Reviewerly/exHarmony huggingface dataset:
Motorcycle Detection using Deep Learning Convolution Neural Network

Detecting and avoiding motorcycles on roads is important for Autonomous Vehicle (AV). This is because a majority of accidents occurring in Malaysia involve motorcycles. Detecting motorcycles is a challenging task due to its low visibility and high velocity. This research attempts to capitalize on Deep Learning Neural Network to detect motorcycles. Training involves various motorcycle models and poses with different resolutions and road conditions. The AlexNet network structure was chosen for implementation due to its proven performance in object detection tasks. Transfer learning was used to repurpose the AlexNet network for the described task. Training and classification were performed using the MATLAB Deep Learning Toolbox. Test results on our custom dataset demonstrates the effectiveness of the approach for the task.
Capture The Flag Simulation Based on STRIDE Threat Model

Capture The Flag (CTF) simulation can improve the player's knowledge of the cyber attacks and how to secure and mitigate those attacks. This research aims to establish a CTF simulation based on injection vulnerability based on the STRIDE threat model (Spoofing, Tampering, Repudiation, Information Disclosure, Denial of Service, and Elevation of Privilege). and then building the CTF simulation with the Injection scenario based on the STRIDE threat model. We develop the CTF simulation environment based on a web application by using a server flask on a virtual machine with various Injection scenarios. The simulation environment consists of two server applications, namely middleware and backend, with different ports to access the Capture The Flag web application. The simulation of each scenario has been tested and can describe the threat model STRIDE. The results show that our CTF web-based simulation can effectively run the STRIDE Injection model.
Comparative analysis of various new activation functions based on convolutional neural network

Abstract With the rapid development of artificial intelligence, deep learning is developing rapidly. As the main framework of in-depth learning, convolution neural network achieved much in semantic segmentation and image classification. Based on the low speed engineering vehicle tracking system project, this paper studies the activation function, which is an important component of convolution neural network. Firstly, it summarizes the advantages and disadvantages of four traditional activation functions such as Sigmoid, and then introduces three new activation functions: Swish, FTS, and Relu-Softplus. Finally, using the TensorFlow framework, a single new activation function and a hybrid new activation function are applied to the convolution layer of the four neural networks constructed, and the image recognition comparison is performed in the data set CIFAR-10. It is concluded that the recognition precision of mixing the new activation functions is higher than that of using a new activation function alone, and the average time is shorter and the convergence speed is faster.
Using Machine Learning Clustering To Find Large Coverage Holes

Identifying large and important coverage holes is a time-consuming process that requires expertise in the design and its verification environment. This paper describes a novel machine learning-based technique for finding large coverage holes when the coverage events are individually defined. The technique is based on clustering the events according to their names and mapping the clusters into cross-products. Our proposed technique is being used in the verification of high-end servers. It has already improved the quality of coverage analysis and helped identify several environment problems.
Wrapper-based feature selection via differential evolution: benchmarking different discretisation techniques

Wrapper-based feature selection approaches reliant on different bio-inspired optimisation algorithms are both effective and widely employed when dealing with classification problems. These algorithms have proven themselves as successful wrappers in finding good feature subsets. However, as a large number of them is defined for the real domain, the small detail of their adaptation to the discrete domain of feature selection is often overlooked. This holds especially true for differential evolution, a prominent wrapper choice among bioinspired optimisation algorithms. As distinct discretisation techniques have been proposed in the literature, the question of which one to incorporate in differential evolution and under which circumstances remains rather unanswered. This paper attempts to provide some answers in that regard by studying the incorporation of discretisation techniques into differential evolution and their influence on the quality of attained feature subsets. Given their differences, some suggestions concerning the selection of discretisation techniques are given based on the obtained results.
WITHDRAWN: Simulation of physical education teaching video recognition based on FPGA and Sobel algorithm

This article has been withdrawn: please see Elsevier Policy on Article Withdrawal (https://www.elsevier.com/about/our-business/policies/article-withdrawal). This article has been withdrawn at the request of the Editor in Chief. Subsequent to acceptance of this special issue paper by the responsible Guest Editor Sundhararajan Mahalingam, the integrity and rigor of the peer-review process was investigated and confirmed to fall beneath the high standards expected by Microprocessors & Microsystems. There are also indications that much of the Special Issue includes unoriginal and heavily paraphrased content. Due to a configuration error in the editorial system, unfortunately the Editor in Chief did not receive these papers for approval as per the journal's standard workflow.
Graph and Word Similarity for Word Sense Disambiguation

