Reviewerly/exHarmony huggingface dataset:
Happy and Sad Classification using HOG Feature Descriptor in SVM Model Selection

Facial Expression Recognition (FER) of the image is one of the potential research fields. It remains some open problems to be solved such as various head positions, backgrounds, occlusion, face attribute etc., where the FER 2013 dataset give such conditions. In this research, the small balanced dataset used to recognize two common fundamental expression, happy and sad face image as our set conditions. Using SVM as classifier and HOG as feature expression method, this research shows best performance, that is 72% accuracy, in quadratic polynomial kernel with intercept constant <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathrm{b}=1$</tex> and tolerance constant <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathrm{C}=0.1$</tex> . By using such conditions, minimized pose variant, a conventional approach in FER such SVM and HOG has shown fair performance in the FER 2013 dataset.
Research on Semantic Interaction Integration Method and Key Technology for Edge Intelligent Instrument

Considering the networking, digitization and intelligence development trend for the traditional instruments, this paper proposes a general method of semantic interaction integration for the edge intelligent instrument, introduces the key techniques about semantic database, information model, uniform model description and semantic shell, etc., constructs a verification system, and performs the experiment verification on the basis of edge intelligent temperature transmitter, whose results conform to the expectations. This method is a general paradigm in semantic interaction integration. It is not only suitable for the instruments but also useful for the semantic interaction integration for CNC and industrial robot, which plays an important role in promoting the transition of industrial digitization.
An Improved LRP-Based Differential Privacy Preserving Deep Learning Framework

Aiming at the privacy leakage risk of the existing differential privacy preserving deep learning framework when calculating relevance, and the problem of model training consuming too much privacy budget, this paper proposes an adaptively allocation of dynamic privacy budget based on LRP (Layer-wise Relevance Propagation) differential privacy preserving deep learning framework. In the process of the relevance calculation, noise is added to the relevance decomposition information, and the privacy budget is dynamically changed during the model training process. Theoretical analysis and experimental results show that the framework proposed in this paper can protect data privacy while maintaining a high accuracy of the model.
Deep Reinforcement Learning and In-Network Caching-Based Martial Arts Physical Training

The martial arts have been regarded as the athletics project at the international competitions, and the corresponding physical training has also brought about widespread attention. However, the traditional physical training evaluation methods are usually performed in the offline way and they are very difficult to achieve the large-scale data evaluation with the high evaluation efficiency. Therefore, this paper leverages Deep Reinforcement Learning (DRL) and in-network caching to realize the high-precision and high-efficiency data evaluation under the large-scale martial arts physical training environment while guarantees the online performance evaluation. Meanwhile, Q-learning based DRL is used to make the large-scale data evaluation. In addition, a communication protocol based on in-network caching is proposed to support the online function. The comparison experiments demonstrate that the proposed conduction method for the martial arts physical training is more efficient than the benchmark.
A multiobjective nonlinear combinatorial model for improved planning of tour visits using a novel binary gaining-sharing knowledge- based optimization algorithm

This chapter proposes a novel binary version of recently developed Gaining-Sharing knowledge-based optimization algorithm (GSK) to solve binary optimization problems. GSK algorithm is based on the concept of how humans acquire and share knowledge during their life span. Binary version of GSK named novel binary Gaining-Sharing knowledge-based optimization algorithm (BGSK) depends on mainly two binary stages: binary junior gaining sharing stage and binary senior gaining sharing stage with knowledge factor 1. These two stages enable BGSK for exploring and exploitation of the search space efficiently and effectively to solve problems in binary space. Many application problems are formulated as nonlinear binary programming models that are hard to be solved using exact algorithms especially in large dimensions. One of these practical applications is to improve planning of tour-visits problem (PTP) in network optimization, it is defined for a director of a tourism company to choose a subset of candidate places comprising maximizing both the number of visited places and the tourist's satisfaction within the tour time limit. A multiobjective nonlinear binary model is introduced with a detailed real application example involving improving the scheduling of a tour to visit five famous touristic places in Cairo, Egypt. The example is solved using a BGSK. Each obtained efficient solution appears several times as multiple solutions with the same values of the objective functions, which gives a room for the decision maker to design many schedules for the planned tour visits.
Research on the CRF-based Sequence Labeling Algorithm Used in Reference Resolution of Mathematical Word Problem Understanding

