Reviewerly/exHarmony huggingface dataset:
Utility Analysis of Horizontally Merged Multi-Party Synthetic Data with Differential Privacy

A large amount of data is often needed to train machine learning algorithms with confidence. One way to achieve the necessary data volume is to share and combine data from multiple parties. On the other hand, how to protect sensitive personal information during data sharing is always a challenge. We focus on data sharing when parties have overlapping attributes but non-overlapping individuals. One approach to achieve privacy protection is through sharing differentially private synthetic data. Each party generates synthetic data at its own preferred privacy budget, which are then released and horizontally merged across the parties. The total privacy cost for this approach is capped at the maximum individual budget employed by a party. We derive the mean squared error bounds for the parameter estimation in common regression analysis based on the merged sanitized data across parties. We identify through theoretical analysis the conditions under which the utility of sharing and merging sanitized data outweighs the perturbation introduced for satisfying differential privacy and surpasses that based on individual party data. The experiments suggest that sanitized HOMM data obtained at a practically reasonable small privacy cost can lead to smaller prediction and estimation errors than individual parties, demonstrating benefits of data sharing while protecting privacy.
Designing D Flip-Flop using a 3×3 reversible gate

Due to the increasing need for miniaturization and power consumption reduction, reversible gates were provided in which, the number of inputs and outputs are equal and the power consumption is zero. In this paper, D flip-flop has been designed using 3×3 retrievable gateways and all of the proposed circuits have been evaluated in terms of cost metrics. In this paper, the reversible gates were finally combined to reduce the delay (the number of gates) in the D flip flop circuit. Here, the Function was used in Simulink and Matlab software, and reversible gates were designed and provided with a D flip-flop.
Efficient Revocation System for Secure Data Deduplication in Cloud Storage

Cloud storage is a service that is increasing the geographic availability of the data to the client with the help of providing unflawed service. A high amount of data redundancy is possible and space is also requiring more in case of availability of data increasing in cloud storage. Data Deduplication is the Technique that can be used to trim back the redundancy and save the storage space in cloud storage. It also assures the availability of data without any loss and proves the data consistency in any situation. The cloud service and storage are more in demand because of increasing the investment, so the data deduplication brings down both amounts of investment and storage space. In this article, various reliable techniques are provided to secure the Deduplication of data and ensure the consistency of data in cloud storage.
Comprehensive Introduction to Neural Networks

Unknowingly, artificial intelligence (AI) has become an inevitable part of our lives. In this chapter, the authors discuss how the neural networks, a sub-part of AI, changed the way we analyse things. In this chapter, the advent of neural networks, inspiration from the human brain, simplification models of biological neuron models are discussed. Later, a detailed overview of various neural network models, their strengths, limitations, applications, and challenges are presented in detail.
LJSing: Large-Scale Singing Voice Corpus of Single Japanese Singer

This paper describes the construction of the LJSing, a large-scale Japanese singing voice corpus by a single female singer for singing voice synthesis based on statistical methods. Singing voice synthesis systems based on machine learning have been widely studied. However, most Japanese singing voice corpora are not enough for the training of recent deep-learningbased synthesis. Furthermore, those corpora were designed without phonetic and prosodic balance. Therefore, we recorded and labeled a five-hour phonetically and prosodically balanced singing corpus sung by a Japanese singer. The corpus consists of two data sets, SongSet and PhraseSet, which are constructed based on songs and phrases, respectively.
Photonic Random Bit Generation Based on Chaotic Laser Diodes for Wireless Distribution

Random bits generated from physical entropy in the timing of microwave bursts are investigated using a laser diode. The bursts are generated by chaotic dynamics for yielding bits at gigabits per second for wireless distribution.
Effects of Science Enlightenment Workshops Using Electronic Musical Instrument “Photomin” for Education

We have developed the electronic musical instruments “Prototype Photomin”, “Breadboard type Photomin”, and “Microcomputer-type Photomin Mbed version” for educational materials. Using these, we conducted workshops for scientific enlightenment activities. For each workshop, the type and version of the Photomin was selected according to the aims and characteristics of the participants, and the workshop was designed to include the fabrication work at an appropriate level of difficulty. As a result, we were able to effectively convey the joy of manufacturing to children, university students, and the general public and deepen their interest. Those cases show that “Photomin” should be effective as an educational tool for science education for a wide range of people.
Meta-path Embedding based Recommendation over Heterogeneous Information Network

