Reviewerly/exHarmony huggingface dataset:
Multifeature extracting CNN with concatenation for image denoising

Convolutional neural networks (CNNs) have made great achievements in the field of image denoising but can still be improved. We introduce a network structure, namely, multifeature extracting CNN with concatenation (McCNN), which can preserve the edge and detail information and make the denoised image easier to view. The McCNN uses different-sized convolutional kernels to extract multiple features from the input image and send them into a forward network structure after cascading these features. The forward network structure consists of five nonlinear mapping modules, which are responsible for extracting more detailed textures and other advanced features. A skip connection is integrated into the forward network structure to pass the feature maps that carry many image details, which helps to reduce image distortion. The skip connection can also reduce gradient disappearance and improve network convergence speed. The potential clean image in the contaminated image contains much more information than the noise image. The noise image is regarded as the learning objective of the network to reduce the learning burden. The experimental results demonstrate that our McCNN denoising method can effectively remove Gaussian noise in grayscale images and offers objective and subjective quality improvement compared to that of the DnCNN-S, SCNN, and DSNet models, as well as other state-of-the-art denoising methods.
Leveraging Multi-task Learning for Biomedical Named Entity Recognition

Biomedical named entity recognition (BioNER) is the task of categorizing biomedical entities. Due to the specific characteristics of the names of biomedical entities, such as ambiguity among different concepts or different ways of referring to the same entity, the BioNER task is usually considered more challenging compared to standard named entity recognition tasks. Recent techniques based on deep learning not only significantly reduce the hand crafted feature engineering phase but also determined relevant improvements in the BioNER task. However, such systems are still facing challenges. One of them is the limited availability of annotated text data. Multi-task learning approaches tackle this problem by training different related tasks simultaneously. This enables multi-task models to learn common features among different tasks where they share some layers. To explore the advantages of the multi-task learning, we propose a model based on convolution neural networks, long-short term memories, and conditional random fields. The model we propose shows comparable results to state-of-the-art approaches. Moreover, we present an empirical analysis considering the impact of different word input representations (word embedding, character embedding, and case embedding) on the model performance.
A Tweak-Cube Color Image Encryption Scheme Jointly Manipulated by Chaos and Hyper-Chaos

This paper proposes a novel tweak-cube color image encryption scheme jointly manipulated by chaos and hyper-chaos. One-dimensional (1D) chaotic maps are effortless to operate, but the key space is relatively small. The hyperchaotic system has complex dynamics properties, which are capable of compensating for the defects of 1D chaotic maps. Thus, we first raise an improved 1D chaotic map with an increased key space. Then, we associate it with a four-dimensional (4D) hyperchaotic system to generate the key streams and further rotate and shift the rows and columns of each component of Red (R), Green (G), and Blue (B) for the color image. The permuting mode is to disturb the original position of the pixels by mimicking the way of twisting the Rubik’s cube. Moreover, the key stream updated by the plain images is also utilized for diffusion and scramble at the bit level. As a consequence, our cryptosystem enhances the security without at the expense of increasing time cost.
An Improved Kalman Filter With Adaptive Estimate of Latency Probability

In this brief, an improved Kalman filter is proposed for a linear system with one-step randomly delayed measurement and unknown latency probability. The measurement likelihood function which is a weighted sum of two Gaussian distributions is transformed into an exponential multiplication form via importing a discrete Bernoulli random variable. Then, an hierarchical Gaussian form of the state-space model is established. Finally, an improved Kalman filter is deduced to estimate jointly the augmented state vector and the unknown parameters employing the variational Bayesian and state augmentation approaches. Simulation study indicates that the improved method has superior performance in estimation accuracy than the existing methods on the basis of accurate estimation of the unknown and time-varying latency probability.
Application of fuzzy logic in online test evaluation in english as a foreign language at university level

The main goal of this research is to introduce and justify the use of fuzzy logic in the process of evaluation of test results in English of university students majoring in Computer Science. More specifically, the authors aim at implementing a fairer assessment of students’ knowledge and skills for borderline cases between two grades without that changing the overall group score.
Multi-Scale Object Detection in Satellite Imagery Based On YOLT

