Reviewerly/exHarmony huggingface dataset:
Read-Once Resolutions in Horn Formulas

In this paper, we discuss the computational complexity of Read-once resolution (ROR) with respect to Horn formulas. Recall that a Horn formula is a boolean formula in conjunctive normal form (CNF), such that each clause has at most one positive literal. Horn formulas find applications in a number of domains such as program verification and logic programming. It is well-known that deduction in ProLog is based on unification, which in turn is based on resolution and instantiation. Resolution is a sound and complete procedure to check whether a boolean formula in CNF is satisfiable. Although inefficient in general, resolution has been used widely in theorem provers, on account of its simplicity and ease of implementation. This paper focuses on two variants of resolution, viz., Read-once resolution and Unit Read-once resolution (UROR). Both these variants are sound, but incomplete. In this paper, the goal is to check for the existence of proofs (refutations) of Horn formulas under these variants. We also discuss the computational complexity of determining optimal length proofs where appropriate.
Differentially Private Greedy Decision Forest

As information security is increasingly valued, privacy-preserving data mining has become a research hotspot in the field of big data and signal processing. We propose a new differentially private greedy decision forest algorithm called DPGDF to help improve the accuracy of privacy-preserving data mining. Unlike previous algorithms that only employed greedy decision trees or random forests, our algorithm uses a combination of greedy trees and parallel combination theory to construct a greedy decision forest and coordinate privacy protection and prediction accuracy to achieve the best balance. Combined with smooth sensitivity, the introduction of noise is minimized, making the prediction accuracy of the algorithm notably better than the current state-of-the-art algorithms. Experiments on the UCI datasets show that the prediction accuracy of our algorithm is about 10% higher than that of those algorithms.
Blind Motion Deblurring via Inceptionresdensenet by Using GAN Model

Deblurring from a motion blurred image has been studied for some times. Recently, convolution neural network(CNN) has been used widely and it can be used on finding the blur kernel or the latent sharp edge of a blurred image. In recent years, the generative adversarial network (GAN) performs well on style transformation. We consider that a deblurring problem as a style transformation problem. We focus on improving the DeblurGAN’s generator, which is the state-of-the-art of the deblurring method and present a new kind of block which combined inception block, residual block and dense block to do deblurring from motion blur. By using the conception of DenseNet which can avoid overfitting. The improved DeblurGAN presents better in both structural similarity measure and by visual effect.
Recognition of handwritten text using long short term memory (LSTM) recurrent neural network (RNN)

Handwriting recognition is a technique which is used to produce machine readable text from a given text image. The hand written text is captured as an image from mobile. Handwritten characters are usually recognized with Optical Character Recognition (OCR) scanners. But with the large usage of mobile phones, detecting text from mobile camera has plenty of applications such as medical script processing, exam script evaluation etc. Camera image has lot of noises when compared to the OCR scanned images. Therefore, the image is pre-processed to reduce noise using image processing techniques such as binarization, thresholding and etc. The letters are segmented and extracted from an image. The features such as binary codes are extracted from the letters. The neural network classifier is built using Long Short Term Memory (LSTM) network which is trained using an already built character dataset. The neural network is used to test the input images. The output is provided as a text document with the recognized words. Since the input feed is obtained from images, the noise will be high compared to the existing system input set which uses scanned images. Noise reduction technique such as low intensity pixel removal is applied to reduce the noise from the input image for improving the efficiency.
Smart Contracts Integration between Blockchain and Internet of Things: Opportunities and Challenges

A Smart Contract is self-executable and self-enforceable program code that runs on the top of blockchain to manage complex business logic. It eliminates the need of extrinsic enforcement of legal agreements. Furthermore, it enforces the terms and conditions of an agreement that lies between untrustworthy parties in which the trusted third parties cannot interfere. The cryptography logic used in smart contract enables the blockchain network to provide trust and authority to all parties in transaction. Network decentralization, data immutability and transparency, resiliency and security make blockchain technology more versatile. Recently, it has become a potential quality and capability of IoT to connect uncountable electronic objects or devices at the same time. The most prominent feature of blockchain-based IoT applications is the integration of smart contracts between blockchain and IoT.A brief comparison has been given in the paper that how the smart contracts react on multiple blockchain platforms with respect to scalability, system complexity and consensus protocol factors. Furthermore, the context of Smart contract integration between blockchain and IoT with highlighting the integration opportunities and challenges along with future research directions. Therefore, we have concluded in the current paper that amalgamation of Blockchain with IoT through Smart Contract can provide a strong framework for distributed application and the newly introduced business communities.
Identifying Vulnerabilities Using Internet-Wide Scanning Data

