Reviewerly/exHarmony huggingface dataset:
Model Predictive Based Unscented Kalman Filter for Hypersonic Vehicle Navigation With INS/GNSS Integration

The INS/GNSS integration is the commonly used technique for hypersonic vehicle navigation.However, owing to the complicated flight dynamics with high maneuverability and large flight envelope, the dynamic model of INS/GNSS integration inevitably exists errors which degrades the navigation performance of a hypersonic vehicle seriously.In this paper, a new model predictive based unscented Kalman filter (MP-UKF) is proposed to address this problem.The MP-UKF employs the concept of model predictive filter for the establishment of a dynamic model error estimator, and it subsequently compensate the model error estimation to UKF for nonlinear state estimation.Since the MP-UKF could predict the dynamic model error persistently and correct the filtering procedure of UKF online, it improves the UKF adaptiveness and is promising for the performance enhancement of INS/GNSS integration for hypersonic vehicle navigation.Simulation results and comparison analysis have been conducted to demonstrate the effectiveness of the proposed method.
Color Image Encryption Algorithm Based on Dynamic Chaos and Matrix Convolution

This paper proposes a color image encryption algorithm based on a cloud model Fibonacci chaotic system, as well as a matrix convolution operation that can protect image content effectively and safely. The algorithm combines the cloud model with the generalized Fibonacci, creating a new complex chaotic system that realizes the dynamic random variation of chaotic sequences. The chaotic sequence is used to scramble the pixel coordinates of the mosaic images of the R, G, and B components of the color image. Then, the chaotic sequence value is used as a matrix convolution cloud algorithm that alternately updates the input value of the matrix convolution operation and the pixel value to obtain the permutation transformation of the original pixel value. Finally, the pixel values of the replacement and cloud model Fibonacci chaotic sequence and the pixel values of the front (rear) adjacent pixel points are subjected to a two-way exclusive XOR operation. Realizing the change of the arbitrary pixel value causes a chain transformation of the pixel values of all of the pixel points, and sequentially generates an encrypted image. Experiments show that the histogram of the encrypted image is smoother and adjacent pixels of the image have low correlation. In addition, this algorithm can resist attack experiments such as differential attack, select plaintext attack and noise attack and provides high encryption security, high anti-interference, and strong robustness. The dynamic chaotic system is used to realize the color image encryption of the dynamic key, and the encryption algorithm has higher security and the validity of the algorithm.
Blockchain technology and supply chains: The paradox of the atheoretical research discourse

Blockchain technology has seen increasingly important practical applications. The literature has expanded greatly in the past four to five years. One area that has seen significant application and growth is in supply chain management—which includes transportation and logistics. The growth of this literature has also resulted in several literature reviews—each has specific scope and emphasis. Of the growing number of literature review and bibliometric analysis studies, the focus has ranged from understanding application cases, themes, trends, and methodologies. Theoretical developments and theorizing have been limited for supply chain management in general and even more limited for sustainable supply chains. Arguably, much of the current research has been relatively atheoretical—a lack of theory development and theory application. Hence, we conclude the existing blockchain research discourse has been centered around application and less around the theory. As a sub-discipline, the nexus of blockchain and supply chain management is still in the early developmental stages. Using a critical synthesis review of the literature, this paper focuses on the most popular theories that have been applied to explain blockchain adoption and implementation within supply chains—how each theory explains the motivations, formalizes the organizational process around the acceptance of blockchain technology, and predicts the outcomes of blockchain-supported business models. Current theoretical gaps and potential future theorizing are discussed. Potential theories that have been used for other technologies (i.e., the internet of things) which have not been applied to the blockchain context are provided and discussed. The comprehensive and systematic theory review has important theoretical and managerial implications for both academia by advancing multiple academic disciplines and practice with insights into actual acceptance and usage of blockchain and other disruptive technologies.
Multi-relational Poincaré Graph Embeddings

Hyperbolic embeddings have recently gained attention in machine learning due to their ability to represent hierarchical data more accurately and succinctly than their Euclidean analogues. However, multi-relational knowledge graphs often exhibit multiple simultaneous hierarchies, which current hyperbolic models do not capture. To address this, we propose a model that embeds multi-relational graph data in the Poincare ball model of hyperbolic space. Our Multi-Relational Poincare model (MuRP) learns relation-specific parameters to transform entity embeddings by Mobius matrix-vector multiplication and Mobius addition. Experiments on the hierarchical WN18RR knowledge graph show that our Poincare embeddings outperform their Euclidean counterpart and existing embedding methods on the link prediction task, particularly at lower dimensionality.
Semantically-Aligned Equation Generation for Solving and Reasoning Math Word Problems

