Reviewerly/exHarmony huggingface dataset:
Natural Brain-Inspired Intelligence for Non-Gaussian and Nonlinear Environments with Finite Memory

The cyber processing layer of smart systems based on a cognitive dynamic system (CDS) can be a good solution for better decision making and situation understanding in non-Gaussian and nonlinear environments (NGNLE). The NGNLE situation understanding means deciding between certain known situations in NGNLE to understand the current state condition. Here, we report on a cognitive decision-making (CDM) system inspired by the human brain decision-making. The simple low-complexity algorithmic design of the proposed CDM system can make it suitable for real-time applications. A case study of the implementation of the CDS on a long-haul fiber-optic orthogonal frequency division multiplexing (OFDM) link was performed. An improvement in Q-factor of ~7 dB and an enhancement in data rate efficiency ~43% were achieved using the proposed algorithms. Furthermore, an extra 20% data rate enhancement was obtained by guaranteeing to keep the CDM error automatically under the system threshold. The proposed system can be extended as a general software-based platform for brain-inspired decision making in smart systems in the presence of nonlinearity and non-Gaussian characteristics. Therefore, it can easily upgrade the conventional systems to a smart one for autonomic CDM applications.
Comparison of Feature Extraction for Sarcasm on Twitter in Bahasa

This study aims to detect the text of sarcasm in Bahasa. Sarcasm detection is very important in the field of affective computing and sentiment analysis because expressions of sarcasm can reverse the polarity of sentences. Sarcasm is difficult to detect in text because there is no intonation of sounds and facial expressions. Therefore, in this study, a system is created to recognize the sentence of sarcasm in text. The data consist of 480 train data and 120 test data collected by crawling on Twitter. Then, the data passed through the preprocessing and feature extraction stages. Classification of sarcasm and non-sarcasm sentences uses the Support Vector Machine (SVM) algorithm. Experiments are done by comparing the accuracy of N-gram, POS Tag, Punctuation, Pragmatic and combining all features. Our proposed approach reaches the highest accuracy of 91.6% with a precision of 92% when all features are combined.
Multiple Flat Projections for Cross-Manifold Clustering

Cross-manifold clustering is an extreme challenge learning problem. Since the low-density hypothesis is not satisfied in cross-manifold problems, many traditional clustering methods failed to discover the cross-manifold structures. In this article, we propose multiple flat projections clustering (MFPC) for cross-manifold clustering. In our MFPC, the given samples are projected into multiple localized flats to discover the global structures of implicit manifolds. Thus, the intersected clusters are distinguished in various projection flats. In MFPC, a series of nonconvex matrix optimization problems is solved by a proposed recursive algorithm. Furthermore, a nonlinear version of MFPC is extended via kernel tricks to deal with a more complex cross-manifold learning situation. The synthetic tests show that our MFPC works on the cross-manifold structures well. Moreover, experimental results on the benchmark datasets and object tracking videos show excellent performance of our MFPC compared with some state-of-the-art manifold clustering methods.
Real-Time, Content-Based Communication Load Reduction in the Internet of Multimedia Things

There is an increasing number of devices available for the Internet of Multimedia Things (IoMT). The demands these ever-more complex devices make are also increasing in terms of energy efficiency, reliability, quality-of-service guarantees, higher data transfer rates, and general security. The IoMT itself faces challenges when processing and storing massive amounts of data, transmitting it over low bandwidths, bringing constrained resources to bear and keeping power consumption under check. This paper’s research focuses on an efficient video compression technique to reduce that communication load, potentially generated by diverse camera sensors, and also improve bit-rates, while ensuring accuracy of representation and completeness of video data. The proposed method applies a video content-based solution, which, depending on the motion present between consecutive frames, decides on whether to send only motion information or no frame information at all. The method is efficient in terms of limiting the data transmitted, potentially conserving device energy, and reducing latencies by means of negotiable processing overheads. Data are also encrypted in the interests of confidentiality. Video quality measurements, along with a good number of Quality-of-Service measurements demonstrated the value of the load reduction, as is also apparent from a comparison with other related methods.
Generative Adversarial Network-based Image Super-Resolution with a Novel Quality Loss

