Reviewerly/exHarmony huggingface dataset:
Machine Learning for Fast and Reliable Source-Location Estimation in Earthquake Early Warning

We develop a random forest (RF) model for rapid earthquake location with an aim to assist earthquake early warning (EEW) systems in fast decision making. This system exploits P-wave arrival times at the first five stations recording an earthquake and computes their respective arrival time differences relative to a reference station (i.e., the first recording station). These differential P-wave arrival times and station locations are classified in the RF model to estimate the epicentral location. We train and test the proposed algorithm with an earthquake catalog from Japan. The RF model predicts the earthquake locations with high accuracy, achieving a mean absolute error (MAE) of 2.88 km. As importantly, the proposed RF model can learn from a limited amount of data (i.e., 10&#x0025; of the dataset) and much fewer (i.e., three) recording stations and still achieve satisfactory results (MAE &#x003C; 5 km). The algorithm is accurate, generalizable, and rapidly responding, thereby offering a powerful new tool for fast and reliable source-location prediction in EEW.
Multi-Party Electronic Contract Signing Protocol Based on Blockchain

With the development of Internet technology, the demand for signing electronic contracts has been greatly increased. The electronic contract generated by the participants in an online way enjoys the same legal effect as paper contract. The fairness is the key issue in jointly signing electronic contracts by the involved participants, so that all participants can either get the same copy of the contract or nothing. Most existing solutions only focus on the fairness of electronic contract generation between two participants, where the digital signature can effectively guarantee the fairness of the exchange of electronic contracts and becomes the conventional technology in designing the contract signing protocol. In this paper, an efficient blockchain-based multi-party electronic contract signing (MECS) protocol is presented, which not only offers the fairness of electronic contract generation for multiple participants, but also allows each participant to aggregate validate the signed copy of others. Security analysis shows that the proposed MECS protocol enjoys unforgeability, non-repudiation and fairness of electronic contracts, and performance analysis demonstrates the high efficiency of our construction.
COVIDSum: A linguistically enriched SciBERT-based summarization model for COVID-19 scientific papers

The coronavirus disease (COVID-19) has claimed the lives of over 350,000 people and infected more than 173 million people worldwide, it triggers researchers from diverse fields are accelerating their research to help diagnostics, therapies, and vaccines. Researchers also publish their recent research progress through scientific papers. However, manually writing the abstract of a paper is time-consuming, and it increases the writing burden of the researchers. Abstractive summarization technique which automatically provides researchers reliable draft abstracts, can alleviate this problem. In this work, we propose a linguistically enriched SciBERT-based summarization model for COVID-19 scientific papers, named COVIDSum. Specifically, we first extract salient sentences from source papers and construct word co-occurrence graphs. Then, we adopt a SciBERT-based sequence encoder and a Graph Attention Networks-based graph encoder to encode sentences and word co-occurrence graphs, respectively. Finally, we fuse the above two encodings and generate an abstractive summary of each scientific paper. When evaluated on the publicly available COVID-19 open research dataset, the performance of our proposed model achieves significant improvement compared with other document summarization models.
Human Activity and Motion Pattern Recognition within Indoor Environment Using Convolutional Neural Networks Clustering and Naive Bayes Classification Algorithms

Human Activity Recognition (HAR) systems are designed to read sensor data and analyse it to classify any detected movement and respond accordingly. However, there is a need for more responsive and near real-time systems to distinguish between false and true alarms. To accurately determine alarm triggers, the motion pattern of legitimate users need to be stored over a certain period and used to train the system to recognise features associated with their movements. This training process is followed by a testing cycle that uses actual data of different patterns of activity that are either similar or different to the training data set. This paper evaluates the use of a combined Convolutional Neural Network (CNN) and Naive Bayes for accuracy and robustness to correctly identify true alarm triggers in the form of a buzzer sound for example. It shows that pattern recognition can be achieved using either of the two approaches, even when a partial motion pattern is derived as a subset out of a full-motion path.
A Consortium Blockchain-Based Secure and Trusted Electronic Portfolio Management Scheme