Ting-Rui Chiang, Yun-Nung Chen. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
SGD on Neural Networks Learns Functions of Increasing Complexity

We perform an experimental study of the dynamics of Stochastic Gradient Descent (SGD) in learning deep neural networks for several real and synthetic classification tasks. We show that in the initial epochs, almost all of the performance improvement of the classifier obtained by SGD can be explained by a linear classifier. More generally, we give evidence for the hypothesis that, as iterations progress, SGD learns functions of increasing complexity. This hypothesis can be helpful in explaining why SGD-learned classifiers tend to generalize well even in the over-parameterized regime. We also show that the linear classifier learned in the initial stages is ``retained'' throughout the execution even if training is continued to the point of zero training error, and complement this with a theoretical result in a simplified model. Key to our work is a new measure of how well one classifier explains the performance of another, based on conditional mutual information.
Transferable Representation Learning in Vision-and-Language Navigation

Vision-and-Language Navigation (VLN) tasks such as Room-to-Room (R2R) require machine agents to interpret natural language instructions and learn to act in visually realistic environments to achieve navigation goals. The overall task requires competence in several perception problems: successful agents combine spatio-temporal, vision and language understanding to produce appropriate action sequences. Our approach adapts pre-trained vision and language representations to relevant in-domain tasks making them more effective for VLN. Specifically, the representations are adapted to solve both a cross-modal sequence alignment and sequence coherence task. In the sequence alignment task, the model determines whether an instruction corresponds to a sequence of visual frames. In the sequence coherence task, the model determines whether the perceptual sequences are predictive sequentially in the instruction-conditioned latent space. By transferring the domain-adapted representations, we improve competitive agents in R2R as measured by the success rate weighted by path length (SPL) metric.
Classification-Based Anomaly Detection for General Data

Anomaly detection, finding patterns that substantially deviate from those seen previously, is one of the fundamental problems of artificial intelligence. Recently, classification-based methods were shown to achieve superior results on this task. In this work, we present a unifying view and propose an open-set method to relax current generalization assumptions. Furthermore, we extend the applicability of transformation-based methods to non-image data using random affine transformations. Our method is shown to obtain state-of-the-art accuracy and is applicable to broad data types. The strong performance of our method is extensively validated on multiple datasets from different domains.
Application of Blockchain and Internet of Things to Ensure Tamper-Proof Data Availability for Food Safety

Food supply chain plays a vital role in human health and food prices. Food supply chain inefficiencies in terms of unfair competition and lack of regulations directly affect the quality of human life and increase food safety risks. This work merges Hyperledger Fabric, an enterprise-ready blockchain platform with existing conventional infrastructure, to trace a food package from farm to fork using an identity unique for each food package while keeping it uncomplicated. It keeps the records of business transactions that are secured and accessible to stakeholders according to the agreed set of policies and rules without involving any centralized authority. This paper focuses on exploring and building an uncomplicated, low-cost solution to quickly link the existing food industry at different geographical locations in a chain to track and trace the food in the market.
Dynamically constraining connectionist networks to produce distributed, orthogonal representations to reduce catastrophic interference

It is well known that when a connectionist network is trained on one set of patterns and then attempts to add new patterns to its repertoire, catastrophic interference may result. The use of sparse, orthogonal hidden-layer representations has been shown to reduce catastrophic interference. The author demonstrates that the use of sparse representations may, in certain cases, actually result in worse performance on catastrophic interference. This paper argues for the necessity of maintaining hidden-layer representations that are both as highly distributed and as highly orthogonal as possible. The author presents a learning algorithm, called context-biasing, that dynamically solves the problem of constraining hiddenlayer representations to simultaneously produce good orthogonality and distributedness. On the data tested for this study, context-biasing is shown to reduce catastrophic interference by more than 50% compared to standard backpropagation. In particular, this technique succeeds in reducing catastrophic interference on data where sparse, orthogonal distributions failed to produce any improvement.
Making Convolutional Networks Shift-Invariant Again

