Reviewerly/exHarmony huggingface dataset:
Multivariate discriminant analysis for branching classification of colonic tubular adenoma glands

Abstract Background Many morphologic findings of histology can be translated into mathematically computerized data, and identifying important parameters is primarily pathologists' task as users. Shape‐specific parameters based on computational geometry properties of glands can be used in the field of pathology. We evaluated the diagnostic utility of three shape‐specific parameters: the chord intersection ratio, convexity ratio, and maximum concave area ratio for branching classification of glands. Methods Seven cases of tubular adenoma were studied. After image analysis, segmented neoplastic glands were classified into nonbranching, mild branching, and moderate branching. Using image analysis formulae for the three shape‐specific parameters, we compared the values of the parameters with the branching classification results for colonic tubular adenoma. Results Multivariate discriminant analysis was used to classify the branching groups. Classification accuracies of nonbranching, mild branching, and moderate branching group based on the three shape‐specific parameters were 98, 94, and 95%, respectively. More branching growth exhibited a higher chord intersection ratio and maximum concave area ratio but lower convexity ratio. We found a statistically significant difference in chord intersection ratio, maximum concave area ratio, and convexity ratio between mild, moderate, and nonbranching groups. Among the three features, the chord intersection ratio was the most significant parameter. Conclusions Shape‐based parameters of chord intersection ratio, convexity ratio, and maximum concave area ratio are valid assessment parameters for irregular branching structures. For the understanding of spatial relationships of histology, the holistic geometric approach using shape‐based parameters can be useful.
Text Classification Using Scope Based Convolutional Neural Network

Text classification is a classic and important task in Natural Language Processing (NLP). Convolutional Neural Network (CNN), as an important model for deep learning, has shown its advantages in the field. For a CNN model, the convolution operation is the key to success. Based on convolution operation, it can effectively capture the local conjunctions of features from high dimensional features. Most of existing approaches utilize a sliding window to conduct convolution operation. Different from existing approaches, we present a scope based convolutional neural network (SCNN) in this paper. Instead of using sliding windows, we propose a concept of scope to capture the local information. Different from a window, a scope only imposes constraint on distance between words. It is more flexible and can effectively handle complicated local feature. We utilize max pooling to keep the most valuable information. In this way, it can capture deeper local information which is hidden from windows based CNN. We conduct extensive experimental study to demonstrate the competitive performance of our model against state-of-the-art approach on real datasets. The result shows the effectiveness of our proposed method.
Finger Recognition Using a Wearable Device while Typing

In the information society, the ability to use a computer is important. To use a computer, users commonly need a keyboard as an input device. If users place their fingers on the home keys and stroke each key using the correct finger, they will lead to improve their typing skills. In this study, we develop a stroked finger recognition method for keyboard typing using Myo, a wearable device that can simply measure the surface electromyography (EMG) signal of the user's arm. Our method detects the user's stroked finger through machine learning that uses the measured EMG. We introduced window functions during feature extraction in order to suppress the influence of the keystroke speed. Our method was capable of recognizing six categories (five fingers and a neutral state) with an accuracy of about 80% when our method was evaluated by a 10-fold cross validation for each subject's data.
Research on Caravan Seaborne Loading Scheme Based on Greedy Algorithm

Seaborne loading is a commercial action that loads people and materials that need to be transported into maritime transport. The merits of this action directly affect the cost of goods. The indicators that judge the merits of a transportation program mainly consider the tuotal loading time and the number of vehicles used. In order to solve the practical problems of the caravan transportation personnel and transportation vessels, this paper analyzes the transportation requirements and loading schemes of each caravan unit. The experimental results fully demonstrate the correctness of the model and the effectiveness of the loading algorithm. The follow-up study will focus on establishing a maritime loading model considering the simultaneous loading of all units, designing the berth configuration algorithm and the material loading algorithm. Under the premise of the shortest loading time, calculate the minimum material loading plan and material distribution for maritime transport scheme.
The Formal Representation of Cyberthreats for Automated Reasoning

Considering the complexity and dynamic nature of cyberthreats, the automation of data-driven analytics in cyberthreat intelligence is highly desired. However, the terminology of cyberthreat intelligence varies between methods, techniques, and applications, and the corresponding expert knowledge is not codified, making threat data inefficient, and sometimes infeasible, to process by semantic software agents. Therefore, various data models, methods, and knowledge organization systems have been proposed over the years, which facilitate knowledge discovery, data aggregation, intrusion detection, incident response, and comprehensive and automated data analysis. This chapter reviews the most influential and widely deployed cyberthreat classification models, machine-readable taxonomies, and machine-interpretable ontologies that are well-utilized in cyberthreat intelligence applications.
Blind Video Quality Assessment Based on Human Visual Characteristic

