Reviewerly/exHarmony huggingface dataset:
Mind Your Tweet: Abusive Tweet Detection

The abusive posts detection problem is more complicated than it seems due to its unseemly, unstructured noisy data and unpredictable context. The learning performance of the neural networks attracts researchers to get the highest performing output. Still, there are some limitations for noisy data while training for a neural network. In our work, we have proposed an approach that considers the assets of both the machine learning and neural network to get the most optimum result. Our approach performs with the F1 score of 92.79.
Design and Implementation of Hotel Big Data Analysis Platform Based on Hadoop and Spark

Abstract In order to provide decision support for user travel and hotel management planning, this paper proposes the design and implementation of a hotel big data analysis platform based on Hadoop and Spark technology, using hotel basic data and user comment data as data sources. First, we design a crawler program to collect basic data and user comment data of city hotels in Qingdao, Shandong Province, and upload them to the hadoop storage platform, use Spark for big data preprocessing, use Hive to build a big data platform data warehouse, and finally use echarts to analyze the results and make decisions perform a visual display.
Multigraph Random Walk for Joint Learning of Multiview Clustering and Semisupervised Classification

Recent researches on multiview learning have received widespread attention due to the increasing generalization of multiview data. As an effective probabilistic model, random walk has also shown encouraging performance in various fields. To further exploit the potential of utilizing random walk schemes to address multiview learning problems, this article proposes a simple yet efficient multigraph random walk scheme for both multiview clustering and semisupervised classification tasks. The proposed model integrates random walk with multiview learning, and recursively learns a globally stable probability distribution matrix from multiple views, on the basis of which the label indicator is obtained in the scene of clustering or semisupervised classification. Furthermore, an adaptive weight vector is learned to incorporate the diversity and complementarity of multiview data. Besides, the relationships between the proposed scheme and spectral clustering, neighborhood embedding and manifold embedding are analyzed theoretically. Finally, comprehensive comparative experiments are conducted with several state-of-the-art multiview clustering and semisupervised classification methods on eight real-world datasets. The experimental results demonstrate the superiority of the proposed method in terms of both clustering and classification performance.
Text Generation with Syntax - Enhanced Variational Autoencoder

Text generation is one of the essential yet challenging tasks in natural language processing. However, the input text alone is usually hard to provide enough information to generate the desired output. Previous work attempts to incorporate syntactic information into the generative models based on variational autoencoder(VAE). But these methods have difficulty in adequately modeling the tree structure of syntactic data. In this paper, we formulate the syntactic structure as a graph and introduce a syntax encoder based on graph neural network(GNN) to model the syntactic information of sentences. Based on the syntax encoder, we propose a novel syntax-enhanced variational autoencoder(SEVAE) with two variants. The variant SEVAE-m merges sentence information and syntactic information into one latent space to enrich the fine-grained syntactic information of latent representations. And the variant SEVAE-s with two separate latent spaces allows the sentence decoder to dynamically attend to semantic and syntactic information from two latent variables. Experiments on two benchmark datasets show that our methods achieve significant and consistent improvements compared with previous work.
Creating Immersive Play Anywhere Location-Based Storytelling Using Mobile AR

Map Story is an Augmented Reality (AR) location-based storytelling app for smartphones, that directs users to nearby real-world locations whilst following a new story. Some of the locations are augmented with virtual content visible on users phones, in order to bring the locations closer in line with those described in the story. In addition to developing the app, a preliminary user study is described, with participants using the app at their own choice of starting location, with data collected through a combination of in-app feedback and questionnaires, alongside post interviews with a selection of those who took part. As well as developing guidelines for such an experience, there is evidence that the real sites visited were reported as being a closer match to the story events, and more believable in terms of the story events playing out there than the AR locations, but with the AR sites potentially beneficial in encouraging a greater focus on the story.
FPGA Implementation of Low Complexity Hybrid Decision Tree Training Accelerator

Decision Tree (DT) algorithms perform classification of data according to decision criteria obtained during training which results in high computational complexity and latency. In this paper, a Hybrid DT (HDT) is proposed which reduces training complexity and achieves 8× speed-up as compared to conventional DT. The critical path of proposed HDT hardware enables the FPGA to operate at a maximum frequency of 125 MHz. Simulation results show that the proposed hardware achieves 1000× speed-up as compared to software-based realisation and 60× speed-up as compared to existing FPGA training accelerator.
An Optimal Transport Analysis on Generalization in Deep Learning

