Reviewerly/exHarmony huggingface dataset:
The Concept of Development of the Integrated Transport System of the Russian Federation

The study analyzes the contradiction between the needs of the practice of the integrated use of the transport system in the interests of ensuring the national security of the country and the state of the scientific and methodological base in this area. The study uses systems analysis, logical and mathematical modeling, systems theory and others, as well as methods of operations research, and economic and mathematical methods. The high dynamics of transport support for economic, military, socio-demographic and environmental security in a market economy requires the improvement of the element base of the concept of the integrated use of transport in the interests of the national security of the country. The survivability of the transport network is ensured by a set of measures aimed at maintaining or restoring its operability in the face of terrorist attacks or the impact of damaging factors of emergency situations (ES). The present study is devoted to the resolution of this contradiction. The scientific task of developing methods and models of economic and military-economic efficiency of the integrated use of various types of transport in the interests of ensuring the national security of the country is formed in this paper. There are problems in the system of functioning of the country’s unified transport system, among which the problem of preparation for work in emergency situations, as well as ensuring the survivability of the country’s transport system, stands out. Preparing transport for work in wartime and in a special period involves solving a set of tasks: defense and mobilization training of modes of transport; technical protection of transport communications.
Multilayered review of safety approaches for machine learning-based systems in the days of AI

The unprecedented advancement of artificial intelligence (AI) in recent years has altered our perspectives on software engineering and systems engineering as a whole. Nowadays, software-intensive intelligent systems rely more on a learning model than thousands of lines of codes. Such alteration has led to new research challenges in the engineering process that can ensure the safe and beneficial behavior of AI systems. This paper presents a literature survey of the significant efforts made in the last fifteen years to foster safety in complex intelligent systems. This survey covers relevant aspects of AI safety research including safety requirements engineering, safety-driven design at both system and machine learning (ML) component level, validation and verification from the perspective of software and system engineers. We categorize these research efforts based on a three-layered conceptual framework for developing and maintaining AI systems. We also perform a gap analysis to emphasize the open research challenges in ensuring safe AI. Finally, we conclude the paper by providing future research directions and a road map for AI safety.
A systematic narrative review of agent persona on learning outcomes and design variables to enhance personification

(2021). A systematic narrative review of agent persona on learning outcomes and design variables to enhance personification. Journal of Research on Technology in Education: Vol. 53, Virtual Humans in Learning, pp. 89-106.
Blockchain-based searchable encryption with efficient result verification and fair payment

Data outsourcing has become a popular application. Searchable encryption (SE) can provide data retrievability as well as keep data privacy. In the threat model of SE schemes, the cloud server is usually assumed to be Honest-But-Curious, but this may not be true in reality. The blockchain-based SE makes malicious cloud servers dare not to deviate from the protocol, and search results are validated by miners. However, rational miners would skip the validation step and accept the block directly to save computational resources. This situation is called the Verifier's Dilemma which will make the blockchain-based SE scheme not real verifiable. Therefore, this paper proposes a verifiable blockchain-based public-key encryption scheme that outsources the verification to the TrueBit network. This design makes our scheme achieves effective verifiability which will minimize the computational cost of miners. Furthermore, our scheme also establishes a fair payment protocol between the multiple data owners and data users. Also, the data owner can revoke the permission of the document he/she shared before. We also present security proofs and analysis of the proposed scheme. Performance evaluation presents the overheads of performing search operations on the cloud server and conducting transactions on smart contracts in Ethereum. The experiment results demonstrate the practicability of our proposed scheme.
Monitoring and Mathematical Modeling of Soil and Groundwater Contamination by Harmful Emissions of Nitrogen Dioxide from Motor Vehicles

The article considers the task and a new approach to monitoring of soil and groundwater contamination by harmful emissions of nitrogen dioxide from motor vehicles. The approach is based on combination of measurement procedure of the harmful emissions concentrations in the surface atmospheric layer and mathematical modeling of the impact of these emissions on soil and groundwater contamination. The scheme of this harmful emission concentrations measurement using a mobile complex Sniffer4D Hyper-local Air Quality Analyzer is given. Based on these results, a mathematical model of diffusion of this harmful substance in the upper layers of soil and water resources is proposed. As a result of the computations, the distribution of concentrations of soil and water contamination in the neighborhood of separate observation points has been built. Also, the field of nitrogen dioxide concentrations at various depths for observation points has been modeled. For a waterbody, the concentration of nitric acid, which is formed due to the interaction of nitrogen dioxide with water, is higher in water than in the surface layer of the soil. This concentration can be significantly increased during prolonged downpours when acidic solutions drain from surrounding soil areas into the waterbodies. The obtained research results are fully consistent with the practical and theoretical results on the diffusion of gases into soils and water resources. It means that the proposed approach can be used instead of the existing very expensive approach to analysis of soil and groundwater contamination in the laboratory.
A secure heuristic semantic searching scheme with blockchain-based verification

