Reviewerly/exHarmony huggingface dataset:
Encouraging Submission of FAIR Data at <i>The Journal of Organic Chemistry</i> and <i>Organic Letters</i>

ADVERTISEMENT RETURN TO ISSUEPREVEditorialNEXTEncouraging Submission of FAIR Data at The Journal of Organic Chemistry and Organic LettersAngela M. HunterAngela M. HunterMore by Angela M. Hunterhttp://orcid.org/0000-0002-3474-525X, Erick M. CarreiraErick M. CarreiraMore by Erick M. Carreirahttp://orcid.org/0000-0003-1472-490X, and Scott J. MillerScott J. MillerMore by Scott J. Millerhttp://orcid.org/0000-0001-7817-1318Cite this: Org. Lett. 2020, 22, 4, 1231–1232Publication Date (Web):February 12, 2020Publication History Received30 January 2020Published online12 February 2020Published inissue 21 February 2020https://doi.org/10.1021/acs.orglett.0c00383Copyright © 2020 American Chemical SocietyRIGHTS & PERMISSIONSArticle Views8339Altmetric-Citations9LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InReddit PDF (366 KB) Get e-AlertsSUBJECTS:Computational chemistry,Inorganic compounds,Nuclear magnetic resonance spectroscopy,Organic chemistry,Students Get e-Alerts
A Fast Recognition Method for Space Targets in ISAR Images Based on Local and Global Structural Fusion Features with Lower Dimensions

Feature extraction is the key step of Inverse Synthetic Aperture Radar (ISAR) image recognition. However, limited by the cost and conditions of ISAR image acquisition, it is relatively difficult to obtain large-scale sample data, which makes it difficult to obtain target deep features with good discriminability by using the currently popular deep learning method. In this paper, a new method for low-dimensional, strongly robust, and fast space target ISAR image recognition based on local and global structural feature fusion is proposed. This method performs the trace transformation along the longest axis of the ISAR image to generate the global trace feature of the space target ISAR image. By introducing the local structural feature, Local Binary Pattern (LBP), the complementary fusion of the global and local features is achieved, which makes up for the missing structural information of the trace feature and ensures the integrity of the ISAR image feature information. The representation of trace and LBP features in a low-dimensional mapping feature space is found by using the manifold learning method. Under the condition of maintaining the local neighborhood relationship in the original feature space, the effective fusion of trace and LBP features is achieved. So, in the practical application process, the target recognition accuracy is no longer affected by trace function, LBP feature block number selection, and other factors, realizing the high robustness of the algorithm. To verify the effectiveness of the proposed algorithm, an ISAR image database containing 1325 samples of 5 types of space targets is used for experiments. The results show that the classification accuracy of the 5 types of space targets can reach more than 99%, and the recognition accuracy is no longer affected by the trace feature and LBP feature selection, which has strong robustness. The proposed method provides a fast and effective high-precision model for space target feature extraction, which can give some references for solving the problem of space object efficient identification under the condition of small sample data.
Feature Selection Based on a Novel Improved Tree Growth Algorithm

Feature selection plays a significant role in the field of data mining and machine learning to reduce the data dimension, speed up the model building process and improve algorithm performance.Tree growth algorithm (TGA) is a recent proposed populationbased metaheuristic, which shows great power of search ability in solving optimization of continuous problems.However, TGA cannot be directly applied to feature selection problems.Also, we find that its efficiency still leave room for improvement.To tackle this problem, in this study, a novel improved TGA (iTGA) is proposed, which can resolve the feature selection problem efficiently.The main contribution includes, (1) a binary TGA is proposed to tackle the feature selection problems, (2) a linearly increasing parameter tuning mechanism is proposed to tune the parameter in TGA, (3) the evolutionary population dynamics (EPD) strategy is applied to improve the exploration and exploitation capabilities of TGA, (4) the efficiency of iTGA is evaluated on fifteen UCI benchmark datasets, the comprehensive results indicate that iTGA can resolve feature selection problems efficiently.Furthermore, the results of comparative experiments also verify the superiority of iTGA compared with other stateof-the-art methods.
A New Approach for Thermal Vision based Fall Detection Using Residual Autoencoder