Single Image Super-Resolution (SISR) has been a very attractive research topic in recent years. Breakthroughs in SISR have been achieved due to deep learning and Generative Adversarial Networks (GANs). However, the generated image still suffers from undesired artifacts. In this paper, we propose a new method named GMGAN for SISR tasks. In this method, to generate images more in line with Human Vision System (HVS), we design a quality loss by integrating an IQA metric named Gradient Magnitude Similarity Deviation (GMSD). To our knowledge, it is the first time to truly integrate an IQA metric into SISR. Moreover, to overcome the instability of the original GAN, we use a variation of GANs named WGAN-GP. Experiments show that GMGAN with quality loss and WGAN-GP can generate visually appealing results and set a new state-of-art.
Bounding Box based Annotation Generation for Semantic Segmentation by Boundary Detection

This paper proposes a new method to generate pseudo-annotations from manual bounding boxes for semantic segmentation. Different from traditional local data driven based methods such as Conditional Random Field (CRF) and GrabCut, we aim at using class-agnostic bounding box based segmentation models. To this end, we propose a new segmentation network, which formulates segmentation task as a sparse boundary point detection task rather than dense pixel label prediction task, and therefore can provide new type of pseudo-annotations. Furthermore, we detect object boundary based on direction, and use multiple directions to handle various shapes of objects. Moreover, we further enhance the pseudo generation by combining different types of segmentation masks. Classical Fully Convolutional Networks (FCN) network based on dense prediction is also modified to generate diverse foreground masks. A simple fusion method based on intersection operation is proposed to combine the two types of pseudo-annotations. We verify the effectiveness of our method on PASCAL VOC 2012 validation dataset. The mIoU value is 67.9%, which outperforms the state-of-the-art method by 1.1%.
Review and Comparative analysis of various Image Interpolation Techniques

In this paper, review and analysis of major image interpolation techniques have been presented. Image interpolation is very useful for fast super resolution (SR) of images. In super resolution a low resolution image is converted to a high resolution image. There are a number of methods to implement the Super resolution, However, the fastest method of SR implementation is always interpolation. In the presented work comparison of the major methods in the field of interpolation has been given. Moreover, simulations of different image interpolation techniques have been implemented for the comparison. The simulation results of the different methods have been compared based on the PSNR, MSE and MSSIM quality matrices.
Identification of Critical-Attacks Set in an Attack-Graph

SCADA/ICS (Supervisory Control and Data Acqui-sition/Industrial Control Systems) networks are becoming targets of advanced multi-faceted attacks, and use of attack-graphs has been proposed to model complex attacks scenarios that exploit interdependence among existing atomic vulnerabilities to stitch together the attack-paths that might compromise a system-level security property. While such analysis of attack scenarios enables security administrators to establish appropriate security measurements to secure the system, practical considerations on time and cost limit their ability to address all system vulnerabilities at once. In this paper, we propose an approach that identifies label-cuts to automatically identify a set of critical-attacks that, when blocked, guarantee system security. We utilize the Strongly-Connected-Components (SCCs) of the given attack graph to generate an abstracted version of the attack-graph, a tree over the SCCs, and next use an iterative backward search over this tree to identify set of backward reachable SCCs, along with their outgoing edges and their labels, to identify a cut with a minimum number of labels that forms a critical-attacks set. We also report the implementation and validation of the proposed algorithm to a real-world case study, a SCADA network for a water treatment cyber-physical system.
Blockchain-Based Model for Software Licensing

Digital assets can be easily copied and it is hard to prove intellectual property rights. The authors of intellectual work, including software developers, often do not receive appropriate remuneration for their work. At the same time, it is also not always easy for the users to examine the way the product has been developed and the quality of the product. The aim of this work is to develop a software transfer model based on the blockchain technology. This research is based on the analysis of research literature with an aim to assess profoundly the opportunities and prospects for the use of blockchain, a method of analysing individual components and a synthesis method that connects its elements and relations in a uniform system.
Deep reinforcement learning method based on DDPG with simulated annealing for satellite attitude control system

Satellite attitude control has been a hot issue in recent years. The rapid development of deep reinforcement learning provides a new choice for the solution of traditional control problems. A deep reinforcement learning algorithm based on DDPG for satellite attitude control system has been proposed by this paper. The method uses a simulated annealing algorithm to seek out the global optimal policy. This method can achieve robust effect in a shorter period of time compared with other algorithms through experiments.
Unsupervised bin-wise pre-training: A fusion of information theory and hypergraph

Pre-training is considered to be a ‘triggering point’ for Deep Neural Networks which gains attraction of the researchers. Though recent research works focus on designing efficient pre-training models, they often fail to capture the relevant information representations across the layers with minimum turns and to maintain the stability of the learning model. This research article presents a novel unsupervised bin-wise pre-training model which fuses Information Theory and Hypergraph that acts as an effective optimizer: speed up the learning process & minimize generalization loss, and also as an impelling regularizer: maintain the stability of the Deep Neural Network. The proposed model is evaluated using three different benchmark datasets and the experimental results confirm the supremacy of the proposed model over the state-of-the-art approaches in speeding up the learning process and minimizing generalization loss without deteriorating the stability of Deep Neural Network.
Design of Guruswami-Sudan List Decoding for Elliptic Codes