Secure semantic searching provides privacy-preserving information retrieval for arbitrary queries semantically related to keywords. However, current verification mechanisms cannot verify the correctness of search results for semantically related queries in mutual distrust scenarios. Moreover, most secure semantic searching schemes still perform exact matching on ciphertext after query expansion, then directly accumulate the weights of the matched keywords as similarity measurements to rank the documents, resulting in the search results with unsatisfied ranking. In this paper, we propose a secure heuristic semantic searching scheme, in which a privacy-preserving word nonlinear matching (PPWNM) method is developed, and a blockchain-based verification is designed to obtain trustworthy search results with high retrieval accuracy. Specifically, we describe several retrieval heuristics and formulate them as a word nonlinear matching problem, then transform it to the PPWNM problem for calculating the similarity measurement between the query and document so that the highly accurate ranked results can be obtained. Also we design a blockchain-based verification mechanism in which the blockchain nodes utilize the proofs generated during the matching process to verify the correctness of the search results and reach a consensus to ensure the trustworthy results and fair payment between the participants in a mutual distrust model. Our security analysis and experimental results show that the proposed scheme is secure and has higher accuracy compared with the existing other schemes.
SalED: Saliency prediction with a pithy encoder-decoder architecture sensing local and global information

This paper proposes a deep convolutional neural network with a concise and effective encoder-decoder architecture for saliency prediction. Local and global contextual features make a considerable contribution to saliency prediction. In order to integrate and exploit these features more thoroughly, in the proposed pithy architecture, we deploy a dense and global context connection structure between the encoder and decoder, after that, a multi-scale readout module is designed to process various information from the previous portion of the decoder with different parallel mapping relationships for full-scale accurate results. Our model ranks first in light of multiple metrics on two famous saliency benchmarks and performs good generalization on other datasets. Besides, we evaluate the precision and the speed of our model with different backbones. The saliency prediction performance of VGGNet-Based, ResNet-based, and DenseNet-based model gradually increases while the speed also drops off. And the experiments illustrate that our model performs better than other models even if replacing the backbone of our model with the same backbone of the compared model. Therefore, we can provide optional versions of our model for different requirements of performance and efficiency.
Garment Recommendation with Memory Augmented Neural Networks

Fashion plays a pivotal role in society. Combining garments appropriately is essential for people to communicate their personality and style. Also different events require outfits to be thoroughly chosen to comply with underlying social clothing rules. Therefore, combining garments appropriately might not be trivial. The fashion industry has turned this into a massive source of income, relying on complex recommendation systems to retrieve and suggest appropriate clothing items for customers. To perform better recommendations, personalized suggestions can be performed, taking into account user preferences or purchase histories. In this paper, we propose a garment recommendation system to pair different clothing items, namely tops and bottoms, exploiting a Memory Augmented Neural Network (MANN). By training a memory writing controller, we are able to store a non-redundant subset of samples, which is then used to retrieve a ranked list of suitable bottoms to complement a given top. In particular, we aim at retrieving a variety of modalities in which a certain garment can be combined. To refine our recommendations, we then include user preferences via Matrix Factorization. We experiment on IQON3000, a dataset collected from an online fashion community, reporting state of the art results.
Knowledge-Infused Abstractive Summarization of Clinical Diagnostic Interviews: Framework Development Study