Resolving the reference phenomenon in application problems is a key step to realize the understanding of mathematical problems. Compared with the Chinese corpus in the general field, this study analyzed the characteristics of reference resolution in mathematical stratified sampling word problems, and explored key factors by combining the C4.5 decision tree algorithm. On this basis, the study proposed a CRF-based sequence labeling algorithm, which was exploited to identify the to-be-solved items of the stratified sampling problem and resolved them. The experimental data is the stratified sampling word problems collected from the math problems in the Chinese college entrance examination and the test problems in the textbook from 2012 to 2020. The results show that the F-values for the antecedents and anaphors of the mathematical stratified sampling word problems based on CRF sequence labeling can reach 81.29% and 90.69%, respectively, and the F-values for reference resolution can reach 84.84%, which is higher than the traditional Bayesian method.
Pattern Recognition and Reconstruction

The topic of the chapter is track and vertex reconstruction in particle detectors. The reconstruction of (charged) tracks and the subsequent reconstruction of interaction vertices are important steps in the data analysis chain of an experiment in high-energy physics. Track reconstruction typically proceeds in three steps: Track Finding; Track Fitting; and Testing the track hypothesis. The most important pattern recognition algorithms and statistical estimation methods applied in this context are presented in the first section of the chapter, along with material on track-based alignment and formulas for the quick assessment of the momentum resolution of a tracking system. The following section deals with the reconstruction of interaction vertices, including both the primary interaction vertex in the collision zone of an experiment and secondary decay vertices of unstable particles. The pattern recognition methods and statical estimators involved are in many respects similar to the ones deployed in track reconstruction. The final section presents a brief overview of track and vertex reconstruction in the four LHC experiments. The methods applied by the experiments are described and their performance is illustrated by results from a few selected physics channels.
Research and Application of Intelligent Question-Answer Algorithm Based on Multi-Channel Dilated Convolution

Electricity is the most important energy driving force for people&#x0027;s production and life. Power customer service is the external service communication window of power enterprises. With the rapid development of Deep Learning and Artificial Intelligence in recent years, it has become the future development trend to use online Question Answer system to solve user&#x0027;s problems in real time. The knowledge base of power customer service is sorted out through the historical dialogue data of State Grid manual customer service in the past. The problem that is most similar to the new problem in the knowledge base is obtained by using the &#x201C;Question-Question&#x201D; matching pattern, and the corresponding answer of the problem is output. To solve the problem that existing text matching models are difficult to obtain multi-scale semantic information, a text matching model based on multi-channel dilated convolution is proposed in this paper. It can capture text features at multiple scales so as to compare sentence similarity according to richer semantic features. Experiments show that the model has excellent performance.
A Rotating Object Detection Method Based on Anchor-Free in Remote Sensing Scene Images

Abstract The core of the object detection task is to be able to accurately classify and regress the object position. The current two-stage algorithm based on convolutional neural networks has achieved good results in horizontal box detection, but the effect in rotating bounding box detection is limited, and the anchor-based detection algorithm has problems, such as complicated hyper-parameter design and imbalance between positive and negative samples. In order to better solve the above problems, the model in this paper adopts a single-stage anchor-free method, uses the high resolution network(HRNet) to obtain the high-resolution feature map of cross fusion, and uses a rotating Gaussian kernel for predicting center point, which can increase the number of positive samples at the center point, simultaneously realize the improvement of detection accuracy and speed. Experimental results on HRSC2016 and DOTA datasets, which demostrates that our method exceed the state-of-art models, and indicates the effectiveness of the proposed methods.
New Conceptual Cohesion Metrics: Assessment for Software Defect Prediction

High cohesion and low coupling is a desirable software design principle as it significantly and positively impacts software quality. While the majority of existing cohesion metrics are structural, this paper proposes a set of new object-oriented metrics capturing the conceptual cohesion of classes. They are derived from the semantic information contained in the source code and involve using Doc2Vec, an artificial neural network based unsupervised model. Two case studies that compare the proposed metrics with an extensive set of relevant existing ones are presented. The empirical validation has been performed on three open source software systems. It includes the assessment of the proposed metrics versus preexisting ones from the perspective of their utility for software defect prediction. The experimental results confirm that the metrics we propose capture additional aspects of cohesion when compared to existing cohesion metrics, while also providing better software defect prediction performance than preexisting related conceptual cohesion metrics.
The handwriting of Image Segmentation Using the K-Means Clustering Algorithm with Contrast Stretching and Histogram Equalization

