Reviewerly/exHarmony huggingface dataset:
Application of Optimized BP Network in Bogie Condition Monitoring

The research and analysis of the technology of vehicle bogie condition monitoring combined with the basic principle of BP network is applied to the condition monitoring of vehicle bogies. The genetic algorithm is used to optimize the BP network, establish the optimal weight and threshold, and establish an ideal BP network model to complete the state monitoring. The dynamic model of the bogie is established by the knowledge of the dynamics of the rail vehicle, and the irregularity of the rail is used as an external stimulus to simulate the state of the vehicle running. Test analysis using an optimized BP network. It was found that the optimized BP network is more stable and accurate. With the continuous development of railway transportation, rail transit has occupied a very important position in the public transportation system. The safety and comfort of rail vehicles is an important factor affecting their development. The suspension system is a key component of the vehicle's travel department, and the performance of the suspension system directly affects the safety and comfort of the vehicle. The online real-time fault condition monitoring of the suspension system plays an important role in the safe and stable operation of the vehicle. Therefore, it is a hot research topic for domestic and foreign scholars to seek real-time and reliable suspension system fault diagnosis methods. At present, there are many fault diagnosis methods for rail vehicle suspension systems at home and abroad. There is a vehicle suspension system fault diagnosis based on IMM algorithm, vehicle suspension system fault diagnosis based on observation method, etc. The fault can only be alerted, and the fault cannot be further determined. Artificial neural network algorithm is a discipline developed on the basis of modern neurology. BP neural network is the most widely used. It adopts parallel distributed processing, learning and memory function, and nonlinear mapping ability, which can perform multi-fault identification, complex pattern recognition, etc., achieved good results in the fault diagnosis system. The combination of genetic algorithm and BP neural network algorithm realizes the recognition of corresponding faults from nonlinear and non-stationary signals. The walking part is an important part of the rail vehicle. It plays the role of carrying, pulling, running, and braking. It is a key link to determine the safety and dynamic performance of the train. This paper establishes a simulation model of orbital dynamics and performs data acquisition and processing. Genetic algorithm is used to optimize BP neural network algorithm for fault diagnosis of key components of vehicles.
Efficient Parallel Subgraph Enumeration

In this chapter, we introduce a novel parallel subgraph enumeration framework, named PSgL, which is built on top of Pregel-like graph computing systems. The PSgL iteratively enumerates subgraph instances and solves the subgraph enumeration in a divide-and-conquer fashion. The framework completely relies on the graph traversal operation instead of the explicit join operation. To achieve the high efficiency of the framework, we propose several algorithm-specific optimization techniques for balancing the workload and reducing the size of intermediate results. In respect to the workload balance, we theoretically prove the problem of partial subgraph instance distribution is NP-hard, and carefully design heuristic strategies. To reduce the massive intermediate results, we develop three mechanisms, which are automorphism breaking of the pattern graph, initial pattern vertex selection based on a cost model, and a pruning method based on a light-weight index. We implemented the prototype of PSgL, and conducted comprehensive experiments of various graph enumeration operations on real-world large graphs. The experimental results clearly demonstrate that PSgL is robust and can achieve performance gain over the existing considerable solutions up to 90%.
Multilingual Symbolic Support for Low Levels of Literacy on the Web

Although literacy rates around the world have increased and there is an expectation that individuals who access web pages will be able to read their content, this is not always the case. The barriers that may be faced can be linked to the way the system is designed and content is written. There may be complex language or a layout that is dense, cluttered and lacks clear markers regarding the key points being made.
Trend Detection Using Time Based Sequential Recommendations

One of the most efficient and common application of data mining and machine learning are recommendation Systems. General recommender and sequential recommender are two applied modelling paradigms for recommendation tasks. General recommender focuses on modelling the general user preferences, ignoring the temporal aspects in user behaviors, whereas sequential recommender focuses on temporal patterns existing in user behavior. Sequential recommendation algorithms are typically designed to learn temporal patterns from user behaviors present in the form of interaction logs. One of the important problems which can be solved using sequential recommendations is trend detection. Trend detection is an important task of detecting trends for individual as well as community. The patterns generated from sequential recommendations are used to predict the users next action within an ongoing session which will be the trends for user itself or to detect short-term trends for community. Deep Learning techniques can be useful for sequential recommendation tasks. Recurrent Neural Networks are successful deep learning models for processing sequences. They are models which can be extended flexibly and also these models can integrate different types of data having temporal patterns. Due to these features, they are perfectly suitable for generating recommendations which are sequential. In this paper, we propose a model based on the extension of recurrent neural network that is Gated Recurrent Neural Network which is used to produce personalized next item recommendation in the sequential settings based on temporal properties present in user behavior and then used these next item recommendation to generate trends for particular user as well as community trends. We also perform experiments on real world dataset to analyze the model on the basis of several evaluation metrics.
Artificial Intelligence and the Law

