Reviewerly/exHarmony huggingface dataset:
Study on two-stage fractional order gradient descend method

In this paper, a new kind of gradient descent method with global search capability is proposed. This method is superior to the conventional gradient method in two aspects. On the one hand, the particle swarm optimization algorithm is used to obtain the initial value near the global optimal solution. On the other hand, the fractional order gradient is used instead of the first order in the iteration. For the characteristics of the fractional calculus, the method can quickly escape the saddle point to accelerate the convergence process. We test the efficiency of the algorithm on a multi extremum function and a medium scale convolution neural network, and compare it with other conventional algorithms. Finally, conclusions and some suggestions for future work are given.
An Enhanced Extreme Learning Machine Model for Improving Accuracy

This research paper focuses on optimization-based extreme learning machine (ELM), in order to enhance generalization ability and learning efficiency of ELM. The study explains new approach which enhances the efficiency of basic ELM algorithm and also reduces the problems of unmodified ELMs of generalization ability by reducing redundant information and low learning efficiency. In most cases, the proposed method can produce the generalization performance on the basis of a few classification and regression problems, and it was found to be competent. The proposed model has a greater potential of reducing the training time, improving the predictive accuracy and robustness. In the future, efforts will be made to attain a higher level of efficiency.
Towards Automated Detection of Higher-Order Command Injection Vulnerabilities in IoT Devices

Command injection vulnerabilities are among the most common and dangerous attack vectors in IoT devices. Current detection approaches can detect single-step injection vulnerabilities well by fuzzing tests. However, an attacker could inject malicious commands in an IoT device via a multi-step exploit if he first abuses an interface to store the injection payload and later use it in a command interpreter through another interface. We identify a large class of such multi-step injection attacks to address these stealthy and harmful threats and define them as higher-order command injection vulnerabilities (HOCIVs). We develop an automatic system named Request Linking (ReLink) to detect data stores that would be transferred to command interpreters and then identify HOCIVs. ReLink is validated on an experimental embedded system injected with 150 HOCIVs. According to the experimental results, ReLink is significantly better than existing command injection detection tools in terms of detection rate, test space and time.
Sentiment Analysis of Barrage Text Based on ALBERT-ATT-BiLSTM Model

For the sentiment analysis of barrage texts, traditional methods cannot distinguish the different meanings of the same word in a sentence in different contexts when performing feature extraction, which cannot take into account the local feature information in the text and the contextual semantic association during the training process. As a result, its classification accuracy is relatively low. To this end, this paper proposes a sentiment classification model for barrage text that combines the ALBERT pre-training language model and BiLSTM with the attention mechanism. First, use the ALBERT pre-training language model to obtain the dynamic feature representation of the barrage text, then use the BiLSTM network to extract the text context relationship features, dynamically adjust the feature weights through the attention mechanism, and then use the Softmax classifier to obtain the sentiment category of the barrage text. Experiments on the barrage text data set obtained by the crawler show that the ALBERT-ATT-BiLSTM model compares W2V-Att-CNN and W2V-Att-LSTM models with attention mechanism, the precision value has increased by 5.34% and 4.32%, the recall value has increased by 5.23% and 4.27%, and the F1 has increased by 5.27% and 4.25%. Compared with the MC-CNN-GRU model, the precision value of ALBERT -ATT -BiLSTM model is also improved by 3.61%, which is more accurate on the data set than some traditional models.
Program Verification: a 70+- Year History

The year 2019 saw the 70th anniversary to Alan Turing’s 1949 paper, “Checking a Large Routine” and the 50th anniversary of Tony Hoare’s paper, “An Axiomatic Basis for Compuer Programming”. In the latter paper, Hoare stated: “When the correctness of a program, its compiler, and the hardware of the computer have all been established with mathematical certainty, it will be possible to place great reliance on the results of the program, and predict their properties with a confidence limited only by the reliability of the electronics.” In this talk, I will review the history of this vision, describing the obstacles, the controversies, and progress milestones. I will conclude with the description of both impressive progress and dramatic failures exhibited over the past few years.
Mastering the Game of Amazons Fast by Decoupling Network Learning

In this work, we propose a deep reinforcement learning (DRL) algorithm DoubleJump which can master the game of Amazons efficiently. To address the bottleneck problem of sparse supervision signal in DRL, we split the neural network into rule network and skill network, using huge amounts of inexpensive data with game rule information and scarce data containing game skill information to train two networks respectively. Besides, we split the three sub-actions of each action into independent states during Monte-Carlo tree search (MCTS), to improve the probability of finding the global optimal state and reduce the average branching factor. The experimental results show our algorithm reaches about 130:70 in the zero-knowledge learning compared with the AlphaGo Zero algorithm, significantly improves the learning speed, and then alleviates the severe dependence on computing resources.
VizSnippets: Compressing Visualization Bundles Into Representative Previews for Browsing Visualization Collections