Multi-scale object detection (MOD) is one of the remaining challenges for satellite imagery. To improve the performance of MOD task, YOLT (You Only Look Twice) has achieved a good accuracy in high resolution remote sensing images. Motivated by the state-of-art object detection method for satellite imagery, we explored and achieved the state-of-the-art accuracy based on the standard YOLT for MOD task by providing a novel method with enough experimental results and model comparison on the typical multi-scale satellite imagery dataset. First, we divide objects into three categories according to the scale of objects. Then, different training strategies are used to train the classifier and detector for different scale objects. Finally, multi-scale detection chips are stitched and fused to get more accurate localization and classification as the final predicted results for MOD in satellite imagery. Experiments have been conducted over dataset from the second stage of AIIA <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> Cup Competition of Typical Object Recognition for Satellite Imagery in Small Samples compared with the standard YOLT and Faster R-CNN, which demonstrates the effectiveness and the comparable detection performance of our proposed pipeline.
The Doors Are Opening for the New Pedigree: A Futuristic View for the Effects of Blockchain Technology on Accounting Applications

Developments in communication technologies have enabled entities to benefit from new technological infrastructures with improved efficiency. In this context, blockchain, as a new, innovative communication technology, can provide a better solution that promises efficiency and improved transparency for every kind of transaction that involves value exchange including stocks, derivatives, interbank transactions, and accounting operations of entities. Today, whenever a value exchange transaction is needed between individuals or institutions, a regulatory and intermediary body is needed in order to ensure trustworthiness and transparency. Supporters of a blockchain called the “internet of values” argue that it sustains a more reliable infrastructure without any intermediary, unlike traditional systems that include regulators and intermediaries. Blockchains established by private institutions for intercompany operations give the decision-making authority of record approvals and monitoring ledgers to the system’s managers. Especially companies or institutions can benefit from these private blockchains without a need for a trust mechanism such as intermediaries. This chapter aims to exhibit the current accounting operation areas that blockchain technology impacts, s as well as the future direction of the integration between the technology and accounting.
Extraction of Digital Wavefront Sets Using Applied Harmonic Analysis and Deep Neural Networks

Microlocal analysis provides deep insight into singularity structures and is often crucial for solving inverse problems, predominately, in imaging sciences. Of particular importance is the analysis of wavefront sets and the correct extraction of those. In this paper, we introduce the first algorithmic approach to extract the wavefront set of images, which combines data-based and model-based methods. Based on a celebrated property of the shearlet transform to unravel information on the wavefront set, we extract the wavefront set of an image by first applying a discrete shearlet transform and then feeding local patches of this transform to a deep convolutional neural network trained on labeled data. The resulting algorithm outperforms all competing algorithms in edge-orientation and ramp-orientation detection.
Learning Physical-Layer Communication With Quantized Feedback

Data-driven optimization of transmitters and receivers can reveal new modulation and detection schemes and enable physical-layer communication over unknown channels. Previous work has shown that practical implementations of this approach require a feedback signal from the receiver to the transmitter. In this paper, we study the impact of quantized feedback on data-driven learning of physical-layer communication. A novel quantization method is proposed, which exploits the specific properties of the feedback signal and is suitable for non-stationary signal distributions. The method is evaluated for linear and nonlinear channels. Simulation results show that feedback quantization does not appreciably affect the learning process and can lead to similar performance as compared to the case where unquantized feedback is used for training, even with 1-bit quantization. In addition, it is shown that learning is surprisingly robust to noisy feedback where random bit flips are applied to the quantization bits.
The Construction of a Majority-Voting Ensemble Based on the Interrelation and Amount of Information of Features

Abstract In this paper, we introduced a new ensemble learning algorithm called VIBES, which is better in terms of performance when compared to 85 machine learning algorithms in WEKA tool. This new algorithm is based on three major processes: (i) making an assumption regarding whether features are dependent on or independent of each other, (ii) computing the amount of information of features when it is assumed that they are dependent on each other and then sorting them in a descending manner based on the amount of information, (iii) speeding up the algorithm by optimizing the forward search algorithm that is used in the construction of the final hypothesis from base learner hypotheses. As a result of these processes, it has been seen in the experiments that choosing the relevant assumption can boost learning performance if features are independent of each other; considering features according to the amount of information provides high accuracy and diversity of base learner models. According to experiment results, the algorithm that has been developed has the highest average classification accuracy rate across the 33 datasets. The highest and the lowest average classification accuracy rates have been found to be 89.80 and 78.03%, respectively.
Pose Estimation on Soccer Robot using Data Fusion from Encoders, Inertial Sensor, and Image Data