Deep neural networks (DNNs) have achieved state-of-the-art performance in various learning tasks, such as computer vision, natural language processing, and speech recognition. However, the fundamental theory of generalization still remains obscure in deep learning-why DNN models can generalize well, despite that they are heavily overparametrized in both depth and width? Recently, some work shows that traditional theory of analyzing the generalization error of learning models fails to explain the generalization of DNNs. The failure is mainly because of one simple fact that the worse case analysis of generalization error for learning models would be too loose for models with large parameter space, such as DNNs. In this work, we propose a new analysis of generalization in DNNs from an optimal transport perspective. Unlike traditional worse-case uniform convergence analysis in learning theory, our analysis of generalization error is dependent on both the learning algorithm and the data distribution and is the average-case analysis. Thus, our theory can be more practical and accurate to describe the generalization behavior of DNNs. More specifically, in this article, we try to answer a fundamental yet unsolved question in deep learning-why deeper models can generalize well than shallow models? The main contribution of this article can be summarized in four aspects. First, under a general learning framework, we derive upper bounds on the generalization error of learning algorithms by their algorithmic transport cost: the expected Wasserstein distance between the output hypothesis and the output hypothesis conditioned on an input example. We further provide several upper bounds on the algorithmic transport cost in terms of total variation distance, relative entropy, and Vapnik-Chervonenkis (VC) dimension. Moreover, we also study different conditions for loss functions under which the generalization error of a learning algorithm can be upper bounded by different probability metrics between distributions relating to the output hypothesis and/or the input data. Finally, under our established framework, we obtain our main results, showing that the generalization error in DNNs decreases exponentially to zero as the number of layers increases.
Trajectory Planning Approach of Mobile Robot Dynamic Obstacle Avoidance with Multiple Constraints

This paper proposes a novel trajectory planning approach based on time elastic band to solve the problem of dynamic obstacle avoidance of mobile robot. Uncertain factors in the scenario need to be considered in trajectory planning. Thus, this approach includes multiple constraints, such as robot motion speed, motion state, and obstacles. First, to solve the optimal speed of the mobile robot, the workspace potential field must be established, and environmental information should be obtained to constrain the robot speed. Second, a costmap needs to be established to detect dynamic obstacles, and obstacle avoidance strategies based on the relative motion relationship between dynamic obstacles and the robot should be proposed to realize dynamic obstacle avoidance. Finally, by combining multiple constraints, the collision-free trajectory planning from the start point to the target point is completed, and the mobile robot realizes collision-free smooth motion. Experimental results show that this approach has satisfactory obstacle avoidance planning effects and superior kinematics characteristics and improves the comfort and safety of the mobile robot.
Automatic Cell Nucleus Segmentation Using Superpixels and Clustering Methods in Histopathological Images

It is seen that there is an increase in cancer and cancer-related deaths day by day. Early diagnosis is vital for the early treatment of the cancerous area. Computer-aided programs allow for early diagnosis of unhealthy cells that specialist pathologists diagnose as a result of efforts. In this study, kMeans and Fuzzy C Means methods, which are among the global segmentation methods, and SLIC, Quickshift, Felzenszwalb, Watershed and ERS algorithms, which are among the superpixel segmentation methods, were used for automatic cell nucleus detection in high resolution histopathological images with computer aided programs. As a result of the study, the success performances of the segmentation algorithms were analyzed and evaluated. It is seen that better success is obtained in watershed and FCM algorithms in high resolution histopathological images used. Quickshift and SLIC methods gave better results in terms of precision. It is seen that there are k-Means and FCM algorithms that provide the best performance in F measure (F-M) and the true negative rate (TNR) is more successful in Quickshift, k-Means and SLIC methods.
Causal analyses of existing databases: the importance of understanding what can be achieved with your data before analysis (commentary on Hernán)

Hernán's short communication provocatively argues that power calculations should not be required for causal analyses of existing databases [1]. His view is thought-provoking and helpful: contrasting it with the opposite extreme helps us to consider where we might sit between the two. We agree with many of Hernán's points. However, the proposal that, regardless of sample size, available data should be analysed because results can be synthesised in later meta-analyses seems to depend on a scientific utopia that does not reflect reality.
Research on the Application of "Micro-learning" in Tai Chi Teaching

This paper adopts the method of literature data, interview method, questionnaire survey method, experimental method and data analysis method. Tai Chi course is used for micro-learning experimental research. The results show that micro-learning can target students' learning needs and effectively stimulate students' interest in learning. Micro-learning is used for pre-class preview and after-class review and consolidation, which is a useful continuation and supplement to classroom teaching. The advantages of teaching grouping method and video feedback method in micro-learning allow teachers to fully grasp the students’ learning situation and carry out timely Improvements in teaching help students better understand and master knowledge. At the same time, the use of micro-learning methods can effectively improve students' participation in physical exercises and physical education performance. On the basis of this research, the following suggestions are put forward: to stimulate students' interest in learning; to improve students' ability to learn independently; to increase the construction of high-quality sports micro-course resources; to improve the cultivation of physical education teachers' teaching information ability.
RoPM: An Algorithm for Computing Typical Testors Based on Recursive Reductions of the Basic Matrix

