Reviewerly/exHarmony huggingface dataset:
Machine-Learning-Based Image Feature Selection

This is the age of big data where aggregating information is simple and keeping it economical. Tragically, as the measure of machine intelligible data builds, the capacity to comprehend and make utilization of it doesn't keep pace with its development. In content-based image retrieval (CBIR) applications, every database needs its comparing parameter setting for feature extraction. CBIR is the application of computer vision techniques to the image retrieval problem that is the problem of searching for digital images in large databases. In any case, the vast majority of the CBIR frameworks perform ordering by an arrangement of settled and pre-particular parameters. All the major machine-learning-based search algorithms have discussed in this chapter for better understanding related with the image retrieval accuracy. The efficiency of FS using machine learning compared with some other search algorithms and observed for the improvement of the CBIR system.
A Method for Integration of Optical and Inertial Data to Determine the Parameters of Orientation and Navigation

An optical-inertial navigation system is considered, in which the orientation and navigation parameters of a moving object are determined by means of an inertial navigation system and two or more video cameras. Unlike well-known correlation-extremal systems, in which navigation is carried out on a map of the area, in the system under consideration, the movement of a moving object is determined by the results of comparing current images from cameras with previous ones.
Detection of Shotgun Surgery and Message Chain Code Smells using Machine Learning Techniques

Code smell is an inherent property of software that results in design problems which makes the software hard to extend, understand, and maintain. In the literature, several tools are used to detect code smell that are informally defined or subjective in nature due to varying results of the code smell. To resolve this, machine leaning (ML) techniques are proposed and learn to distinguish the characteristics of smelly and non-smelly code elements (classes or methods). However, the dataset constructed by the ML techniques are based on the tools and manually validated code smell samples. In this article, instead of using tools and manual validation, the authors considered detection rules for identifying the smell then applied unsupervised learning for validation to construct two smell datasets. Then, applied classification algorithms are used on the datasets to detect the code smells. The researchers found that all algorithms have achieved high performance in terms of accuracy, F-measure and area under ROC, yet the tree-based classifiers are performing better than other classifiers.
Analysis of Machine Learning Algorithms for Breast Cancer Detection

As per the latest health ministry registries of 2017-2018, breast cancer among women has ranked number one in India and number two in United States. Despite the fact that breast cancer affects men also, pervasiveness is lower in men than women. This is the reason breast cancer is such a vital concern among ladies. Roughly 80% of cancer malignancies emerge from epithelial cells inside breast tissues. In breast cancer spectrum, ductal carcinoma in situ (DCIS) and invasive ductal carcinoma (IDC) are considered malignant cancers that need treatment and care. This chapter mainly deals with breast cancer and machine learning (ML) applications. All through this chapter, different issues related to breast cancer prognosis and early detection and diagnostic techniques using various ML algorithms are addressed.
APPLICATION OF AUGMENTED REALITY AS A LEARNING MEDIA OF MOLLUSCA GROUP ANIMAL RECOGNITION AND ITS HABITAT BASED ON ANDROID

Augmented Reality is a technology that is currently developing, this technology can combine the virtual world with the real world. Augmented Reality technology has been developed in many fields. One application of Augmented Reality technology is in the field of education, as a learning medium. Currently, there are still many learning media that use books and blackboards as a means of delivering material so that students feel bored to learn because the material obtained is only in the form of text and two-dimensional images. Submission of material like this is considered less informative and interactive for students so that it has an impact on decreasing student interest in learning. This Mollusca animal application was created to provide an alternative to easy teaching for biology teachers at SMA N 1 Penawar Aji in providing Mollusca animal material to students of class X majoring in Science, by describing an object in three-dimensional form. With this, students will also understand and be more interested in studying Molluscs. And can make it easier for students to learn how the shape of Mollusca animals and their habitats. This application is made using Unity 3D software. The system development method used in making this application uses the MDLC (Multimedia Development Life Cycle) method. The way this application works is that the camera on the android device functions as an input device that will capture the marker, after the marker is detected the three-dimensional object that has been created will be displayed on the screen. The results of the development of this application can provide convenience for teachers in conveying material about Mollusca animals and students can clearly see the shape of Mollusca animals and their habitats
English Proficiency Evaluation Model of College Students by Using Intuitive Fuzzy Information Theory