Middle Size League (MSL) is a division of soccer robot in the Robocup event. The research area of the robot focuses more on the ball detection and the individual capabilities of the robot and also the cooperation between one robot and the other. From the focus of the research area, the determination of the robot to the field pose is the basic ability that each robot must have to cooperate with other robots, or to improve the individual capabilities of the robot itself. One method to get a pose for a robot is the Odometry method. The main component of this method is the rotary encoder used to figure out how far the robot has moved from its initial position. However, this method is easily affected by the slip on the rotary encoder wheel, resulting in an integral error value that makes the error value bigger as the robot moves. Based on the results of experiments that has been done, in normal conditions, fusion method is more accurate than Odometry method, where the average error of fusion method is 2.07 cm at x coordinate, 2.53 cm at y coordinate, and 1.53 degrees at the robot orientation and in the Odometry method is 22.07 cm at x coordinate, 20.00 cm at y coordinate, 5.13 degrees at the robot orientation. While in the condition of a robot moving at a high speed, it turns out that the fusion method is no more accurate than the Odometry method, where the average error in the Odometry method is 17.67 cm at the x coordinate, 16.33 cm at the y coordinate, 15.5 degrees at the robot orientation and in the fusion method is 23.67 cm at x coordinate, 20.67 cm at y coordinate, 16.67 degrees at the robot orientation.
No‐reference image quality assessment via structural information fluctuation

Image quality assessment (IQA) is a meaningful research topic to meet the increasing demand of high-quality image. The degradation of image quality will cause changes in image structural information. Meanwhile, human visual system is sensitive to changes in structural information. This finding motivates us to utilise structural information for proposing IQA method which is consistent with human visual perception. Recently, IQA methods are mainly focused on individual image type, e.g. natural image or screen content image (SCI), thus, the authors proposed a novel no-reference IQA method which can be suitable for both natural image and SCI. The proposed method is based on structural information analysis. For each image, they first obtain the grey-scale fluctuation maps (GFMs) in four detection directions. After that, the grey-scale fluctuation direction map (GFD) of certain image can be acquired via its GFMs. Based on the GFMs and GFD, the structural features of each image are extracted, and then collected and transformed to feature vectors. Subsequently, the IQA model is trained by support vector regression. The experimental results on the public databases demonstrate the proposed method can predict image quality accurately for both natural image and SCI, and the performance is competitive with prevalent methods.
High Precision Dimensional Measurement with Convolutional Neural Network and Bi-Directional Long Short-Term Memory (LSTM)

In modern industries, high precision dimensional measurement plays a pivotal role in product inspection and sub-pixel edge detection is the core algorithm. Traditional interpolation and moment methods have achieved some success. However, those methods still have shortcomings. For example, the accuracy is still insufficient with the resolution limitation of the image sensor. Moreover, prediction results can be affected by image noise. With the recent success of deep learning technology, we propose a sub-pixel edge detection method based on convolution neural network (CNN) and bi-directional long short-term memory (LSTM). First, one-dimensional visual geometry group-16 (VGG-16) is employed to extract edge features. Then, a transformation operation is developed to generate sequence information. Lastly, bi-directional LSTM with fully-connected layers is introduced to output edge positions. Experimental results on our steel plate dataset demonstrate that our method achieves superior accuracy and anti-noise ability than traditional methods.
Improved differential evolution for noisy optimization

A novel approach is proposed in this paper to improve the optimization proficiency of the differential evolution (DE) algorithm in the presence of stochastic noise in the objective surface by utilizing the composite benefit of four strategies. The first strategy is devised with an aim to employ reinforcement learning scheme of stochastic learning automata for autonomous selection of the sample size of a trial solution (for its repeated fitness evaluation) based on the characteristics of the fitness landscape in its local neighborhood. The second stratagem is proposed to estimate the effective fitness measure from multiple fitness samples of a trial solution, resulting from sampling. The novelty of the second policy lies in considering the distribution of noisy samples during effective fitness evaluation, instead of their direct averaging. The third strategy deals with amelioration of the DE/current-to-best/1 mutation scheme to judiciously direct the search in promising region, even in prevailing existence of noise in the objective surface. Finally, the greedy selection policy of the traditional DE is modified by introducing the principle of probabilistic crowding induced niching to ensure both the population quality and the population diversity. Comparative analysis performed on simulation results for diverse noisy benchmark functions reveal the statistically significant superiority of the proposed algorithm to its contenders with respect to function error value.
Distance Based Training for Cross-Modality Person Re-Identification