In recent times, electronic portfolios (e-portfolios) are being increasingly used by students and lifelong learners as digital online multimedia résumés that showcase their skill sets and achievements. E-portfolios require secure, reliable, and privacy-preserving credential issuance and verification mechanisms to prove learning achievements. However, existing systems provide private institution-wide centralized solutions that primarily rely on trusted third parties to issue and verify credentials. Furthermore, they do not enable learners to own, control, and share their e-portfolio information across organizations, which increases the risk of forged and fraudulent credentials. Therefore, we propose a consortium blockchain-based e-portfolio management scheme that is decentralized, secure, and trustworthy. Smart contracts are leveraged to enable learners to completely own, publish, and manage their e-portfolios, and also enable potential employers to verify e-portfolio credentials and artifacts without relying on trusted third parties. Blockchain is used as an immutable distributed ledger that records all transactions and logs for tamper-proof trusted data provenance, accountability, and traceability. This system guarantees the authenticity and integrity of user credentials and e-portfolio data. Decentralized identifiers and verifiable credentials are used for user profile identification, authentication, and authorization, whereas verifiable claims are used for e-portfolio credential proof authentication and verification. We have designed and implemented a prototype of the proposed scheme using a Quorum consortium blockchain network. Based on the evaluations, our solution is feasible, secure, and privacy-preserving. It offers excellent performance.
FHI-Unet: Faster Heterogeneous Images Semantic Segmentation Design and Edge AI Implementation for Visible and Thermal Images Processing

The same class of objects clustering process in a frame is known as semantic segmentation. The deep convolutional neural network-based semantic segmentation needs large-scale computations and annotations for data training to reach real-time inference speeds. The heterogeneous image segmentation is a more challenging task to categorize each pixel of an image. However, the heterogeneous image semantic segmentation method extracts the features of visible and thermal images separately. We designed an efficient architecture with the multi-hybrid-autoencoder and decoder for Faster Heterogeneous Image (FHI) Semantic Segmentation. The proposed corresponding architecture has fewer layers resulting in lower parameters, higher inference speed, and Intersection over Union (IoU). The specialty of this architecture is the discrete autonomous feature extraction framework for RGB image and Thermal (T) image inputs with individual convolutional layers. Later, we combined the 4-channels (RGBT) convolution features to reduce computational complexity and robust the model performances. The proposed FHI-Unet semantic segmentation model experimented on NVIDIA Xavier NX edge AI platforms with standard accuracy under the real-time inference requirement. The proposed FHI-Unet model has the highest mIoU of 43.67 and the fastest real-time inference of 83.39 frames per second on edge AI implementation. The proposed approach improves 31.36% inference speed, 7.16% mAcc, and 5.1% mIoU on the Multi-spectral Semantic Segmentation Dataset compared with the existing works.
Complexity for an open quantum system

We study the complexity for an open quantum system. Our system is a harmonic oscillator coupled to a one-dimensional massless scalar field, which acts as the bath. Specifically, we consider the reduced density matrix by tracing out the bath degrees of freedom for both regular and inverted oscillator and computed the complexity of purification (COP) and complexity by using the operator-state mapping. We found that when the oscillator is regular the COP saturates quickly for both underdamped and overdamped oscillators. Interestingly, when the oscillator is underdamped, we discover a kink like behaviour for the saturation value of COP with varying damping coefficient. For the inverted oscillator, we found a linear growth of COP with time for all values of bath-system interaction. However, when the interaction is increased the slope of the linear growth decreases, implying that the unstable nature of the system can be regulated by the bath.
Cross-Dataset Learning for Age Estimation

Age estimation from a single human face image has been an important yet challenging task in computer vision and multimedia. Due to the large individual differences in human faces, including the differences in races and genders, the performance of a learning model depends largely on training data. The existing learning methods are challenged by insufficient numbers of images and poor-quality images in datasets, as well as by new low-precision data that are dissimilar to existing training data. In this paper, we propose a learning method called the cross-dataset training convolutional neural network (CDCNN), which uses a general framework for cross-dataset training in age estimation. We adopted convolutional neural networks (CNNs) with VGG-16 architectures pretrained on ImageNet and treated the age estimation problem as a classification problem. For the classification results, softmax is utilized to map the output and provide value refinement. We conducted a series of experiments on the Craniofacial Longitudinal Morphological Face Database (MORPH), Cross-Age Celebrity Dataset (CACD), and Asian Face Age Dataset (AFAD). The results show that simultaneous training on multiple datasets using additional labeled data achieves a more impressive performance when compared to training on a single, independent dataset. Our proposed cross-dataset training model achieves state-of-the-art results on both the AFAD and CACD age estimation benchmarks with great generalizability.
Single-Image Reflection Removal Using Deep Learning: A Systematic Review