Visualization collections, accessed by platforms such as Tableau Online or Power Bl, are used by millions of people to share and access diverse analytical knowledge in the form of interactive visualization bundles. Result snippets, compact previews of these bundles, are presented to users to help them identify relevant content when browsing collections. Our engagement with Tableau product teams and review of existing snippet designs on five platforms showed us that current practices fail to help people judge the relevance of bundles because they include only the title and one image. Users frequently need to undertake the time-consuming endeavour of opening a bundle within its visualization system to examine its many views and dashboards. In response, we contribute the first systematic approach to visualization snippet design. We propose a framework for snippet design that addresses eight key challenges that we identify. We present a computational pipeline to compress the visual and textual content of bundles into representative previews that is adaptive to a provided pixel budget and provides high information density with multiple images and carefully chosen keywords. We also reflect on the method of visual inspection through random sampling to gain confidence in model and parameter choices.
Evaluation of Data Integration Plans based on Graph Data

In data management, new data storage models and technologies are gradually adopted, including graph-based data models that have been enabled by technologies such as RDF, GraphQL and LPG. These are particularly fit when dealing with the coexistence of multitude and heterogeneous data sources that require an integration architecture in a data-centric organization due to intrinsic data connectedness. Two prominent approaches are distinguished in our study: (1) embracing a unifying graph format, where, following a series of transformations, all data sources are lifted to a common format in a consolidated graph repository, (2) creating a mediator tier where legacy data sources hold their data but are accessed through a virtual graph integration layer. Each approach comes with a specific way to query data and a plethora of query languages were created for this purpose. In the context of a data integration project we investigated two approaches to data retrieval and data modeling that have gained momentum during recent years: RDF graphs holding data lifted from non-graph data sources and GraphQL acting as a proxy for retrieving virtually connected data from heterogeneous sources. The paper reports on comparative experiments with technological instantiations of the two approaches, which can inform further IT strategies in an institutional project advocating a migration from app-centricity towards data connectedness.
Aerial Image Object Detection Based on Superpixel-Related Patch

Aerial image object detection and recognition has attracted increasing attention in recent years. Many excellent detectors have been proposed. However, due to the high-resolution of aerial images, these detectors are difficult to directly apply to aerial images. In order to solve the problem of hard processing caused by high resolution, it is generally to resize the high-resolution images into low-resolution images or cut the high-resolution images into small image patches. Cutting high-resolution aerial images into small image patches without overlap may cut an object into multiple parts which may lose the integrity of the object and causes one object to be detected as multiple objects. We design a new baseline to cut high-resolution aerial images into small image patches by using superpixel. Firstly, we use pixel-related GMM (Gaussian mixture model) to segment the high-resolution aerial images into superpixel images. Then we utilize superpixel label to cut high-resolution aerial images into low-resolution image patches with integrity of the object. Finally, we use YOLOv5 with CSL (Circular Smooth Label) to detect oriented objects. Our method effectively preserves the integrity of the object and improves the AP (Average Precision) of the object detection. This baseline can be applied not only to object detection, but also to aerial image segmentation, classification and so on. Experiments on the UCAS-AOD dataset show the effectiveness of the proposed method.
MDCount: A Lightweight Encoder-Decoder Architecture for Resource-Saving Crowd Counting

Abstract This work put forwards a lightweight encoder-decoder architecture MDCount designed unambiguously for real-time high-precision crowd counting with constricted calculation resources. The lightweight backbone network MobileNetV2 is tailored to decrease parameter numbers in an acceptable accuracy. The encoder-decoder architecture with atrous spatial pyramid pooling modules is proposed to recover the spatial and contextual information at multiple scales. Experiments on realistic and challenging datasets and comparing contemporary approaches demonstrate that our method MDCount accomplishes equivalent performance with smaller computation costs.
Averaging and Boosting Methods in Ensemble-Based Classifiers for Text Readability

The purpose of this paper is to investigate whether it is possible to predict text readability with ensemble-based classifiers. In this article, the authors calculated and analyzed the readability indices. In the next stage, they defined additional features for each text and determined the relationships between readability and features. Among the various tasks of machine learning, they chose the classification problem. The authors calculated and compared the accuracy of different machine learning models. After building the models, they proceeded to the Random decision forests model interpretation step using the SHAP method. The authors show that machine learning models based on only three features are capable of predicting text readability. Long sentences and a low percentage of stop words can cause low readability. The machine learning model shown in this paper allows to classify texts according to readability with a model accuracy of 0.9.
Deep Adaptive Multi-intention Inverse Reinforcement Learning