There is quick technological developments is taking place in pace with that there is no comprehensive legislation to regulate this growing industry has been formulated in the country till date. There are both merits and demerits of artificial intelligence. It helps advocates to get case laws easily viewed and will in their hands the information accessible with them at the same time it also can replace the advocates. Hence it is high time that the legal issues and implications relating to civil and criminal laws, commercial and competition laws property and intellectual property laws. Thus, there is urgent need to address all the legal concerns.
The Human Integration into Sustainable 3D Printing Systems Part II: Design Experimentation

This is the second of a two-parts study concerning the integration of the human factors, and in general the integration of all human centred aspects, into fully sustainable and sustainable-oriented 3D printing systems. In particular, this second part of the study will present the design experimentations obtained after the methodological implementation with the Human Centred Design approach on a relevant case study, which is only one of the twenty experimentations carried out. Results obtained in this paper integrate the theoretical and methodological ones developed in the Part I of the study: ‘Methodological Setting and Human-System Integration Strategy’.
MC-PKS: A Collaboration Public Key Services System for Mobile Applications

Digital certificates provided by PKI are commonly used for identification and authentication services. The corresponding private key can be protected by USB keys, TEE/SE, etc., in the hardware environment. But in the software environment, the private key is relatively easy for an attacker to steal. This paper presents the design and implementation of a collaboration public key services system for mobile applications, which is named MC-PKS. The MC-PKS system provides a set of mobile digital certificate signatures and decryption schemes for PKI in the form of APP-server collaboration. It protects the private key utilizing information hiding and secret splitting on two-party signature schemes, which ensures that the split private key which has been split can still generate a digital signature if and only if with the cooperation of the server. We then analyze the security of the proposed system and show that it satisfies all known security requirements in practical applications. The performance analysis demonstrates that the MC-PKS system also achieves the resource-constrained requirements of mobile applications.
Architectural Models for Predicting the Amount of Natural Disasters and their Effects Using Batch Training

Abstract The Batch Training method is one of the methods of Artificial Neural Networks that can be used to make predictions, especially in times series data. This method is able to make predictions by learning from data that has never happened before by forming the right network architecture model. Therefore, this research will discuss the best network architecture model that is appropriate for making predictions using the Batch Training method. The data used in this study is the data of Natural Disasters in Indonesia, sourced from the National Disaster Management Agency. There are 12 variables used, namely Time of disaster, Number of disasters, Death and missing victims, injured victims, victims suffering and displaced, seriously damaged houses, lightly damaged homes, submerged houses, damage to health facilities, damage to worship facilities, and damage to facilities education. Based on this data will be formed and determined the network architecture model used, including 4-5-1, 4-10-1 and 4-15-1. From these 3 models after training and testing, the best architectural model is obtained 4-10-1 with an accuracy level of 91% with MSE Training and testing values of 0.0245532940 and 0.0579265906.
Quantum bicyclic hyperbolic codes

Bicyclic codes are a generalization of the one-dimensional (1D) cyclic codes to two dimensions (2D). Similar to the 1D case, in some cases, 2D cyclic codes can also be constructed to guarantee a specified minimum distance. Many aspects of these codes are yet unexplored. Motivated by the problem of constructing quantum codes, we study some structural properties of certain bicyclic codes. We show that a primitive narrow-sense bicyclic hyperbolic code of length $$n^2$$ contains its dual if and only if its design distance is lower than $$n-O(\sqrt{n})$$ . We extend the sufficiency condition to the non-primitive case as well. We also show that over quadratic extension fields, a primitive bicyclic hyperbolic code of length $$n^2$$ contains Hermitian dual if and only if its design distance is lower than $$n-O(\sqrt{n})$$ . Our results are analogous to some structural results known for BCH and Reed–Solomon codes. They further our understanding of bicyclic codes. We also give an application of these results by showing that we can construct two classes of quantum bicyclic codes based on our results.
Pedestrian Recognition Based On Improved Semantic Segmentation Neural Network