Advancing from Reed-Solomon (RS) codes, the length of algebraic-geometric (AG) codes can exceed the size of finite field, resulting in a greater error-correction capability. However, this is realized with a genus penalty. Usually, they are not maximum distance separable (MDS) codes. One-point elliptic codes are either MDS or almost MDS, yielding a good tradeoff between codeword length and distance property. This paper proposes the Guruswami-Sudan (GS) list decoding algorithm for elliptic codes. To define the interpolated polynomial Q(x, y, z), an explicit construction for the zero basis of each affine point is introduced. Given an interpolation multiplicity m, the error-correction capability τ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sub> and the maximum decoding output cardinality l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sub> of the GS algorithm are characterized. An efficient interpolation algorithm is further presented for elliptic codes. Performance of elliptic codes is shown for the first time, demonstrating their advantage over RS codes.
HBUNN - Hybrid Binary-Unary Neural Network: Realizing a Complete CNN on an FPGA

Most modern neural networks use the basic Multiply-Accumulate (MAC) Operation in some form or another, and as networks get larger, the computational needs for these larger networks grow rapidly. Typically neural networks implemented on FPGAs use variable multipliers so that any weight can be used in the MAC, but this also forces the accelerator designer to store the weights of the model in off-chip memory (DRAM). Moreover, because of high computational power and high memory bandwidth requirements of today's CNNs, it is harder for FPGA platforms to deliver the best performance. In this paper, we propose a fully parallel-pipeline Hybrid Binary-Unary Neural Network (HBUNN) architecture to implement a low-cost and high-performance ResNet-18 convolutional neural network. We use a hybrid binary-unary method to implement constant-coefficient multipliers and batch normalization units. These two units reduce hardware cost by 30.7% and 47.97% on average compared to the conventional binary equivalent, respectively. Moreover, we propose a novel training scheme using our hardware cost-aware regularizers that not only improves the area cost of the proposed architecture and the conventional binary architecture by 59.3% and 76.7% respectively, but also maintains the same accuracy. Finally, we have implemented three trained networks using different regularizers. The proposed HBUNN architectures reduce the area cost by 30%, and the area × delay cost by 69% on average compared to the conventional binary architectures. The error rate of the proposed work is 12.93%, while its throughput is 278 Kfps.
An Overview of Traditional and Recent Trends in Video Processing

Video processing is a significant field of research interest in recent years. Before going into the recent advancement of video processing, an overview about the traditional video processing is a matter of interest. Knowing about this, its advantages and limitations help to give a strong base and invoke an insight into the further development of this research area. This paper introduces the concept of video processing in early era followed by hybrid video processing, MJPEG and describes the traditional video processing methodologies in hierarchal order. Also, the paper summarizes the recent trends in video processing with NN, CNN, deep learning and focus on the applications in this area. It focus mainly on the video processing development and its application till current era.
Identifying Generic Features for Malicious URL Detection System

Malicious URLs pose serious cybersecurity threats to the Internet users. It is critical to detect malicious URLs so that they could be blocked from user access. Several techniques have been proposed to differentiate malicious URLs from benign ones. However, the goal of our work is to find the list of substantial features that can be used to classify most of the malicious URLs. In this paper, we select the most significant lexical features from different datasets using Chi-Square and ANOVA F-value. Later, we apply a voting classifier that combines several machine learning algorithms on those selected features.
A Comparative Evaluation of Jpeg Steganography

With the dependency on internet and smart gadgets, security of data has become a major concern. Steganography is one of the methods adopted to enhance the security of data by hiding it behind some cover which can be any amongst text, image, audio or video. Images are the most preferred medium to apply steganography as it contains large amount of redundant data. Image steganography hides the data behind the image in a manner that alterations are hardly noticeable to human eye. Images are available in different formats, out of which jpeg images have become the most popular image format due to its small size. These images undergo lossy compression resulting in small sized images, but hiding data behind these images is a challenge. Many algorithms have been suggested till date. This paper intends to give understanding and evolution of different techniques for jpeg steganography. It summarizes various algorithms and techniques used in the past and in present. This paper also analysis's these algorithms based on the basic parameters of image steganography: Undetectability, robustness and embedding capacity.
Exploring Technology-Enhanced Learning Key Terms using TF-IDF Weighting

