protection and water security. The electrochemical treatment technology has attracted a great deal of attention because of its compact, high particulate removal, free from chemicals, automation, and minimum sludge generation. The objective of the study is to review the existing literature on COD (Chemical oxygen demand) removal from various industrial effluents using electrocoagulation technology, as well as the factors that influence the process. Electricity is passed through electro plates dipped in wastewater during the electrocoagulation process. Metal hydroxide formations occur, which removes pollutants from wastewater via the sedimentation and flotation mechanisms. After a thorough review of various literatures, a detailed discussion on the process influencing parameters such as pH, Current Density, Electrolysis Time, Conductivity, Stirring Speed, and Retention Time has been done which gives useful information on future scope of research in this area.
Satellite Video Intrinsic Decomposition

Existing satellite video processing methods are mainly based on original video, ignoring the use of invariant background characteristics of staring satellites, and easy to be disturbed by rapid light changes. In order to improve application capability of satellite video, this paper establishes the satellite video intrinsic decomposition (SVID) model, including satellite video signal composition model, decomposition constraint with time-spatial unity similarity constraint, static and dynamic components separation by improving TRPCA, and decomposition acceleration based on reflectance transfer. With SVID, intrinsic decomposition and dynamic and static component separation are realized. Five Jilin-1 satellite videos are used to verify the validity, superiority and the potential applications of the proposed algorithm. By comparing with state-of-the-art intrinsic image decomposition method and intrinsic video decomposition method, the experimental results prove the superiority of the SVID method in extracting reflectance component. In addition, the experimental results also prove SVID has excellent application ability in scene background analysis and moving target tracking.
Automobile insurance claim occurrence prediction model based on ensemble learning

Abstract The generalized linear model (GLM) is a widely used method in traditional automobile insurance loss prediction. Ensemble learning algorithms have recently shown promising results in the realm of automobile insurance, providing a new option for loss prediction. In the age of big data, how to predict loss in automobile insurance more accurately is an urgent problem to be solved. Stacking is a hot issue in ensemble learning that has been effectively used in many fields, but few researchers have applied it to the field of automobile insurance. In this research, Stacking was innovatively introduced into loss prediction for automobile insurance to solve this problem. Three datasets related to automobile insurance were used. Adopting the Synthetic Minority Oversampling Technique for class balance, a model of claim occurrence was established using four methods, namely logistic regression in GLM and bagging, boosting, stacking in ensemble learning. The area under the receiver operating characteristic curve values and F1‐scores achieved with the four methods were then compared to assess classification performance. Ensemble algorithms were used to rank the importance of features in the FRE dataset. Finally, we combined probability with the bonus‐malus system to formulate a fairer transfer strategy. The results showed that the proposed approach performed better than the other methods on all datasets, with significantly enhanced prediction accuracy.
Toward Fast and Scalable Random Walks over Disk-Resident Graphs via Efficient I/O Management

Traditional graph systems mainly use the iteration-based model, which iteratively loads graph blocks into memory for analysis so as to reduce random I/Os. However, this iteration-based model limits the efficiency and scalability of running random walk, which is a fundamental technique to analyze large graphs. In this article, we first propose a state-aware I/O model to improve the I/O efficiency of running random walk, then we develop a block-centric indexing and buffering scheme for managing walk data, and leverage an asynchronous walk updating strategy to improve random walk efficiency. We implement an I/O-efficient graph system, GraphWalker , which is efficient to handle very large disk-resident graphs and also scalable to run tens of billions of random walks with only a single commodity machine. Experiments show that GraphWalker can achieve more than an order of magnitude speedup when compared with DrunkardMob, which is tailored for random walks based on the classical graph system GraphChi, as well as two state-of-the-art single-machine graph systems, Graphene and GraFSoft. Furthermore, when compared with the most recent distributed system KnightKing, GraphWalker still achieves comparable performance with only a single machine, thereby making it a more cost-effective alternative.
Environment Challenges of E-Learning in Higher Education—The Teachers’ Perspective