This paper focuses on falling of the elderly people which is considered as one of the most critical issue that can face them in their life.To deal with such issue, we propose a new approach named a Spatio-temporal Residual AutoEncoder (SRAE) model.This model is an unsupervised fall detector based on utilizing the deep learning technique to detect falls of the elderly people.Our proposed model uses autoencoder based on convolutional neural network, convolutional long short term memory (ConvLSTM) network, and residual connections to extract spatial and temporal features of videos captured from thermal cameras.The reconstruction error of an autoencoder is used to detect falls recorded in such thermal videos.Furthermore, SRAE model is tested on the publicly available thermal dataset where thermal images conserve the privacy of the elderly under observation which is a very important issue.The obtained results show that the our proposed model detects falls with high receiver operating characteristic area under curve (ROC AUC) (97%) ,and precision recall area under curve (PR AUC) (93%) compared to denoising autoencoder (DAE), convolutional autoencoder (CAE), and convolutional long short term memory autoencoder (CLSTMAE) introduced in the literature.
PLSD: A Perceptually Accurate Line Segment Detection Approach

Most existing line segment detection methods suffer from the over-segmentation phenomenon. An improved line segment detection method is presented in this work, which can generate more and longer line segments, yet still accurately reflect the structural details of the image. Line segment grouping, line segment validation and a multiscale framework are adopted to reach this end. Specifically, smart grouping rules are introduced to locate potential homologous line segments (derived from the same boundaries). Novel merging criteria based on Helmholtz principle is then used to evaluate the meaningfulness between separate line segments and their merged ones. The improved multiscale framework facilitates line segments merging in detection and post-detection processes, yielding more high-quality line segments. Finally, the proposed method is compared with four leading methods on the famous public dataset, YorkUrban-LineSegment. Experimental results demonstrate that the method has good continuity and outperforms the leading methods in F-measure.
Panoramic image mosaic technology based on SIFT algorithm in power monitoring

Panoramic image plays a very important role in power monitoring, which is conducive to a comprehensive understanding of the surrounding environment for transmission line operators and maintenance personnel and provides an important guarantee for line safety and information collection. In this paper, based on SIFT (Scale-invariant feature transform) algorithm, feature matching of video surveillance images is carried out, and then multi-image fusion and natural mosaic are realized. The software and experiment results show that the algorithm can realize the full mosaic of the power environment panorama, and can be applied to the power environment monitoring system. It effectively provides the omnidirectional information of the surrounding environment of the transmission line and has been effectively applied.
Synthesis and Analysis of the Fixed-Point Hodgkin–Huxley Neuron Model

In many tasks related to realistic neurons and neural network simulation, the performance of desktop computers is nowhere near enough. To overcome this obstacle, researchers are developing FPGA-based simulators that naturally use fixed-point arithmetic. In these implementations, little attention is usually paid to the choice of numerical method for the discretization of the continuous neuron model. In our study, the implementation accuracy of a neuron described by simplified Hodgkin–Huxley equations in fixed-point arithmetic is under investigation. The principle of constructing a fixed-point neuron model with various numerical methods is described. Interspike diagrams and refractory period analysis are used for the experimental study of the synthesized discrete maps of the simplified Hodgkin–Huxley neuron model. We show that the explicit midpoint method is much better suited to simulate the neuron dynamics on an FPGA than the explicit Euler method which is in common use.
Unified GPU Technique to Boost Confidentiality, Integrity and Trim Data Loss in Big Data Transmission

