Reviewerly/exHarmony huggingface dataset:
Distortion Correction using Virtual PCG Pattern for Precise Stereo-based Large-scale 3D Measurement

Three-dimensional (3D) measurement is an essential procedure in various manufacturing industries including shipbuilding. Since binocular systems are convenient and time-saving, they are proposed for ship block measurement. However, because of the very large scale of the ship blocks, working distance is about 10 m, resulting in a fatal limitation of calibration: the size of image portion corresponding to the checkerboard for calibration included in the whole image is extremely small. This prevents the distortion parameter of camera lens which most affects 3D reconstruction accuracy, from being accurately estimated in the calibration. To overcome this limitation, this paper proposes a method that pre-estimates the distortion correction map that covers the entire image area. A phase-shift circular grating (PCG) pattern displayed on a monitor is captured by the camera set to large scale. Since PCG patterns are generated by computer software, infinite number of patterns corresponding to desired orientations and positions can be generated, which are useful to measure the center of distortion and more accurate vanishing points. Based on the estimated vanishing points, the accurate distortion correction is performed using perspective projection invariants, and the distortion values in pixels are measured for each grid points to estimate the distortion correction map of the entire image area. An experimental 3D measurement was conducted with an estimated distortion correction map. As a result, Mean and standard deviation of 3D reconstruction error by proposed method were improved by 15.84% and 6.77% compared with the Zhang’s method, respectively.
PROJECTION IN DIALOGUE ROMEO AND JULIET SHAKESPEARE’S

This thesis studies projection in the complex clauses which are found in Shakespeare’s Romeo and Juliet dialogue. The aims of this study are (1) to find out the types of projection in Shakespeare’s Romeo and Juliet dialogue, and (2) to find out the realization of projections in Shakespeare’s Romeo and Juliet dialogue based on the data that have been analyzed in line with the theory of projection. The data of this study were clause complexes found in Shakespeare’s Romeo and Juliet dialogue. This research is conducted by using a descriptive qualitative approach. The data are analyzed by using the Systemic Functional Linguistics (SFL) theory. The result shows that in (1) In Shakespeare’s Romeo and Juliet dialogue, four types of projection were found, there were 55 clauses complexes of projection. Of the four types of projection that occurred, the highest number of occurrences is Paratactic Locution for 40 clauses (72,7%), followed by Hypotactic Locution for 8 clauses (14,5%), Paratactic Idea for 5 clauses (9,1%), and 2 clauses (3,7%) of Hypotactic Idea. (2) The realization of projection in Shakespeare’s Romeo and Juliet dialogue in terms of verbal locutions and mental ideas. The verbal locution consists of 40 clause complexes for Paratactic Locution, and 8 clause complexes for Hypotactic Locution whereas the mental idea consists of 5 clause complexes for Hypotactic Idea and, 2 complex clauses for Paratactic Idea.
Communication Efficient Federated Learning

Federated learning, as a branch of distributed learning, suffers from the high cost and concurrency of communication. The situation gets even worse in training deep models on large-scale mobile devices. Hence, communication efficiency is an avoidable topic in multi-party federated learning systems. Focusing on this issue, this chapter first discusses the background, the main methodologies and potential directions for communication efficient federated learning. Then, we introduce two methods for reducing the communication cost of federated learning, which are layer-wise asynchronous update and model quantization. Extensive experiments are performed to show the efficiency of these two approaches.
Secure Federated Learning

Although federated learning does not require any participant to share its private data to the central server, local data information still has a potential risk of being revealed taking advantage of the uploaded model parameters or gradients from each client, if the server is honest-but-curious. To address this issue, homomorphic encryption is one of the main stream privacy preservation technologies used in federated learning due to its extraordinary model protection ability. However, most encryption-based federated learning systems are computationally intensive and consume a large number of communication resources. Moreover, a trusted third party is always needed to generate key pairs for both encryption and decryption operations, not only increasing the complexity of the system topology but also causing additional security threats. Therefore, in this chapter, we introduce two secure federated learning frameworks. The first one is for horizontal federated learning, where the global key pairs are jointly generated between the server and connected clients without the help of a trusted third party. In addition, model quantization and approximated model aggregation techniques are adopted to significantly improve the encryption efficiency during the training period. The second framework is tailored for vertical federated learning, where labels are distributed on different local devices. To achieve secure node splitting and construction as well as label aggregation of a XGBoost tree, both homomorphic encryption and differential privacy are adopted.
Evolutionary Multi-objective Federated Learning