This paper presents a deep Inverse Reinforcement Learning (IRL) framework that can learn an a priori unknown number of nonlinear reward functions from unlabeled experts' demonstrations. For this purpose, we employ the tools from Dirichlet processes and propose an adaptive approach to simultaneously account for both complex and unknown number of reward functions. Using the conditional maximum entropy principle, we model the experts' multi-intention behaviors as a mixture of latent intention distributions and derive two algorithms to estimate the parameters of the deep reward network along with the number of experts' intentions from unlabeled demonstrations. The proposed algorithms are evaluated on three benchmarks, two of which have been specifically extended in this study for multi-intention IRL, and compared with well-known baselines. We demonstrate through several experiments the advantages of our algorithms over the existing approaches and the benefits of online inferring, rather than fixing beforehand, the number of expert's intentions.
Controller Design for Affine Nonlinear System Based on Constructing Optimization T–S Fuzzy Model and Disturbance Reconstruction

Affine nonlinear systems are special kinds of nonlinear systems, and they can represent many practical systems. Dealing with affine nonlinear system based on T–S fuzzy model has drawn much attention in the literature recently. In this article, a robust control method is proposed for affine nonlinear system with disturbance based on T–S fuzzy model. First, an optimization T–S fuzzy model is constructed focusing on optimizing the parameters of the weight functions by using differential evolution algorithm. Second, in order to reconstruct the disturbance, an interval observer is designed for the affine nonlinear system, and then a novelty disturbance reconstruction method based on the interval observer is developed. The disturbance reconstruction value can asymptotically approach the actual disturbance value, and furthermore, it decouples the control input. Third, by taking the optimization T–S fuzzy model as design model and by introducing the reconstruction of disturbance into controller, a feedback robust controller with disturbance compensation is designed to stabilize the affine nonlinear system. Finally, a simulation example of the crane model is given and some comparisons are provided to verify the effectiveness and show the advantages of the proposed method.
Bangla Topic Classification Using Supervised Learning

We are living in the era of an information technology-driven world. A lot of data is being generated at every moment with the help of the Internet of Things. To extract information from that data, it becomes essential to classify those data into various categories. Classification of text involves extracting features from text and then classifying them. Categorizing this big chunk of data is cumbersome and time-consuming, and, in some cases, impossible without the machine’s involvement. That has imaged a need to model a machine-based classification algorithm. However, the recent surge in machine learning and deep learning has grown more attraction in this research domain. Literature shows a significant number of research work in topic classification that deals with only English language. But, there are very few topic classification research in the Bangla language due to the scarcity of the Bangla topics database and other linguistic constraints. Among those topic classification work in Bangla, there are few works that involve machine learning or deep learning implementation. In this article, we present the Bangla topic classification methodology using a supervised learning model. We have implemented various word embedding algorithms to embed the text of Bangla newspaper datasets and machine learning algorithms to classify the embedded text. We have selected the best embedding and classification algorithm pair considering the performance metrics.Kowsher, Md. Tahabilder, Anik Jahan Prottasha, Nusrat Abdur-Rakib, Md. Moyez Uddin, Md. Saha, Puspita
EACoupledCF: An Enhanced Attention-based Coupled Collaborative Filtering Approach for Recommendation

Recommender system is the core to solve the problem of information overload. Meanwhile, non-IID (non-Independently Identically Distribution) recommender system shows its potential in improving recommendation quality and solving the problems such as sparsity and cold start. With the development of deep learning, recommendation has become a hot topic and a large number of studies have proved the effectiveness of deep learning in recommender system. In this work, we contribute a new multi-layer neural network framework, EACoupledCF (Enhanced Attention-based Coupled Collaborative Filtering), to perform collaborative filtering. The idea of EACoupledCF is to use an outer product to explicitly model the pairwise correlations between the dimensions of the embedding space, utilize the convolutional neural network and introduce spatial attention mechanism to learn high-order features between embedded dimensions. At the same time, it also proposes a novel model called DCCF (Deep Combination Collaborative Filtering) for implicit feedback learning in order to capture the interactive information better. In contrast to the existing neural recommendation models, the experimental results obtained on two real-word large datasets show the effectiveness of our proposed model.
Optimized Texture Spectral Similarity Criteria