The COVID-19 pandemic enforced higher education institutions worldwide to start using various digital technologies for providing synchronous digital online teaching and learning support. The new e-learning environment became a main challenge for the teachers, students, and university management. This paper is based on a study that aims to investigate the main challenges of digital teaching (a teacher-centered aspect of e-learning) from the teachers’ perspective. The data for this research is gathered through the ENLIVEN Video Podcasts, using the method of semi-structured interviews, in different locations of the Paris Lodron University of Salzburg. Qualitative analysis was used for data analysis. The results of this study provide five common themes which reflect teachers’ challenges: personal barriers to digital teaching, structural barriers to digital teaching, challenging factors to ensure the quality of teaching, recommendations for colleagues, and future vision. Furthermore, this paper presents a collective story of teachers’ experiences related to digital teaching practices and gives an overview of the major challenges when teaching via an e-learning environment.
Feedback neural network for constrained bi-objective convex optimization

Bi-objective optimization problems occur widely in engineering and science. This paper considers lexicographic method solution of the bi-objective optimization problems, feedback neural networks are adopted as optimization tools. The two objectives are sorted according to their priority, and then the bi-objective optimization problem is converted to two optimization problems with single-objective, namely, the high-priority optimization problem and the low-priority optimization problem. First, a classical neural network is applied for the high-priority optimization problem. Then, a novel feedback neural network is constructed to find an optimal solution of the low-priority optimization problem in the optimal solution set of the high-priority optimization problem. It is proved that any accumulation point of the state sequence of the proposed feedback neural network is a Pareto optimal solution to the considered bi-objective optimization problem. In contrast with the existing neural networks, the proposed neural network has simple structure and relies on weaker convergence conditions. Finally, some numerical examples and an application in fuzzy optimization show the effectiveness of the proposed neural network.
Revisiting the dataset gap problem – On availability, assessment and perspective of mobile forensic corpora

Digital forensic corpora are essential for education, academic research, tool development and testing. Due to the increasing pervasiveness of mobile devices like smartphones or tablets, the need for mobile forensic datasets is growing, too. However, publications in the IT forensic community show that there is a large gap in publicly available datasets. In this work we focus on mobile digital forensic corpora as one of the main fields of missing datasets and aim at shifting the focus of the digital forensic community on this topic. In order to do so, we provide three main contributions. We first perform a structured search for mobile forensic corpora and show that 31 publicly available mobile forensic corpora exist, 9 of them more than 5 years old and 18 of them more than 3 years old. Second, we assess these datasets with respect to its content compared to an ordinary real mobile image and conclude that most of the 31 datasets contain too few traces to be considered as realistic. Finally, we propose how to proceed to solve the presumable problem of missing mobile forensic datasets.
HPFL: Federated Learning by Fusing Multiple Sensor Modalities with Heterogeneous Privacy Sensitivity Levels

Solving classification problems to understand multi-modality sensor data has become popular, but rich-media sensors, e.g., RGB cameras and microphones, are privacy-invasive. Though existing Federated Learning (FL) algorithms allow clients to keep their sensor data private, they suffer from degraded performance, particularly lower classification accuracy and longer training time, than centralized learning. We propose a Heterogeneous Privacy Federated Learning (HPFL) paradigm to capitalize on the information in the privacy insensitive data (such as mmWave point clouds) while keeping the privacy sensitive data (such as RGB images) private because sensor data are of diverse sensitivity levels. We evaluate the HPFL paradigm on two representative classification problems: semantic segmentation and emotion recognition. Extensive experiments demonstrate that the HPFL paradigm outperforms: (i) the popular FedAvg by 18.20% in foreground accuracy (semantic segmentation) and 4.20% in F1-score (emotion recognition) under non-i.i.d. sample distributions and (ii) the state-of-the-art FL algorithms by 12.40%--17.70% in foreground accuracy and 2.54%--4.10% in F1-score.
Adversarial Attacks Against Blind Image Quality Assessment Models

Several deep models for blind image quality assessment (BIQA) have been proposed during the past few years, with promising results on standard image quality datasets. However, generalization of BIQA models beyond the standard content remains a challenge. In this paper, we study basic adversarial attack techniques to assess the robustness of representative deep BIQA models. Our results show that adversarial images created for a simple substitute BIQA model (i.e. white-box scenario) are transferable as such and able to deceive also several other more complex BIQA models (i.e. black-box scenario). We also investigated some basic defense mechanisms. Our results indicate that re-training BIQA models with a dataset augmented with adversarial images improves robustness of several models, but at the cost of decreased quality prediction accuracy on genuine images.
OrthoMAD: Morphing Attack Detection Through Orthogonal Identity Disentanglement