Data integrity, confidentiality and data loss are the issues that arise during transmission because of the use of an inadequate security scheme.These issues become particularly critical for big data transmission due to its own individual overhead causes.Moreover, multiple executions of distinct security algorithms for maintaining confidentiality and integrity reduce throughput and add a large number of additional bits as security overhead that hampers the robustness against data loss.Conversely, an efficient compression technique minimizes data confidentiality, as it eliminates redundant data during compression.Contemporary studies shows the lack of security policies for solving the mentioned issues in a combinatorial manner.The current study proposes an innovative integrated technique to collectively addresses the above security issues.It increases confidentiality and offers a backup for accidental data loss by combining the simplified data encryption standard (SDES) and an advanced pattern generation technique that uses a unique pattern generation table.A novel dual round of error control technique has been introduced to maximize data integrity by considering an arbitrary number of transmission errors.A new compression technique is adopted to enhance the robustness against data loss along with high compression efficiency and resistance against transmission errors.Confidentiality and integrity are further enhanced by integrating advanced audio steganography that uses a distinctive sample selection for hiding bits.Additionally, the implementation of the proposed innovative integrated technique in the graphics processing unit (GPU) environment increases the execution speed and reduces time complexity with extended parallel processing power.Furthermore, the application of a GPU enhances the execution speed at least 28-fold compared to the CPU performance.Experiments are performed using the standard Calgary Corpuses, text files (sized up to 1 TB), and audio files to validate the objectives.The proposed method offers a higher signal-to-noise ratio (SNR), entropy, and avalanche effect (AE) and lower amplitude difference (AD), and uncorrectable error rate (UER) as well as a lower percentage of information loss (IL), which substantiates its potential to offer higher data confidentiality and integrity.The capacity to reduce the computational complexity is further measured with compression ratio (CR) and throughput.The results further depicts the method's superiority in offering confidentiality and integrity over contemporary approaches.INDEX TERMS SDES, pattern string generation and the incorporation technique, dual round XOR operations to control transmission error, data compression, LSB-based audio steganography, GPU-oriented integrated technique, signal-to-noise ratio (SNR), entropy, avalanche effect (AE), amplitude difference (AD), uncorrectable error rate (UER), percentage of information loss (IL), compression ratio (CR) and throughput.
Robust and Efficient Linear Discriminant Analysis With <i>L</i> <sub>2,1</sub>-Norm for Feature Selection

Feature selection and feature transformation are the two main approaches to reduce dimensionality, and they are often presented separately.In this study, a novel robust and efficient feature selection method, called FS-VLDA-L21 (feature selection based on variant of linear discriminant analysis and L 2,1 -norm), is proposed by combining a new variant of linear discriminant analysis and L 2,1 sparsity regularization.Here, feature transformation and feature selection are integrated into a unified optimization objective.To obtain significant discriminative power between classes, all the data in the same class are expected to be regressed to a single vector, and the important task is to explore a transformation matrix such that the squared regression error is minimized.Therefore, we derive a new discriminant analysis from a novel view of least squares regression.In addition, we impose row sparsity on the transformation matrix through L 2,1 -norm regularized term to achieve feature selection.Consequently, the most discriminative features are selected, simultaneously eliminating the redundant ones.To address the L 2,1 -norm based optimization problem, we design a new efficient iterative re-weighted algorithm and prove its convergence.Extensive experimental results on four well-known datasets demonstrate the performance of our feature selection method.
Blending containers and virtual machines

With serverless computing, providers deploy application code and manage resource allocation dynamically, eliminating infrastructure management from application development.
A YOLOv3 Inference Approach for Student Attendance Face Recognition System

Checking attendance in a classroom is a factor contributing to the final performance of the students in the course.For both students and professors, attendance checking by name is very time-consuming and, in particular, the latter is very susceptible to simple attendance fraud.The study used a Face Recognition based attendance method using the YOLOv3 approach as an alternative.The system, based on face-detection and face-recognition algorithms, automatically recognizes, and marks attendance by recognizing the student.The experimental result shows that by using the trained model with a training accuracy of 98.01%, the proposed attendance system achieved 94% face recognition efficiency.
Is Bitcoin a decentralized payment mechanism?

Abstract We make a distinction between centralized, decentralized, and distributed payment mechanisms. A centralized payment mechanism processes a transaction using a trusted third party. A decentralized payment mechanism processes a transaction between the parties to the transaction. A distributed payment mechanism relies on the network of users to process a transaction on a shared ledger. We maintain that bitcoin is neither a centralized nor a decentralized payment mechanism. It is, instead, a distributed payment mechanism. We then consider decentralized and centralized aspects of the broader bitcoin payment space.
A Novel 3D Vector Decomposition for Color-Image Encryption

