Reviewerly/exHarmony huggingface dataset:
Blockchain-Enabled Trade Finance Innovation: A Potential Paradigm Shift on Using Letter of Credit

This paper explores a potential paradigm shift in trade finance utilizing blockchain technology. Traditionally, the centralized operating model has governed trade finance and the manner in which traders handle business processes. However, such heavy reliance on centralized authorities has made for poor performance, the lack of flexibility and transparency, and vulnerability to malicious alteration. The blockchain, as a distributed ledger technology (DLT), has attracted growing attention and has the potential to disrupt legacy finance procedures such as payment by letter of credit (L/C). International trade players may benefit from the technological reengineering of financial processes through the implementation of blockchain- and smart contract-based platforms. From the conceptual perspective of a paradigm shift, this study analyzes the feasibility of blockchain innovation in trade finance through modern blockchain-based L/C initiatives. Moreover, this study also explores blockchain applications in terms of logistics tracking and how it integrates with trade finance procedures. This study contributes to the understanding of a blockchain paradigm shift with a multi-case study. The results may illuminate the potential future application of blockchain finance and provide researchers with an illustrative example of other finance-related capabilities. Studies of trade-related topics such as customs clearances, insurance, and logistics applications need to be addressed in the future to create a comprehensively trustless environment and facilitate the automation of trade.
An Efficient Hardware Implementation of Reinforcement Learning: The Q-Learning Algorithm

In this paper we propose an efficient hardware architecture that implements the Q-Learning algorithm, suitable for real-time applications. Its main features are low-power, high throughput and limited hardware resources. We also propose a technique based on approximated multipliers to reduce the hardware complexity of the algorithm. We implemented the design on a Xilinx Zynq Ultrascale+ MPSoC ZCU106 Evaluation Kit. The implementation results are evaluated in terms of hardware resources, throughput and power consumption. The architecture is compared to the state of the art of Q-Learning hardware accelerators presented in the literature obtaining better results in speed, power and hardware resources. Experiments using different sizes for the Q-Matrix and different wordlengths for the fixed point arithmetic are presented. With a Q-Matrix of size 8 × 4 (8 bit data) we achieved a throughput of 222 MSPS (Mega Samples Per Second) and a dynamic power consumption of 37 mW, while with a Q-Matrix of size 256 × 16 (32 bit data) we achieved a throughput of 93 MSPS and a power consumption 611 mW. Due to the small amount of hardware resources required by the accelerator, our system is suitable for multi-agent IoT applications. Moreover, the architecture can be used to implement the SARSA (State-Action-Reward-StateAction) Reinforcement Learning algorithm with minor modifications.
A comprehensive study on the multi-class cervical cancer diagnostic prediction on pap smear images using a fusion-based decision from ensemble deep convolutional neural network

The diagnosis of cervical dysplasia, carcinoma in situ and confirmed carcinoma cases is more easily perceived by commercially available and current research-based decision support systems when the scenario of pathologists to patient ratio is small. The treatment modalities for such diagnosis rely exclusively on precise identification of dysplasia stages as followed by The Bethesda System. The classification based on The Bethesda System is a multiclass problem, which is highly relevant and vital. Reliance on image interpretation, when done manually, introduces inter-observer variability and makes the microscope observation tedious and time-consuming. Taking this into account, a computer-assisted screening system built on deep learning can significantly assist pathologists to screen with correct predictions at a faster rate. The current study explores six different deep convolutional neural networks- Alexnet, Vggnet (vgg-16 and vgg-19), Resnet (resnet-50 and resnet-101) and Googlenet architectures for multi-class (four-class) diagnosis of cervical pre-cancerous as well as cancer lesions and incorporates their relative assessment. The study highlights the addition of an ensemble classifier with three of the best deep learning models for yielding a high accuracy multi-class classification. All six deep models including ensemble classifier were trained and validated on a hospital-based pap smear dataset collected through both conventional and liquid-based cytology methods along with the benchmark Herlev dataset.
Dynamic Group-Based Cooperative Optimization Algorithm

Several optimization problems from various types of applications have been efficiently resolved using available meta-heuristic algorithms such as Particle Swarm Optimization and Genetic Algorithm. Recently, many meta-heuristic optimization techniques have been extensively reported in the literature. Nevertheless, there is still room for new optimization techniques and strategies since, according to the literature, there is no meta-heuristic optimization algorithm that may be considered as the best choice to cope with all modern optimization problems. This paper introduces a novel meta-heuristic optimization algorithm named Dynamic Group-based Optimization Algorithm (DGCO). The proposed algorithm is inspired by the cooperative behavior adopted by swarm individuals to achieve their global goals. DGCO has been validated and tested against twenty-three mathematical optimization problems, and the results have been verified by a comparative study with respect to state-of-the-art optimization algorithms that are already available. The results have shown the high exploration capabilities of DGCO as well as its ability to avoid local optima. Moreover, the performance of DGCO has also been verified against five constrained engineering design problems. The results demonstrate the competitive performance and capabilities of DGCO with respect to well-known state-of-the-art meta-heuristic optimization algorithms. Finally, a sensitivity analysis is performed to study the effect of different parameters on the performance of the DGCO algorithm.
COVID-19 Contact Tracing in Two Counties — North Carolina, June–July 2020