English is a required university course in China and is a long-standing and consistent undergraduate-level course in China’s universities. It uses English as the medium and language of instruction to promote general education. As the most important content of undergraduate foreign language education, college English has different characteristics from other courses. It is only when its objectives, content, teaching activities, and assessment system fully reflect its unique requirements that it can truly achieve the ultimate goal of teaching this subject. With the advent of the information age, global economic integration and international exchange are deepening in various industries and fields. It is increasingly common for countries to require professionals to have foreign language skills, especially English as an international language. As a result, the demand for deeper English proficiency has become more prominent. In international communication, the English intercultural communication ability of key personnel directly affects the process of international communication, science and technology exchange, and the smooth transition of cultural conflicts in various fields and even determines the ultimate success of project cooperation. In this context, the effective use of English and cross-cultural communication skills is very important. Therefore, our higher education institutions are mainly responsible for training foreign language talents. However, the core problem facing English teaching in Chinese universities is the teaching value orientation. In particular, it seriously restricts the effectiveness of university English teaching. First, the value orientation of university English teaching deviates from the teaching objectives. It emphasizes the basic knowledge and skills of the English language, which makes teaching mainly stay on the foundation of the language. In other words, it is still limited to learning English for the sake of learning English, and the examination-oriented education is obvious, with a preference for instrumental teaching. Secondly, university English teaching focuses too much on the accumulation of English grammar rules and imitation drills, and the teaching style is dull and the teaching context is empty. This approach deviates from the characteristics of English intercultural communication, which makes the gap between the actual university English teaching and the real intercultural communication more and more obvious. Third, university English teaching lacks integration with other teaching values, especially with the humanistic orientation of university English teaching. Nowadays, a single teaching value orientation dominates university English teaching. As a result, the teaching around various English examinations has become a strange phenomenon in college English teaching.
Multithreshold Image Segmentation and Computer Simulation Based on Interactive Processing System

With the increasing application of interactive processing systems at all levels, in order to better and efficiently spread the application range of interactive processing systems, this study has researched and developed an application system for interactive online processing of DVB-SMODIS data. In addition, correct and improved algorithms are proposed for most of the problems in DVB-SMODIS data. In order to better improve the overall search ability of the BBO algorithm in multithreshold image segmentation, this study discusses a new BBO algorithm. Image segmentation is an important processing part of digital image processing systems and computer vision systems. The use of computer simulation programming technology can simulate and replace the process of objects to produce simulation maps. According to the original object calculated by the computer system, it can be reconstructed and reproduced by referring to the mathematical formula of light waves and using traditional simulation technology experiments. Therefore, this article uses interactive processing technology to study the multithreshold graph segmentation and computer simulation technology.
Refined attention Siamese network for real-time object tracking

The Siamese tracker shows great potential in achieving a balance between accuracy and speed, but the twin structure of the Siamese network makes the tracker vulnerable to background interference in the tracking scene. To deal with this problem, a tracking algorithm based on the attention mechanism is proposed. The algorithm introduces the channel attention module based on the Siamese network and dynamically enhances the robust channel feature response by modeling the context relationship between channels. This paper also verified the network performance on the OTB and VOT benchmark. Experimental results show that the proposed algorithm can achieve robust tracking results on challenging datasets, and achieves the goal of improving network performance with a slight increase in computational cost.
Reducing circuit complexity in optical quantum computation using 3D architectures

Integrated photonic architectures based on optical waveguides are one of the leading candidates for the future realisation of large-scale quantum computation. One of the central challenges in realising this goal is simultaneously minimising loss whilst maximising interferometric visibility within waveguide circuits. One approach is to reduce circuit complexity and depth. A major constraint in most planar waveguide systems is that beamsplitter transformations between distant optical modes require numerous intermediate SWAP operations to couple them into nearest neighbour proximity, each of which introduces loss and scattering. Here, we propose a 3D architecture which can significantly mitigate this problem by geometrically bypassing trivial intermediate operations. We demonstrate the viability of this concept by considering a worst-case 2D scenario, where we interfere the two most distant optical modes in a planar structure. Using femtosecond laser direct-writing technology we experimentally construct a 2D architecture to implement Hong-Ou-Mandel interference between its most distant modes, and a 3D one with corresponding physical dimensions, demonstrating significant improvement in both fidelity and efficiency in the latter case. In addition to improving fidelity and efficiency of individual non-adjacent beamsplitter operations, this approach provides an avenue for reducing the optical depth of circuits comprising complex arrays of beamsplitter operations.
Advanced deep learning enhancement algorithm based on retinex model guidance

