Reviewerly/exHarmony huggingface dataset:
Restore the Original data by Embedded data

Network is an important representation method to describe related objects. For network, the most core research is to reasonably represent the characteristic information of nodes in the network, which is called network representation learning. In recent years, many scholars have proposed many excellent representation learning algorithms, through which the original network data can be embedded in low-dimensional representation, which can help us classify the nodes in the network, and the nodes can also be used as point coordinates in Euclidean space for visualization. Existing algorithms are all aimed at embedding the original data, but how to use the embedded data to restore the original data when the original data is incomplete has not been studied by scholars. In order to solve this problem, this paper proposes two solutions of deep learning, one is the artificial neural network method based on deep learning, and the other is the attention mechanism method based on deep learning. The experimental results of this paper show that these two methods are very effective.
An Adaptive Image Encryption Scheme Based on Iterated Function System

A novel 2D dynamical system is derived from the iterated function system which is used to generate the Sierpinski carpet-like fractal images. Experimental results show that the derived system has better chaotic performances. Therefore we can apply the derived chaotic system to generate pseudo-random sequences to design image encryption schemes. To achieve better ability of resisting chosen-plaintext and known-plaintext attacks, we design an adaptive image encryption scheme with a permutation-diffusion structure. In the permutation stage, we generate plain-image content dependent pseudo-random sequence to change the pixels positions. Consequently, different plain-images produce distinct features for the diffusion stage. The performance and security analysis of the proposed image encryption scheme have been carried out, including histogram analysis, correlation analysis, entropy analysis, and differential attack analysis, etc. The experimental simulations demonstrate that the proposed image encryption scheme is highly secure and reliable for practical image encryption.
Bilingual Auto-Categorization Comparison of Two LSTM Text Classifiers

Multi linguistic problems such as auto-categorization is not an easy task. It is possible to train different models for each language, another way to do auto-categorization is to build the model in one base language and use automatic translation from other languages to that base language. Different languages have a bias to a language specific grammar and syntax and will therefore pose problems to be expressed in other languages. Translating from one language into a non-verbal language could potentially have a positive impact of the categorization results. A non-verbal language could for example be pure information in form of a knowledge graph relation extraction from the text. In this article a comparison is conducted between Chinese and Swedish languages. Two categorization models are developed and validated on each dataset. The purpose is to make an auto-categorization model that works for n'importe quel langage. One model is built upon LSTM and optimized for Swedish and the other is an improved Bidirectional-LSTM Convolution model optimized for Chinese. The improved algorithm is trained on both languages and compared with the LSTM algorithm. The Bidirectional-LSTM algorithm performs approximately 20% units better than the LSTM algorithm, which is significant.
Investigation of the performance of high-frequency phase spectrum components in audio replay spoofing attack detection

Automatic speaker verification (ASV) systems are being used in various areas such as mobile banking, security, and military. Due to widespread use of ASV systems, spoofing attacks are developing themselves and getting more important problem for ASV systems. In this study, we investigated currently proposed features to solve this problem and proposed the usage of high-frequency components of phase spectrum based feature modified group delay (MGD) for audio replay spoofing attack detection. Then, spoofing detection performances of magnitude spectrum based feature constant Q cepstral coefficients (CQCC) and high frequency components of CQCC are compared with high frequency MGD performance. We used the ASVspoof 2017 database to test our countermeasure system. As a result, high-frequency components are performing %40-55 better performance than low frequency for both magnitude and phase spectrum based features and it improves the baseline system performance which is proposed by ASVspoof 2017 by %40-%45 in total over the development sub-dataset.
Modified Feature Descriptors to enhance Secure Content-based Image Retrieval in Cloud

With the emergence of cloud, content-based image retrieval (CBIR) on encrypted domain gain enormous importance due to the ever increasing need for ensuring confidentiality, authentication, integrity and privacy of data. CBIR on outsourced encrypted images can be done by extracting features from unencrypted images and generating searchable encrypted index based on it. Visual descriptors like color descriptors, shape and texture descriptors, etc. are employed for similarity search. Since visual descriptors used to represent an image have crucial role in retrieving most similar results, an attempt to combine them has been made in this paper. The effect of combining different visual descriptors on retrieval precision in secure CBIR scheme proposed by Xia et al. is analyzed. Experimental results show that combining visual descriptors can significantly enhance retrieval precision of the secure CBIR scheme.
U-Net in Constraint Few-Shot Settings