This paper proposes a graph and word similarity method for word sense disambiguation. Perform word sense mapping on the context words of a given ambiguous sentence to obtain the corresponding English words; compute the English word similarity of the obtained English words, and take the English words as vertices, the semantic relations between words as edges, and the similarity values as edges to construct a disambiguation graph; the graph scoring algorithm is used to score the importance of each word sense of the disambiguated words, and the highest score is selected as the final correct word sense. The experimental results show it can effectively use English knowledge resources and obtain a disambiguation accuracy rate of 0.451 on the SemEval-2007 task#5 dataset, which can improve the accuracy of Chinese word sense disambiguation.
Correlation Filter-Based Visual Tracking Using Confidence Map and Adaptive Model

Recently, visual trackers based on the framework of kernelized correlation filter (KCF) achieve the robustness and accuracy results. These trackers need to learn information on the object from each frame, thus the state change of the object affects the tracking performances. In order to deal with the state change, we propose a novel KCF tracker using the filter response map, namely a confidence map, and adaptive model. This method firstly takes a skipped scale pool method which utilizes variable window size at every two frames. Secondly, the location of the object is estimated using the combination of the filter response and the similarity of the luminance histogram at multiple points in the confidence map. Moreover, we use the re-detection of the multiple peaks of the confidence map to prevent the target drift and reduce the influence of illumination. Thirdly, the learning rate to obtain the model of the object is adjusted, using the filter response and the similarity of the luminance histogram, considering the state of the object. Experimentally, the proposed tracker (CFCA) achieves outstanding performance for the challenging benchmark sequence (OTB2013 and OTB2015).
Creating A Syntactically Felicitous Constituency Treebank For Turkish

In this study, Bakay et. al [1] and Yıldız et. al.'s [2] work on Turkish constituency treebanks were developed further. Compared to the previous work, the most prominent feature of this study is the fact that every annotation and refinement process is held manually. In addition, constituency treebank created as a result of this study abides by the syntactic rules and typologic features of Turkish while the trees created by previous studies convey only the translated and simply inverted trees that completely ignore the syntactic properties of Turkish. The methodology followed in this study resulted in a significantly more accurate representation of Turkish language and simpler, relatively flatter trees. The straightforward style of trees in this study reduces the complexity and offers a better training dataset for learning algorithms.
Time series forecasting method based on frequent pattern mining

Abstract Pattern Sequence-based Forecasting (PSF) is an effective method for time series prediction. However, the accuracy of this method depends on the selection of parameters such as the length of the pattern sequence and the number of clusters. In diverse time series data sets, these parameters are often priori unknown. This paper innovatively introduces a pattern mining method before the PSF pattern clustering to guide the clustering process and realize the automation of initial parameter selection. Experimental results show that the method proposed in this paper effectively eliminates the uncertainty of PSF caused by the selection of initial parameters. Compared with the original model, it improves the efficiency while ensuring the advantage of prediction accuracy.
ShadowSparrow: An Ambient Display for Information Visualization and Notification

We present the design and prototype of ShadowSparrow, an ambient display utilizing lights & shadows. The concept uses illustrative bird images and other relevant visuals (e.g., dishwasher and e-mail icons) cast on the surroundings for ambient information visualizations and notifications. Our work creates an alternative channel to convey non-critical information in an unobtrusive and aesthetic way.
First-Round and Last-Round Power Analysis Attack Against AES Devices

Power analysis attacks are a known method of breaking cryptographic algorithms such as AES. The scenario is a first-round and last-round attack based on CPA and hamming distance, each of which provides candidates for 16 secret state key. This paper demonstrates a series of attacks on a secure AES microcontroller device, where the f rmware f les have been encrypted with AES128 EBC. A correlation power analysis (CPA) attack is performed on AES128 to recover the complete master key. The use of CPA attacks is carried out on the first-byte state at First- Round and Last-Round. The correlation coefficient for the entire state in AES in the first-round is 0.712 <<0.8455. Meanwhile, the coefficient value in the second test is 0.1724 <<0.2635. We can conclude the main vulnerability of the AES128 is in the power estimate value that is easy to guess after the SubBytes and INVSubbytes function in each round.
Explainable AI in Decision Support Systems : A Case Study: Predicting Hospital Readmission Within 30 Days of Discharge

Explainable models are a critical requirement for predictive analytics applications in the healthcare domain. In this work we develop a hypothetical clinical decision support system for the classification task of predicting hospital readmission within 30 days of discharge. We compare a baseline logistic regression model with an implementation of the coordinate descent algorithm known as lasso. We choose lasso because it inherently performs variable selection during optimization which leads to an explainable model. Using model evaluation data we achieve an area under the ROC curve score of 0.795 improving on the baseline score of 0.683 without inflating the feature space.
Improving offline HTR in small datasets by purging unreliable labels