Images captured through the glass often consist of undesirable specular reflections. These reflections detected in front of the glass remarkably reduce the quality and visibility of the scenes behind it. The process of reflection removal from images through the glass has many important applications in computer vision projects. Recently deep learning-based methods are being utilized for reflection removal so widely. In this article, we proposed a systematic literature review on the topic of single-image reflection removal using deep learning methods which were published between the years 2015 to 2021. A total number of 1600 research papers were extracted from five different online databases and digital libraries (IEEE Xplore, Google Scholar, Science Direct, SpringerLink and ACM Digital Library). After following the study selection procedure, 25 research papers were selected for this systematic review. The selected research papers were then analyzed to answer 7 key research questions that we have come up with to comprehensively explore the use of deep learning and neural networks for single-image reflection removal. After reading this article, future researchers will have a solid idea in the research field and will be able to work on their own research. The results provided in this proposed systematic review illustrate the main challenges that are encountered by researchers in this field and recommend encouraging directions for future research work. This review will also be helpful for researchers in discovering accessible datasets that can be used as benchmarks for comparing their proposed deep learning techniques with other studies in this research area.
IEFHAC: Image encryption framework based on hessenberg transform and chaotic theory for smart health

Smart cities aim to improve the quality of life by utilizing technological advancements. One of the main areas of innovation includes the design, implementation, and management of data-intensive medical systems also known as big-data Smart Healthcare systems. Smart health systems need to be supported by highly efficient and resilient security frameworks. One of the important aspects that smart health systems need to provide, is timely access to high-resolution medical images, that form about 80% of the medical data. These images contain sensitive information about the patient and as such need to be secured completely. To prevent unauthorized access to medical images, the process of image encryption has become an imperative task for researchers all over the world. Chaos-based encryption has paved the way for the protection of sensitive data from being altered, modified, or hacked. In this paper, we present an Image Encryption Framework based on Hessenberg transform and Chaotic encryption (IEFHAC), for improving security and reducing computational time while encrypting patient data. IEFHAC uses two 1D-chaotic maps: Logistic map and Sine map for the confusion of data, while diffusion has been achieved by applying the Hessenberg household transform. The Sin and Logistic maps are used to regeneratively affect each other's output, as such dynamically changing the key parameters. The experimental analysis demonstrates that IEFHAC shows better results like NPCR ranging from 99.66 to 100%, UACI of 37.39%, lesser computational time of 0.36 s, and is more robust to statistical attacks.
MetaBalance: Improving Multi-Task Recommendations via Adapting Gradient Magnitudes of Auxiliary Tasks

In many personalized recommendation scenarios, the generalization ability of a target task can be improved via learning with additional auxiliary tasks alongside this target task on a multi-task network. However, this method often suffers from a serious optimization imbalance problem. On the one hand, one or more auxiliary tasks might have a larger influence than the target task and even dominate the network weights, resulting in worse recommendation accuracy for the target task. On the other hand, the influence of one or more auxiliary tasks might be too weak to assist the target task. More challenging is that this imbalance dynamically changes throughout the training process and varies across the parts of the same network. We propose a new method: MetaBalance to balance auxiliary losses via directly manipulating their gradients w.r.t the shared parameters in the multi-task network. Specifically, in each training iteration and adaptively for each part of the network, the gradient of an auxiliary loss is carefully reduced or enlarged to have a closer magnitude to the gradient of the target loss, preventing auxiliary tasks from being so strong that dominate the target task or too weak to help the target task. Moreover, the proximity between the gradient magnitudes can be flexibly adjusted to adapt MetaBalance to different scenarios. The experiments show that our proposed method achieves a significant improvement of 8.34% in terms of [email protected] upon the strongest baseline on two real-world datasets. The code of our approach can be found at here.1
A novel approach for spam detection using horse herd optimization algorithm

Abstract Communication via email has expanded dramatically in recent decades due to its cost-effectiveness, convenience, speed, and utility for a variety of contexts, including social, scientific, cultural, political, authentication, and advertising applications. Spam is an email sent to a large number of individuals or organizations without the recipient's desire or request. It is increasingly becoming a harmful part of email traffic and can negatively affect the usability of email systems. Such emails consume network bandwidth as well as storage space, causing email systems to slow down, wasting time and effort scanning and eliminating enormous amounts of useless information. Spam is also used for distributing offensive and harmful content on the Internet. The objective of the current study was to develop a new method for email spam detection with high accuracy and a low error rate. There are several methods to recognize, detect, filter, categorize, and delete spam emails, and almost the majority of the proposed methods have some extent of error rate. None of the spam detection techniques, despite the optimizations performed, have been effective alone. A step in text mining and message classification is feature selection, and one of the best approaches for feature selection is the use of metaheuristic algorithms. This article introduces a new method for detecting spam using the Horse herd metaheuristic Optimization Algorithm (HOA). First, the continuous HOA was transformed into a discrete algorithm. The inputs of the resulting algorithm then became opposition-based and then converted to multiobjective. Finally, it was used for spam detection, which is a discrete and multiobjective problem. The evaluation results indicate that the proposed method performs better compared to other methods such as K-nearest neighbours-grey wolf optimisation, K-nearest neighbours, multilayer perceptron, support vector machine, and Naive Bayesian. The results show that the new multiobjective opposition-based binary horse herd optimizer, running on the UCI data set, has been more successful in the average selection size and classification accuracy compared with other standard metaheuristic methods. According to the findings, the proposed algorithm is substantially more accurate in detecting spam emails in the data set in comparison with other similar algorithms, and it shows lower computational complexity.
Evaluation Method of Classroom Teaching Effect Under Intelligent Teaching Mode