Heterogeneous information network (HIN) based models aim to make recommendations as much as possible using meta-path information. It would be more advisable if they consider the scenario information provided by meta-path and explore the relatedness of meta-paths. To address these issues, we propose an End-to-end model based on Meta-Embedding for the Recommendation over HIN, called EMER. Here, meta-embedding refers to the explicit representation of meta-path under the user and item pair. Learning the meta-embedding can capture the scenario information provided by meta-path, and can directly explore the relatedness between meta-paths. Furthermore, we leverage the meta-embedding to design three loss functions of structure loss, relation loss and rating loss to capture three aspect information. We use structure loss to calculate the similarity between nodes based on meta-embedding to approximate the similarity calculated based on meta-path for preserving the structural characteristics. The relation loss models meta-paths by interpreting them as translations operating on the embeddings of users and items. We use rating loss to characterize the difference between the predicted ratings and the truth. Experiments over three real datasets indicate that our model achieves state-of-the-art performance.
A Copyright Protection Scheme for Grayscale Images Using Wavelet Transform and Arnold Transform

Due to the gigantic development of Internet and computer networks, safeguarding the prolific content and intellectual property in digital form is intractable. It could be conceivable to have a same image on laptop, desktop, mobile, etc. While plenty of money and time is invested in protecting the intellectual property but none of the techniques turned up as an ideal solution for the problem. Digital watermarking is one possible solution for the problem which has its root from steganography, an art of concealing a message or file inside another message or file. A digital watermark hides embedded information from illicit viewer. Digital watermarks aid in detecting copyright infringement and can be used as a legitimate proof for resolving the disputes. The proposed scheme uses Arnold Transform and Discrete Wavelet Transform (DWT) to increase the robustness of extracted watermark from various kinds of attacks.
Sentence Pair Similarity Modeling Based on Weighted Interaction of Multi-semantic Embedding Matrix

In this paper, we focus on measuring the similarity of sentence pair. Noting that a single sentence vector may lose fine-grained semantic information which is important for sentence matching, we propose an embedding matrix to calculate a multi-granularity similarity matrix and find the true semantic alignment of two sentences. We also propose a semantic importance calculation and semantic decomposition that are simple but effective. The proposed model does not require any sparse features or external resources such as WordNet. Compared with other state-of-the-art models, we successfully train in a short time and achieve competitive results on similarity measurement and paraphrase identification tasks. Experiments and visual analysis show the good performance and interpretability of the model.
StarGAN-ZSVC: Towards Zero-Shot Voice Conversion in Low-Resource Contexts

Voice conversion is the task of converting a spoken utterance from a source speaker so that it appears to be said by a different target speaker while retaining the linguistic content of the utterance. Recent advances have led to major improvements in the quality of voice conversion systems. However, to be useful in a wider range of contexts, voice conversion systems would need to be (i) trainable without access to parallel data, (ii) work in a zero-shot setting where both the source and target speakers are unseen during training, and (iii) run in real time or faster. Recent techniques fulfill one or two of these requirements, but not all three. This paper extends recent voice conversion models based on generative adversarial networks (GANs), to satisfy all three of these conditions. We specifically extend the recent StarGAN-VC model by conditioning it on a speaker embedding (from a potentially unseen speaker). This allows the model to be used in a zero-shot setting, and we therefore call it StarGAN-ZSVC. We compare StarGAN-ZSVC against other voice conversion techniques in a low-resource setting using a small 9-min training set. Compared to AutoVC—another recent neural zero-shot approach—we observe that StarGAN-ZSVC gives small improvements in the zero-shot setting, showing that real-time zero-shot voice conversion is possible even for a model trained on very little data. Further work is required to see whether scaling up StarGAN-ZSVC will also improve zero-shot voice conversion quality in high-resource contexts.
How to Prototype a Client-Side Route Planner for Helsinki with Routable Tiles and Linked Connections

Route planning is key in application domains such as delivery services, tourism advice and ride sharing. Today’s route planning as a service solutions do not cover all requirements of each use case, forcing application developers to build their own self-hosted route planners. This quickly becomes expensive to develop and maintain, especially when it requires integrating data from different sources. We demo a configurable route planner that takes advantage of strategically designed data publishing approaches and performs data integration and query execution on the client. For this demonstrator, we (i) publish a Linked Connections interface for the public transit data in Helsinki, including live updates; (ii) integrate Routable Tiles, a tiled Linked Data version of OpenStreetMap road network and (iii) implement a graphical user interface, on top of the Planner.js SDK we have built, to display the query results. By moving the data integration to the client, we provide higher flexibility for application developers to customize their solutions according to their needs. While the querying might be slow today, these preliminary results already hint at different data publishing strategies that may increase query evaluation performance on the client-side.
상호문화 감수성 신장을 위한 문학교육 방안 연구 -대만 대학교의 부전공 과정생을 대상으로-