Different from model quantization and partial model uploads presented in the previous chapter, evolutionary federated learning, more specifically, evolutionary federated neural architecture search, aims to optimize the architecture of neural network models, thereby reducing the communication costs caused by frequent model transmissions, generating lightweight neural models that are better suited for mobile and other edge devices, and also enhancing the final global model performance. To achieve this, scalable and efficient encoding methods for deep neural architectures must be designed and evolved using multi-objective evolutionary algorithms. This chapter presents two multi-objective evolutionary algorithms for federated neural architecture search. The first one employs a probabilistic representation of deep neural architectures that describes the connectivity between two neighboring layers and simultaneously maximizing the performance and minimizing the complexity of the neural architectures using a multi-objective evolutionary algorithm. However, this evolutionary framework is not practical for real-time optimization of the neural architectures in a federated environment. To tackle this challenge, a real-time federated evolutionary neural architecture search is then introduced. In addition to adopting a different neural search space, a double sampling strategy, including sampling subnetworks from a pretrained supernet and sampling clients for model update, is proposed so that the performance of the neural architectures becomes more stable, and each client needs to train one local model in one communication round, thereby preventing sudden performance drops during the optimization and avoiding training multiple submodels in one communication round. This way, evolutionary neural architecture search is made practical for real-time real-world applications.
Introduction

This chapter introduces the background knowledge of the book, including the most widely used artificial neural network models and decision trees, gradient based learning methods, evolutionary algorithms and their applications to single- and multi-objective machine learning, traditional privacy-preserving computing methods such as multi-party secure computation, differential privacy, and homomorphic encryption, and the federated learning paradigm for privacy-preserving machine learning. An overview of horizontal and vertical federated learning, together with a description of the basic federated learning algorithm, known as federated averaging, is presented, before knowledge transfer in federated learning is briefly explained. Finally, the main challenges of federated learning over non independent and identically distributed data are discussed in detail.
Healthor: Heterogeneity-aware Flow Control in DLTs to Increase Performance and Decentralization

Permissionless reputation-based distributed ledger technologies (DLTs) have been proposed to overcome blockchains’ shortcomings in terms of performance and scalability, and to enable feeless messages to power the machine-to-machine economy. These DLTs allow machines with widely heterogeneous capabilities to actively participate in message generation and consensus. However, the open nature of such DLTs can lead to the centralization of decision-making power, thus defeating the purpose of building a decentralized network. In this article, we introduce Healthor, a novel heterogeneity-aware flow-control mechanism for permissionless reputation-based DLTs. Healthor formalizes node heterogeneity by defining a health value as a function of its incoming message queue occupancy. We show that health signals can be used effectively by neighboring nodes to dynamically flow control messages while maintaining high decentralization. We perform extensive simulations, and show a 23% increase in throughput, a 76% decrease in latency and four times increased node participation in consensus compared to state-of-the-art. To the best of our knowledge, Healthor is the first system to systematically explore the ramifications of heterogeneity on DLTs and proposes a dynamic, heterogeneity-aware flow control. Healthor’s source code ( https://github.com/jonastheis/healthor ) and simulation result data set ( https://zenodo.org/record/4573698 ) are both publicly available.
Location and Time-Based Reminder Android Application – A Survey

In this study, a location and time-based reminder android application is developed to help people remember their daily tasks which are location specific. Also enabling them to provide a time context to these reminders. Many times, it cannot be predicted when we will be at a particular location and at what time to perform tasks that are location specific. It is beneficial if we get an alert when we are actually present near that location. When combined with the context of location and time reminders can be much more efficient and drastically increase the productivity of users. The system uses Google Maps API, GPS, and Geo-Fencing Algorithms to provide accurate reminders. Timely reminders increase productivity by reducing the chances of the missing location of interest and the task to be performed..
Symbolic Regression for Data Storage with Side Information

There are various ways to use machine learning to improve data storage techniques. In this paper, we introduce symbolic regression, a machine-learning method for recovering the symbolic form of a function from its samples. We present a new symbolic regression scheme that utilizes side information for higher accuracy and speed in function recovery. The scheme enhances latest results on symbolic regression that were based on recurrent neural networks and genetic programming. The scheme is tested on a new benchmark of functions for data storage.
Ontology web language implementation in the strategic level of ArchiMate language in the context of smart cities

TOGAF and its modelling language ArchiMate, are the most common standards used for this area of planning, designing, implementation as well as architectural planning. The modelling tool ArchiMate is very popular and advanced, yet it still does not reflect all demands. In this regard, it is essential to point out that there are numerous studies, focusing on its improvement and also the improvement of models for business architecture. Authors tried to enable villages and cities and other potential customers to transparently assign their goals, motivations, requests etc. into a prepared environment/model. The core idea was to use modelling language ArchiMate 3.1., which was complimented with selected features of IT ontology.
The Design of Concurrent Signature Combined with Blind Signature Generation and Its Application on E-commerce