In this paper, a novel 3D vector decomposition is proposed for color-image encryption, in which a 3D vector is decomposed into two 3D vectors with random size in a random plane for providing a reliable security constraint.The technique of 3D vector decomposition, as far as we know, firstly offers a three-component encryption with one action, which fits well color-image encryption, and outputs a real ciphertext, which is convenient for recording and transmission.Furthermore, we employ an 1D chaos, which has strong chaotic properties, and reality-preserving fractional Hartley transform to cooperate 3D vector decomposition for constructing a color-image cryptosystem.Therefore, the proposed cryptosystem accomplishes improved security by reducing the single-channel attack risk in individual color-image encryption and avoiding the vulnerable channel in sequential color-image encryption as well as reduced the amount of data of transform-based cryptosystem by avoiding the complex output.Also, it has the advantages of strong chaotic performances, large key space and high key sensitivity, which is highly robust against various attacks.Experimental results show the effectiveness and superiority of the proposed cryptosystem.
Impact of Foreign Direct Investment on the Financial Performance of Listed Deposit Banks in Nigeria

In this study, we examine the impact of foreign direct investment (FDI) on the financial performance of Nigerian listed deposit banks. We collected secondary data from the annual reports and accounts of 14 banks between 2010 and 2017. We employed the Tobin Q quantitative method for the analysis. We adopted the theoretical framework of pecking order theory since the analysis of the impact of FDI on the financial performance of these banks are both inward and outward FDI. The Tobin Q method was used as the dependent variable and FDI as an independent variable. Board size, firm size, equity capital and reinvested earnings were all financial performance indicators employed to test the impact of FDI on the financial performance of the banks on understudy in Nigeria. The result of the data analysis and findings showed that FDI had contributed positively to the development and performance of the deposit banks over the period under consideration. Our theoretical findings suggest a positive relationship between FDI and profit maximization. This support the FDI theory that banks or organisations are financed partly with debt-equity, both used by the banks to balance the cost and benefit financing decisions by the management. In the case of the empirical findings, the results of hypothesis testing show a significant effect on the banks’ financial performances. Given these results, we conclude that FDI has made a positive impact on the development and financial performances of the listed deposit banks under study which resulted in some of the banks’ growth from local banks in Nigeria into some of the leading international banks in Africa.
Authorship Attribution in Bangla literature using Character-level CNN

Characters are the smallest unit of text that can extract stylometric signals to determine the author of a text. In this paper, we investigate the effectiveness of character-level signals in Authorship Attribution of Bangla Literature and show that the results are promising but improvable. The time and memory efficiency of the proposed model is much higher than the word level counterparts but accuracy is 2-5% less than the best performing word-level models. Comparison of various word-based models is performed and shown that the proposed model performs increasingly better with larger datasets. We also analyze the effect of pre-training character embedding of diverse Bangla character set in authorship attribution. It is seen that the performance is improved by up to 10% on pre-training. We used 2 datasets from 6 to 14 authors, balancing them before training and compare the results.
Learning Vertex Representations for Bipartite Networks

Recent years have witnessed a widespread increase of interest in network representation learning (NRL). By far most research efforts have focused on NRL for homogeneous networks like social networks where vertices are of the same type, or heterogeneous networks like knowledge graphs where vertices (and/or edges) are of different types. There has been relatively little research dedicated to NRL for bipartite networks. Arguably, generic network embedding methods like node2vec and LINE can also be applied to learn vertex embeddings for bipartite networks by ignoring the vertex type information. However, these methods are suboptimal in doing so, since real-world bipartite networks concern the relationship between two types of entities, which usually exhibit different properties and patterns from other types of network data. For example, E-Commerce recommender systems need to capture the collaborative filtering patterns between customers and products, and search engines need to consider the matching signals between queries and webpages. This work addresses the research gap of learning vertex representations for bipartite networks. We present a new solution BiNE, short for <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"/> <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Bi</b> <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">partite</i> <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</b> <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">etwork</i> <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">E</b> <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">mbedding</i> , which accounts for two special properties of bipartite networks: long-tail distribution of vertex degrees and implicit connectivity relations between vertices of the same type. Technically speaking, we make three contributions: (1) We design a biased random walk generator to generate vertex sequences that preserve the long-tail distribution of vertices; (2) We propose a new optimization framework by simultaneously modeling the explicit relations (i.e., observed links) and implicit relations (i.e., unobserved but transitive links); (3) We explore the theoretical foundations of BiNE to shed light on how it works, proving that BiNE can be interpreted as factorizing multiple matrices. We perform extensive experiments on five real datasets covering the tasks of link prediction (classification) and recommendation (ranking), empirically verifying the effectiveness and rationality of BiNE. Our experiment codes are available at: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/clhchtcjj/BiNE</uri> .