Reviewerly/exHarmony huggingface dataset:
The Architecture of Micro-services and the Separation of Frond-end and Back-end Applied in a Campus Information System

In traditional Java Web project, the code of front-end and back-end are highly mixed together. Since the static resources and dynamic resources are merged together in server, the pressure of server is huge. If some problems arise in server, the front-end and back-end will collapse at the same time so that user experience is terrible. In the age of big data, the performance requirements of Internet project are becoming higher and higher, so the architecture of mixing together front-end and back-end can’t gradually meet the need of applications. The separation of front-end and back-end has become a direction for Web project. Along with the development and iteration of front-end technique, some front-end frameworks appear such as React, Argluar and Vue. In this paper, we introduce a campus information system in which software architecture is based on Spring Cloud for micro-services and React for the separation of front-end and backend. The key technique and realization used in the system have been described in this paper. Through decoupling of front-end and back-end, load capacity of the system has been raised by big percentages and good performance of the system embody the design advantage of micro-services and separation of front-end and back-end.
Lex-Pos Feature-Based Grammar Error Detection System for the English Language

This work focuses on designing a grammar detection system that understands both structural and contextual information of sentences for validating whether the English sentences are grammatically correct. Most existing systems model a grammar detector by translating the sentences into sequences of either words appearing in the sentences or syntactic tags holding the grammar knowledge of the sentences. In this paper, we show that both these sequencing approaches have limitations. The former model is over specific, whereas the latter model is over generalized, which in turn affects the performance of the grammar classifier. Therefore, the paper proposes a new sequencing approach that contains both information, linguistic as well as syntactic, of a sentence. We call this sequence a Lex-Pos sequence. The main objective of the paper is to demonstrate that the proposed Lex-Pos sequence has the potential to imbibe the specific nature of the linguistic words (i.e., lexicals) and generic structural characteristics of a sentence via Part-Of-Speech (POS) tags, and so, can lead to a significant improvement in detecting grammar errors. Furthermore, the paper proposes a new vector representation technique, Word Embedding One-Hot Encoding (WEOE) to transform this Lex-Pos into mathematical values. The paper also introduces a new error induction technique to artificially generate the POS tag specific incorrect sentences for training. The classifier is trained using two corpora of incorrect sentences, one with general errors and another with POS tag specific errors. Long Short-Term Memory (LSTM) neural network architecture has been employed to build the grammar classifier. The study conducts nine experiments to validate the strength of the Lex-Pos sequences. The Lex-Pos -based models are observed as superior in two ways: (1) they give more accurate predictions; and (2) they are more stable as lesser accuracy drops have been recorded from training to testing. To further prove the potential of the proposed Lex-Pos -based model, we compare it with some well known existing studies.
Framework of Forecast Verification of Surface Solar Irradiance From a Numerical Weather Prediction Model Using Classification With a Gaussian Mixture Model

Abstract A clustering and classification method using a Gaussian mixture model (GMM) is used to summarize and simplify meteorological data from a numerical weather prediction (NWP) model. Each horizontal grid in the integration domain of the NWP model is characterized by a feature vector, which consists of a multivariable with multiple pressure levels. All horizontal grids at every forecast time are classified based on the GMM clustering. The classification results show that grids are clustered into air masses or disturbances with the same meteorological characteristics. This paper describes application of the proposed classification method as a framework to verify the forecast of surface solar irradiance from the NWP model. Satellite observation data are used as the reference so that verification can be performed over the integration domain of the NWP model for each air mass or disturbance that moves and changes shape over time. The mean square error (MSE) is decomposed into the square of the mean error and the MSE between variables centered on zero, the square root of which is called the centered root mean square error (CRMSE). The analyses are performed for forecast data over a 2 day forecast horizon. The change in mean error is not significant until the second day, whereas the CRMSE is maintained only during the first day. Each air mass has a different forecast error structure. The proposed framework clarifies the structure of the forecast error of the surface solar irradiance.
Identifying click-requests for the network-side through traffic behavior