Abstract At present, the existing Re-ID methods can’t align the target and the target in the image that should to be detected, which has been affecting the recognition accuracy. Although using semantic segmentation method can solve this problem, it brings new problems. The region which is considered as background by semantic segmentation neural network and discarded is likely to contain feature representations that are helpful for recognition. In this paper, an improved semantic segmentation method is proposed to re-divide the foreground and background regions. In order to avoid the influence of semantic segmentation errors on recognition accuracy, a new method is used to combine background and foreground regions. Experiments show that the accuracy of Re-ID is 84.52%, which is 3.33% higher than before.
A Visualization System for Exploring Logo Trend and Design Shape Patterns

A logo is an effective way of expressing a brand’s identity and an essential element in conveying the values and image of the company. The development process of a competitive logo should be based on a design that is future-proof in a rapidly changing global market; hence, understanding the design trends for successful logo design is key. In this study, the design shape elements of logo trend models were analyzed and made into a database. Then, a trend analysis system was produced using radial visualization (RadViz) and circular parallel coordinates data visualization techniques. RadViz allows observation of clusters of logos that have similar shape elements, whereas with circular parallel coordinates plots, detailed information of the shape elements of each logo trend can be seen. Using the system, it was confirmed that shape elements—such as transformation to surface, overlapping, artificiality, concept of color and rhythm—play a major role in driving a trend. It was observed that trends change over time as various shape elements are added or removed. In addition, our study is expected to help predict the logo trend models that will come into style in the future. While similar efforts have been made in the past, our proposed system improves upon them by utilizing standard design elements as the categorizing criteria, using a unique combination of RadViz and circular parallel coordinates data visualization techniques. Using our system as a guideline, many users would be able to create logos that reflect what is trending.
New Ideas to Build Noise-Free Homomorphic Cryptosystems

We design a very simple private-key encryption scheme whose decryption function is a rational function. This scheme is not born naturally homomorphic. To get homomorphic properties, a nonlinear additive homomorphic operator is specifically developed. The security analysis is based on symmetry considerations and we prove some formal results under the factoring assumption. In particular, we prove IND-CPA security in the generic ring model. Even if our security proof is not complete, we think that it is convincing and that the technical tools considered in this paper are interesting by themselves. Moreover, the factoring assumption is just needed to ensure that solving nonlinear equations or finding non-null polynomials with many roots is difficult. Consequently, the ideas behind our construction could be re-used in rings satisfying these properties. As motivating perspectives, we then propose to develop a simple multiplicative operator. To achieve this, randomness is added in our construction giving hope to remove the factoring assumption in order to get a pure multivariate encryption scheme.
Grammar Book for Speakitaly NYC Classes

Grammar Book for Speakitaly NYC Classes Grammar Book suggested for all  Speakitaly NYC Classes (Levels A1-B1). Special price compared to other sellers online. Free Pick up at School *Due to major USPS delays, Speakitaly NYC is not responsible for any lost or damaged packages once they are shipped and the tracking number is provided.
Comprehensive Analysis on Intelligent Retail Management System using Classification Techniques

Retailers/Businessmen search for quick benefits with fewer speculations. This paper focuses on structuring an application to yield more profits for retailers by utilizing Machine Learning. By considering the properties such as the spot of retail, the season of retail, the impact of season on the product(s), and many more to produce a yield where the product(s) can gain benefits for the retailers/business people. By knowing the proper item to the right season and spot, benefits the retailers to purchase the required item through the application. By utilizing Machine Learning it helps to discover the "Pace of Recommendation (exactness)." Through that precision, finding whether the item is best for that season to sell or for that spot to sell, and the retailer can acquire benefits as indicated by the season and item. At last, the need to consolidate prescient qualities and prescriptive qualities by accepting the perceptive conditions as a contribution to the further calculations, and the application gives the rate of suggestion for the recommended item. This paper focuses on using models Rpart, Naïve Bayes, and ID3 Algorithm.
Perancangan Sistem Deteksi Vena Pada Punggung Tangan Berbasis Inframerah Untuk Sistem Biometrik

AbstractVein detection is one of the imaging techniques required for biometric authentication purposes. The vein is chosen as an object because of its strength in terms of universality and uniqueness especially in adults because the venous pattern will only change slightly with age and each person has a different pattern, besides from the object side, the veins have no influence from temperature and humidity, and physical contactless with the other user. The purpose of this research is to create design that can visualize the veins as well and determine the optimum parameters to produce a better quality of vein image than the original image. The data for this research were obtained from 10 naracoba people who were varied of Boddy Mass Index and skin pigment. From the research, dorsal hand vein visualization tool for biometric system has been successfully designed using 726 nm infrared LED and image processing by convert of image to grayscale, image sharpening, histogram equalisation until segmentation by local thresholding method and the optimum parameters obtained to obtain a good image based on contrast analysis in this study is with a distance of 6 cm exposure.Keyword : Vein Detection, Biometrik, Near Infrared, Segmentation
Super Resolution Reconstruction of Low Resolution Video Using Sparse Technique