In order to improve the accuracy and performance of classroom teaching effect evaluation, an intelligent teaching mode classroom teaching effect evaluation method is proposed. Based on the characteristics of intelligent teaching mode, an intelligent teaching effect evaluation index system including five indexes of basic quality, teaching attitude, teaching method, teaching ability and teaching effect is constructed. After obtaining the scores of each index by expert scoring, it is input into the cuckoo search algorithm extreme learning machine evaluation model and solved by objective function, obtain the final score of teaching effect evaluation. The experimental results show that the proposed method can effectively improve the evaluation accuracy of classroom teaching effect of intelligent teaching mode, and provide a new method for classroom teaching effect evaluation.
Radar Signal Intrapulse Modulation Recognition Based on a Denoising-Guided Disentangled Network

Accurate recognition of radar modulation mode helps to better estimate radar echo parameters, thereby occupying an advantageous position in the radar electronic warfare (EW). However, under low signal-to-noise ratio environments, recent deep-learning-based radar signal recognition methods often perform poorly due to the unsuitable denoising preprocess. In this paper, a denoising-guided disentangled network based on an inception structure is proposed to simultaneously complete the denoising and recognition of radar signals in an end-to-end manner. The pure radar signal representation (PSR) is disentangled from the noise signal representation (NSR) through a feature disentangler and used to learn a radar signal modulation recognizer under low-SNR environments. Signal noise mutual information loss is proposed to enlarge the gap between the PSR and the NSR. Experimental results demonstrate that our method can obtain a recognition accuracy of 98.75% in the −8 dB SNR and 89.25% in the −10 dB environment of 12 modulation formats.
From Little Things Big Things Grow

Medical systematic review query formulation is a highly complex task done by trained information specialists. Complexity comes from the reliance on lengthy Boolean queries, which express a detailed research question. To aid query formulation, information specialists use a set of exemplar documents, called `seed studies', prior to query formulation. Seed studies help verify the effectiveness of a query prior to the full assessment of retrieved studies. Beyond this use of seeds, specific IR methods can exploit seed studies for guiding both automatic query formulation and new retrieval models. One major limitation of work to date is that these methods exploit `pseudo seed studies' through retrospective use of included studies (i.e., relevance assessments). However, we show pseudo seed studies are not representative of real seed studies used by information specialists. Hence, we provide a test collection with real world seed studies used to assist with the formulation of queries. To support our collection, we provide an analysis, previously not possible, on how seed studies impact retrieval and perform several experiments using seed-study based methods to compare the effectiveness of using seed studies versus pseudo seed studies. We make our test collection and the results of all of our experiments and analysis available at http://github.com/ielab/sysrev-seed-collection
Uncertainty-aware Pseudo Label Refinery for Entity Alignment

Entity alignment (EA), which aims to discover equivalent entities in knowledge graphs (KGs), bridges heterogeneous sources of information and facilitates the integration of knowledge. Recently, based on translational models, EA has achieved impressive performance in utilizing graph structures or by adopting auxiliary information. However, existing entity alignment methods mainly rely on manually labeled entity alignment seeds, limiting their applicability in real scenarios. In this paper, a simple but effective Uncertainty-aware Pseudo Label Refinery (UPLR) framework is proposed without manually labeling requirement and is capable of learning high-quality entity embeddings from pseudo-labeled data sets containing noisy data. Our proposed model relies on two key factors: First, a non-sampling calibration strategy is provided that does not require artificially designed thresholds to reduce the influence of noise labels. Second, the entity alignment model achieves goal-oriented uncertainty correction through a gradual enhancement strategy. Experimental results on benchmark datasets demonstrate that our proposed model outperforms the existing supervised methods in cross-lingual knowledge graph tasks. Our source code is available at: https://github.com/Jia-Li2/UPLR/.