This paper focuses on the offline handwriting text recognition problem (HTR) with small training data sets. Some techniques such as transfer learning or data augmentation have recently been applied to this problem, improving the performance of the recognition. In these scenarios, we found that errors in the labelling of the training samples, present in some databases, have a great impact in the character error rates (CER). Accordingly, we propose a novel cross validation technique to remove incorrect labelled lines. In this approach, after a first training stage, transcript lines with CER above a threshold are discarded, where the threshold is a function of the available data. Less available data favours larger CER, even for healthy lines, suggesting higher thresholds for fewer lines. This new technique and the validation of the threshold are analyzed over the ICFHR 2018 competition on automated HTR and other well known databases such as Washington and Parzival. For the Ricordi database in the ICFHR 2018, with transcription errors, we report a reduction of CER by 2%.
A novel software defect prediction approach using modified objective cluster analysis

Abstract A novel approach for software defect prediction of unlabeled datasets is proposed using modified objective cluster analysis (OCA). In this approach, the first step is to construct the distance matrix of instances in the datasets by utilizing the automatically determined clusters through the modified OCA. Then the dipoles within different instances are categorized into two different groups. Finally, the clusters of instances are produced, and software defects can be predicted by imposing a modified consistency criterion. Case study and comparative experiments were conducted based on 12 public datasets selected from the databases of Promise and ReLink using multiple different unsupervised algorithms and cross‐project approaches. There are two experimental settings: experiments with datasets that contain all metrics and experiments with datasets that contain only module size metrics. The results were evaluated by the index of precision, recall, F ‐measure, and receiver operating characteristic curve (AUC). Furthermore, a complexity analysis of the tested algorithms was conducted as well. In experiments with datasets with all metrics, the proposed OCA gets the best results in four indexes, and the average values of precision, recall, F ‐measure, and AUC were improved by a minimum of 1.52%, 2.78%, 19.84%, and 0.93%, respectively. In experiments with datasets with only module size metrics, the proposed OCA performed the best results in four indexes also, and the average values of precision, F ‐measure, and AUC were improved by a minimum of 8.8%, 2.59%, and 8.36%, respectively. The proposed algorithm is of low complexity and provides a new way to efficiently predict software defects with unlabeled datasets.
Azimuth Thruster PMSM Optimization using Symbiotic Organisms Search Algorithm

The azimuth thruster is widely used in electric propulsion ships due to its excellent performance in contrast to their mechanical counterparts which present an inherent drawback regarding to maintenance needs, high costs and overall system deficiency. At the same time, according to up-to-date manufacturers' reports, permanent magnet motors make azimuth thrusters more compact, lighter and more efficient compared to other types of motors. Thus, the replacement of a conventional drive with a properly designed low-speed permanent magnet synchronous motor (PMSM) directly coupled to the thruster propeller, provides an attractive alternative. This paper, based on authors' previous work on low-speed surface mounted PMSM design for azimuth thruster application, presents and proposes here an optimization procedure using a meta-heuristic population based technique namely the symbiotic organism search (SOS) algorithm. The results obtained reveal that the SOS algorithm provides effectively the required design solutions and performance.
Towards refinable choreographies

We investigate refinement in the context of choreographies. We introduce refinable global choreographies allowing for the underspecification of protocols, whose interactions can be refined into actual protocols. Arbitrary refinements may spoil well-formedness, which are sufficient conditions that guarantee a protocol to be implementable. We introduce a typing discipline that enforces well-formedness of typed choreographies. Then we unveil the relation among refinable choreographies and their admissible refinements in terms of an axiom scheme.
Spatial-Temporal Network for No Reference Video Quality Assessment Based on Saliency

With the increasing use of digital video, the importance of establishing a high-performance no reference video quality assessment (NR VQA) model is increasing. How to effectively assess the properties of the human visual system (HVS) in a data-driven manner is one of the difficulties in NR VQA. In this paper, we propose a spatio-temporal network model based on saliency. The model has two branches: the spatio-temporal branch and the saliency branch. We propose a basic spatio-temporal network model in the spatio-temporal branch and use it to predict the score after the spatial distortion effected by temporal information. Then we extract the salient features of the current video frame and merge it with the spatial distortion to predict a result that is more in line with human perception. Finally, the two scores are automatically weighted to obtain the score of the current video. The performance of the proposed method has been verified on two databases, LIVE and CSIQ. The training results show that the method proposed in this paper can basically conform to the subjective perception of human eyes, and the performance of the network can also be better than most current methods without reference video.
A Classification Method of Breast Pathological Image Based on Residual Learning