Background In clinical diagnostic interviews, mental health professionals (MHPs) implement a care practice that involves asking open questions (eg, “What do you want from your life?” “What have you tried before to bring change in your life?”) while listening empathetically to patients. During these interviews, MHPs attempted to build a trusting human-centered relationship while collecting data necessary for professional medical and psychiatric care. Often, because of the social stigma of mental health disorders, patient discomfort in discussing their presenting problem may add additional complexities and nuances to the language they use, that is, hidden signals among noisy content. Therefore, a focused, well-formed, and elaborative summary of clinical interviews is critical to MHPs in making informed decisions by enabling a more profound exploration of a patient’s behavior, especially when it endangers life. Objective The aim of this study is to propose an unsupervised, knowledge-infused abstractive summarization (KiAS) approach that generates summaries to enable MHPs to perform a well-informed follow-up with patients to improve the existing summarization methods built on frequency heuristics by creating more informative summaries. Methods Our approach incorporated domain knowledge from the Patient Health Questionnaire-9 lexicon into an integer linear programming framework that optimizes linguistic quality and informativeness. We used 3 baseline approaches: extractive summarization using the SumBasic algorithm, abstractive summarization using integer linear programming without the infusion of knowledge, and abstraction over extractive summarization to evaluate the performance of KiAS. The capability of KiAS on the Distress Analysis Interview Corpus-Wizard of Oz data set was demonstrated through interpretable qualitative and quantitative evaluations. Results KiAS generates summaries (7 sentences on average) that capture informative questions and responses exchanged during long (58 sentences on average), ambiguous, and sparse clinical diagnostic interviews. The summaries generated using KiAS improved upon the 3 baselines by 23.3%, 4.4%, 2.5%, and 2.2% for thematic overlap, Flesch Reading Ease, contextual similarity, and Jensen Shannon divergence, respectively. On the Recall-Oriented Understudy for Gisting Evaluation-2 and Recall-Oriented Understudy for Gisting Evaluation-L metrics, KiAS showed an improvement of 61% and 49%, respectively. We validated the quality of the generated summaries through visual inspection and substantial interrater agreement from MHPs. Conclusions Our collaborator MHPs observed the potential utility and significant impact of KiAS in leveraging valuable but voluminous communications that take place outside of normally scheduled clinical appointments. This study shows promise in generating semantically relevant summaries that will help MHPs make informed decisions about patient status.
Quadratic surface support vector machine with L1 norm regularization

&lt;p style='text-indent:20px;'&gt;We propose &lt;inline-formula&gt;&lt;tex-math id="M1"&gt;\begin{document}$ \ell_1 $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt; norm regularized quadratic surface support vector machine models for binary classification in supervised learning. We establish some desired theoretical properties, including the existence and uniqueness of the optimal solution, reduction to the standard SVMs over (almost) linearly separable data sets, and detection of true sparsity pattern over (almost) quadratically separable data sets if the penalty parameter on the &lt;inline-formula&gt;&lt;tex-math id="M2"&gt;\begin{document}$ \ell_1 $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt; norm is large enough. We also demonstrate their promising practical efficiency by conducting various numerical experiments on both synthetic and publicly available benchmark data sets.&lt;/p&gt;
Polygonal Coordinate System: Visualizing high-dimensional data using geometric DR, and a deterministic version of t-SNE

Dimensionality Reduction (DR) is useful to understand high-dimensional data. It attracts wide attention from industry and academia and is employed in areas such as machine learning, data mining, and pattern recognition. This work presents a geometric approach to DR termed Polygonal Coordinate System (PCS), capable of representing multidimensional data in two or three dimensions while preserving their inherent overall structure by taking advantage of a polygonal interface bridging high- and low-dimensional spaces. PCS can handle Big Data by adopting an incremental, geometric DR with linear-time complexity. A new version of t-Distributed Stochastic Neighbor Embedding (t-SNE), a state-of-the-art algorithm for DR, is also provided. It employs a PCS-based deterministic strategy and is named t-Distributed Deterministic Neighbor Embedding (t-DNE). Several synthetic and real data sets were used as well-known real-world problem archetypes in our benchmark, providing a means to evaluate PCS and t-DNE against four embedding-based DR algorithms: two linear-transformation ones (Principal Component Analysis and Non-negative Matrix Factorization) and two nonlinear ones (t-SNE and Sammon's Mapping). Statistical comparisons of the execution times of these algorithms, by the Friedman's significance test, highlight the efficiency of PCS in data embedding. PCS tends to surpass its counterparts in several aspects explored in this work, including asymptotic time and space complexity, preservation of global data-inherent structures, number of hyperparameters, and applicability to unobserved data.
Federated Learning Algorithm Based on Knowledge Distillation

Federated learning is a new scheme of distributed machine learning, which enables a large number of edge computing devices to jointly learn a shared model without private data sharing. Federated learning allows nodes to synchronize only the locally trained models instead of their own private data, which provides a guarantee for privacy and security. However, due to the challenges of heterogeneity in federated learning, which are: (1) heterogeneous model architecture among devices; (2) statistical heterogeneity in real federated dataset, which do not obey independent-identical-distribution, resulting in poor performance of traditional federated learning algorithms. To solve the problems above, this paper proposes FedDistill, a new distributed training method based on knowledge distillation. By introducing personalized model on each device, the personalized model aims to improve the local performance even in a situation that global model fails to adapt to the local dataset, thereby improving the ability and robustness of the global model. The improvement of the performance of local device benefits from the effect of knowledge distillation, which can guide the improvement of global model by knowledge transfer between heterogeneous networks. Experiments show that FedDistill can significantly improve the accuracy of classification tasks and meet the needs of heterogeneous users.
High-capacity reversible data hiding in encrypted image based on Huffman coding and differences of high nibbles of pixels