Modern convolutional networks are not shift-invariant, as small input shifts or translations can cause drastic changes in the output. Commonly used downsampling methods, such as max-pooling, strided-convolution, and average-pooling, ignore the sampling theorem. The well-known signal processing fix is anti-aliasing by low-pass filtering before downsampling. However, simply inserting this module into deep networks degrades performance; as a result, it is seldomly used today. We show that when integrated correctly, it is compatible with existing architectural components, such as max-pooling and strided-convolution. We observe \textit{increased accuracy} in ImageNet classification, across several commonly-used architectures, such as ResNet, DenseNet, and MobileNet, indicating effective regularization. Furthermore, we observe \textit{better generalization}, in terms of stability and robustness to input corruptions. Our results demonstrate that this classical signal processing technique has been undeservingly overlooked in modern deep networks. Code and anti-aliased versions of popular networks are available at this https URL .
Findings of the WMT 2019 Shared Task on Parallel Corpus Filtering for Low-Resource Conditions

Following the WMT 2018 Shared Task on Parallel Corpus Filtering, we posed the challenge of assigning sentence-level quality scores for very noisy corpora of sentence pairs crawled from the web, with the goal of sub-selecting 2% and 10% of the highest-quality data to be used to train machine translation systems. This year, the task tackled the low resource condition of Nepali-English and Sinhala-English. Eleven participants from companies, national research labs, and universities participated in this task.
BIOMEDICAL IMAGE ANALYSIS USING SEMANTIC SEGMENTATION

Semantic Segmentation is a very active area of research in the examining the medical images. The failure in the conventional segmentation methods to preserve the full resolution throughout the network led to the research’s that developed methods to protect the resolution of the images. The proposed method involves the semantic segmentation model for the biomedical images by utilizing the encoder/decoder structure to down sample the spatial resolution of the input data and develop a lower resolution feature mapping that are very effective at distinguishing between the classes and then perform the up samples to have a full-resolution segmentation map of the biomedical images reducing the diagnostic time. The frame work put forth utilizes a pixel to pixel fully trained cascaded convolutional neural network for the task of image segmentation. The evaluation biomedical image analysis using the semantic segmentation shows the performance improvement achieved by the minimization of the time required in testing and the augmentation in the analysis performed by the radiologist.
Orthogonally-designed adapted grasshopper optimization: A comprehensive analysis

Grasshopper optimization algorithm (GOA) is a newly proposed meta-heuristic algorithm that simulates the biological habits of grasshopper seeking for food sources. Nonetheless, some shortcomings exist in the basic version of GOA. It may quickly drop into local optima and show slow convergence rates when facing some complex basins. In this work, an improved GOA is proposed to alleviate the core shortcomings of GOA and handle continuous optimization problems more efficiently. For this purpose, two strategies, including orthogonal learning and chaotic exploitation, are introduced into the conventional GOA to find a more stable trade-off between the exploration and exploitation cores. Adding orthogonal learning to GOA can enhance the diversity of agents, whereas a chaotic exploitation strategy can update the position of grasshoppers within a limited local region. To confirm the efficacy of GOA, we compared it with a variety of famous classical meta-heuristic algorithms performed on 30 IEEE CEC2017 benchmark functions. Also, it is applied to feature selection cases, and three structural design problems are employed to validate its efficacy in terms of different metrics. The experimental results illustrate that the above tactics can mitigate the deficiencies of GOA, and the improved variant can reach high-quality solutions for different problems.
Review Study of Interpretation Methods for Future Interpretable Machine Learning

In recent years, black-box models have developed rapidly because of their high accuracy.Balancing the interpretability and accuracy is increasingly important.The lack of interpretability severely limits the application of the model in academia and industry.Despite the various interpretable machine learning methods, the perspective and meaning of the interpretation are also different.We provide a review of the current interpretable methods and divide them based on the model being applied.We divide them into two categories: interpretable methods with the self-explanatory model and interpretable methods with external co-explanation.And the interpretable methods with external co-explanation are further divided into subbranch methods based on instances, SHAP, knowledge graph, deep learning, and clustering model.The classification aims to help us understand the model characteristics applied in the interpretable method better.This survey makes the researcher find a suitable model to solve interpretability problems more easily.And the comparison experiments contribute to discovering complementary features from different methods.At the same time, we explore the future challenges and trends of interpretable machine learning to promote the development of interpretable machine learning.INDEX TERMS Deep learning, interpretable machine learning, interpretable methods with the selfexplanatory model, interpretable methods with external co-explanation, influential instance, knowledge graph.
Granulated RCNN and Multi-Class Deep SORT for Multi-Object Detection and Tracking