Traditional Retinex model-based image enhancement methods require careful design of constraints and parameters to handle this highly ill-conditioned decomposition. With the advancement of deep learning algorithms, low-light image enhancement has also achieved deep processing. However, image enhancement based on the RGB color space model is prone to color distortions when enhancing images under the influence of the correlation of the three primary colors of RGB. In this report we apply the HSV color space technique to a Retinex-based network model. Simulations and experiments show that using HSV space-improved deep neural networks can effectively avoid the color distortion problem.
Machine Learning Applications for Anomaly Detection

The aim of this chapter is to describe and analyze the application of machine learning for anomaly detection. The study regarding the anomaly detection is a very important thing. The various phenomena often occur related to the anomaly study, such as the occurrence of an extreme climate change, the intrusion detection for the network security, the fraud detection for e-banking, the diagnosis for engines fault, the spacecraft anomaly detection, the vessel track, and the airline safety. This chapter is an attempt to provide a structured and a broad overview of extensive research on anomaly detection techniques spanning multiple research areas and application domains. Quantitative analysis meta-approach is used to see the development of the research concerned with those matters. The learning is done on the method side, the techniques utilized, the application development, the technology utilized, and the research trend, which is developed.
A text summary-based method to detect new events from streams of online news articles

• We develop a summary-based event detection (SED) method for online new event detection. • We construct an effective text summarization model to produce news summaries. • We empirically evaluate SED, using three full-text-based methods as the benchmarks. • The evaluation results show that SED outperforms its benchmark techniques. New event detection (NED), which is crucial to firms’ environmental surveillance, requires timely access to and effective analysis of live streams of news articles from various online sources. These news articles, available in unprecedent frequency and quantity, are difficult to sift through manually. Most of existing techniques for NED are full-text-based; typically, they perform full-text analysis to measure the similarity between a new article and previous articles. This full-text-based approach is potentially ineffective, because a news article often contains sentences that are less relevant to define the focal event being reported and the inclusion of these less relevant sentences into the similarity estimation can impair the effectiveness of NED. To address the limitation of the full-text-based approach and support NED more effectively and efficiently, this study proposes and develops a summary-based event detection method that first selects relevant sentences of each article as a summary, then uses the resulting summaries to detect new events. We empirically evaluate our proposed method in comparison with some prevalent full-text-based techniques, including a vector space model and two deep-learning-based models. Our evaluation results confirm that the proposed method provides greater utilities for detecting new events from online news articles. This study demonstrates the value and feasibility of the text summarization approach for generating news article summaries for detecting new events from live streams of online news articles, proposes a new method more effective and efficient than the benchmark techniques, and contributes to NED research in several important ways.
Ambiguous file system partitions

We investigate the problem of creating ambiguous file system partitions, i.e., the possibility to have two fully functional file systems within a single file system partition. The problem is different from steganographic data hiding since there is no real distinction between content and cover data, and no translation process may be applied to the content data. Since typical file systems that occur in forensic analysis are usually unambiguous, ambiguous file system partitions may be useful corner cases in forensic tools and processes. We show that it is possible to create ambiguous file system partitions by integrating a guest file system into the structures of a host file system in two cases: We integrate a fully functional FAT32 into Ext3 and HFS+. In a third example we even integrate two guest file systems (HFS+ and FAT32) into a single Btrfs file system partition. We test common forensic tools on these examples and exhibit some deficiencies. Moreover, we develop a taxonomy of ambiguous file system partitions and argue that the existence of essential data at fixed positions still is a way to distinguish host from guest and so to heuristically reduce the ambiguity, without removing it completely.
Instant inpainting using multiscale prior conditioned propagation optimization