This study aims to discuss how the incorporation of literature in curricula can not only develop students’ knowledge on the Korean language but also enhance their intercultural sensibility. The subjects of the study are students taking “Intermediate Korean” in one of the Taiwanese universities, and the piece of literature used is the translated version of a Taiwanese tale called “GujiGuji”. Students were first instructed to read the book along with the instructor’s book report, then were asked to write their own book reports incorporating their personal experiences. As a result of analyzing their reports, the author found that the unique cultures and emotions embedded in the piece of literature, class material provided by the instructor, and book reports written by students (which embodies the author’s, instructor’s, and students’ personal experiences, respectively) interact with one another. Throughout this process, the author was also able to discover that the improvement of students’ intercultural sensibility usually occurs at the top level of the Development Model of Intercultural Sensibility (DMIS) proposed by Bennett (1986). As such, the study suggests that the incorporation of literature in curricula can serve as a method to improve intercultural sensibility, especially for foreign students who have limited access to communicating with people fluent in the target language they are trying to learn. (Center for Korean Studies, National University of Kaohsiung)
Comparative Analysis between Machine Learning Methods in Tones Classification

This paper presents an analysis between the indicators in synthesis of models based on machine learning techniques for RMS noise levels recognition of tones with different frequencies. Discriminant classification models were performed in MATLAB as pseudo-quadratic model with the highest accuracy of 84.650% was selected. Naïve Bayes algorithm with Gaussian and Kernel distributions is implemented in the classification process, as the better results were obtained in the second approach. In selection of the metric distance by the k-NN method an accuracy range from 89.800% to 91.050% is observed.
Arbitrary Propositional Network Announcement Logic

Modal logics for reasoning about interaction in social networks is an active area of research. In this paper we introduce modalities for quantifying over possible “tweets”, i.e., simultaneous messages sent to all an agent’s “followers”, into an existing basic framework for reasoning about this type of network events. Modalities that quantify over informational events in general, and over agent announcements in particular, is also an active area in the study of the dynamics of knowledge and belief. We combine these two directions by interpreting such modalities in social networks. We study the resulting logic, and provide a sound and strongly complete (infinitary) axiomatisation.
Classification of historic buildings in Indonesia with svd algorithm based on texture and color characteristics (case: temples, historic mosques, historic churches, and historic Confucian temples)

Abstract This study aims to make an application that able to make classification for historical building in Indonesia. The building types being used in this study are: temples, historic mosques, historic churches and historic confucian temples. This application classifies historical buildings based on texture, Color and combination of two. GLCM method is utilzied for texture characteristics classification. Histogram method is being used for Color characteristics. SVD method is used to reduce dimensions, which can make classification process becomes faster.
LET´S TALK EASILY ABOUT STEREOCHEMISTRY: A HOMEMADE EXPERIMENT TO UNDERGRADUATE STUDENTS UNDER SOCIAL ISOLATION

Under social isolation due COVID-19 we are adapting to the new educational scenario.Taking methodologies previous developed in distance education using video conference the world is establishing a new area called technopedagogy.Stereochemistry topic in Organic Chemistry is always difficult to understand even in class-courses.The rotation of plane-polarized light is challenging to teach because it requires high level of abstraction about the mental experiment.Thus, in this work we propose the development of a tool to be used in remote way to teach stereochemistry.Thus we have made a tutorial video teaching college students to perform a hand-made experiment in stereochemistry topic.This video was nicely scored by teachers and post graduate students.
Performance Monitoring of Industrial Rotary Equipment using AI/ML Techniques

Industrial AI is the application of Artificial Intelligence and Machine Learning techniques to optimise production, increase safety and reliability of industrial equipment and manufacturing plants. The purpose of this paper is to document and compare different AI/ML techniques and their efficacy when used with data in the industrial manufacturing context. These techniques have been compared with each other in their performance on datasets recorded from select mechanical equipment used in the factories. The paper details the use of methods such as ANN and LSTM and their applicability to this domain. The comparison of results and discussion will enable the reader to choose the appropriate method for a given scenario.
Empirical analysis of generalization through augmentation for classifying images of vernacular handwritten texts