Internet-wide scanning projects such as Shodan and Censys, scan the Internet and collect active reconnaissance results for online devices. Access to this information is provided through associated websites. The Internet-wide scanning data can be used to identify devices and services which are exposed on the Internet. It is possible to identify services as being susceptible to known-vulnerabilities by analysing the data. Analysing this information is classed as passive reconnaissance, as the target devices are not being directly communicated with. This paper goes on to define this as contactless active reconnaissance. The vulnerability identification functionality in these Internet-wide scanning tools is currently limited to a small number of high profile vulnerabilities. This work looks towards extending these features through the creation of a tool Scout which combines data from Censys and the National Vulnerability Database to passively identify potential vulnerabilities. This is possible by analysing Common Platform Enumerations and associated Common Vulnerability and Exposures. Through this novel approach, active vulnerability scanning results can be gained, while mitigating the associated issues of active scanning, such as possible disruption to the target network and devices. In initial experiments performed on 2571 services across 7 local academic intuitions, 12967 potential known-vulnerabilities were identified. More focused experiments to evaluate the results and compare accuracy with industry standard vulnerability assessment tools were carried out and Scout was found to successfully identify vulnerabilities with an effectiveness score of up to 74 percent when compared to OpenVAS.
Visualization and analysis of schema and instances of ontologies for improving user tasks and knowledge discovery

Ontologies are an important resource for knowledge representation. Their structure can be complex due to role relations between several concepts, distinct attributes, and different instances. In this paper, we discuss a Visual Analytics solution, relying on the use of multiple coordinated views for exploring different ontology aspects and a novel use of the degree of interest (DoI) suppression technique to reduce the complexity of the ontology visual representation. Visual Analytics facilitates the understanding of the domain and tasks represented by ontologies, thus allowing to carry out exploratory analysis to optimize the comprehension of data semantics including non-explicit relationships between data. Through the DoI technique, we place the main concept in focus, distinguishing it from the unnecessary information and facilitating the analysis and understanding of correlated data. We evaluated all the devised solutions, and the results reinforce the importance of providing visualization and analysis techniques dedicated to the schema and instances levels of ontologies for the discovery of non-explicit information.
A Novel Hybrid Method of Global Optimization Based on the Grey Wolf Optimizer and the Bees Algorithm

A new hybrid evolutionary algorithm based on the Grey wolf optimizer and the Bees algorithm is proposed. Embedded hybridization allows combining the strengths of original methods. A comparative analysis of the new hybrid method is performed on benchmark functions.
Domain Adversarial Training for Improving Keyword Spotting Performance of ESL Speech

A second language (L2) learner usually cannot speak L2 well in both pronunciations and forming-of-words. Hence his/her L2 speech cannot be well recognized by a recognizer trained with native data. Domain adversarial training (DAT), capable of reducing the acoustic mismatch between training and testing, can be useful for improving speech recognition of L2 learners. To get around the ungrammatical L2 speech in scenario-based conversation training, keyword spotting (KWS) is an effective solution by relaxing the language model constraint in decoding. On the acoustic pronunciation side, DAT is investigated in this study for training a neural net-based acoustic model. DAT model is trained with both native and English as second language (ESL) learners' speech to extract more invariant features from native to ESL speech by equalizing their intrinsic difference. The model is jointly optimized for improved senone classification in training. Testing on ESL learners' speech and native English, the DAT model improves recognition performance which is comparable to jointly trained multi-condition model but significantly improves the performance of native speech recognition. In KWS, DAT shows a consistent better performance than the multi-condition training. The improved performance of proposed model is also obtained without increasing its computation complexity or the model size.
Classification of XSS Attacks by Machine Learning with Frequency of Appearance and Co-occurrence

Cross site scripting (XSS) attack is one of the attacks on the web. It brings session hijack with HTTP cookies, information collection with fake HTML input form and phishing with dummy sites. As a countermeasure of XSS attack, machine learning has attracted a lot of attention. There are existing researches in which SVM, Random Forest and SCW are used for the detection of the attack. However, in the researches, there are problems that the size of data set is too small or unbalanced, and that preprocessing method for vectorization of strings causes misclassification. The highest accuracy of the classification was 98% in existing researches. Therefore, in this paper, we improved the preprocessing method for vectorization by using word2vec to find the frequency of appearance and co-occurrence of the words in XSS attack scripts. Moreover, we also used a large data set to decrease the deviation of the data. Furthermore, we evaluated the classification results with two procedures. One is an inappropriate procedure which some researchers tend to select by mistake. The other is an appropriate procedure which can be applied to an attack detection filter in the real environment.
GMM-VRD: A Gaussian Mixture Model for Dealing With Virtual and Real Concept Drifts