Contact tracing is a strategy implemented to minimize the spread of communicable diseases (1,2). Prompt contact tracing, testing, and self-quarantine can reduce the transmission of SARS-CoV-2, the virus that causes coronavirus disease 2019 (COVID-19) (3,4). Community engagement is important to encourage participation in and cooperation with SARS-CoV-2 contact tracing (5). Substantial investments have been made to scale up contact tracing for COVID-19 in the United States. During June 1-July 12, 2020, the incidence of COVID-19 cases in North Carolina increased 183%, from seven to 19 per 100,000 persons per day* (6). To assess local COVID-19 contact tracing implementation, data from two counties in North Carolina were analyzed during a period of high incidence. Health department staff members investigated 5,514 (77%) persons with COVID-19 in Mecklenburg County and 584 (99%) in Randolph Counties. No contacts were reported for 48% of cases in Mecklenburg and for 35% in Randolph. Among contacts provided, 25% in Mecklenburg and 48% in Randolph could not be reached by telephone and were classified as nonresponsive after at least one attempt on 3 consecutive days of failed attempts. The median interval from specimen collection from the index patient to notification of identified contacts was 6 days in both counties. Despite aggressive efforts by health department staff members to perform case investigations and contact tracing, many persons with COVID-19 did not report contacts, and many contacts were not reached. These findings indicate that improved timeliness of contact tracing, community engagement, and increased use of community-wide mitigation are needed to interrupt SARS-CoV-2 transmission.
A satellite image data based ultra-short-term solar PV power forecasting method considering cloud information from neighboring plant

Accurate ultra-short-term PV power forecasting is essential for the power system with a high proportion of renewable energy integration, which can provide power fluctuation information hours ahead and help to mitigate the interference of the random PV power output. Most of the PV power forecasting methods mainly focus on employing local ground-based observation data, ignoring the spatial and temporal distribution and correlation characteristics of solar energy and meteorological impact factors. Therefore, a novel ultra-short-term PV power forecasting method based on the satellite image data is proposed in this paper, which combines the spatio-temporal correlation between multiple plants with power and cloud information. The associated neighboring plant is first selected by spatial-temporal cross-correlation analysis. Then the global distribution information of the cloud is extracted from satellite images as additional inputs with other general meteorological and power inputs to train the forecasting model. The proposed method is compared with several benchmark methods without considering the information of neighboring plants. Results show that the proposed method outperforms the benchmark methods and achieves a higher accuracy at 4.73%, 10.54%, and 4.88%, 11.04% for two target PV plants on a four-month validation dataset, in terms of root mean squared error and mean absolute error value, respectively.
Estimation of the fundamental learning loss and learning poverty related to COVID-19 pandemic in Mexico

There is evidence of learning loss due to the COVID-19 pandemic, but there is no related information for Mexico. The objective of the study is to estimate learning loss in reading and numeracy in Mexico. We compared the results of two household surveys conducted in 2019 and 2021. We interviewed 3161 children between 10 and 15 years. We estimated a learning loss according with SES in a range from 0.34–0.45 SD in reading and 0.62–0.82 SD in mathematics by COVID-19 pandemic, and an increase in learning poverty in a range of 25.7%–15.4% in reading and 29.8%–28.8% in numeracy. Gaps in fundamental learning by gender and SES increased. There is an urgent need develop a clear strategy to perform personalized diagnoses and implement remedial courses to address learning loss.
FPGA implementation and image encryption application of a new PRNG based on a memristive Hopfield neural network with a special activation gradient

A memristive Hopfield neural network (MHNN) with a special activation gradient is proposed by adding a suitable memristor to the Hopfield neural network (HNN) with a special activation gradient. The MHNN is simulated and dynamically analyzed, and implemented on FPGA. Then, a new pseudo-random number generator (PRNG) based on MHNN is proposed. The post-processing unit of the PRNG is composed of nonlinear post-processor and XOR calculator, which effectively ensures the randomness of PRNG. The experiments in this paper comply with the IEEE 754-1985 high precision 32-bit floating point standard and are done on the Vivado design tool using a Xilinx XC7Z020CLG400-2 FPGA chip and the Verilog-HDL hardware programming language. The random sequence generated by the PRNG proposed in this paper has passed the NIST SP800-22 test suite and security analysis, proving its randomness and high performance. Finally, an image encryption system based on PRNG is proposed and implemented on FPGA, which proves the value of the image encryption system in the field of data encryption connected to the Internet of Things (IoT).
TPFlow: Progressive Partition and Multidimensional Pattern Extraction for Large-Scale Spatio-Temporal Data Analysis