With the rapid development of web-based applications, clicking on hyperlinks has become a general means for accessing various network services. Understanding the visiting behavior of web users not only helps improve the personalized service quality and user experience, but also plays an important role in network management and early threat detection. Click-stream identification is a fundamental issue for user behavior analysis. However, most existing approaches are designed for non-encrypted HTTP requests and only focus on server-side scenarios, which makes them inapplicable to the increasingly popular HTTPS and network-side management. In this work, we propose an encryption-independent scheme from a network-side perspective that adopts the web traffic collected at the network boundary to identify the HTTP(S) requests generated by the click actions of web users. The proposed scheme employs hidden Markov models (HMMs) to describe the time-varying behavior of click and non-click web traffic. A deep neural network (DNN) is integrated into the HMMs to capture the context of web traffic, which eliminates the limitations caused by the independence hypothesis of the traditional HMMs. Finally, a DNN-based rear classifier is proposed to determine the type of HTTP(S) requests according to the fitting degree between the HTTP(S) requests and the HMM-based behavior models. We derive the algorithms for model learning and click identification. Experiments are conducted to validate the proposed approach. Performance-related issues and comparisons are discussed. Results show that both the average precision and recall rate of the proposed approach exceed 92%, which is better than most existing benchmark methods in terms of performance and stability.
Counterfeit Currency Detection Using Supervised Machine Learning Algorithms

Counterfeit currency is an extremely common yet pertinent problem of presence of fake, inauthentic or copies of real currency in the market and economy that is faced by various nations across the globe. Cash transactions still make up for over 80% of all transactions. Fake and inauthentic notes, therefore, continue to be a major source of nuisance for the economy. In this paper, the aim is to identify the authenticity of the currency notes by using various machine learning algorithms and also to compare and contrast which of these algorithms is best suited for the same. The machine learning algorithms classify the currency notes on the basis of features extracted from images. The dataset was taken from UCI Machine Learning Repository. We got the best results from K-nearest neighbours classifier with 99.8% accuracy and F-score of 0.992 (β = 2).
Learning to Fool the Speaker Recognition

Due to the widespread deployment of fingerprint/face/speaker recognition systems, attacking deep learning based biometric systems has drawn more and more attention. Previous research mainly studied the attack to the vision-based system, such as fingerprint and face recognition. While the attack for speaker recognition has not been investigated yet, although it has been widely used in our daily life. In this paper, we attempt to fool the state-of-the-art speaker recognition model and present speaker recognition attacker, a lightweight model to fool the deep speaker recognition model by adding imperceptible perturbations onto the raw speech waveform. We find that the speaker recognition system is also vulnerable to the attack, and we achieve a high success rate on the non-targeted attack. Besides, we also present an effective method to optimize the speaker recognition attacker to obtain a trade-off between the attack success rate with the perceptual quality. Experiments on the TIMIT dataset show that we can achieve a sentence error rate of 99.2% with an average SNR 57.2dB and PESQ 4.2 with speed rather faster than real-time.
Domain Adaptation Using Class Similarity for Robust Speech Recognition

When only limited target domain data is available, domain adaptation could be used to promote performance of deep neural network (DNN) acoustic model by leveraging well-trained source model and target domain data. However, suffering from domain mismatch and data sparsity, domain adaptation is very challenging. This paper proposes a novel adaptation method for DNN acoustic model using class similarity. Since the output distribution of DNN model contains the knowledge of similarity among classes, which is applicable to both source and target domain, it could be transferred from source to target model for the performance improvement. In our approach, we first compute the frame level posterior probabilities of source samples using source model. Then, for each class, probabilities of this class are used to compute a mean vector, which we refer to as mean soft labels. During adaptation, these mean soft labels are used in a regularization term to train the target model. Experiments showed that our approach outperforms fine-tuning using one-hot labels on both accent and noise adaptation task, especially when source and target domain are highly mismatched.
StegoPNet: Image Steganography With Generalization Ability Based on Pyramid Pooling Module

In terms of payload capacity and visual effects, the existing image steganography technology based on deep neural networks still needs improvement, to solve this problem, this article proposes a new deep convolutional steganography network based on the pyramid pooling module to achieve better image steganography. The deep convolutional neural network itself can extract features efficiently. Based on the combination of up-sampling structure, we added a pyramid pool module, under the premise of ensuring safety, fully integrated the previous important global features, achieved good hiding and extraction effects, fully integrated the previous important global features, and effective it reduces the loss of contextual information between different sub-regions in the feature extraction process and achieves better hiding and extraction effects under the premise of ensuring security. Experiments show that the average peak signal-to-noise ratio (PSNR)/structure similarity (SSIM) and other indicators between the images obtained by this method have achieved good results in the experiment. Also, we have verified through ablation experiments that the pyramid pooling module can enhance the steganography effect of the network model and can further cut down the loss function of the model.
Unsupervised Training of Siamese Networks for Speaker Verification