In this paper, we propose a new reversible data hiding method in encrypted images. Due to spatial correlation, there is a large probability that the adjacent pixels of the image have small differences, which is especially obvious on the high four most significant bits (high nibbles) of the pixels. If the high nibble of each pixel is regarded as a 4-bit value, the differences between the high nibbles of the adjacent pixels are mostly concentrated in a small range. Based on this fact, Huffman coding was used to encode all the differences between the high nibbles of the adjacent pixels in order to compress the four most significant bit (MSB) planes efficiently and create a large-capacity room. After creating room, a stream cipher is used to encrypt the image, and the room is reserved in the encrypted image for data hiding without losing information. The experimental results showed that the proposed method can achieve a larger embedding rate and better visual quality of the marked decrypted image than other related methods.
Document Image Binarization With Stroke Boundary Feature Guided Network

Text is the most crucial element in a document image but is often disconnected in document image binarization.Most of the previous methods based on deep learning do not focus on structure information such as stroke boundary, leading to disconnected strokes when the stroke is ambiguous or weak.In this paper, we propose a multi-task learning with an auxiliary task for learning stroke boundary features in an adversarial manner.The learned boundary features are integrated into the main task for the binarization.Specifically, in the first step, in addition to using shared global location features with the main task, the auxiliary task leverages additional local edges to obtain stroke boundary features.In the second step, we use adversarial loss based on boundary ground truth to supervise the obtained stroke boundary feature in the auxiliary task.The adversarial training is to embed expert knowledge, especially structure information, in the model.In the third step, the learned boundary feature from the auxiliary task supports the main task directly.The fusion module of the main task refines the final binarized image.Experiments show that our method achieves better-preserved stroke and better performance than existing methods on benchmark H-DBCO and DIBCO datasets.
Exploiting Pull-In/Pull-Out Hysteresis in Electrostatic MEMS Sensor Networks to Realize a Novel Sensing Continuous-Time Recurrent Neural Network

The goal of this paper is to provide a novel computing approach that can be used to reduce the power consumption, size, and cost of wearable electronics. To achieve this goal, the use of microelectromechanical systems (MEMS) sensors for simultaneous sensing and computing is introduced. Specifically, by enabling sensing and computing locally at the MEMS sensor node and utilizing the usually unwanted pull in/out hysteresis, we may eliminate the need for cloud computing and reduce the use of analog-to-digital converters, sampling circuits, and digital processors. As a proof of concept, we show that a simulation model of a network of three commercially available MEMS accelerometers can classify a train of square and triangular acceleration signals inherently using pull-in and release hysteresis. Furthermore, we develop and fabricate a network with finger arrays of parallel plate actuators to facilitate coupling between MEMS devices in the network using actuating assemblies and biasing assemblies, thus bypassing the previously reported coupling challenge in MEMS neural networks.
Neural Networks with Fixed Binary Random Projections Improve Accuracy in Classifying Noisy Data

The trend of Artificial Neural Networks becoming\bigger"and \deeper" persists. Training these networks using back-propagation is considered biologically implausible and a time-consuming task. Hence, we investigate how far we can go with fixed binary random projections (BRPs), an approach which reduces the number of trainable parameters using localized receptive fields and binary weights. Evaluating this approach on the MNIST dataset we discovered that contrary to models with fully-trained dense weights, models using fixed localized sparse BRPs yield equally good performance in terms of accuracy, saving 98% computations when generating the hidden representation for the input. Furthermore, we discovered that using BRPs leads to a more robust performance – up to 56% better compared to dense models – in terms of classifying noisy inputs.
Proposal and experimental case study on building ventilating fan fault diagnosis based on cuckoo search algorithm optimized extreme learning machine

As the necessary auxiliary equipment in the fields of building energy system, the fan's healthy condition is very important for energy saving and public safety. Machine-learning fault diagnosis can improve fan performance and bring benefits of energy, economy, and safety. However, the vibration signal of the fan is particularly susceptible to interference from other factors, which brings great trouble to the existing machine-learning fault diagnosis Therefore, in order to learn useful fault features more effectively, a fan fault diagnosis method based on cuckoo search (CS) algorithm optimized extreme learning machine (ELM) is proposed in this paper. Firstly, wavelet packet extracts the original signal features. Secondly, with the help of the powerful search ability of the CS algorithm, the ELM model parameters are optimized to fully learn the signal features. Finally, the optimized ELM is tested through two cases. The experimental fault data of two types of fan are collected and employed to verify the effectiveness of this method, and the superiority compared with other existing methods. The results show that the proposed method can increase total recognition rate by at least 2.25% and at most 21.49%, indicating good progress and application potential.