Today, breast cancer has become a globally recognized tumor disease with high incidence and high mortality. The use of advanced convolutional neural network for the auxiliary diagnosis of breast pathological images is the general trend. Due to the complex background of the medical pathology image and more noise, a bilateral filter is used for noise reduction. Aiming at the problem of refinement, complexity and weakening of the morphological texture features of breast pathological images, the idea of using residual network residual units to simplify the learning process and enhance gradient propagation is adopted. The deep residual network is used to divide the image into benign and malignant tumors. For medical images with billions of pixels, the strategy of randomly extracting image patches is employed for data enhancement, and the model is evaluated using summation rules. Experimental results show that the recognition accuracy of the model reaches 96%.
Research and design of activation function hardware implementation methods

Abstract Activation function is an important part of neural network. Many artificial intelligence accelerators specially design hardware component supporting for activation function. In this article, we analyze normal activation functions, study kinds of activation function hardware implementation methods, and propose a parallel look-up table based piecewise linear fitting method. In many-core AI processor, we determine the table precision by memory size, and propose the basic algorithm and detailed structural design scheme. After the design is completed, we verify its correctness and evaluate its performance. The result shows that proposed method meets the need of correctness, and can finish the parallel computation of sixteen activation function fitting results within thirteen cycles. It greatly improves the efficiency of activation function hardware design.
New Color and Texture Features Coding Method Combined to the Simulated Annealing Algorithm for Content Based Image Retrieval

Content based image retrieval is a method that can find similar images to a query image in a large database. This technique is based on the perceptible contents and visual features of the image. In this work, we implemented a new method of coding the visual features of the image based on strings. The aim of this work is to improve our previous method by reducing the retrieval time and increasing the number of results by improving the two measures: precision and recall. Therefore, from a query image we extract the color signature. Then, we create the gray level co-occurrence matrix to extract four texture features. Meanwhile, we develop our previous method which is color string coding while combining color and texture features in the purpose of creating an efficient codification technique which improve enormously the size of coding the image descriptors. Finally, the simulated annealing is implemented as a low complexity meta-heuristic algorithm for image retrieval.
MIX’EM: Unsupervised Image Classification Using a Mixture of Embeddings

We present MIX’EM, a novel solution for unsupervised image classification. MIX’EM generates representations that by themselves are sufficient to drive a general-purpose clustering algorithm to deliver high-quality classification. This is accomplished by building a mixture of embeddings module into a contrastive visual representation learning framework in order to disentangle representations at the category level. It first generates a set of embedding and mixing coefficients from a given visual representation, and then combines them into a single embedding. We introduce three techniques to successfully train MIX’EM and avoid degenerate solutions; (i) diversify mixture components by maximizing entropy, (ii) minimize instance conditioned component entropy to enforce a clustered embedding space, and (iii) use an associative embedding loss to enforce semantic separability. By applying (i) and (ii), semantic categories emerge through the mixture coefficients, making it possible to apply (iii). Subsequently, we run K-means on the representations to acquire semantic classification. We conduct extensive experiments and analyses on STL10, CIFAR10, and CIFAR100-20 datasets, achieving state-of-the-art classification accuracy of 78%, 82%, and 44%, respectively. To achieve robust and high accuracy, it is essential to use the mixture components to initialize K-means. Finally, we report competitive baselines (70% on STL10) obtained by applying K-means to the “normalized” representations learned using the contrastive loss.
CLASS: Cross-Level Attention and Supervision for Salient Objects Detection

Salient object detection (SOD) is a fundamental computer vision task. Recently, with the revival of deep neural networks, SOD has made great progresses. However, there still exist two thorny issues that cannot be well addressed by existing methods, indistinguishable regions and complex structures. To address these two issues, in this paper we propose a novel deep network for accurate SOD, named CLASS. First, in order to leverage the different advantages of low-level and high-level features, we propose a novel non-local cross-level attention (CLA), which can capture the long-range feature dependencies to enhance the distinction of complete salient object. Second, a novel cross-level supervision (CLS) is designed to learn complementary context for complex structures through pixel-level, region-level and object-level. Then the fine structures and boundaries of salient objects can be well restored. In experiments, with the proposed CLA and CLS, our CLASS net consistently outperforms 13 state-of-the-art methods on five datasets.