Concept drift is a change in the joint probability distribution of the problem.This term can be subdivided into two types: real drifts that affect the conditional probabilities p(y|x) or virtual drifts that affect the unconditional probability distribution p(x).Most existing work focuses on dealing with real concept drifts.However, virtual drifts can also cause degradation in predictive performance, requiring mechanisms to be tackled.Moreover, as virtual drifts frequently mean that part of the old knowledge remains useful, they require different strategies from real drifts to be effectively tackled.Motivated on this, we propose an approach called Gaussian Mixture Model for Dealing With Virtual and Real Concept Drifts (GMM-VRD), which updates and creates Gaussians to tackle virtual drifts and resets the system to deal with real drifts.The main results show that the proposed approach obtained the best results, in terms of average accuracy, in relation to the literature methods, which propose to solve that same problem.In terms of accuracy over time, the proposed approach showed lower degradation on concept drifts, which indicates that the proposed approach was efficient.
Recognizing offline handwritten mathematical expressions efficiently

In this study, we propose a system that can recognize offline handwritten mathematical expressions using limited training data. The purpose of the system is to exhibit a high recognition accuracy with a few training data and to allow everyone to form a recognition module with a small sample of his or her own handwriting. The system comprises three main parts: segmentation, symbol recognition, and structural analysis. A recursive cortical network is used to form the recognition part of the system and a new type of linked list is proposed to analyze the complex structure of the expressions. We prepared 400 real handwritten mathematical expressions from 20 different people, containing a total of 60,103 symbols from 100 symbol classes to evaluate the performance. The system was trained using one image per class and achieved 80% accuracy on the correct segmentation result.
Training of reduced-rank linear transformations for multi-layer polynomial acoustic features for speech recognition

The use of higher-order polynomial acoustic features can improve the performance of automatic speech recognition (ASR). However, dimensionality of polynomial representation can be prohibitively large, making acoustic model training using polynomial features infeasible for large vocabulary ASR systems. This paper presents a multi-layer polynomial training framework for acoustic modeling, which recursively expands the acoustic features into their second-order polynomial feature space. After each expansion the dimensionality of resultant features is reduced by a linear transformation. Experimental results obtained for two large-vocabulary continuous speech recognition tasks show that the proposed method outperforms conventional mixture models. More recently the acoustic modelling community has shifted its focus to deep neural networks. We also train multi-layer polynomial features in a similar way: allowing backpropagation and using mean-normalized stochastic gradient descent algorithm. This has led to encouraging results. Specifically, appending a sigmoid-based feed-forward deep neural network with a final polynomial layer has resulted in significant word error rate improvement.
On Using 2D Sequence-to-sequence Models for Speech Recognition

Attention-based sequence-to-sequence models have shown promising results in automatic speech recognition. Using these architectures, one-dimensional input and output sequences are related by an attention approach, thereby replacing more explicit alignment processes, like in classical HMM-based modeling. In contrast, here we apply a novel two-dimensional long short-term memory (2DLSTM) architecture to directly model the input/output relation between audio/feature vector sequences and word sequences. The proposed model is an alternative model such that instead of using any type of attention components, we apply a 2DLSTM layer to assimilate the context from both input observations and output transcriptions. The experimental evaluation on the Switchboard 300h automatic speech recognition task shows word error rates for the 2DLSTM model that are competitive to end-to-end attention-based model.
Empirical Evaluation and Combination of Punctuation Prediction Models Applied to Broadcast News

Natural language processing techniques are dependent upon punctuation to work well.When their input is taken from speech recognition, it is necessary to reconstruct the punctuation; in particular sentence boundaries.We define a range of features from low level acoustics to those with high level lexical semantics, including deep and recurrent models; these in turn are representative of a broad range of approaches used by previous authors for punctuation prediction.We combine the features using a gradient boosting machine that is also capable of indicating the relative importance of each feature.In an empirical study, we show that features from different semantic levels are in fact complementary, that combining statistical and deep learning methods yields better prediction results, and that generalization across different speaking styles is difficult to achieve without adaptation.Our best model achieves an F-Measure of 82.8 on a challenging broadcast news dataset.
Speaker Change Detection Using Fundamental Frequency with Application to Multi-talker Segmentation