This paper introduces an accelerated algorithm for evaluating criteria for comparing the spectral similarity of color, Bidirectional Texture Functions (BTF), and hyperspectral textures. The criteria credibly compare texture pixels by simultaneously considering the pixels with similar values and their mutual ratios. Such a comparison can determine the optimal modeling or acquisition setup by comparing the original data with their synthetic simulations. Other applications of the criteria can be spectral-based texture retrieval or classification. Together with existing alternatives, the suggested methods were extensively tested and compared on a wide variety of color, BTF, and hyper-spectral textures. The methods’ performance quality was examined in a long series of specially designed experiments where proposed ones outperform all tested alternatives.
Audio Watermarking in Linear Canonical Transform Domain Using Frequency-Dependent Clustering

Audio watermarking is considered as a challenging domain owing to the requirement of imperceptibility post watermarking, while keeping the robustness intact (Dhar PK, Robust FFT based watermarking scheme for copyright protection of digital audio data, IEEE, China, pp. 181–184, 2011). This paper proposes an effective methodology for watermarking of the audio signal using linear canonical transform (LCT), which has found its use in a multitude of signal processing applications, due to its flexibility (Xiang et al., The linear canonical transform and its application to time-frequency signal analysis, IEEE, Wuhan, China, pp 1–4, 2009). The proposed work uses a watermark sequence of complex numbers which is suitably embedded into the transformed audio signal using frequency-dependent clustering. The watermark detection is blind and performed reversibly. It is found that the watermark is extremely resilient against a wide range of signal processing and feature distortive attacks. The technique is also secure, imperceptible, stable and offers sufficient payload capacity of nearly 15% of the audio signal length.
DYNAMIC HANDWRITTEN SIGNATURE IDENTIFICATION USING SPIKING NEURAL NETWORK

The article proposes a method for dynamic signature identification based on a spiking neural network. Three dynamic signature parameters l(t), xy(t), p(t) are used, which are invariant to the signature slope angle, and after their normalization, also to the signature spatial and temporal scales. These dynamic parameters are fed to the spiking neural network for recognition simultaneously in the form of time series without preliminary transformation into a vector of static features, which, on the one hand, simplifies the method due to the absence of complex computational transformation procedures, and on the other hand, prevents the loss of useful information, and therefore increases the accuracy and reliability of signature identification and recognition (especially when recognizing forged signatures that are highly correlated with the genuine). The spiking neural network used has a simple training procedure, and not all neurons of the network are trained, but only the output ones. If it is necessary to add new signatures, it is not necessary to retrain the entire network as a whole, but it is enough to add several output neurons and learn only their connections. In the results of experimental studies of the software implementation of the proposed system, it’s EER = 3.9% was found when identifying skilled forgeries and EER = 0.17% when identifying random forgeries.
Camera Calibration Through Camera Projection Loss

Camera calibration is a necessity in various tasks including 3D reconstruction, hand-eye coordination for a robotic interaction, autonomous driving, etc. In this work we propose a novel method to predict extrinsic (baseline, pitch, and translation), intrinsic (focal length and principal point offset) parameters using an image pair. Unlike existing methods, instead of designing an end-to-end solution, we proposed a new representation that incorporates camera model equations as a neural network in a multi-task learning framework. We estimate the desired parameters via novel camera projection loss (CPL) that uses the camera model neural network to reconstruct the 3D points and uses the reconstruction loss to estimate the camera parameters. To the best of our knowledge, ours is the first method to jointly estimate both the intrinsic and extrinsic parameters via a multi-task learning methodology that combines analytical equations in learning framework for the estimation of camera parameters. We also proposed a novel CVGL Camera Calibration dataset using CARLA Simulator [1]. Empirically, we demonstrate that our proposed approach achieves better performance with respect to both deep learning-based and traditional methods on 8 out of 10 parameters evaluated using both synthetic and real data. Our code and generated dataset are available at https://github.com/thanif/Camera-Calibration-through-Camera-Projection-Loss.
Improved convergence guarantees for learning Gaussian mixture models by EM and gradient EM

We consider the problem of estimating the parameters a Gaussian Mixture Model with K components of known weights, all with an identity covariance matrix. We make two contributions. First, at the population level, we present a sharper analysis of the local convergence of EM and gradient EM, compared to previous works. Assuming a separation of Ω( logK), we prove convergence of both methods to the global optima from an initialization region larger than those of previous works. Specifically, the initial guess of each component can be as far as (almost) half its distance to the nearest Gaussian. This is essentially the largest possible contraction region. Our second contribution are improved sample size requirements for accurate estimation by EM and gradient EM. In previous works, the required number of samples had a quadratic dependence on the maximal separation between the K components, and the resulting error estimate increased linearly with this maximal separation. In this manuscript we show that both quantities depend only logarithmically on the maximal separation.