In this paper, we investigate the feasibility of using a standard U-Net for Few-Shot segmentation tasks in very constraint settings. We demonstrate on the example of femur segmentation in X-ray images, that a U-Net architecture only needs few samples to generate accurate segmentations, if the images and the structure of interest only show little variance in appearance and perspective. This is often the case in medical imaging. We also present a novel training strategy for the UNet, leveraging U-Net’s Few-Shot capability for inter-patient consistent protocols. We propose repeatedly enforcing Few-Sample-Fitting the network for faster convergence. The results of our experiments indicate that incrementally fitting the network to an increasing sample set can lead to faster network convergence in constraint few-shot settings.
Semantic segmentation with Recurrent Neural Networks on RGB-D videos

Fully Convolutional Network (FCN) based on convolutional neural network is the mainstream method for image semantic segmentation, but its performance on RGB-D video is not good. In this paper, we propose a new semantic segmentation method for RGB-D video using a Recurrent Neural Network architecture. Our novel method combines visual simultaneous localization and mapping (SLAM) technology to transfer semantic information across frames, so the data association between each frame of RGB-D video can be captured. We also show that the proposed approach allows us to improve over existing method by a comparison study on a RGB-D scene dataset.
Multiple Feature Subspaces Based Multi-Task Learning for Person Re-Identification

Person re-identification(Re-ID) is an important task with the purpose of retrieving the person with the same identity across multiple cameras. Multi-task learning(MTL) is a useful learning paradigm in machine learning, and the aim is to leverage useful information included in multiple related tasks to help improve the generalization performance of all the tasks. So the multi-task learning may be an effective way to improve the performance of a model. Existing works in person Re-ID using multi-task learning often require the use of additional information or the design of complex multiple tasks. This paper presents a new simple multi-task learning method for Person Re-ID, which is based on multiple feature subspaces. The idea of this method is to regard the training of each feature subspace as a task in multi-task learning, and we can improve the performance by jointly training multiple feature subspaces. Our method is simple and effective compared to other methods because the feature subspace is easy to obtain. Experiments show that the proposed method can significantly improve performance for person Re-ID.
Revisiting the Reed-Muller Locally Correctable Codes

Local codes are a special kind of error-correcting codes. Locally correctable codes (LCCs) are one type of local codes. LCCs can efficiently recover any coordinate of its corrupted encoding by probing only a few but not all fraction of the corrupted word. A q-ary LCC which encodes length k messages to length N codewords with relative distance Δ has three parameters: r, δ and ε. r is called query complexity recording the number of queries. δ is called tolerance fraction measuring the relative distance between encoding codewords and its corrupted codes which can be locally decoded. ε is called error probability showing the coordinate of its corrupted encoding fail to be recovered with probability at most ε. One fundamental problem in LCCs is to determine the trade-off among rate, distance and query complexity. But for a specific LCC, focus is on query complexity, tolerance fraction and error probability. Reed-Muller codes (RM codes) are the most presentative LCCs. In order to understand the "local" more clearly, we revisit local correctors for RM codes and analyze them in detail: 1)The decoding procedures; 2)The role of Reed-Solomon codes (RS codes) in decoding RM LCCs; 3)Other local correctors for RM codes. How parameters including r, δ and ε change in RM LCCs have been analyzed in different correctors. We believe this paper can help us understand local codes better and grasp the main soul of this research direction.
Secure Transfer of Image-Acquired Text Using a Combination of Cryptography and Steganography

Information security is a matter of growing concern. The threats to data being transmitted over networks is increasing day by day. The data being transmitted is thus vulnerable to different types of attacks. Cryptography is a technique used to protect the data by converting it into an unintelligible form of text known as cipher text. On the other hand, steganography based algorithms have proved to be an efficient way of hiding the existence of essential data during transfer. The proposed method uses a combination of cryptography and steganography for encryption thereby increasing the level of security provided. The text is recognized from images and documents using a deep learning model and further encrypted using an improved encryption method and then hidden into an image using image steganography.
Heating furnace temperature based on WM-GA fuzzy control

The heating furnace is a kind of nonlinear, pure hysteresis, large inertia, strong coupling, time-varying system. It is difficult for the traditional PID control to achieve stable and energy-saving effects. Fuzzy control is an effective control method, but depends on expert experience and faces the dilemma that rules are difficult to extract. In this paper, a WM-GA(Wang Mendel and genetic algorithm) algorithm is proposed to extract and optimize fuzzy rules. Firstly, the initial rules are extracted by WM algorithm, then the proximity rules of the initial rules are added to the candidate rule optimization space, and then the GA targeting the engegy-saving function,is using to search in the candidate rules to obtain the final fuzzy rule table. Through simulation analysis, the constructed fuzzy controller has good characteristics.
Automobile Maintenance Case Matching Method Based on the Continuous Space Language Model

