Reviewerly/exHarmony huggingface dataset:
A Collaborative Approach Toward Scientific Paper Recommendation Using Citation Context

Researchers face difficulties in finding relevant papers to their research interest as the number of scientific publication is rapidly increasing on the web. Scientific paper recommenders have emerged as a leading solution to help researchers by automatically suggesting relevant and useful publications. Several approaches have been proposed on improving recommender systems. However, most existing approaches depend on priori user profiles, and thus they cannot recommend papers to new user. Furthermore, the existing approaches utilize non-public contextual information, and thus it cannot adequately find similarities between papers due to copyright restrictions. Also, the existing approaches consider only single level paper-citation relation to identify similarities between papers. Considering the above challenges, this paper presents a collaborative filtering based recommendation approach for scientific papers that does not depend on priori user profiles and which utilizes only public contextual information. Using citation context, we utilized 2-level paper-citation relations to find hidden associations between papers. The rational underlying this approach is that, two papers are co-occurred with same cited paper(s) and two papers are co-occurring with same citing paper(s) are significantly similar to some extent. To evaluate the performance of the proposed approach, publicly available datasets are used to conduct extensive experiments. The experimental results demonstrate that the proposed approach has significantly outperforms the baseline approaches in terms of precision, recall, F1, mean average precision, and mean reciprocal rank, which are commonly used information retrieval metrics. The novelty of this study is that, with the proposed approach, researchers are able to find relevant and useful publications over the internet regardless of their previous research experiences and research area.
Joint Watermarking-Encryption-ECC for Patient Record Security in Wavelet Domain

Maintaining electronic patient records is the primary role for the fruitful implementation of the telehealth services in urban areas to enhance the quality of healthcare. These records frequently shared in-between professionals and healthcare centers so as to develop efficient tool(s) for accurate diagnosis purpose. However, protection of these records is a challenging task in the current era. In this article, we develop a joint watermarking-encryption- error correcting code (ECC) technique for the purpose of securing patient records. The suggested method involves three major steps as follows: 1) watermarks generation and scrambling, 2) encryption of cover and generated watermark via Paillier cryptosystem, and 3) imperceptibly embedding and robustly recovery/extraction of the watermarks. Experimental results indicate that the proposed technique offers high robustness and fulfills all watermarking needs for telehealth applications in terms of imperceptibility, robustness, capacity, and security. Furthermore, the proposed method achieves better robustness against varying attacks, and outperforms former techniques.
Linking project complexity to project success: a hybrid SEM–FCM method

Purpose The purpose of this study is to develop a novel hybrid approach that incorporates the structural equation model (SEM) and fuzzy cognitive map (FCM) to investigate the impacts of the variation in project complexity on project success. Design/methodology/approach This study adopts SEM to identify and validate a correlation between project complexity variables and PS. Standardized causal coefficients estimated in SEM are used to construct an FCM model to illustrate the effect of complexity on PS with linkage direction and weights. Predictive and diagnostic analyses are performed to dynamically model the variation in project complexity on the evolution of PS. Findings Results indicate that (1) the hybrid SEM–FCM approach is capable of modeling the dynamic interactions between project complexity and PS; (2) information, goal and environmental complexities are negatively correlated with PS, and technological, task and organizational complexities are positively correlated with PS and (3) the recommendations of complexity management for construction projects are put forward under the guideline of success monitoring. Originality/value This research contributes to (1) the state of knowledge by proposing a hybrid methodology that can model the dynamic interactions between project complexity and PS and (2) the state of practice by providing a new perspective of PS evaluation to enhance the probability of success in complex construction projects.
Intention Detection Based on Siamese Neural Network With Triplet Loss

Understanding the user's intention is an essential task for the spoken language understanding (SLU) module in the dialogue system, which further illustrates vital information for managing and generating future action and response. In this paper, we propose a triplet training framework based on the multiclass classification approach to conduct the training for the intention detection task. Precisely, we utilize a Siamese neural network architecture with metric learning to construct a robust and discriminative utterance feature embedding model. We modified the RMCNN model and fine-tuned BERT model as Siamese encoders to train utterance triplets from different semantic aspects. The triplet loss can effectively distinguish the details of two input data by learning a mapping from sequence utterances to a compact Euclidean space. After generating the mapping, the intention detection task can be easily implemented using standard techniques with pre-trained embeddings as feature vectors. Besides, we use the fusion strategy to enhance utterance feature representation in the downstream of intention detection task. We conduct experiments on several benchmark datasets of intention detection task: Snips dataset, ATIS dataset, Facebook multilingual task-oriented datasets, Daily Dialogue dataset, and MRDA dataset. The results illustrate that the proposed method can effectively improve the recognition performance of these datasets and achieves new state-of-the-art results on single-turn task-oriented datasets (Snips dataset, Facebook dataset), and a multi-turn dataset (Daily Dialogue dataset).
Hockey activity recognition using pre-trained deep learning model