Based human visual speed perception characteristic, a video quanlity assessment(VQA) algorithm is introduced in the paper. Natural video statistics features extraction and weighting factors are incorporated in the scheme. In the VQA, considering the impact both video content itself and HVS's characteristic on human subjective perception, we propose weighting factors to scale the effect of those features, and it contains two parts: motion information and perception noise. And natural video statistics features relate to the spatial and temporal domain are extracted. The weighting factors would be used to combine both the temporal and spatial features, then generate the quality of each frame. Finally, the video quality score can be obtained by pooling scheme. LIVE database, EPFL-PoliMI database and some other generated test videos were used in our experiments, and the results indicate our model has outstanding performance.
Personalized Recommendation using Similarity Powered Pairwise Amplifier Network

Online advertising, one typical application of recommendation system, calls for effective and accurate recommendations of keywords. Extreme sparse and large scale data makes online advertising a challenging problem. To achieve better performance and accuracy of the recommendation, a better model with a short turnaround time is needed. In this paper, we address the problem of personalized online advertising for extreme sparse and large scale data. We develop a novel machine learning model (Similarity Powered Pairwise Amplifier Network, SPPAN for short). The complexity of this model (a.k.a. the number of parameters) grows with the amount of observed data, which makes it suitable to extremely sparse data. The training algorithm based on gradient descent makes it easy to parallelize. The similarity model combines the user neighborhood and item neighborhood ideas in collaborative filtering smartly, obtaining a cost-effective way to handle large scale data. The proposed framework is evaluated on a large set of real-world data set from a large internet company (expressed by "Company A"). The experiment results demonstrate that the proposed SPPAN model can greatly improve the prediction and recommendation accuracy on that extreme sparse data set compared with existing approaches.
Neural Networks with Poincare Chaos

To make research of chaos more amenable to investigating the neural networks through differential equations, we apply the results initiated in [1,2], where the Poincare chaos is introduced. The presence of chaos is approved by existence of unpredictable solutions. The present research considers the existence and uniqueness of asymptotically stable unpredictable solution for a shunting inhibitory cellular neural network (SICNN). Appropriate examples with simulations that support the theoretical results are provided.
DnM3Net: Multi-Scale &amp; Multi-Level Shuffle-CNN Via Multi-Level Attention for Image Denoising

Recently, based on novel convolutional neural net-work architectures proposed, tremendous advances have been achieved in image denoising task. An effective and efficient multi-level network architecture for image denoising refers to restore the latent clean image from a coarser scale to finer scales and pass features through multiple levels of the model. Unfortunately, the bottleneck of applying multi-level network architecture lies in the multi-scale information from input images is not effectively captured and the fine-to-coarse feature fusion strategy to be ignored in image denoising task. To solve these problems, we propose a multi-scale & multi-level shuffle-CNN Via multi-level attention (DnM3Net), which plugs the multi-scale feature extraction, fine-to-coarse feature fusion strategy and multi-level attention module into the new network architecture in image denoising task. The advantage of this approach are two-fold: (1) It solve the multi-scale information extraction issue of multi-level network architecture, making it more effective and efficient for the image denoising task. (2) It is impressive performance because the better trade-off between denoising and detail preservation. The proposed novel network architecture is validated by applying on synthetic gaussian noise gray and RGB images. Experimental results show that the DnM3Net effectively improve the quantitative metrics and visual quality compared to the state-of-the-art denoising methods.
Dependency bugs

For years, the software reuse community (including the variability community) has used cost reduction as the main argument for software reuse. Software reuse methods are often contrasted with cloning[9]. We commonly hear that already when a piece of code, a subsystem or a product, would be copied three times, it pays off to manage a single generalized reusable artifact [8]. Evolving software from cloned to integrated variants is seen as a maturation [2].
Digital Media Copyright Protection Technology in the Age of All Media

With the development of technology in the information age, digital products are increasingly integrated into people’s lives. People enter a full-media era. However, digital media files are characterized by easy copying and rapid dissemination, resulting in a large number of Internet. Piracy and infringement issues, and even some documents involving confidential areas, have been stolen by lawless elements, seriously infringing on the intellectual property and economic interests of the file owners. This article briefly introduces what is digital media in the era of all-media, analyzes what is the key technology of digital copyright protection, and proposes a series of digital media copyright policy-keeping system architecture to protect the best interests of copyright owners.
International Journal Quartile Classification Using the K-Nearest Neighbor Method