This paper shows that time varying pitch properties can be used advantageously within the segmentation step of a multi-talker diarization system. First a study is conducted to verify that changes in pitch are strong indicators of changes in the speaker. It is then highlighted that an individual's pitch is smoothly varying and, therefore, can be predicted by means of a Kalman filter. Subsequently it is shown that if the pitch is not predictable then this is most likely due to a change in the speaker. Finally, a novel system is proposed that uses this approach of pitch prediction for speaker change detection. This system is then evaluated against a commonly used MFCC segmentation system. The proposed system is shown to increase the speaker change detection rate from 43.3% to 70.5% on meetings in the AMI corpus. Therefore, there are two equally weighted contributions in this paper: 1. We address the question of whether a change in pitch is a reliable estimator of a speaker change in multi-talk meeting audio. 2. We develop a method to extract such speaker changes and test them on a widely available meeting corpus.
Image Encryption Algorithm Methodology Based on Multi-mapping Image Pixel

Image encryption and decryption using a key sequence based on the multi-mapping method of an image pixel is proposed in this paper. The generated key sequences are based on the logistic map, elliptic curves (EC) and Henon map. In the beginning, the one-dimensional sequence is generated using logistic map for the bifurcation parameter and initial value. The generated sequences are XORed with image pixels as a first mapping step. Then the resulted pixels are XORed with key sequence based on elliptic curve random number generator (ECRNG) as a second mapping step. Finally, the resulted pixels XORed with key sequence generated by Henon map. The obtained cipher image introduces good cryptographic properties in the case of multi-mapping. The performance of the proposed scheme is analyzed by computing Histogram, Entropy, and Correlation between the original and encrypted images. The proposed method provides an immense improvement in the encrypted image.
Exploring Weighted Dual Graph Regularized Non-Negative Matrix Tri-Factorization Based Collaborative Filtering Framework for Multi-Label Annotation of Remote Sensing Images

Manually annotating remote sensing images is laborious work, especially on large-scale datasets. To improve the efficiency of this work, we propose an automatic annotation method for remote sensing images. The proposed method formulates the multi-label annotation task as a recommended problem, based on non-negative matrix tri-factorization (NMTF). The labels of remote sensing images can be recommended directly by recovering the image–label matrix. To learn more efficient latent feature matrices, two graph regularization terms are added to NMTF that explore the affiliated relationships on the image graph and label graph simultaneously. In order to reduce the gap between semantic concepts and visual content, both low-level visual features and high-level semantic features are exploited to construct the image graph. Meanwhile, label co-occurrence information is used to build the label graph, which discovers the semantic meaning to enhance the label prediction for unlabeled images. By employing the information from images and labels, the proposed method can efficiently deal with the sparsity and cold-start problem brought by limited image–label pairs. Experimental results on the UCMerced and Corel5k datasets show that our model outperforms most baseline algorithms for multi-label annotation of remote sensing images and performs efficiently on large-scale unlabeled datasets.
Discrete-Time Zhang Neural Networks for Time-Varying Nonlinear Optimization

As a special kind of recurrent neural networks, Zhang neural network (ZNN) has been successfully applied to various time-variant problems solving. In this paper, we present three Zhang et al. discretization (ZeaD) formulas, including a special two-step ZeaD formula, a general two-step ZeaD formula, and a general five-step ZeaD formula, and prove that the special and general two-step ZeaD formulas are convergent while the general five-step ZeaD formula is not zero-stable and thus is divergent. Then, to solve the time-varying nonlinear optimization (TVNO) in real time, based on the Taylor series expansion and the above two convergent two-step ZeaD formulas, we discrete the continuous-time ZNN (CTZNN) model of TVNO and thus get a special two-step discrete-time ZNN (DTZNN) model and a general two-step DTZNN model. Theoretical analyses indicate that the sequence generated by the first DTZNN model is divergent, while the sequence generated by the second DTZNN model is convergent. Furthermore, for the step-size of the second DTZNN model, its tight upper bound and the optimal step-size are also discussed. Finally, some numerical results and comparisons are provided and analyzed to substantiate the efficacy of the proposed DTZNN models.
Neural Tensor Network Training Using Meta-Heuristic Algorithms for RDF Knowledge Bases Completion

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Multi-feature Fusion Based on Supervised Multi-view Multi-label Canonical Correlation Projection

This paper presents multi-feature fusion based on supervised multi-view multi-label canonical correlation projection (sM2CP). The proposed method applies sM2CP-based feature fusion to multiple features obtained from various convolutional neural networks (CNNs) whose characteristics are different. Since new fused features with high representation ability can be obtained, performance improvement of multi-label classification is realized. Specifically, in order to tackle the multi-label problem, sM2CP introduces a label similarity information of label vectors into the objective function of supervised multi-view canonical correlation analysis. Thus, sM2CP can deal with complex label information such as multi-label annotation. The main contribution of this paper is the realization of feature fusion of multiple CNN features for the multi-label problem by introducing multi-label similarity information into the canonical correlation analysis-based feature fusion approach. Experimental results show the effectiveness of sM2CP, which enables effective fusion of multiple CNN features.