Reviewerly/exHarmony huggingface dataset:
Measurement of multiple mechanical properties for polymer composites using digital image correlation at elevated temperatures

This work presents a cost-effective experimental method for measuring multiple mechanical properties for carbon fiber reinforced modified epoxy matrix composites using the digital image correlation method at elevated temperatures. The method is based on combining a calculated stress distribution and a surface full-field strain measurement in a custom short-beam shear specimen. As the in-plane shear strain exceeds 0.01, all unidirectional SBS specimens exhibited highly nonlinear shear stress-strain behavior. Shear delamination failure occurred at elevated temperatures up to 150oC. Axial elastic modulus and in-plane shear modulus were determined from 2-mm-wide gage regions mid-way between the loading nose and lower support in a single experiment using the closed-form approximation and finite-element-model-updating method. The average axial moduli determined using two different methods are highly consistent. The variation of multiple mechanical properties for the composite material with the temperature rising to 150oC has been obtained simultaneously. The experimental results demonstrate that the axial elastic modulus decreased by only 5.2% at 100oC and 7.8% at 150oC, respectively. However, the in-plane shear modulus decreased by 20.8% at 150oC. It indicates that the degradation of the in-plane shear behavior is sensitive to temperature rising since it is dominated by the matrix, which is sensitive to the glass transition temperature Tg of epoxy resin.
A Competence-Aware Curriculum for Visual Concepts Learning via Question Answering

Humans can progressively learn visual concepts from easy to hard questions. To mimic this efficient learning ability, we propose a competence-aware curriculum for visual concept learning in a question-answering manner. Specifically, we design a neural-symbolic concept learner for learning the visual concepts and a multi-dimensional Item Response Theory (mIRT) model for guiding the learning process with an adaptive curriculum. The mIRT effectively estimates the concept difficulty and the model competence at each learning step from accumulated model responses. The estimated concept difficulty and model competence are further utilized to select the most profitable training samples. Experimental results on CLEVR show that with a competence-aware curriculum, the proposed method achieves state-of-the-art performances with superior data efficiency and convergence speed. Specifically, the proposed model only uses 40% of training data and converges three times faster compared with other state-of-the-art methods.
An original multi-criteria decision-making algorithm for solar panels selection in buildings

Nowadays, there are several thousand solar panels (SP) in the market. These panels vary in size, efficiency, cost, warranty, technology, etc. Selecting the best SP becomes complicated when other factors are added to the list, such as the available roof area, weather conditions, electricity tariffs, energy consumptions, etc. Therefore, it becomes crucial to propose an algorithm that selects the best SP for a specific client and provides the best combination of quality-cost-satisfaction. This paper presents an original multi-criteria decision-making algorithm based on the concept of Rank-Weight-Rank to select the best SP by considering many criteria. For validation purposes, our method is compared to TOPSIS, and it shows advantages, especially regarding the simulation time and the accuracy of the selection. Our approach is faster than TOPSIS, and the speed of finding the solution increases exponentially when the number of criteria and alternatives increases. Moreover, it is reliable and accurate since the probability is more than 78% for similarity of 90%–100% between both methods in selecting the same best alternative. Results show that the highest SP efficiency and the most expensive ones are not the best alternatives, nor the lowest efficient neither the cheapest SPs are the worst alternatives.
Heterogeneous Signcryption Scheme Supporting Equality Test from PKI to CLC toward IoT

Abstract The prosperity of Internet of Things (IoT) has brought numerous advantages to individuals and organizations. Since the transmission channel is usually considered as untrusted, the notion of signcryption seems to be the desired method to ease the defect. Nevertheless, this primitive inevitably faces the new obstacle that how to search the required data without decrypting it. Therefore, inheriting the excellence of signcryption and equality test, we present a heterogeneous signcryption protocol supporting the equality test from public key infrastructure (PKI) to certificateless cryptosystem (CLC) toward IoT environment (PK‐CLET). In this way, the introduced construction not only preserves the integrity, confidentiality and unforgeability of data, but also allows the cloud server to perform the equality test on different ciphertexts. Moreover, the security of PK‐CLET as well as the performance evaluation are both shown that it is more suitable for IoT.
Predicting critical transitions in multiscale dynamical systems using reservoir computing

We study the problem of predicting rare critical transition events for a class of slow–fast nonlinear dynamical systems. The state of the system of interest is described by a slow process, whereas a faster process drives its evolution and induces critical transitions. By taking advantage of recent advances in reservoir computing, we present a data-driven method to predict the future evolution of the state. We show that our method is capable of predicting a critical transition event at least several numerical time steps in advance. We demonstrate the success as well as the limitations of our method using numerical experiments on three examples of systems, ranging from low dimensional to high dimensional. We discuss the mathematical and broader implications of our results.
An Intelligent Augmented Reality Training Framework for Neonatal Endotracheal Intubation