This paper proposes a method for automobile maintenance case extraction based on a continuous language model to improve the context understanding accuracy and reduce the result redundancy of the traditional keyword matching technology in the car maintenance case diagnosis process. First, the SimHash algorithm is used to find the candidate case diagnosis result sets. Then, a deep learning technology word embedding is used to create a distributive map of the keywords and the candidate case sets and queries. Finally, a continuous space language model-based inference algorithm is developed to produce more accurate matching results between the keywords and the candidate case sets. Experiments carried out on vehicle maintenance case data prove that the continuous language model can effectively improve the case matching accuracy.
SLAOE-NN:A Deep Network with Structure Learning for Aspect and Opining co-Extraction for NLP

The task of co-extracting aspects and opinion terms is intended to explicitly extract aspect terms that describe entity features and opinion terms that express emotions from user-generated text. An effective way to accomplish this task is to exploit the relationship between aspect terms and opinion terms by parsing the syntax structure of each sentence. However, this method requires a lot of effort to parse and is highly dependent on the quality of the parsing results. In this paper, we present a deep learning model called SLAOE-NN (Structural Learning and Aspect and Opining Extraction Neural Networks). The proposed model provides an end-to-end solution and does not require any other language resources for preprocessing. Particularly, we use ON-LSTM to generate hidden layer with language structure information which can generate constituency tree unsupervised and we serve it as an auxiliary task for aspect and opinion terms extraction. For aspect terms and opinion terms extract task, we propose different attention mechanism, which can exploit the indirect relationship between aspect term and corresponding opining term to achieve more accurate information extraction. The experimental results of SemEval's three benchmark datasets in 2014 and 2015 show that our model achieves the-state-of-art performance compared to several baselines.
Differential Evolution Using Linkage Identification

Optimization problems become more difficult as the number of decision variables increases. A problem, however, can be divided into multiple independent subproblems in many cases. Therefore, it is possible to efficiently search for the optimal solution or a near-optimal one by performing optimization for each subproblem. In genetic algorithms (GAs), the relationship between two decision variables is called a linkage, and checking whether there is the linkage is called linkage identification. Identifying linkages enables GAs to perform the optimization for each subproblem. Such GAs use a crossover that does not damage the linkage. However, differential evolution (DE) has better search performance than GAs. Moreover, DE has a procedure similar to the crossover of GA. In this paper, we propose DE using the linkage identification. The effectiveness of the proposed method is evaluated through numerical experiments of comparing with DE not using the linkage identification and GA using the linkage identification.
Application Research of Circuit Fault Finding Algorithm Based on Model Diagnosis in System-on-Chip

In order to make the circuit fault diagnosis system more intelligent and efficient. In this paper, the smart car is taken as the research object, and the MBD fault diagnosis based on the proposition can be studied according to the model-based diagnosis. Taking the robot with 20 components as the model, the car is modeled and observed, and the solution of the MBD fault diagnosis algorithm based on the proposition can be solved by the minisat solver, which is automatically run after the system on the chip is started. The experimental results show that the main factor affecting the system diagnosis time is the system coding scale.
Learning Short Text Representation using Non-Negative Matrix Factorization and Word Semantic Correlations

With the emergence of online medias, short texts have been increasingly available. Because the short texts have a limited contextual information, they are sparse, noisy and ambiguous. Conventional models to text representation are hence not applicable. In this paper, we propose a representation learning model for short texts to tackle this challenge. Our proposed model is based on Non-Negative Matrix Factorization (NNMF). Due to the problem lacking word co-occurrences information, it effectively learns the representation model by incorporating the semantic relationship between words generated by unsupervised learning methods. Finally, the NNMF-based model is effectively solved by using a gradient descent-based algorithm. Experimental results on two benchmark data sets, including sentiment140 and 20newsgroups, demonstrate that our proposed approach achieved the improvement of classification accuracy to state-of-the-art topic discovery techniques.
DSNet:Multi-resolution Dense Encoder and Stack Decoder Network for Aerial Image Segmentation

Semantic segmentation in high resolution aerial image is faced with a challenge caused by ubiquitous fine-structure objects. Traditional encoder-decoder structure losses some detail information during the process of down-sampling, which is harmful to the location of fine-structure objects. In this work, we present a multi-resolution dense encoder and stack decoder network to deal with this problem. On the one hand, the dense encoder embeds shallow detailed feature into deep semantic feature through proposed information-reserved down-sampling method called CE-Pooling. On the other hand, the stack decoder gradually enhances the detailed feature through iterative attention fusion. Extensive experiments on several benchmark datasets have been conducted, which shows that our method is superior than the state-of-the-art approaches.
Convolutional Neural Network Features Selection Based On Analysis Of Variance