An analysis of handwritten documents is a scientific technique to understand a writer's personality using handwriting scratches and patterns. Graphologists have identified human characters using visual observations. The identification process requires a long time because the observations are conducted comprehensively and examining one by one of the letters or words of the overall handwriting. Therefore, we need a system that automatically identifies the characters of human personalities based on handwriting, requires a shorter period, and provides objectivity. Handwriting image processing to identify human characters has been developed in various fields, such as education, medicine, psychology, and criminology. In image processing, segmentation is an important stage to separate an object from its background. On the other hand, the k-means clustering algorithm is an algorithm to classify some cluster regions based on certain characteristics. Therefore, it can be implemented at the segmentation stage of handwriting images. This research started with data acquisition. The data employed constituted scans of handwriting obtained from graphologists. Then, the image quality improvement employed contrast stretching and histogram equalization. The next step was image segmentation using the k-means clustering algorithm. Segmentation was conducted by varying k values to gain the best segmentation results. The evaluation was conducted by comparing the results of segmentation images with the results of reference images. The reference images were obtained from segmentation images using Otsu's thresholding method. Otsu's method (1979) has been widely applied in various research on segmentation and produced good accuracy. Therefore, this study applied the image segmentation results with Otsu's method as a reference. The results showed that (1) the highest evaluation indicator was in the segmentation results without pre-processing, and (2) the k value was = 2 with the average accuracy of 100%, the average sensitivity of 100%, and the average specificity of 100%.
Importance of Histopathology Images in disease detection and Cancer Survival Prediction Analysis

Illness has been portrayed as a heterogeneous polluting such as a huge stage of subtypes. The early preference and doubt for a damage kind have end up a want in contamination research, as it is able to empower the going with the clinical courting of sufferers. The significance of depicting pollutants sufferers into high or all-round secure social affairs hosts pushed one-of-a-kind appraisal gatherings, from the biomedical and the bioinformatics subject, to have a look at using AI and ML technique. thinking about everything, these methods were used as an association to reveal the new flip of occasions and remedy of peril inflicting situations. moreover, the prerequisite of ML gadgets to look key highlights from complicated datasets uncovers their importance.We constructed up a Deep studying layout to count on illness specific excitement throughout 10 ruinous improvement kinds from the most cancers Genome Atlas (TCGA). We applied a hopelessly orchestrated framework without pixel-degree explanations and endeavored three irrefutable regular fine disturbance limits.Our assessment indicates the ability for this manner to cope with oversee direct give head prognostic information in distinct peril sorts, and even inner express pathologic tiers. anyways, given the normally unnoticeable number of cases and saw scientific activities for a essential learning undertaking of this type, we noticed absolute sureness levels for model execution, as a result inclusive of that destiny work will profit through more obvious datasets gathered for the reasons for fidelity acting.
Filter Selection and Feature Extraction to Distinguish Types of CT Scan Images

The study aims to select the most powerful filtering method as input for feature extraction to distinguish the types of Head CT Scan images. Visually determining the scanned medical image (head CT Scan) has difficulty because it has similar results. So that research is needed that aims to determine the types of digital images scanned by using image processing methods, filtering, and feature extraction. This research used a medical image taken from the head CT-Scan of the patient. To be processed using a computer, the data is scanned to obtain digital image data. Furthermore, various filtering methods were selected, such as median, bandpass filter, XYZ colour transformer filter, enhanced local contrast filter, and histogram equalization. The most significant filtered image results are then segmented with the graph cut segmentation method and extracted using the statistical feature extraction method. The results showed that histogram equalization and enhanced local contrast filter methods were the most significant filtering methods. While the mean and standard deviation are the two most important characteristics that can distinguish the three classes of head CT Scan
Automated Flag Detection and Participant Performance Evaluation for Pwnable CTF

Abstract The demand for cyber security awareness, education, evaluation of learning levels of students etc., has increased in the past few years. In order to meet this rising demand, several cyber security learning and training platforms have been developed. Capture the flag (CTF) platforms and cyber ranges have become primary tools that facilitate education, training and recruitment of cyber security personnel. These tools evaluate and rank the participants on the basis of challenges solved by them. A discrete evaluation mechanism focusing only on flags solved, fails to ensure that the effort and knowledge demonstrated by the participants while solving the challenge, are factored into the scoring system. Most of these tools do not even distinguish between participants actually solving the flags vs. those who might copy a captured flag without actually working on the problem. Further, in flag only scoring systems, participants feel discouraged as they fail to score without finding the flags – despite putting in enormous time and effort. In this paper, we present our novel approach to quantify participant’s learning, efforts, and any unethical practices. We award partial scores by automatically capturing their behavior while solving the CTF problems. We also provide an accurate ranking system with automated solved challenge detection which replaces the need for manual flag submission. In our system, participants get hybrid scores based on their efforts, and organizations get a better and an effective evaluation tool.
Symbolic Specialization of Rewriting Logic Theories with <tt>Presto</tt>