Activity recognition in sports is often complex task resulting from the rapid dynamic interaction within players. In this paper, pre-trained VGG-16, deep learning based hockey activity recognition model has been proposed. Own hockey dataset consisting of four main activity includes free hit, goal, penalty corner and long corner was constructed as there are no existing field hockey datasets available. Experimental results indicate that the pre-trained deep learning model generates comparative results on this challenging dataset by tweaking the hyperparameters of this pre-trained model.
Viewport prediction for 360° videos

An important component for viewport-adaptive streaming of 360° videos is viewport prediction. Increasing viewport prediction horizon enables the client to prefetch more chunks into the playback buffer. Having longer buffer results in less rebuffering under fluctuating network conditions. We analyzed the recorded viewport traces of viewers who watched various 360° videos. We propose a clustering-based viewport prediction method that incorporates viewport pattern information from previous video streaming sessions. For several videos, specifically those with well-defined region of interest, the proposed approach increases the viewport prediction horizon and/or prediction accuracy.
Deep Variational Quantum Eigensolver: A Divide-And-Conquer Method for Solving a Larger Problem with Smaller Size Quantum Computers

We propose a divide-and-conquer method for the quantum-classical hybrid algorithm to solve larger problems with small-scale quantum computers. Specifically, we concatenate a variational quantum eigensolver (VQE) with a reduction in the system dimension, where the interactions between divided subsystems are taken as an effective Hamiltonian expanded by the reduced basis. Then the effective Hamiltonian is further solved by the VQE, which we call deep VQE. Deep VQE allows us to apply quantum-classical hybrid algorithms on small-scale quantum computers to large systems with strong intrasubsystem interactions and weak intersubsystem interactions, or strongly correlated spin models on large regular lattices. As proof-of-principle numerical demonstrations, we use the proposed method for quasi-one-dimensional models, including one-dimensionally coupled 12-qubit Heisenberg antiferromagnetic models on kagome lattices as well as two-dimensional Heisenberg antiferromagnetic models on square lattices. The largest problem size of 64 qubits is solved by simulating 20-qubit quantum computers with a reasonably good accuracy approximately a few %. The proposed scheme enables us to handle the problems of >1000 qubits by concatenating VQEs with a few tens of qubits. While it is unclear how accurate ground-state energy can be obtained for such a large system, our numerical results on a 64-qubit system suggest that deep VQE provides a good approximation (discrepancy within a few percent) and has room for further improvement. Therefore, deep VQE provides us a promising pathway to solve practically important problems on noisy intermediate-scale quantum computers.Received 2 October 2020Revised 25 January 2022Accepted 16 February 2022DOI:https://doi.org/10.1103/PRXQuantum.3.010346Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasQuantum algorithmsQuantum computationQuantum simulationQuantum Information
A Bisection Algorithm for Time-Optimal Trajectory Planning Along Fully Specified Paths

The time-optimal trajectory planning problem involves minimizing the time required to follow a path defined in space, subject to kinematic and dynamic constraints. Here, we introduce a novel technique, called the bisection algorithm (BA), which is fully implemented in C++ and extends dynamic programming approaches to the problem. These approaches, which rely on dividing the global problem into a series of simpler subproblems, become increasingly advantageous compared to direct transcription methods as the number of problem constraints increases. In contrast to nearly all other dynamic programming approaches, BA does not rely on finding a maximum-velocity curve or explicitly finding acceleration switching points during the trajectory planning process. Additionally, only one forward and one backward integration are used, during which all constraints are imposed. This approach is made feasible through careful control of the numerical integration process and the use of a bisection algorithm to resolve constraint violations during integration. BA is shown to be significantly simpler, faster, and more robust than recently proposed algorithms: a direct comparison is made for a series of paths to be followed by a serial manipulator, subject to kinematic constraints. The wide applicability of BA is then established by solving the time-optimal problem for a parallel manipulator following a complex path, subject to both kinematic and dynamic constraints.
Exploring Carbon Mineral Systems: Recent Advances in C Mineral Evolution, Mineral Ecology, and Network Analysis