Technology-enhanced learning (TEL) covers a broad spectrum of discussion. Having a holistic viewpoint and use it to augment TEL is a challenge. We need extensive literature reviews requiring coverage of as many articles as possible discussing TEL. Accordingly, we may look for the key terms with discriminant power to explain this topic. This study processed 40 TEL articles, published no earlier than 2010, taken from IEEE Xplore research database. In previous work, we applied Luhn's significant words as a qualitative approach. However, the reliability of subjective justification become an issue. This study answers the issue by applying term frequency-inverse document frequency (TF-IDF) weight, to find the key terms. This research produces 23 key terms from 685 TF-IDF important words compared to 381 significant words. The finding indicates that some of the significant words also appear in the highest TF-IDF weight cluster. Further analysis could be done using other research databases for more articles.
The Multi Layer Auto Encoder Neural Network (ML-AENN) for Encryption and Decryption of Text Message

Efficient key generation techniques require highly secure cryptosystems. The traditional key generation technique is very systematic that it is easy to attack. The Deep Learning algorithm is one of the research paths into the automated extraction of complex data representations (features) at a high level of abstraction. Auto Encoder Neural Network (AENN), one of the deep learning's architectures, play an essential role in unsupervised learning in deep architecture for learning transfer and other tasks. The AENN are simple learning networks that aim to convert inputs into outputs with the least amount of distortion. This study proposes the Deep Learning approach for text message encryption and decryption by using the Multi-Layer AENN where the outputs of each of the deepest layer as the secret-key generator and hash value generator. The result of this research showed that the proposed method has a high degree of confidentiality because each training always produces a different secret key. Furthermore, the tampered data/information can be seen from the presence of different hash values that occur.
Is Usable Security an Oxymoron?

Current multipronged cybersecurity measures require the active support and participation of users for their successful deployment. Although no formal definition of usable security exists, it is time to make it a reality for users.
Cross-view hashing via supervised deep discrete matrix factorization

Matrix factorization has been utilized for the task of cross-view hashing, where basis functions are learned to map data from different views to the same hamming embedding. It is possible that the basis functions between the hamming embedding and the original data matrix contain rather complex hierarchical information, which existing work can not capture. In addition, previous work employs relaxation technique in the matrix factorization based hashing which may lead to large quantization error. To address these issues, this paper presents a novel Supervised Discrete Deep Matrix Factorization (SDDMF) for cross-view hashing. We introduce deep matrix factorization so that SDDMF is able to learn a set of hierarchical basis functions and unified binary codes from different views. In addition, a classification error term is incorporated into the objective to learn discriminative binary codes. We then employ a linearization technique to directly optimize the discrete constraints which can significantly reduce the quantization error. Experimental results on three standard datasets with image-text modalities verify that SDDMF significantly outperforms several state-of-the-art methods.
An Iterative Dynamic Programming Approach to the Multipoint Markov-Dubins Problem

A new solution to the multipoint Markov-Dubins problem via iterative dynamic programming is herein presented. The shortest path problem connecting a sequence of given points in the plane while maintaining angle continuity and bounded curvature is presented. As in the classic two points Dubins problem, the solution is a juxtaposition of line segments and circle arcs. This problem is relevant for the path planning of a non-holonomic robot, such as a wheeled vehicle. The proposed method is robust and computationally inexpensive with respect to existing solutions and is therefore suitable to be integrated as motion primitive into Dubins-based applications, e.g. orienteering problems or waypoint following robotics.
On Robustness of Neural Architecture Search Under Label Noise

Neural architecture search (NAS), which aims at automatically seeking proper neural architectures given a specific task, has attracted extensive attention recently in supervised learning applications. In most real-world situations, the class labels provided in the training data would be noisy due to many reasons, such as subjective judgments, inadequate information, and random human errors. Existing work has demonstrated the adverse effects of label noise on the learning of weights of neural networks. These effects could become more critical in NAS since the architectures are not only trained with noisy labels but are also compared based on their performances on noisy validation sets. In this paper, we systematically explore the robustness of NAS under label noise. We show that label noise in the training and/or validation data can lead to various degrees of performance variations. Through empirical experiments, using robust loss functions can mitigate the performance degradation under symmetric label noise as well as under a simple model of class conditional label noise. We also provide a theoretical justification for this. Both empirical and theoretical results provide a strong argument in favor of employing the robust loss function in NAS under high-level noise.