Speaker labeled background data is an essential requirement for most state-of-the-art approaches in speaker recognition, e.g., xvectors and i-vector/PLDA.However, in reality it is difficult to access large amount of labeled data.In this work, we propose siamese networks for speaker verification without using speaker labels.We propose two different siamese networks having two and three branches, respectively, where each branch is a CNN encoder.Since the goal is to avoid speaker labels, we propose to generate the training pairs in an unsupervised manner.The client samples are selected within one database according to highest cosine scores with the anchor in i-vector space.The impostor samples are selected in the same way but from another database.Our double-branch siamese performs binary classification using cross entropy loss during training.In testing phase, we obtain speaker verification scores directly from its output layer.Whereas, our triple-branch siamese is trained to learn speaker embeddings using triplet loss.During testing, we extract speaker embeddings from its output layer, which are scored in the experiments using cosine scoring.The evaluation is performed on VoxCeleb-1 database, which show that using the proposed unsupervised systems, solely or in fusion, the results get closer to supervised baseline.
Optimum Design of Function-Link Type-2 Fuzzy Asymmetric CMAC Based on Self-Organizing Algorithm and Modified Jaya Algorithm

In this work, a novel self-organizing interval type-2 fuzzy cerebellar model articulation controller design for chaotic systems synchronization is presented.A function-link network and an asymmetric-membership function are employed to improve the learning ability and flexibility of the proposed network.The gradient-descent method is used to derive the adaptive laws for online updating the network parameters.Suitable learning rates for adaptive laws can be achieved by employing the modified Jaya algorithm.Furthermore, the proposed self-organizing algorithm is used to generate new fuzzy rules or delete less-active fuzzy rules.The control system's exponential stability is analyzed by applying the Lyapunov theory.Finally, numerical simulations on the synchronization of four-dimensional chaotic systems (considering external disturbances and system uncertainties) are conducted to validate the efficiency and performance of the proposed method.
A Novel Study of the Multi-verse Optimizer and Its Applications on Multiple Areas of Computer Science

The purpose of this paper is to study the applications of the metaheuristic multi-verse optimizer in a variety of problems involving optimization in the distinct areas of computer science, keeping a focus on fuzzy logic. This is a state-of-the-art study, so it is only showing different cases where the multi-verse optimizer has been implemented and showed positive and negative results (mostly positive), so we can conclude that it can be applied to fuzzy control with positive results in further investigations. In this review, we can observe different population-based optimization algorithms in various applications, and many of them come from other colleagues that apply these algorithms for different problems, so we can compare with the applications of MVO over his short time along other algorithms. The paper shows multiple applications of many known metaheuristics such as PSO, GWO, HSA; and has nine applications of the MVO in different works.
Learning to Learn Words from Visual Scenes

Language acquisition is the process of learning words from the surrounding scene. We introduce a meta-learning framework that learns how to learn word representations from unconstrained scenes. We leverage the natural compositional structure of language to create training episodes that cause a meta-learner to learn strong policies for language acquisition. Experiments on two datasets show that our approach is able to more rapidly acquire novel words as well as more robustly generalize to unseen compositions, significantly outperforming established baselines. A key advantage of our approach is that it is data efficient, allowing representations to be learned from scratch without language pre-training. Visualizations and analysis suggest visual information helps our approach learn a rich cross-modal representation from minimal examples.
OTNEL: A Distributed Online Deep Learning Semantic Annotation Methodology

Semantic representation of unstructured text is crucial in modern artificial intelligence and information retrieval applications. The semantic information extraction process from an unstructured text fragment to a corresponding representation from a concept ontology is known as named entity disambiguation. In this work, we introduce a distributed, supervised deep learning methodology employing a long short-term memory-based deep learning architecture model for entity linking with Wikipedia. In the context of a frequently changing online world, we introduce and study the domain of online training named entity disambiguation, featuring on-the-fly adaptation to underlying knowledge changes. Our novel methodology evaluates polysemous anchor mentions with sense compatibility based on thematic segmentation of the Wikipedia knowledge graph representation. We aim at both robust performance and high entity-linking accuracy results. The introduced modeling process efficiently addresses conceptualization, formalization, and computational challenges for the online training entity-linking task. The novel online training concept can be exploited for wider adoption, as it is considerably beneficial for targeted topic, online global context consensus for entity disambiguation.
The Mereological Structure of Informational Entities