Neonatal Endotracheal Intubation (ETI) is a critical resuscitation skill that requires tremendous practice of trainees before clinical exposure. However, current manikin-based training regimen is ineffective in providing satisfactory real-time procedural guidance for accurate assessment due to the lack of see-through visualization within the manikin. The training efficiency is further reduced by the limited availability of expert instructors, which inevitably results in a long learning curve for trainees. To this end, we propose an intelligent Augmented Reality (AR) training framework that provides trainees with a complete visualization of the ETI procedure for real-time guidance and assessment. Specifically, the proposed framework is capable of capturing the motions of the laryngoscope and the manikin and offer 3D see-through visualization rendered to the head-mounted display (HMD). Furthermore, an attention-based Convolutional Neural Network (CNN) model is developed to automatically assess the ETI performance from the captured motions as well as identify regions of motions that significantly contribute to the performance evaluation. Lastly, augmented user-friendly feedback is delivered with interpretable results with the ETI scoring rubric through the color-coded motion trajectory that classifies highlighted regions that need more practice. The classification accuracy of our machine learning model is 84.6%.
User Perspectives on Blockchain Technology: User-Centered Evaluation and Design Strategies for DApps

Emerging technologies have played an important role in driving major changes in human society.However, the advent of most technologies is typically initially accompanied by confusion; this is often because technology developers overlook the user perspective.This study was conducted to systematically determine the fundamental causes of problems that users encounter when they interact with blockchain technology, one of the promising emerging technologies today, and to suggest relevant design strategies.To this end, usability evaluation was conducted for the KDEX decentralized exchange application.To ensure the effective identification of the significant usability problems, heuristic evaluation with four experts and usability testing with 23 experimental participants were carried out.The results obtained show that more user-centered design is necessary to enable the widespread use of decentralized applications.Based on the experimental findings, actionable design strategies that facilitate the effective utilization of emerging technologies are suggested.The proposed strategies are expected to enable users to easily understand and navigate applications based on these technologies.
Distributed maximum correntropy linear and nonlinear filters for systems with non-Gaussian noises

In this paper, we investigate the distributed state estimation of non-Gaussian systems, where every sensor only exchanges information within its neighborhoods in the absence of a fusion center. Taking advantage of the Gaussian correntropy in processing non-Gaussian signals, we first derive a centralised maximum correntropy Kalman filter for linear multi-sensor systems, and then obtain its information form with some approximations. After that, a distributed maximum correntropy information filter is designed to approximate the centralised information filter using the consensus average method, and its extension to nonlinear systems is also provided based on statistical linearization. Simulation results on the typical target tracking example over a sensor network are given to illustrate the effectiveness of the proposed algorithms.
Improved Binary Symbiotic Organism Search Algorithm With Transfer Functions for Feature Selection

Symbiotic Organism Search (SOS) algorithm is highly praised by researchers for its excellent convergence performance, global optimization ability and simplicity in solving various continuous practical problems.However, in the real world, there are many binary problems, which can only take values of 0 and 1, that still need to be solved.Since the original SOS algorithm cannot directly solve the binary problem, the original ASOS Binary SOS (BSOS) algorithm has the disadvantage of premature convergence.In order to improve the limitations of the ASBSOS algorithm, we propose an Improved BSOS (IBSOS) algorithm.As we all know, the transfer function is very important in the binarization of continuous optimization algorithms.Therefore, we used 9 transfer functions in the IBSOS algorithm to binarize the continuous SOS algorithm and analyzed the impact of each transfer function on the performance of the BSOS algorithm.Moreover, we use the same three biological symbiosis strategies as the continuous SOS algorithm in our proposed IBSOS algorithm to binarize the SOS algorithm to improve The diversity of the algorithm execution process and the ability to balance algorithm exploration and development.In order to verify the performance of IBSOS using different transfer functions, we use 13 benchmark functions to show the global optimization capability and convergence speed of the BSOS algorithm.Finally, we apply the algorithm to feature selection in the ten data sets of UCI.The experimental results with low classification error and few features further verify the excellent performance of the IBSOS algorithm.
TopoX: Topology Refactorization for Minimizing Network Communication in Graph Computations