In this paper, we aim to reduce the redundancy in deep Convolutional Neural Networks (CNNs) while preserving significantly the image classification performance. The main novelty is to select a subset of most discriminative features in the final response layer by pruning neurons with the least importance. To achieve that, the Analysis of Variance (ANOVA) technique is used to determine feature maps with higher neuron responses variation. The proposed method handles effectively the neurons with the least important and may prove useful in identifying the image regions most informative regarding classification. Experimental results, using several datasets, show that our proposed neurons pruning strategy yields a significant improvement in feature selection ability, and then fine-tuned network to retain its predictive power.
Ore mineralogy, trace element distribution and 3D X-ray tomography of the polymetallic sulphide deposits at Mavres Petres and Piavitsa, Greece

The Mavres Petres mine, operated by Hellas Gold, and the Piavitsa prospect are both located along the Stratoni fault in the Kassandra mining district, northern Greece. These Tertiary deposits are b ...
Contextual Coefficients Excitation Feature: Focal Visual Representation for Relationship Detection

Visual relationship detection (VRD), a challenging task in the image understanding, suffers from vague connection between relationship patterns and visual appearance. This issue is caused by the high diversity of relationship-independent visual appearance, where inexplicit and redundant cues may not contribute to the relationship detection, even confuse the detector. Previous relationship detection models have shown remarkable progress in leveraging external textual information or scene-level interaction to complement relationship detection cues. In this work, we propose Contextual Coefficients Excitation Feature (CCEF), a focal visual representation, which is adaptively recalibrated from original visual feature responses by explicitly modeling the interdependencies between features and their contextual coefficients. Specifically, contextual coefficients are obtained by calculation of both the spatial coefficients and generated-label ones. In addition, a conditional Wasserstein Generative Adversarial Network (WGAN) regularized with a relationship classification loss is designed to alleviate inadequate training of generated-label coefficients due to long tail distribution of relationship. Experimental results demonstrate the effective improvements of our method on relationship detection. In particular, our method improves the recall from 8.5% to 23.2% of predicting unseen relationship from zero-shot set.
Signal Detection Scheme Based on Deep Learning in OFDM systems

The quality of any communication system relies on efficient signal estimation with minimum computational and economic payoffs. Usually, the characteristics of the multipath channel used for transmission decreases the efficiency of the system. Traditionally, channel estimation is done to alleviate the effects of the channel which in turn needs a great deal of computation and bandwidth. In this paper, Deep Neural Network (DNN) is used for estimating the signal thus avoiding the extra effort of estimating the channel. The calamitous effect of Rayleigh channel in BPSK modulated OFDM systems are being extricated by the DNN and its effects are compared between different multipaths. Also, in OFDM, Cyclic Prefix(CP) is used to improve BER. Using the proposed method, CP can be untied from OFDM with a marginal increase in SNR, thus saving priceless bandwidth.
The Guiding Role of Scientific and Technological Methodology in the Development of Sensing Technology

The methodology of science and technology provides mankind with a correct way to understand and transform the world. Taking the process development and research application of quartz resonator cluster as an example, this paper further analyses the relationship between the methodology of science and technology and sensor technology development, and expounds the guiding and catalytic role of the methodology in the rapid development of natural science.
A Flexible Outlier Detector Based on a Topology Given by Graph Communities

Outlier detection is essential for optimal performance of machine learning methods and statistical predictive models. Their detection is especially determinant in small sample size unbalanced problems, since in such settings outliers become highly influential and significantly bias models. This particular experimental settings are usual in medical applications, like diagnosis of rare pathologies, outcome of experimental personalized treatments or pandemic emergencies. In contrast to population-based methods, neighborhood based local approaches compute an outlier score from the neighbors of each sample, are simple flexible methods that have the potential to perform well in small sample size unbalanced problems. A main concern of local approaches is the impact that the computation of each sample neighborhood has on the method performance. Most approaches use a distance in the feature space to define a single neighborhood that requires careful selection of several parameters, like the number of neighbors. This work presents a local approach based on a local measure of the heterogeneity of sample labels in the feature space considered as a topological manifold. Topology is computed using the communities of a weighted graph codifying mutual nearest neighbors in the feature space. This way, we provide with a set of multiple neighborhoods able to describe the structure of complex spaces without parameter fine tuning. The extensive experiments on real-world and synthetic data sets show that our approach outperforms, both, local and global strategies in multi and single view settings.