This article provides the basis of a formal axiomatic system for a mereology of informational entities based on the idea of information fillers that can occupy information slots, such as the same word that can be used in different sentences. It is inspired by Karen Bennett’s mereological system that enables a whole to have a part “twice over”, but differs from it in several key points, such as the acceptance of empty slots, and the possibility for slots to have slots. Information slots are analyzed as informational entities that can carry aboutness.
Performance Evaluation of Data Compression Algorithms for IoT-Based Smart Water Network Management Applications

IoT-based smart water supply network management applications generate a huge volume of data from the installed sensing devices which are required to be processed (sometimes in-network), stored and transmitted to a remote centre for decision making. When the volume of data produced by diverse IoT smart sensing devices intensify, processing and storage of these data begin to be a serious issue. The large data size acquired from these applications increases the computational complexities, occupies the scarce bandwidth of data transmission and increases the storage space. Thus, data size reduction through the use of data compression algorithms is essential in IoT-based smart water network management applications. In this paper, the performance evaluation of four different data compression algorithms used for this purpose is presented. These algorithms, which include RLE, Huffman, LZW and Shanon-Fano encoding were realised using MATLAB software and tested on six water supply system data. The performance of each of these algorithms was evaluated based on their compression ratio, compression factor, percentage space savings, as well as the compression gain. The results obtained showed that the LZW algorithm shows better performance base on the compression ratio, compression factor, space savings and the compression gain. However, its execution time is relatively slow compared to the RLE and the two other algorithms investigated. Most importantly, the LZW algorithm has a significant reduction in the data sizes of the tested files than all other algorithms
TSER: A Two-Stage Character Segmentation Network With Two-Stream Attention and Edge Refinement

Segmenting characters in an image is a classic yet challenging task in computer vision. Correctly determining boundaries of adhesive characters with various scales and shapes is essential for character segmentation, especially for separating handwritten characters. Nevertheless, there is seldom work in the literature which can achieve satisfactory performance. In this article, by leveraging the ability of deep neural networks, we proposed a two-stage character segmentation network with two-stream attention and edge refinement (TSER) to tackle this problem. TSER firstly locates every character by object detection, then extracts their corresponding contours. In the process, a novel two-stream attention mechanism (TSAM) is proposed to make the network focus more on the discrepancy of character boundaries. Furthermore, a novel generating method is used to dynamically generate anchors on different feature levels to improve model’s sensitivity on the shapes and scales of characters. Eventually a cascaded edge refinement network is used to obtain contour of each character. To prove the efficiency and generalization ability of our model, we compared TSER with traditional algorithms and other deep learning models on two commonly used datasets in different segmentation tasks. The comparative result indicated that TSER reached state-of-the-art performance.
Data Agnostic Filter Gating For Efficient Deep Networks

Filter pruning is essential for deploying a well-trained CNN model on edge computation devices with a target computation budget (e.g., FLOPs). Current filter pruning methods mainly focus on leveraging feature maps to analyze the importance of filters, and prune those with less impact on the value of the CNN’s loss function, thereby ignoring the variance of input batches to differences in sparse structure over the filters. In this paper, we propose a data-agnostic filter pruning method that uses an auxiliary network named Dagger module to induce pruning with the pre-trained weights as input. Besides, to help prune filters with a preset FLOPs constraint, we utilize an explicit FLOPs-aware regularisation mechanism to directly promote pruning filters toward the target FLOPs. Experimental results on CIFAR-10 and ImageNet datasets show that the proposed filter pruning method surpasses the state-of-the-art.
Naturalness Enhancement with Linguistic Information in End-to-End TTS Using Unsupervised Parallel Encoding

State-of-the-art end-to-end speech synthesis models have reached levels of quality close to human capabilities.However, there is still room for improvement in terms of naturalness, related to prosody, which is essential for human-machine interaction.Therefore, part of current research has shift its focus on improving this aspect with many solutions, which mainly involve prosody adaptability or control.In this work, we explored a way to include linguistic features into the sequenceto-sequence Tacotron2 system to improve the naturalness of the generated voice.That is, making the prosody of the synthesis looking more like the real human speaker.Specifically we embedded with an additional encoder part-of-speech tags and punctuation mark locations of the input text to condition Tacotron2 generation.We propose two different architectures for this parallel encoder: one based on a stack of convolutional plus recurrent layers, and another formed by a stack of bidirectional recurrent plus linear layers.To evaluate the similarity between real read-speech and synthesis, we carried out an objective test using signal processing metrics and a perceptual test.The presented results show that we achieved an improvement in naturalness.