Large and growing data resources on the spatial and temporal diversity and distribution of the more than 400 carbon-bearing mineral species reveal patterns of mineral evolution and ecology. Recent advances in analytical and visualization techniques leverage these data and are propelling mineralogy from a largely descriptive field into one of prediction within complex, integrated, multidimensional systems. These discoveries include: (1) systematic changes in the character of carbon minerals and their networks of coexisting species through deep time; (2) improved statistical predictions of the number and types of carbon minerals that occur on Earth but are yet to be discovered and described; and (3) a range of proposed and ongoing studies related to the quantification of network structures and trends, relation of mineral “natural kinds” to their genetic environments, prediction of the location of mineral species across the globe, examination of the tectonic drivers of mineralization through deep time, quantification of preservational and sampling bias in the mineralogical record, and characterization of feedback relationships between minerals and geochemical environments with microbial populations. These aspects of Earth’s carbon mineralogy underscore the complex co-evolution of the geosphere and biosphere and highlight the possibility for scientific discovery in Earth and planetary systems.
The composition and weathering of the continents over geologic time

The composition of continental crust records the balance between construction by tectonics and destruction by physical and chemical erosion.Quantitative constraints on how igneous addition and chemical weathering have modified the continents' bulk composition are essential for understanding the evolution of geodynamics and climate.Using novel data analytic techniques we have extracted temporal trends in sediments' protolith composition and weathering intensity from the largest available compilation of sedimentary major element compositions: ∼15,000 samples from 4.0 Ga to the present.We find that the average Archean upper continental crust was silica-rich and had a similar compositional diversity to modern continents.This is consistent with an early Archean, or earlier, onset of plate tectonics.In the Archean, chemical weathering sequestered ∼25 % more CO 2 per mass eroded for the same weathering intensity than in subsequent time periods, consistent with carbon mass balance despite higher Archean outgassing rates and more limited continental exposure.Since 2.0 Ga, over long (>0.5 Gyr) timescales, crustal weathering intensity has remained relatively constant.On shorter timescales over the Phanerozoic, weathering intensity is correlated to global climate state, consistent with a weathering feedback acting in response to changes in CO 2 sources or sinks.
Emergence of Blockchain Technology: Fundamentals, Working and its Various Implementations

In this paper, we discuss that blockchain is a decentralized system which provides peer-to-peer network over which thousands of clients get connected without the involvement of trusted third parties. Involvement of trusted third parties provides security, privacy and trust and also limits the transaction size and increases time and cost to settle transaction. Blockchain is made up of several blocks and these blocks contain several transactions before initiating a transaction from sender, it is first authenticated and after successful authentication, a block is created. This block is again authenticated by all the nodes in the network and later this block is connected to a chain of blocks and all the blocks in the network are updated about this transaction and finally receiver at other end receives money. Layers of blockchain play significant role in designing architecture of blockchain since each layer filters out unnecessary info and lighters node traffic. Further we discuss blockchain applications, benefits for effective businesses and its adoption in several industrial sectors, financial sector, health and research sector.
Table Fact Verification with Structure-Aware Transformer

Verifying fact on semi-structured evidence like tables requires the ability to encode structural information and perform symbolic reasoning. Pre-trained language models trained on natural language could not be directly applied to encode tables, because simply linearizing tables into sequences will lose the cell alignment information. To better utilize pre-trained transformers for table representation, we propose a Structure-Aware Transformer (SAT), which injects the table structural information into the mask of the self-attention layer. A method to combine symbolic and linguistic reasoning is also explored for this task. Our method outperforms baseline with 4.93% on TabFact, a large scale table verification dataset.
Improving AMR Parsing with Sequence-to-Sequence Pre-training

In the literature, the research on abstract meaning representation (AMR) parsing is much restricted by the size of human-curated dataset which is critical to build an AMR parser with good performance.To alleviate such data size restriction, pre-trained models have been drawing more and more attention in AMR parsing.However, previous pre-trained models, like BERT, are implemented for general purpose which may not work as expected for the specific task of AMR parsing.In this paper, we focus on sequence-to-sequence (seq2seq) AMR parsing and propose a seq2seq pre-training approach to build pre-trained models in both single and joint way on three relevant tasks, i.e., machine translation, syntactic parsing, and AMR parsing itself.Moreover, we extend the vanilla fine-tuning method to a multi-task learning fine-tuning method that optimizes for the performance of AMR parsing while endeavors to preserve the response of pre-trained models.Extensive experimental results on two English benchmark datasets show that both the single and joint pre-trained models significantly improve the performance (e.g., from 71.5 to 80.2 on AMR 2.0), which reaches the state of the art.The result is very encouraging since we achieve this with seq2seq models rather than complex models.We make our code and model available at https:// github.com/xdqkid/S2S-AMR-Parser.
Immersive 5G Virtual Reality Visualization Display System Based on Big-Data Digital City Technology