Efficient graph partitioning is vital for high-performance graph-parallel systems. Traditional graph partitioning methods attempt to both minimize communication cost and guarantee load balancing in computation. However, the skewed degree distribution of natural graphs makes it difficult to simultaneously achieve the two objectives. This article proposes topology refactorization (TR), a topology-aware method allowing graph-parallel systems to separately handle the two objectives: refactorization is mainly focused on reducing communication cost, and partitioning is mainly targeted for balancing the load. TR transforms a skewed graph into a more communication-efficient topology through fusion and fission, where the fusion operation organizes a set of neighboring low-degree vertices into a super-vertex, and the fission operation splits a high-degree vertex into a set of sibling sub-vertices. Based on TR, we design an efficient graph-parallel system (TopoX) which pipelines refactorization with partitioning to both reduce communication cost and balance computation load. Prototype evaluation shows that TopoX outperforms PowerLyra by up to 78.5% (from 37.2%) on real-world graphs and is faster than most other graph-parallel systems, while only introducing small overhead and memory consumption.
Analyzing the role of national culture on content creation and user engagement on Twitter: The case of Indian Premier League cricket franchises

The content generation strategy of a sports franchise determines whether the user engagement increases or decreases on social media platforms. Thus, the role of Chief Operating Officer (COO) is profound who generally decides and governs social media policies of the franchises. We show that the cultural differences between local-COO vis-à-vis foreign-COO-governed sports franchises reflect in their content generation strategy and are also associated with user engagement. We use Hofstede's cultural dimensions theory and extract relevant features from the tweets. Overall, the results show that user engagement is more when the content generation strategy is in alignment with fans' national culture. The first contribution of our work is towards showing the incremental impact of power distance, individualism and collectivism on user engagement. The second contribution of our work is towards feature construction, feature selection and building authorship attribution classifiers to understand the content generation strategy. Prior literature shows that national culture impacts writing of online reviews. We investigate the role of national culture in social media content generation and user engagement and extend the literature. Our study is useful for organizations to understand the role of national culture in content generation and how it is related to user engagement.
A Depthwise Separable Convolution Architecture for CNN Accelerator

Convolutional Neural Network (CNN) give an unmatched performance in image classification, object detection and object tracking. As many of the modern embedded systems for portable devices deals with similar tasks, they often deploy CNN based algorithms. The intensive computational workload associated with CNN inference demands powerful computing platforms like Graphics Processing Units. However, deploying CNN on mobile devices demands low power, application specific computing platforms like Field-Programmable Gate Array (FPGA) and Application-Specific Integrated Circuit (ASIC) which can work as computation accelerator units. Moreover, using certain algorithmic optimizations like using Depthwise Separable Convolution instead of standard convolution, significantly reduces the computational burden of CNN inference. This paper discusses a pipelined architecture of Depthwise Separable Convolution followed by activation and pooling operations for a single layer of CNN. The architecture is implemented on Xilinx 7 series FPGA and works at a clock period of 40ns. It can be used as a building block for an integrated system of CNN accelerator for implementation on FPGAs of different sizes. This work focuses on speeding up the convolution process, instead of implementing large design of an integrated system of CNN accelerator which makes it difficult to focus on performance of the subsystems. To the best of the knowledge of the authors, earlier works have implemented an integrated system of CNN accelerator but the blueprint for architecture of a single layer of CNN is not discussed individually, which can be a great support for the beginners in understanding FPGA based computing accelerators for CNN.
Solving the Max-Flow Problem on a Quantum Annealing Computer

This article addresses the question of implementing a maximum flow algorithm on directed graphs in a formulation suitable for a quantum annealing computer. Three distinct approaches are presented. In all three cases, the flow problem is formulated as a quadratic unconstrained binary optimization (QUBO) problem amenable to quantum annealing. The first implementation augments a graph with integral edge capacities into a multigraph with unit-capacity edges and encodes the fundamental objective and constraints of the maximum flow problem using a number of qubits equal to the total capacity of the graph $\sum _i{c_i}$. The second implementation, which encodes flows through edges using a binary representation, reduces the required number of qubits to $\mathcal {O}(|E| \log C_{\max })$, where $|E|$ and $C_{\max }$ denote the number of edges and maximum edge capacity of the graph, respectively. The third implementation adapts the dual minimum cut formulation and encodes the problem instance using $|V|$ qubits, where $|V|$ is the number of vertices in the graph. Scaling factors for penalty terms and coupling matrix construction times are made explicit in this article.
Knowledge-driven Egocentric Multimodal Activity Recognition