Handwritten image recognition [1] is a challenging problem that has seen tremendous research efforts in recent years from the computer vision community. Although many deep learning-based classifications algorithms are studied in the literature for handwritten image recognition [2], most of the works are concentrated on a few widely used benchmark datasets [3]. Hence, it is difficult to understand how well a certain model generalizes across different optical characters with vast complexity levels. The existing research works for classification from handwritten image datasets are primarily focused on developing better model architectures, better training procedures, better regularization methods such as Image Augmentation, Dropout, Dropblock, etc. [4-6], hard example mining techniques [7], better Loss Functions such as Focal loss [8], Weighted Cross Entropy loss, etc. In this paper, we focus on image augmentation techniques along with an example mining technique to establish a robust classification baseline for handwritten datasets using a novel combination of augmentation methods and online hard example mining. Through our experiments, we show the combination of advanced image augmentation techniques best suited for achieving the highest macro averaged recall score and highlight a few important trends therein.
Fall Detection Based on an Inertial Sensor and a Customized Artificial Neural Network Algorithm

With an aging population, falls have become a significant safety hazard, especially for the elderly. This paper proposes a fall detection system based on a 6-axis inertial sensor to collect body movement information and a customized artificial neural network to process the signals. Both the acceleration and the angular velocity are utilized for accuracy measurement. Instead of the typical threshold-based algorithm, a MIMO neural network is customized for fall detection. Rather than simply distinguish between falls and other activities, the system is able to recognize the non-fall behaviors, including running, sitting and walking. The whole device is implemented on a pegboard. Experiment results show that the detection specificities of these behaviors are all above 96% and the whole system accuracy reaches 96.8%. Keywords-fall detection, inertial sensor, neural network, behavior recognition.
On the influence of using quantized images on machine learning algorithms

Recently, applying quantized images to machine learning algorithms has been expected to enhance robustness against adversarial examples. In this paper, three quantization methods: linear quantization, lloyd-max quantization and error diffusion are considered for producing quantized images, respectively, and the influence of using the quantized images is discussed in an image classification experiment under the use of typical machine learning algorithms including deep learning ones.
Artificial Neural Networks For Pattern Recognize Handwritten

Abstract Backpropagation is a supervised learning method on artificial neural networks. The process of training artificial neural networks that are built will be tested several structures of artificial neural networks, with the hope that the best structure is obtained to produce the output with the smallest error. The evaluation purposes use the same data set to form a neural model with a hidden level and the neuron model with two hidden levels then compare these three models both in terms of classification accuracy and training time. A neural network can be concluded that for each architecture, the neural network architecture used to recognize type 3 is 82,42%, type 2 77,5%, type 1 98,1%.
Application: LatticeWalks and Smooth ACSV

We now apply the asymptotic techniques developed in Chapter 5 to lattice path enumeration problems, using the generating function expressions derived in Chapter 4. As seen in Chapter 4, there is a correspondence between lattice path models having nice combinatorial properties and those whose generating functions admit particularly nice representations as diagonals of rational functions. Because Chapter 5 gives asymptotics in the presence of smooth contributing singularities, here we will consider lattice path models whose step sets have lots of symmetries. In addition to the precise results we derive, this chapter serves as an extended illustration of how to apply the powerful machinery of analytic combinatorics in several variables, including the determination of higher-order constants in the resulting asymptotic expansions. Chapter 10 of Part III will return to lattice path enumeration after a theory of analytic combinatorics for non-smooth points is developed in Chapter 9. Several of the asymptotic results in this chapter were originally given by Melczer and Mishna [3] and Melczer [2], on which our presentation is based.
Unbiased FIR Filtering under Bernoulli-Distributed Binary Randomly Delayed and Missing Data

This paper develops an unbiased finite impulse response (UFIR) filtering algorithm for networked systems where uncertain delays and packet dropouts can happen due to measurement failures and unreliable communication. The binary Bernoulli distribution with known delay probability is used to model the randomly arrived measures. A novel representation of the stochastic model is presented for FIR-type filter structures. To avoid packet dropouts and improve the estimation accuracy when a message arrives with no data, a predictive algorithm is used. An advantage of the UFIR filtering approach is demonstrated by comparing the mean square errors with the Kalman and H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> filters under the same conditions. Experimental verifications are provided based on GPS vehicle tracking.
Using a Boolean derivative to evaluate the significance of properties of recognized objects

This paper offers an approach for evaluating the significance of individual characteristics of recognized objects. The scope of this approach is not the subject area where objects and characteristics of these objects are specified, but a trained ∑Π - neural network that works correctly on the specified subject area. In this paper, we propose a method for constructing a crucial function based on the weight characteristics of a correctly functioning ∑Π - neuron. A logical derivative is used to evaluate the significance of object characteristics. This makes it possible to track how the decision function will change its value if one or more object characteristics change their value. This will allow us to draw a conclusion about the most important properties of the subject area under consideration.