Journal is one of the media used for the publication of scientific work in the form of the latest research supported by strong, relevant and comprehensive evidence to prove the validity of the research. Research results published in a journal are often used as references in other studies as a development effort from previous research. In referring to scientific work, the public can utilize journal ranking sites to find the best quality journals. SCImago Journal Rank (SJR) is one of the reputable journal ranking sites that are integrated with the Scopus database. However, there is an inequality between the value of the SJR indicator and its quartile label in several journals. One solution that can be proposed to correct inequality that occurs in journal ranking data is the classification method. This study uses the K-Nearest Neighbor (K-NN) algorithm as a classification method and K-fold Cross-Validation as a validation method. The classification process is carried out in nine scenarios using 2-fold to 10-fold cross-validation. Each scenario gets 25 classification results with 1 to 25 nearest neighbors. The goal is to get the best classification performance based on the nearest neighbor parameters and the number of folds used. The best classification performance is obtained in the fifth scenario using 6-fold cross-validation and 16 nearest neighbors. Even so, the best average performance achieved from all scenarios based on accuracy scores only reached 63%. This raises the assumption that the K-NN method is considered unable to produce an optimal performance that approaches the SJR classification system.
Encountered, Habituated, Estranged and Overridden by Machines

Where does human agency remain in the era of automation and intelligent machineries? Technological affordances create instant ways of accomplishing challenging tasks, where a lot of individual agency is mitigated. In this installation, we present an autonomous musical instrument that invites volunteers to eventually take a subordinate role to machines. Over time, the instrument gradually raises its level of autonomy, eventually overriding through musical patterns impractical to play by users. Juxtaposing machine uprising and an artifact that manifests human creativity, we question the relationship between technology and human on the continuum of symbiosis and adversary, through orders of algorithmic complexity and intensity in the act of music.
Feature Extraction and Analysis of Zircon Cathodoluminescence Images Based on Multiscale Segmentation

The digital image processing method is introduced into the zircon Cathodoluminescence (CL) images, and method extracting the zircon CL image particle feature information on multi-scale segmentation is proposed. We choose the magmatic and metamorphic zircon CL images as basic samples image segmentation and counting the morphological parameters, including their sizes, perimeters, axis lengths, ratios and shape indexes. Among these parameters: the magmatic zircon aspect ratio is mostly concentrated in 1.3-2.5, the average ratio is 1.83, the metamorphic zircon aspect ratio is concentrated in 1.0-1.5, the average ratio is 1.28; The zircon has a ring density average ratio of 0.097, the zircon has a ring density average ratio of 0.045. These diagnostic parameters have allowed us to discriminate the magmatic and metamorphic zircons, furtherly to distinguish zircons from basic rocks and those from acid rocks respectively. This method of morphological feature extraction quantitative analysis contributes to the geologic understanding of zircon CL images.
Augmented Reality in the Context of Automotive Manufacturing

The 4th industrial revolution based on the digitization of industrial processes, on the connection of the equipment between them, and on new interfaces with the humans, will require new work contexts and new organizations (Cyber-Physical System and Internet of Things). Virtual Reality (VR) and Augmented Reality (AR) will contribute in a large scale for the automotive industry transition to the Industry 4.0 paradigm. This chapter provides an overview of these versatile technologies in the context of industrial production, where these technologies will allow the real world of the shop floor to merge with a digital world of simulations, predictions, and automation.
Comparative Study on Obstacle Detection and Avoidance System by Using Real-Time Image Processing and Artificial Intelligence in Autonomous Wheelchair Application

The research objective of this project conducts a comparative performance study between real time image processing and Artificial Intelligence object recognition in the application of obstacle avoidance system for an autonomous wheelchair. The proposed obstacle detection system is achieved through the application of camera sensor with the implementation of Artificial Intelligence techniques in image processing. A pre-trained Convolutional Neural Network model known as MobileNet SSD, and Deep Neural Network (DNN) module in OpenCV library (for live video streams) are utilized in developing the object recognition algorithm. An Arduino and a pair of DC brushed motors together with Smart Drive 40 motor drivers made up the motion control system that acts correspondingly to the inputs obtained from the obstacle detection system(s). Comparative researches on the performance of both obstacle detection algorithms were carried out experimentally. The considered performance is the success rate in obstacle avoidance and this performance was analyzed based on varying light intensity, motor speed, and wall detection. The results obtained indicate that Artificial Intelligence based obstacle detection algorithm performed better than real time image processing with vary light intensity environment. However, it has a relatively low performance with varying motor speed and wall detection when compared to the Image Processing based obstacle detection system.
Immersion Domes