Patch-based inpainting is widely used in interactive photo editing scenarios. It iteratively fills the target region by searching the candidate from the source region. However, the high computational cost is a long-lasting concern that prevents users from seeing instant results, bringing limitation to its further application. In this paper, we present a novel instant inpainting technique based on a multi-scale framework, pushing the speed to an unprecedented interactive level (around seconds). Our key insights are that filling process becomes very efficient at low scale. Also, scale changes do not significantly affect the match correspondence, allowing the source-target match correspondence to be quickly collected at low scale, and to be delivered as a priori to higher scales. At high scale, thanks to the built-in coherence in natural images, we propose a mechanism named propagation optimization to further fine-tune the match correspondence based on prior, eliminating the side effects caused by scale recovery. Experiments demonstrate that our method is 10-300 times faster while keeping the same image quality as previous works did. We believe our work contributes a new strategy to real-time application of image inpainting techniques.
Nuclei Segmentation in Breast Histopathology Images using FCM

Recently, computerized methods have been rapidly evolving in the area of digital pathology, with growing applications related to nuclei detection. Cell nuclei segmentation is a fundamental yet crucial step in pleomorphism detection based on the Nottingham Histopathology Grading (NHG) system. The information of the segmented nuclei such as size and morphology properties can be used to determine the scoring decision of the breast histopathology images in terms of pleomorphism. The main purpose of this project is to segment the cell nuclei using Hematoxylin and Eosin (H&E) stained breast histopathology images. In this paper, noise removal associated with Fuzzy C-Mean (FCM) clustering algorithm is introduced which can extract information about each object and then revised and eliminate those irrelevant regions. The RGB input images would be first pre-processed to normalize the color of the input images. The color normalization stage is essential to facilitate the segmentation stage in the later step. For the segmentation process, FCM clustering techniques are applied to better allocate similar pixels into the same clustering while having significant differences between each cluster. Next, the noise region reduction method is performed to discard those pixels which are not related to the properties of cell nuclei. The proposed method is measured by Performance Matrix and the experimental result shows that it demonstrates more desirable performance than the convention FCM clustering method which has average accuracy of 77.30% (±3.332).
Network Flow Anomaly Detection Based on Improved Echo State Network

Aiming at the problems existing in the current network flow anomaly detection, a network flow prediction model based on echo state network of double loop reserve pool is designed, which solves the problem of reserve pool generated randomly in traditional echo state network and improves the accuracy and instantaneity of network flow prediction. Then, an anomaly detection method based on dynamic threshold is proposed, which takes the difference between the predicted value and the real value as the basis for judging the occurrence of anomalies. Simulation results show that the improved prediction model and anomaly detection method can effectively detect the abnormal behavior of network flow, and the detection effect is better than other models.
A Non-Mitosis Reduction Method using Semantic Descriptors for Breast Cancer Mitosis Detection Application

Based on the Nottingham Histopathology Grading system, mitosis count is one of the important criteria that contribute to the overall grade of breast cancer. Over the years, many automated mitosis detection methods have been proposed. Nonetheless, the ever-increasing demand for quality detection continues by seeking optimization in each stage in the detection pipeline. This paper aims to focus on the optimization of the non-mitosis cells reduction stage by proposing three semantic descriptors: solidity, eccentricity, and area to eliminate the non-mitosis cells in breast histopathology images. The proposed method consists of three stages: (1) color normalization, (2) nucleus segmentation, and (3) non-mitosis reduction and its performance was evaluated using 40 histopathology images. The proposed three semantic descriptors were found to be useful and effective in reducing non-mitosis cells, achieving 96.18% (with standard deviation tabulated at ±1.6374%) across the dataset.
Genetic Programming as Supervised Machine Learning Algorithm

This chapter presents the theory and procedures behind supervised machine learning and how genetic programming can be applied to be an effective machine learning algorithm. Due to simple and powerful concept of computer programs, genetic programming can solve many supervised machine learning problems, especially regression and classifications. The chapter starts with theory of supervised machine learning by describing the three main groups of modelling: regression, binary, and multiclass classification. Through those kinds of modelling, the most important performance parameters and skill scores are introduced. The chapter also describes procedures of the model evaluation and construction of confusion matrix for binary and multiclass classification. The second part describes in detail how to use genetic programming in order to build high performance GP models for regression and classifications. It also describes the procedure of generating computer programs for binary and multiclass calcification problems by introducing the concept of predefined root node.