Abstract This paper introduces $\tt{{Presto}}$ , a symbolic partial evaluator for Maude’s rewriting logic theories that can improve system analysis and verification. In $\tt{{Presto}}$ , the automated optimization of a conditional rewrite theory $\mathcal{R}$ (whose rules define the concurrent transitions of a system) is achieved by partially evaluating, with respect to the rules of $\mathcal{R}$ , an underlying, companion equational logic theory $\mathcal{E}$ that specifies the algebraic structure of the system states of $\mathcal{R}$ . This can be particularly useful for specializing an overly general equational theory $\mathcal{E}$ whose operators may obey complex combinations of associativity, commutativity, and/or identity axioms, when being plugged into a host rewrite theory $\mathcal{R}$ as happens, for instance, in protocol analysis, where sophisticated equational theories for cryptography are used. $\tt{{Presto}}$ implements different unfolding operators that are based on folding variant narrowing (the symbolic engine of Maude’s equational theories). When combined with an appropriate abstraction algorithm, they allow the specialization to be adapted to the theory termination behavior and bring significant improvement while ensuring strong correctness and termination of the specialization. We demonstrate the effectiveness of $\tt{{Presto}}$ in several examples of protocol analysis where it achieves a significant speed-up. Actually, the transformation provided by $\tt{{Presto}}$ may cut down an infinite folding variant narrowing space to a finite one, and moreover, some of the costly algebraic axioms and rule conditions may be eliminated as well. As far as we know, this is the first partial evaluator for Maude that respects the semantics of functional, logic, concurrent, and object-oriented computations.
Automated Localization and Segmentation of Left Ventricle in Cardiac MRI using Faster R-CNN

Convolution neural networks are good choice for localization and extracting ROI (Region of Interest) in MR images. But such algorithms are data hungry and need huge training dataset. Unfortunately, the publically available LV segmentation datasets are not large enough to cover all variations and dynamics of heart chambers. The accuracy of traditionally employed very deep neural networks tend to get reduce due to over-fitting on small datasets and, due to this factor, these algorithms are inefficient whenever the heart shape is outside learning dataset and there is a large bias in the segmentation. We tried to solve this problem by using a special kind of deep neural networks called faster R-CNN Inception-ResNet networks. Such networks are equipped with the power of inception as well as residual modules. The inception modules, in our architecture, help to learn information in image locally and globally whereas it also empowered with residual modules which are computationally less expensive because we can get higher accuracy in lower epochs. This fact makes our R-CNN architecture faster than traditional deep neural network architectures employed for LV localization and segmentation task. Also our R-CNN architecture can extract training features with multiple abstractions which is suitable for our problem as the publically available cardiac MRI image datasets are small, captured through different viewpoints and have inhomogeneous spatial and intensity level resolution along with inherent noise.
Research on the Computer Assisted Design of the Intelligent Information Platform Using Blockchain

The construction of the &#x201C;credit bank chain&#x201D; based on the &#x201C;blockchain&#x201D; technology has become an important starting point for the agglomeration and development of private higher education in the Guangdong-Hong Kong-Macao Greater Bay Area. The informatization construction of the credit bank is not only one of the people&#x0027;s livelihood projects of the national vocational education, but also fully reflects the characteristics and needs of the current private higher education reform and development. At present, the construction of credit banks with new technologies such as blockchain, artificial intelligence, and big data has become an irreversible trend. The construction of an information platform for private higher vocational credit banks in the Guangdong-Hong Kong-Macao Greater Bay Area supports the accumulation and transfer of credits between different private higher vocational schools by building a model for mutual recognition of learning achievements. And based on the blockchain technology to establish learners&#x0027; lifelong learning files, create a cloud-chain integrated &#x201C;credit bank chain&#x201D;, reconstruct the trust system in the field of private vocational education, and form a lifelong education system that runs through pre-employment, mid-career, and post-employment. The agglomeration and development of private higher education has opened up the key link of mutual recognition of credits.
Designing Fashion Technology Garments for Awareness

Fashion and fashion technology is a popular medium for bringing awareness to social issues through runway show presentations. However, we do not know what design decisions of fashion technology garments and runway show elements affect how messages of social issues are translated to the audience. My thesis work aims to understand how the design and presentation of fashion technology garments can create a deeper understanding of these messages in the audience. In this work, I outline the process of understanding technology for awareness and how the runway conveys activism through fashion technology. I also present two project designs to explore (I) the creation of an activism fashion technology garment for the runway and (II) ways to make runway events more interactive.