Super resolution (SR) deals with enhancing the quality of low resolution (LR) image to obtain high resolution (HR) image. It is required to design the system for SR using sparse representation method. Sparsity-based SR techniques deal with spacing the pixels of LR image and filling them by estimating new pixels. SR based on sparsity concept, collective with structure modulated technique, upgrades its modeling capability. The technique can be beneficial to favor the pursuance of image SR based on sparsity. Patch wise each LR frame is divided and processed iteratively for better results. Firstly, LR frame of the video input is given to the system. After converting it into YCbCr color space with bi-cubic interpolation; LR patch features are extracted and corresponding HR patches will be generated. After iterative back-projection, HR image is converted back to RGB color space and given out as the HR sparse output. It is ambitious to the state-of-the-art methods like Bicubic, SRCNN, ASDS, NCSR and NA-VSR, both visually and quantitatively. The quality of the HR image is decided by PSNR and SSIM values.
Co-located Social Engineering Through Novel Technology Design

The focus of my Ph.D. research is on exploring ubiquitous and embodied technology interventions to enhance co-located social interactions. I engage in a Research-through-Design approach and leverage state-of-the-art technologies to envision and create novel designs. I then deploy and evaluate these designs to generate insights and identify key concepts and questions to consider when designing technology to engineer co-located social experience.
Application of a combination between Principal Component Analysis and Logistic Regression Based on Support Vector Machine on Educational Data Mining with Overlapping Data Problem

Abstract In 2019, the government of the Republic of Indonesia issued a zoning-based policy for New Student Admissions (PPDB) from the level of elementary school (SD) to high school (SMA), especially for public schools. The policy is documented in Permendikbud No.51 / 2018. The government policy aims to ensure the equality of education and make prospective students not focus only on favorite schools. However, this policy raises new problems. One of them is that if the potential student has got a medium UN (National Examination) score or medium distance of the house to the destination school, then his potential to be accepted at the destination school is very small. It is even worse if the potential students do not know the lowest score and the farthest distance the destination school can accept. Thus, potential students will choose schools by only guessing without basing on valid data, so their chances of being accepted will be very small. This current research focused on Educational Data Mining at PPDB Public High School (SMA) in Jombang in the academic year 2019/2020 which aims to accommodate the needs of potential students to predict the destination schools based on their own grades and home distances using classification techniques of data mining. However, another problem emerged in this study. An overlapping data occurred where one data was also owned by more than one class. For example, a potential student of SMA Negeri 2 Jombang (SMAN 2 Jombang) has got a score of 80 in Bahasa Indonesia subject, which is the same as that of a student from SMA Negeri 3 Jombang (SMAN 3 Jombang). Data overlapping does not only occur in one data but almost all of the data. The data used in this study were 600 data, consisting of 308 from PPDB 2019 of SMAN 2 Jombang, and the rest were from SMAN 3 Jombang. The attributes used were the home distance from the destination school, overall UN scores, UN scores of Mathematics, Natural Sciences, Bahasa Indonesia, and English subjects. The algorithm used was a combination of Principal Component Analysis (PCA) with Logistic Regression (LR)-based Support Vector Machine (SVM) with Anova kernel. The validation applied 10-fold cross-validation and the evaluation of algorithm performance used the aspects of accuracy, precision, and recall. The results of this current study showed an accuracy of 94.33%, a precision of 96.28%, and a recall of 92.53%. The results were better than those that did not apply PCA (70.83% accuracy, 69.62% precision, and 76.62% recall). By PCA, data could be seen from another angle that could separate or differentiate one class from the others. Even though there were 100% overlapping data, none of them, from all attributes, was 100% exactly the same.
Detecting Identity Deception in Online Context: A Practical Approach Based on Keystroke Dynamics

Keystroke dynamics has been recently proved to be an effective behavioral measure to detect subjects who provide false demographic information in online contexts. However, current techniques still suffer from some limits that restrict their practical application, such as the use of errors as a key feature to train the lie detectors and the absence of normalized features. Here, an extension of a keystroke dynamics technique, which was recently proposed to detect faked identities, is reported with the goal to overcome these limitations. Using a Quadratic Discriminant Analysis an accuracy up to 92% in the identification of faked identities has been reached, even if errors were excluded from predictors and normalized features were included. The classification model performs similarly to those previously proposed, with a slightly lower accuracy (−3%) but overcoming their important practical limitations.