Morphing attacks are one of the many threats that are constantly affecting deep face recognition systems. It consists of selecting two faces from different individuals and fusing them into a final image that contains the identity information of both. In this work, we propose a novel regularisation term that takes into account the existent identity information in both and promotes the creation of two orthogonal latent vectors. We evaluate our proposed method (OrthoMAD) in five different types of morphing in the FRLL dataset and evaluate the performance of our model when trained on five distinct datasets. With a small ResNet-18 as the backbone, we achieve state-of-the-art results in the majority of the experiments, and competitive results in the others.
Optimization of the Structure of Variable-Valued Logical Functions when Adding New Production Rules

In this paper, a theoretical justification and an algorithm for the optimal construction of variable-valued logical functions based on it are proposed, provided that new production rules are added to an already existing (within the original subject area) logical classifier. The proposed algorithm is based on the method of constructive transformation of variable-valued logical classifiers built on the basis of variable-valued predicates. The properties of changing the structures of production clauses and clauses of free knowledge in the process of adding new production rules are studied. The conditions under which these clauses are guaranteed to turn into a logical zero, or remain unchanged, are revealed. Accounting for these conditions in the proposed algorithm makes it possible to reduce the number of logical operations performed and ensure a reduction in computational costs to perform the required transformations.
Open-Set Recognition: an Inexpensive Strategy to Increase DNN Reliability

Deep Neural Networks (DNNs) are nowadays widely used in low-cost accelerators, characterized by limited computational resources. These models, and in particular DNNs for image classification, are becoming increasingly popular in safety-critical applications, where they are required to be highly reliable. Unfortunately, increasing DNNs reliability without computational overheads, which might not be affordable in low-power devices, is a non-trivial task. Our intuition is to detect network executions affected by faults as outliers with respect to the distribution of normal network’s output. To this purpose, we propose to exploit Open-Set Recognition (OSR) techniques to perform Fault Detection in an extremely low-cost manner. In particuar, we analyze the Maximum Logit Score (MLS), which is an established Open-Set Recognition technique, and compare it against other well-known OSR methods, namely OpenMax, energy-based outof-distribution detection and ODIN. Our experiments, performed on a ResNet-20 classifier trained on CIFAR-10 and SVHN datasets, demonstrate that MLS guarantees satisfactory detection performance while adding a negligible computational overhead. Most remarkably, MLS is extremely convenient to conFigure and deploy, as it does not require any modification or re-training of the existing network. A discussion of the advantages and limitations of the analysed solutions concludes the paper.
Understanding the Impact of Cutting in Quantum Circuits Reliability to Transient Faults

Quantum Computing is a highly promising new computation paradigm. Unfortunately, quantum bits (qubits) are extremely fragile and their state can be gradually or suddenly modified by intrinsic noise or external perturbation. In this paper, we target the sensitivity of quantum circuits to radiation-induced transient faults. We consider quantum circuit cuts that split the circuit into smaller independent portions, and understand how faults propagate in each portion. As we show, the cuts have different vulnerabilities, and our methodology successfully identifies the circuit portion that is more likely to contribute to the overall circuit error rate. Our evaluation shows that a circuit cut can have a 4.6 x higher probability than the other cuts, when corrupted, to modify the circuit output. Our study, identifying the most critical cuts, moves towards the possibility of implementing a selective hardening for quantum circuits.
Multistrategy Improved Sparrow Search Algorithm Optimized Deep Neural Network for Esophageal Cancer

Deep neural network is a complex pattern recognition network system. It is widely favored by scholars for its strong nonlinear fitting ability. However, training deep neural network models on small datasets typically realizes worse performance than shallow neural network. In this study, a strategy to improve the sparrow search algorithm based on the iterative map, iterative perturbation, and Gaussian mutation is developed. This optimized strategy improved the sparrow search algorithm validated by fourteen benchmark functions, and the algorithm has the best search accuracy and the fastest convergence speed. An algorithm based on the iterative map, iterative perturbation, and Gaussian mutation improved sparrow search algorithm is designed to optimize deep neural networks. The modified sparrow algorithm is exploited to search for the optimal connection weights of deep neural network. This algorithm is implemented for the esophageal cancer dataset along with the other six algorithms. The proposed model is able to achieve 0.92 under all the eight scoring criteria, which is better than the performance of the other six algorithms. Therefore, an optimized deep neural network based on an improved sparrow search algorithm with iterative map, iterative perturbation, and Gaussian mutation is an effective approach to predict the survival rate of esophageal cancer.