In this article, two new models, namely granulated RCNN (G-RCNN) and multi-class deep SORT (MCD-SORT), for object detection and tracking, respectively from videos are developed. Object detection has two stages: object localization (region of interest RoI) and classification. G-RCNN is an improved version of the well-known Fast RCNN and Faster RCNN for extracting RoIs by incorporating the unique concept of granulation in a deep convolutional neural network. Granulation with spatio-temporal information enables more accurate extraction of RoIs (object regions) in unsupervised mode. Compared to Fast and Faster RCNNs, G-RCNN uses (i) granules (clusters) formed over the pooling feature map, instead of its all feature values, in defining RoIs, (ii) only the positive RoIs during training, instead of the whole RoI-map, (iii) videos directly as input, rather than static images, and (iv) only the objects in RoIs, instead of the entire feature map, for performing object classification. All these lead to the improvement in real-time detection speed and accuracy. MCD-SORT is an advanced form of the popular Deep SORT. In MCD-SORT, the searching for association of objects with trajectories is restricted only within the same categories. This increases the performance in multi-class tracking. These characteristic features have been demonstrated over 37 videos containing single-class, two-class, and multi-class objects. Superiority of the models over several state-of-the-art methodologies is also established extensively, both qualitatively and quantitatively.
A spatial attentive and temporal dilated (SATD) GCN for skeleton‐based action recognition

Current studies have shown that the spatial-temporal graph convolutional network (ST-GCN) is effective for skeleton-based action recognition. However, for the existing ST-GCN-based methods, their temporal kernel size is usually fixed over all layers, which makes them cannot fully exploit the temporal dependency between discontinuous frames and different sequence lengths. Besides, most of these methods use average pooling to obtain global graph feature from vertex features, resulting in losing much fine-grained information for action classification. To address these issues, in this work, the authors propose a novel spatial attentive and temporal dilated graph convolutional network (SATD-GCN). It contains two important components, that is, a spatial attention pooling module (SAP) and a temporal dilated graph convolution module (TDGC). Specifically, the SAP module can select the human body joints which are beneficial for action recognition by a self-attention mechanism and alleviates the influence of data redundancy and noise. The TDGC module can effectively extract the temporal features at different time scales, which is useful to improve the temporal perception field and enhance the robustness of the model to different motion speed and sequence length. Importantly, both the SAP module and the TDGC module can be easily integrated into the ST-GCN-based models, and significantly improve their performance. Extensive experiments on two large-scale benchmark datasets, that is, NTU-RGB + D and Kinetics-Skeleton, demonstrate that the authors’ method achieves the state-of-the-art performance for skeleton-based action recognition.
Ant-TD: Ant colony optimization plus temporal difference reinforcement learning for multi-label feature selection

In recent years, multi-label learning becomes a trending topic in machine learning and data mining. This type of learning deals with data that each instance is associated with more than one label. Feature selection is a pre-processing method, which can significantly improve the performance of the multi-label classification. In this paper, we propose a new multi-label feature selection method based on Ant Colony Optimization (ACO). The proposed method makes a significant difference among all ACO-based feature selection methods so that instead of using a static heuristic function, it uses a heuristic learning approach. Because heuristic functions influence the decision-making process of the ACO during the search process, using a heuristic learning approach can help the algorithm to search better in the search space. Here we propose a heuristic learning approach for the ACO heuristic function, which learns from experiences directly by using the Temporal Difference (TD) reinforcement learning algorithm. For heuristic learning, we need to model the ACO problem into a reinforcement learning problem. Thus, we model the feature selection search space into a Markovian Decision Process (MDP) where features represent the states (S), and selecting the unvisited features by each ant represents a set of actions (A). Reward signals (R) are composed of two criteria when ants take action. The ACO state transition rule, which is a combination of both probabilistic and greedy rules, forms the transition function (T) in MDP. In addition to the pheromone that is updated by the “global updating rule” in the ACO, the state-value function (V) is directly updated by the temporal difference formulation to form a learned heuristic function. We conduct various experiments on nine benchmark data and compare the classification performance over three multi-label evaluation measures against nine state-of-the-art multi-label feature selection methods. The results show that the proposed method outperforms competing methods significantly.
Trade openness, FDI and economic growth in sub-Saharan Africa: do institutions matter?

The paper investigates empirically the role of institutions as an interactive factor in the FDI, trade and growth nexus in sub-Saharan Africa (SSA). We use the Structural Equation Modelling (SEM) technique with data from 34 SSA countries covering the period 1996–2016. We find evidence of a decreasing effect of FDI on economic growth, which increases monotonically without institutions. On the trade openness – growth nexus, we find a positive effect of institutions on trade openness. We also find a positive institutional quality effect on growth; however, no such effect is found on FDI. Human capital development, financial development, and resource rent are equally found to exhibit positive effects on economic growth in SSA. In conclusion, our findings indicate the need for a targeted approach towards improving institutional quality to enhance economic growth and development in SSA.