Feature selection plays an important role in pattern recognition and smart computing. The full set of typical testors constitutes a useful tool for solving feature selection problems, especially those problems in which the objects are described by both quantitative and qualitative features. However, finding the typical testors involves a high computational cost. That is why even the most efficient methods become unsuitable to solve some problems. In this work, a new algorithm was introduced in order to reduce the long runtimes involved in the search of typical testors. The performance of the proposed algorithm was evaluated by means of several tests, which use both real-world and simulation data. MATLAB and Java language on Eclipse SDK platform were used to build the simulation dataset and to perform the tests, respectively. The runtimes achieved by the proposed algorithm were significantly shorter than those obtained by fast-BR and GCreduct (the two fastest algorithms) mainly when the latter ones exhibited excessively long runtimes.
High-level Features in Deeper Deep Learning Layers for Breast Cancer Classification

Early detection of breast cancer is crucial when treating than cure in later mammogram screening processes. To date, researchers extensively proposed the implementation of artificial intelligence to develop a computer-aided system (CAD) to determine types of breast tumour lesion, whether benign or malignant. This approach is significant to minimise the rate of misinterpretation in false positive and false negative diagnosis results among radiologists. Lack of established medical datasets publicly available has become the main reason why the system is not fully implemented in clinical settings yet. This study is aimed to investigate the performance of a convolutional neural network (CNN) to detect cancerous lesion types. The pre-trained CNN networks are tested on two established public datasets, CBIS-DDSM and INbreast. Pre-processing using denoising and contrast limited adaptive histogram equalisation (CLAHE) and augmented to lessen the effect of overfitting. The pre-trained CNNs AlexNet and InceptionV3 represent shallow and deeper neural networks respectively, trained using the transfer learning method. Performance of the system is tested and its accuracy, losses, sensitivity, specificity, and receiver operating characteristic curve (ROC) are evaluated. The InceptionV3 network performs better with the highest testing and area under the curve (AUC) at 99.93% compared to shallower AlexNet at 98.92% using INbreast dataset. Training the system using augmented data is proven to improve testing accuracy at 86.7% from 60.26% using a non-augmented dataset in low-quality input images. Meanwhile, using a shallower network for transfer learning produces high accuracy results without compromising computational cost. This study serves as the platform to improve the system’s performance by varying the pretrained networks used and getting different features from each convolutional layer to be trained in the future.
Multiple Object Tracking with GRU Association and Kalman Prediction

Multiple Object Tracking (MOT) has been a useful yet challenging task in many real-world applications such as video surveillance, intelligent retail, and smart city. The challenge is how to model long-term temporal dependencies in an efficient manner. Some recent works employ Recurrent Neural Networks (RNN) to obtain good performance, which, however, requires a large amount of training data. In this paper, we proposed a novel tracking method that integrates the auto-tuning Kalman method for prediction and the Gated Recurrent Unit (GRU), and achieves a near-optimum with a small amount of training data. Experimental results show that our new algorithm can achieve competitive performance on the challenging MOT benchmark, with higher efficiency and more robustness compared to the state-of-the-art RNN-based online MOT algorithms.
The Idiosyncratic Effects of Adversarial Training on Bias in Personalized Recommendation Learning

Recently, recommendation systems have been proven to be susceptible to malicious perturbations of the model weights. To overcome this vulnerability, Adversarial Regularization emerged as one of the most effective solutions. Interestingly, the technique not only robustifies the model, but also significantly increases its accuracy. To date, unfortunately, the effect of Adversarial Regularization beyond-accuracy evaluation dimensions is unknown. This paper sheds light on these aspects and investigates how Adversarial Regularization impacts the amplification of popularity bias, and the deterioration of novelty and coverage of the recommendation list. The results highlight that, with imbalanced data distribution, Adversarial Regularization amplifies the popularity bias. Moreover, the empirical validation on five datasets confirms that it degrades the diversity and novelty of the generated recommendation. Code and data are available at https://github.com/sisinflab/The-Idiosyncratic-Effects-of-Adversarial-Training.
Identity-linked Group Channel Pruning for Deep Neural Networks