The virtual reality visual display system creates a realistic virtual product display system, allowing users to swim in a three-dimensional virtual environment and perform interactive operations, fully simulating the process of shopping selection and payment in reality, so that users have an immersive feeling. The purpose of this article is to realize the design of an immersive 5G virtual reality visual display system through big-data digital city technology. This paper uses big-data digital city technology to design and implement an immersive virtual reality visualization system from the three-dimensional display mode of vision, hearing, and touch, creating a real and interactive three-dimensional visualization environment for users to have a more intuitive visual experience. The experimental results of this paper show that the smoothness of the virtual reality visualization system test can reach 60FPS, the excellent rate reaches nearly 33%, and the model scene-realistic feedback excellent rate is about 62.5%.
Deep Declarative Networks

We explore a class of end-to-end learnable models wherein data processing nodes (or network layers) are defined in terms of desired behavior rather than an explicit forward function. Specifically, the forward function is implicitly defined as the solution to a mathematical optimization problem. Consistent with nomenclature in the programming languages community, we name these models deep declarative networks. Importantly, it can be shown that the class of deep declarative networks subsumes current deep learning models. Moreover, invoking the implicit function theorem, we show how gradients can be back-propagated through many declaratively defined data processing nodes thereby enabling end-to-end learning. We discuss how these declarative processing nodes can be implemented in the popular PyTorch deep learning software library allowing declarative and imperative nodes to co-exist within the same network. We also provide numerous insights and illustrative examples of declarative nodes and demonstrate their application for image and point cloud classification tasks.
Lens Learning Sparrow Search Algorithm

In this paper, a lens learning sparrow search algorithm (LLSSA) is proposed to improve the defects of the new sparrow search algorithm, which is random and easy to fall into local optimum. The algorithm has achieved good results in function optimization and has planned a safer and less costly path to the three-dimensional UAV path planning. In the discoverer stage, the algorithm introduces the reverse learning strategy based on the lens principle to improve the search range of sparrow individuals and then proposes a variable spiral search strategy to make the follower's search more detailed and flexible. Finally, it combines the simulated annealing algorithm to judge and obtain the optimal solution. Through 15 standard test functions, it is verified that the improved algorithm has strong search ability and mining ability. At the same time, the improved algorithm is applied to the path planning of 3D complex terrain, and a clear, simple, and safe route is found, which verifies the effectiveness and practicability of the improved algorithm.
Finite-sample Analysis of Interpolating Linear Classifiers in the Overparameterized Regime

We prove bounds on the population risk of the maximum margin algorithm for two-class linear classification. For linearly separable training data, the maximum margin algorithm has been shown in previous work to be equivalent to a limit of training with logistic loss using gradient descent, as the training error is driven to zero. We analyze this algorithm applied to random data including misclassification noise. Our assumptions on the clean data include the case in which the class-conditional distributions are standard normal distributions. The misclassification noise may be chosen by an adversary, subject to a limit on the fraction of corrupted labels. Our bounds show that, with sufficient over-parameterization, the maximum margin algorithm trained on noisy data can achieve nearly optimal population risk.
UTSA: Urdu Text Sentiment Analysis Using Deep Learning Methods

The Internet has seen substantial growth of regional language data in recent years. It enables people to express their opinion by incapacitating the language barriers. Urdu is a language used by 170.2 million people for communication. Sentiment analysis is used to get insight of people opinion. In recent years, researchers' interest in Urdu sentiment analysis has grown. Application of deep learning methods for Urdu sentiment analysis has been least explored. There is a lot of ground to cover in terms of text processing in Urdu since it is a morphologically rich language. In this paper, we propose a framework for Urdu Text Sentiment Analysis (UTSA) by exploring deep learning techniques in combination with various word vector representations. The performance of deep learning methods such as Long Short-Term Memory (LSTM), attention-based Bidirectional LSTM (BiLSTM-ATT), Convolutional Neural Networks (CNN) and CNN-LSTM is evaluated for sentiment analysis. Stacked layers are applied in sequential model LSTM, BiLSTM-ATT, and C-LSTM. In CNN, various filters are used with single convolution layer. Role of pre-trained and unsupervised self-trained embedding models is investigated on sentiment classification task. The results obtained show that the BiLSTM-ATT outperformed other deep learning models by accomplishing 77.9% accuracy and 72.7% F1 score.