Large-scale immersion domes are specialized embodiments of spatial augmented reality allowing large groups to be immersed in real-time animated or cinematic virtual worlds with strong sense-of-presence. Also called fulldome theaters, these spaces currently serve as giant screen cinemas, planetariums, themed entertainment attractions, and immersive classrooms. This chapter presents case studies for emerging applications of digital domes, reviews dome theater design basics, and suggests that these venues are on track to become mainstream arts and entertainment centers delivering global impact at scale. Standard venue designs will be necessary to realize the full potential of an immersive media arts and entertainment distribution network. This chapter provides rationale for standardization of immersion domes for multi-use events spaces, immersive cinemas, and live performing arts theaters.
Set of Experience and Decisional DNA: Experience-Based Knowledge Structures

This chapter presents a description of Set of Experience Knowledge Structure (SOEKS) and Decisional DNA (DDNA), argumentation for a knowledge representation, composition, configuration and metrics. SOEKS is a combination of filtered and amalgamated information obtained from formal decision events. It facilitates effective explicit representation of decisional experience taken from different technologies. SOEKS comprises variables, functions, constraints and rules associated in a DNA shape, allowing the construction of enterprises’ fingerprints called Decisional DNA. SOEKS possesses characteristics that potentialize it as a more precise knowledge representation in a world guided by sensitive dependence and uncertainty, that is, SOEKS is a suitable representation for decisional explicit knowledge that has been gifted with capabilities to manage uncertainty, preciseness and incompleteness. Furthermore, SOEKS extends into the so-called DDNA due to the characterization and aggrupation of SOEKS into different classes termed decisional chromosomes. Such decisional chromosomes simulate specialized genes that when placed together create the decisional experience of an enterprise, the Decisional DNA.
Multi-Layered Encryption Method

Information is categorized as precious thing when only current specific people have the opportunity to gain it, or it is so called confidential. Confidential information is secure if it contains Cryptograph method which contains process of encryption and decryption since their function is to present data and to process it into confidential information. The aim is to keep the information confidential for current people, and they are the ones who can understand and reveal the information. The multi-layered encryption process itself makes the level of difficulties in analysing the information hard to predict. According to that, this process is best applied on securing information which contains alphabetic on plaintext. The quality of multilayered encryption is evaluated with avalanche effect parameter which results value 34% in which a bit variation on a plaintext keeps the consistent of an encryption. Aftermath comparison to the method layer of multi-layered encryption avalanche effect it produce values lower at 33% obtained by change the structure decryption is being encryption layer.
INNOVATIVE METHODS FOR TEACHING AND LEARNING IN MACHINE ELEMENTS IN DUAL TRAINING

In modern school the problem of interest in learning is up to date, since interest can boost the private creative and cognitive activity of students. The teacher who created the enduring interest to a subject, achieved high results in training and provoking the desire of students to the selection, preparation and effective practice of the profession. The objective of this study was to showcase some innovative methods to achieve a higher success rate in the educational process. The study will help to understand what is the pedagogical effect of the introduction of the technology project-based learning in the process of mastering the knowledge and skills of the students in the context of the dual training. To outlining the readiness of students to work in an hour, their expectations and their achievements at the end of the school year. Analyse how by building skills and habits for private work in the learning process on machine elements developed mindset for a private creative cognitive activity. The enrichment of the academic practice of this method provides a means to raise interest in the learning process, makes it possible, through private participation in lessons on machine elements, students take control of their education and to gradually switch to the actual workplace. Dual training system provides a real connection of class theory with practice. Create opportunities students directly participate in their education and to gradually switch to professional development.
Convolutional neural network based on multi-channel feature fusion and dynamic sample weights

In order to solve the problem of insufficient feature extraction and slow convergence in face recognition, a convolutional neural network face recognition algorithm based on multi-channel feature fusion and dynamic sample weights is designed. Firstly, the theoretical analysis of the shortcomings of traditional CNN in face recognition is carried out. Then multi-channel feature fusion and dynamic sample weight theory are designed. The network structure of multi-channel feature fusion and dynamic sample weight CNN is determined through multiple experiments. In addition, the above algorithm is used to identify the partial occlusion and rotation test set. In the end, the method designed in this paper is compared with other methods for face recognition rate and convergence speed. It is finally proved that compared with other face recognition methods, the method designed in this paper not only has higher recognition rate and faster. Convergence speed, and strong robustness in image occlusion and rotation.