Channel pruning is a commonly used model compression in convolutional neural network. The structured pruning using sparse constraints can automatically learn the importance of parameters during the training process by imposing sparse constraints on parameters. However, existing pruning methods based on sparse constraints cannot process the final convolutional layer of the residual module with complex connections. Due to the existence of residual connection, if the final convolutional layer of the residual module is pruned, the sparse channel of the feature map from residual connection does not correspond to the feature map from module output, which will cause the parameters to be unable to be pruned. This paper studies this problem and proposes an identity association group pruning algorithm, which we call IGP. IGP groups the parameters and channels that generate the corresponding feature maps, uses Group Lasso to sparse the same group of parameters as a whole, and forces the sparseness of the parameters with sparse correlation to be consistent with each other. Experiments show that when IGP compresses ResNet56 60% parameters, the model performance only drops 0.36 %, which is better than the existing pruning method based on sparse constraints. In the case of high compression ratio, IGP can compresses ResNet-50 compressesed with 87% parameters and the performance drops only 0.76%, which is 5.17 % higher than the existing methods.
An Image Captioning Approach Using Dynamical Attention

In recent years, as an active topic in the field of vision and language, image captioning has made great progress. Previous approaches have demonstrated the superiority of spatial and channel attentions in image captioning task. However, such attention-based approaches ignore the difference between function words (e.g., “to”, “for” and “out”) and notional words (e.g., “girl”, “teddy” and “bear”). To address above issue, in this paper we propose a dynamical balancing attention model (BAM) based on attention variation for image captioning, which uses attention variation to fuse channel attention and region attention. Generating function and notional words, it effectively balances the contribution of image channel feature and that of image region feature. Further, the proposed approach dynamically focuses on the most relevant attention features in word prediction. Extensive experimental results on typical datasets show our approach outperforms the attention based approaches and achieves competitive performance over existing end-to-end leading approaches.
An infinite family of Griesmer quasi-cyclic self-orthogonal codes

Our aim for this paper is to find the construction method for quasi-cyclic self-orthogonal codes over the finite field Fpm. We first explicitly determine the generators of α-constacyclic codes over the finite Frobenius non-chain ring Rp,m=Fpm[u,v]/〈u2=v2=0,uv=vu〉, where m is a positive integer, α=a+ub+vc+uvd is a unit of Rp,m, a,b,c,d∈Fpm, and a is nonzero. We then find a Gray map from Rp,m[x]/〈xn−α〉 (with respect to homogeneous weights) to Fpm[x]/〈xp3m+1n−a〉 (with respect to Hamming weights), which is linear and preserves minimum weights. We present an efficient algorithm for finding the Gray images of α-constacyclic codes over Rp,m of length n, which produces infinitely many quasi-cyclic self-orthogonal codes over Fpm of length p3m+1 and index p3m. In particular, some family turns out to be "Griesmer" codes; these Griesmer quasi-cyclic self-orthogonal codes are "new" codes compared with previously known Griesmer codes of dimension 4.
A Dynamically Adaptive Approach to Reducing Strategic Interference for Multiagent Systems

Multiagent reinforcement learning (RL) is widely used and can successfully solve many problems in the real world. In the multiagent RL system, a global critic network is used to guide each agent’s strategy to update to learn the most beneficial strategy for the collective. However, the global critic network also makes the current agent’s learning be affected by other agents’ strategies, which leads to unstable learning. To solve this problem, we propose dynamic decomposed multiagent deep deterministic policy gradient (DD-MADDPG): a new network that considers both global and local evaluations and adaptively adjusts the agent’s attention to the two evaluations. Besides, the use of the experience replay buffer by multiagent deep deterministic policy gradient (MADDPG) produces outdated experience, and the outdated strategies of other agents further affect the learning of the current agent. To reduce the influence of other agents’ outdated experience, we propose TD-Error and Time-based experience sampling (T2-PER) based on DD-MADDPG. We evaluate the proposed algorithm’s performance according to the learning stability and the average return obtained by the agents. We have conducted experiments in the MPE environment. The results show that the proposed method has better stability and higher learning efficiency than MADDPG and has a certain generalization ability.
A Systematic Review of Blockchain Technology to Find Current Scalability Issues and Solutions

Blockchain technology has proven the success of its security technology and need for transparency of transaction in the present area. This paper covers in-depth review of all the existing blockchain technology with its issue, limits throughput, high latency, storage issues, etc. Blockchain technology having different variants with different consensus mechanisms, methods, and techniques each have its own advantage and limitation. In this research review, we also study different existing solutions for the scalability challenge. There are large number of blockchain scalability solutions are exist but to overcome completely, it requires further research and more number of scalability solutions.