Consider a multi-dimensional spatio-temporal (ST) dataset where each entry is a numerical measure defined by the corresponding temporal, spatial and other domain-specific dimensions. A typical approach to explore such data utilizes interactive visualizations with multiple coordinated views. Each view displays the aggregated measures along one or two dimensions. By brushing on the views, analysts can obtain detailed information. However, this approach often cannot provide sufficient guidance for analysts to identify patterns hidden within subsets of data. Without a priori hypotheses, analysts need to manually select and iterate through different slices to search for patterns, which can be a tedious and lengthy process. In this work, we model multidimensional ST data as tensors and propose a novel piecewise rank-one tensor decomposition algorithm which supports automatically slicing the data into homogeneous partitions and extracting the latent patterns in each partition for comparison and visual summarization. The algorithm optimizes a quantitative measure about how faithfully the extracted patterns visually represent the original data. Based on the algorithm we further propose a visual analytics framework that supports a top-down, progressive partitioning workflow for level-of-detail multidimensional ST data exploration. We demonstrate the general applicability and effectiveness of our technique on three datasets from different application domains: regional sales trend analysis, customer traffic analysis in department stores, and taxi trip analysis with origin-destination (OD) data. We further interview domain experts to verify the usability of the prototype.
Communication-Aware Container Placement and Reassignment in Large-Scale Internet Data Centers

Containerization has been used in many applications for isolation purposes due to its lightweight, scalable, and highly portable properties. However, to apply containerization in large-scale Internet data centers faces a big challenge. Services in data centers are always instantiated as a group of containers, which often generate heavy communication workloads and therefore resulting in inefficient communications and downgraded service performance. Although assigning the containers of the same service to the same server can reduce the communication overhead, this may cause heavily imbalanced resource utilization since containers of the same service are usually intensive to the same resource. To reduce communication cost as well as balance the resource utilization in large-scale data centers, we further explore the container distribution issues in a real industrial environment and find that such conflict lies in two phases-container placement and container reassignment. The objective of this paper is to address the container distribution problem in these two phases. For the container placement problem, we propose an efficient communication aware worst fit decreasing algorithm to place a set of new containers into data centers. For the container reassignment problem, we propose a two-stage algorithm called Sweep&Search to optimize a given initial distribution of containers by migrating containers among servers. We implement the proposed algorithms in Baidu's data centers and conduct extensive evaluations. Compared with the state-of-the-art strategies, the evaluation results show that our algorithms perform better up to 70% and increase the overall service throughput up to 90% simultaneously.
A Novel Deep-Learning-Based Bug Severity Classification Technique Using Convolutional Neural Networks and Random Forest with Boosting

The accurate severity classification of a bug report is an important aspect of bug fixing. The bug reports are submitted into the bug tracking system with high speed, and owing to this, bug repository size has been increasing at an enormous rate. This increased bug repository size introduces biases in the bug triage process. Therefore, it is necessary to classify the severity of a bug report to balance the bug triaging process. Previously, many machine learning models were proposed for automation of bug severity classification. The accuracy of these models is not up to the mark because they do not extract the important feature patterns for learning the classifier. This paper proposes a novel deep learning model for multiclass severity classification called Bug Severity classification to address these challenges by using a Convolutional Neural Network and Random forest with Boosting (BCR). This model directly learns the latent and highly representative features. Initially, the natural language techniques preprocess the bug report text, and then n-gram is used to extract the features. Further, the Convolutional Neural Network extracts the important feature patterns of respective severity classes. Lastly, the random forest with boosting classifies the multiple bug severity classes. The average accuracy of the proposed model is 96.34% on multiclass severity of five open source projects. The average F-measures of the proposed BCR and the existing approach were 96.43% and 84.24%, respectively, on binary class severity classification. The results prove that the proposed BCR approach enhances the performance of bug severity classification over the state-of-the-art techniques.
Deep Multi-Interest Network for Click-through Rate Prediction