Recognizing activities from egocentric multimodal data collected by wearable cameras and sensors, is gaining interest, as multimodal methods always benefit from the complementarity of different modalities. However, since high-dimensional videos contain rich high-level semantic information while low-dimensional sensor signals describe simple motion patterns of the wearer, the large modality gap between the videos and the sensor signals raises a challenge for fusing the raw data. Moreover, the lack of large-scale egocentric multimodal datasets due to the cost of data collection and annotation processes makes another challenge for employing complex deep learning models. To jointly deal with the above two challenges, we propose a knowledge-driven multimodal activity recognition framework that exploits external knowledge to fuse multimodal data and reduce the dependence on large-scale training samples. Specifically, we design a dual-GCLSTM (Graph Convolutional LSTM) and a multi-layer GCN (Graph Convolutional Network) to collectively model the relations among activities and intermediate objects. The dual-GCLSTM is designed to fuse temporal multimodal features with top-down relation-aware guidance. In addition, we apply a co-attention mechanism to adaptively attend to the features of different modalities at different timesteps. The multi-layer GCN aims to learn relation-aware classifiers of activity categories. Experimental results on three publicly available egocentric multimodal datasets show the effectiveness of the proposed model.
A Novel Image Encryption Algorithm Based on Chaotic Sequences and Cross-Diffusion of Bits

To protect image information security, we propose an encryption algorithm based on chaotic sequences and bits' cross-diffusion. First, two chaotic sequences are generated by two-dimensional logic-sine coupling mapping, and the original image is scrambled with the two sequences. Secondly, the scrambled image is transformed into a one-dimensional sequence, and the low-order bits between every two pixels are fused to change the detailed information of the image, improved the ability to resist differential attacks. Finally, chaotic sequences are generated by iterative logical mapping to perform pixel replacement and ciphertext diffusion. The known plaintext attack can be effectively resisted because of the stochastic combination of diffusion scheme and chaotic sequence. It also has a significant effect on resisting differential attack. Take Lena image as an example, NPCR and UACI can reach 99.6063% and 33.4477%, respectively, which are very close to the ideal value. The encryption scheme has good key sensitivity and strong ability to resist statistical attacks, which shows that the algorithm has adequate security.
StegoNet: Turn Deep Neural Network into a Stegomalware

Deep Neural Networks (DNNs) are now presenting human-level performance on many real-world applications, and DNN-based intelligent services are becoming more and more popular across all aspects of our lives. Unfortunately, the ever-increasing DNN service implies a dangerous feature which has not yet been well studied–allowing the marriage of existing malware and DNN model for any pre-defined malicious purpose. In this paper, we comprehensively investigate how to turn DNN into a new breed evasive self-contained stegomalware, namely StegoNet, using model parameter as a novel payload injection channel, with no service quality degradation (i.e. accuracy) and the triggering event connected to the physical world by specified DNN inputs. A series of payload injection techniques which take advantage of a variety of unique neural network natures like complex structure, high error resilience capability and huge parameter size, are developed for both uncompressed models (with model redundancy) and deeply compressed models tailored for resource-limited devices (no model redundancy), including LSB substitution, resilience training, value mapping, and sign-mapping. We also proposed a set of triggering techniques like logits trigger, rank trigger and fine-tuned rank trigger to trigger StegoNet by specific physical events under realistic environment variations. We implement the StegoNet prototype on Nvidia Jetson TX2 testbed. Extensive experimental results and discussions on the evasiveness, integrity of proposed payload injection techniques, and the reliability and sensitivity of the triggering techniques, well demonstrate the feasibility and practicality of StegoNet.
Deep Normalization for Speaker Vectors

Deep speaker embedding has demonstrated state-of-the-art performance in speaker recognition tasks. However, one potential issue with this approach is that the speaker vectors derived from deep embedding models tend to be non-Gaussian for each individual speaker, and non-homogeneous for distributions of different speakers. These irregular distributions can seriously impact speaker recognition performance, especially with the popular PLDA scoring method, which assumes homogeneous Gaussian distribution. In this article, we argue that deep speaker vectors require deep normalization, and propose a deep normalization approach based on a novel discriminative normalization flow (DNF) model. We demonstrate the effectiveness of the proposed approach with experiments using the widely used SITW and CNCeleb corpora. In these experiments, the DNF-based normalization delivered substantial performance gains and also showed strong generalization capability in out-of-domain tests.
Using Blockchain Data Security Management for E-Voting Systems

In this era of disruption, voting methods in elections have been carried out using the system. But in the process, there are still some obstacles, such as the calculation of election results can still be manipulated. In the 2019 election between candidates A and B, according to the fast-counting technology, media candidate A gets the highest votes, but in electronic media states candidate, B gets the highest votes. This raises the possibility of manipulation of the vote. This study addresses the issues involved in delivering valid results and also authenticates the validity of voting data. The method in developing this research uses blockchain technology that can verify data from voting with a decentralized system. Decentralization on the blockchain allows each server to connect and have the same role and form a peer-to-peer network. Data tracking is easier, and when one server is in trouble, backups are easily performed by other servers, and the problematic server is temporarily removed from the blockchain network. The application of blockchain technology in electronic voting can produce data that cannot be manipulated and will be maintained authenticity.