Cross-modality person re-identification between infrared (IR) and visible (VIS) domains is a challenging problem, which aims to identify persons in different spectrums, variety of camera specs, and broad illumination conditions. This paper proposes distance based training on an one-stream convolutional neural network architecture, in which network weights are shared between IR and VIS domains to learn discriminative features for person re-identification. The distance based score layer enables to train the network using distance metrics instead of the fully connected layer. Different distance metrics can be used for training and ranking stages. The proposed structure enables to extract discriminative features in the cross-modality data without using dedicated structures for each domain. Experimental results on a cross-modality person re-identification dataset indicate that the proposed approach outperforms the state-of-the-art methods.
Full Characterization of Parikh's Relevance-Sensitive Axiom for Belief Revision

In this article, the epistemic-entrenchment and partial-meet characterizations of Parikh's relevance-sensitive axiom for belief revision, known as axiom (P), are provided. In short, axiom (P) states that, if a belief set $K$ can be divided into two disjoint compartments, and the new information $\varphi$ relates only to the first compartment, then the revision of $K$ by $\varphi$ should not affect the second compartment. Accordingly, we identify the subclass of epistemic-entrenchment and that of selection-function preorders, inducing AGM revision functions that satisfy axiom (P). Hence, together with the faithful-preorders characterization of (P) that has already been provided, Parikh's axiom is fully characterized in terms of all popular constructive models of Belief Revision. Since the notions of relevance and local change are inherent in almost all intellectual activity, the completion of the constructive view of (P) has a significant impact on many theoretical, as well as applied, domains of Artificial Intelligence.
Automated Real-time Anomaly Detection in Human Trajectories using Sequence to Sequence Networks

Detection of anomalous trajectories is an important problem with potential applications to various domains, such as video surveillance, risk assessment, vessel monitoring and high-energy physics. Modeling the distribution of trajectories with statistical approaches has been a challenging task due to the fact that such time series are usually non stationary and highly dimensional. However, modern machine learning techniques provide robust approaches for data-driven modeling and critical information extraction. In this paper, we propose a Sequence to Sequence architecture for real-time detection of anomalies in human trajectories, in the context of risk-based security. Our detection scheme is tested on a synthetic dataset of diverse and realistic trajectories generated by the ISL iCrowd simulator [11], [12]. The experimental results indicate that our scheme accurately detects motions that deviate from normal behaviors and is promising for future real-world applications.
The Thin Line Between Hate and Profanity

Hate speech can be defined as a language used to demean people within a specific group. Hate speech often contains explicitly profane words, however, the presence of these words does not always mean that the text instance is hateful. In some cases, text instances with profane words are just offensive language and they do not target any specific group, and so cannot be classified as hate speech. In this work, we build on existing studies to find a better demarcation between hate speech and offensive language. Our main contribution is to introduce the use of typed dependency as new features in our feature set. This new feature enables us to consider the relationship between long distance words in a text instance, thereby provides more identifying information than single word-based features. We evaluate our approach using a dataset with the classes: hate, offensive and neither. Comparing our work with existing studies, our feature set is much smaller but we achieve better accuracy and show comparable results in further analysis. Our detailed analysis also showed instances missed by the lexical features that were correctly predicted by the proposed feature set.
Personalized content recommendation scheme based on trust in online social networks

Summary As various social network services have developed, users are creating and sharing a large amount of content. Concurrently, there have been many studies on recommendation schemes for providing users with content that matches their preferences. In this paper, we propose a trust‐based personalized content recommendation scheme using collaborative filtering in online social network services. The user trust is calculated by analyzing social activities, content usages, and social relationships. In addition, the content trust is calculated by analyzing user expertise and reputations. Collaborative filtering is performed on users who are filtered through the user trust, and recommendation priorities are determined according to the content trust. The proposed scheme can improve the performance of collaborative filtering by eliminating untrustworthy users using user trust. It also improves the accuracy of recommendations since it provides recommendations based on content trust. Therefore, the proposed scheme can improve the performance of recommendation services using collaborative filtering in online social network services that share multimedia content. Performance evaluation is performed in terms of MAE and RMSE, which assesses errors in recommended results to demonstrate the superiority of the proposed scheme. Performance evaluations have shown that errors in the proposed scheme are reduced compared to the existing schemes, improving the accuracy of recommendations.