Click-through rate prediction plays an important role in many fields, such as recommender and advertising systems. It is one of the crucial parts to improve user experience and increase industry revenue. Recently, several deep learning-based models are successfully applied to this area. Some existing studies further model user representation based on user historical behavior sequence, in order to capture dynamic and evolving interests. We observe that users usually have multiple interests at a time and the latent dominant interest is expressed by the behavior. The switch of latent dominant interest results in the behavior changes. Thus, modeling and tracking latent multiple interests would be beneficial. In this paper, we propose a novel method named as Deep Multi-Interest Network (DMIN) which models user's latent multiple interests for click-through rate prediction task. Specifically, we design a Behavior Refiner Layer using multi-head self-attention to capture better user historical item representations. Then the Multi-Interest Extractor Layer is applied to extract multiple user interests. We evaluate our method on three real-world datasets. Experimental results show that the proposed DMIN outperforms various state-of-the-art baselines in terms of click-through rate prediction task.
Spatial-Aware Feature Aggregation for Image based Cross-View Geo-Localization

In this paper, we develop a new deep network to explicitly address these inherent differences between ground and aerial views. We observe there exist some approximate domain correspondences between ground and aerial images. Specifically, pixels lying on the same azimuth direction in an aerial image approximately correspond to a vertical image column in the ground view image. Thus, we propose a two-step approach to exploit this prior knowledge. The first step is to apply a regular polar transform to warp an aerial image such that its domain is closer to that of a ground-view panorama. Note that polar transform as a pure geometric transformation is agnostic to scene content, hence cannot bring the two domains into full alignment. Then, we add a subsequent spatial-attention mechanism which further brings corresponding deep features closer in the embedding space. To improve the robustness of feature representation, we introduce a feature aggregation strategy via learning multiple spatial embeddings. By the above two-step approach, we achieve more discriminative deep representations, facilitating cross-view Geo-localization more accurate. Our experiments on standard benchmark datasets show significant performance boosting, achieving more than doubled recall rate compared with the previous state of the art.
Divide and Conquer: Question-Guided Spatio-Temporal Contextual Attention for Video Question Answering

Understanding questions and finding clues for answers are the key for video question answering. Compared with image question answering, video question answering (Video QA) requires to find the clues accurately on both spatial and temporal dimension simultaneously, and thus is more challenging. However, the relationship between spatio-temporal information and question still has not been well utilized in most existing methods for Video QA. To tackle this problem, we propose a Question-Guided Spatio-Temporal Contextual Attention Network (QueST) method. In QueST, we divide the semantic features generated from question into two separate parts: the spatial part and the temporal part, respectively guiding the process of constructing the contextual attention on spatial and temporal dimension. Under the guidance of the corresponding contextual attention, visual features can be better exploited on both spatial and temporal dimensions. To evaluate the effectiveness of the proposed method, experiments are conducted on TGIF-QA dataset, MSRVTT-QA dataset and MSVD-QA dataset. Experimental results and comparisons with the state-of-the-art methods have shown that our method can achieve superior performance.
FTIR photoacoustic spectroscopy for identification and assessment of soil components: Chernozems and their size fractions

FTIR photoacoustic spectroscopy was used to approach inorganic matrix components and organic-matter constituents of chernozem size fractions (1-5000 μm, by dry sieving) with a different history of use (from intact steppe to permanent bare fallow, a continuous long-term field experiment). The conditions of FTIR photoacoustic measurements in continuous-scan modes were compared with attenuated total reflection measurements, the advantages of photoacoustic measurements resulting from a higher intensity of the incident radiation and signal-generating volume were discussed. Overtone peaks of quartz as a soil matrix component at 2000-1700 cm-1 were selected as a possible internal-standard (guide) bands for the comparison of photoacoustic spectra. For different land-use samples, differences in the composition were found, which are differently manifested in normalized spectra of size fractions, with millimeter-size, 20-100 μm, and silt fraction bearing the maximum information.
A New High Capacity Image Steganography Method Combined With Image Elliptic Curve Cryptography and Deep Neural Network

Image steganography is a technology that hides sensitive information into an image. The traditional image steganography method tends to securely embed secret information in the host image so that the payload capacity is almost ignored and the steganographic image quality needs to be improved for the Human Visual System(HVS). Therefore, in this work, we propose a new high capacity image steganography method based on deep learning. The Discrete Cosine Transform(DCT) is used to transform the secret image, and then the transformed image is encrypted by Elliptic Curve Cryptography(ECC) to improve the anti-detection property of the obtained image. To improve steganographic capacity, the SegNet Deep Neural Network with a set of Hiding and Extraction networks enables steganography and extraction of full-size images. The experimental results show that the method can effectively allocate each pixel in the image so that the relative capacity of steganography reaches 1. Besides, the image obtained using this steganography method has higher Peak Signal-to-Noise Ratio(PSNR) and Structural Similarity Index(SSIM) values, reaching 40dB and 0.96, respectively.