On the approaches of E-cash and E-payment transactions, most of them using blind signature to achieve the protection of untraceability. However there exists one critical problem that is in some commercial applications, they usually need additional evidence signed by the two parties to prove the facticity of the transaction process. For example, a client withdraws an e-cash from the bank by applying the blind signature scheme. The two parties, the client and the bank can simultaneously obtain the signed transaction evidence from the other side at the end of the procedure. To solve the problem, we combine the blind signature and concurrent signature schemes to guarantee that the two parties can concurrently obtain the transaction evidence if they honestly carry out the protocol; at the same time, a blind signature can be generated and received by the requester for the use in commercial applications.
Regulation of dubious currency transactions and development of the digital yuan in China

Aim. The presented study aims to investigate the regulation of dubious currency transactions in the People’s Republic of China (PRC), the application of the anti-money laundering/combating the financing of terroristm regime, and the development of the digital yuan system. Tasks. The author reveals the essence of the concept of dubious currency transactions in modern China and their legal regulation by Chinese state authorities; considers the legal regime for anti-money laundering/combating the financing of terrorism (AML/CFT); describes the concept of “digital yuan” as a Chinese analog of previously banned cryptocurrencies in China. Methods . This study uses the methods of analysis, grouping, and systematization, a method of synthesizing information from various sources in Chinese and English, and general scientific methods of induction and deduction. Results. The strict system for combating dubious currency transactions developed by the government of the People’s Republic of China has become a significant step in the fight against the legalization of criminal proceeds. Nowadays, Chinese financial regulators pay special attention to any transactions that can be characterized as “dubious”. To effectively combat illegal financial transactions, Chinese AML/CFT standards have been developed and are regularly updated. The Chinese government has regarded cryptocurrencies as a threat to the AML/CFT regime, which led to their complete prohibition on the territory of the PRC. However, the Chinese government has offered an alternative in the form of the digital yuan. Attempts to put it into circulation have been made since 2020. Conclusions. In China, there are strict criteria for declaring foreign exchange transactions as dubious, and Chinese financial institutions have broad powers for their regulation. China’s financial institutions should monitor suspicious transactions at all levels of business management, taking reasonable measures to identify suspicious transactions not only in the process of identifying customers, but also by checking, studying, and analyzing transaction data. The digital yuan, which differs significantly from other cryptocurrencies, is recognized as one of the most developed cryptocurrencies controlled by centralized authorities. The Chinese digital currency is controlled by the Central Bank, minimizing one of the main advantages of cryptocurrencies — user anonymity. If China continues to successfully put the digital yuan into circulation, it may help China protect its so-called monetary sovereignty in the context of US sanctions.
A Novel Defense Mechanism Against Label-Flipping Attacks for Support Vector Machines

Datasets are important for machine learning for training, and they can be searched on the Internet these days. However, attackers may attack datasets by label flipping attacks. Therefore, a method to defend against the attack is needed. In this paper, we propose an algorithm with two processes, namely sanitizer and defender (SPD), to protect against uncertain datasets and label flipping attacks. We also conduct a series experiments to validate the relabeled dataset and maintain the accuracy of the support vector machine (SVM). Our experiment results show that the proposed SPD algorithm can effectively block the attack by relabeling the dataset and gain high accuracy with relabeled data.
Attention, Please! Adversarial Defense via Activation Rectification and Preservation

This study provides a new understanding of the adversarial attack problem by examining the correlation between adversarial attack and visual attention change. In particular, we observed that: (1) images with incomplete attention regions are more vulnerable to adversarial attacks; and (2) successful adversarial attacks lead to deviated and scattered activation map. Therefore, we use the mask method to design an attention-preserving loss and a contrast method to design a loss that makes the model’s attention rectification. Accordingly, an attention-based adversarial defense framework is designed, under which better adversarial training or stronger adversarial attacks can be performed through the above constraints. We hope the attention-related data analysis and defense solution in this study will shed some light on the mechanism behind the adversarial attack and also facilitate future adversarial defense/attack model design.
Comparison Functionalities of HTTP and MQTT Protocols

The mission and goal of this paper was to compare functionalities for both MQTT and HTTP protocol on the same example to see the advantages and disadvantages of sending and receiving data from server/device. Used tools are Android Studio, Kotlin, NestJS, TypeScript, PostgreSQL and in implementation two protocols were used: HTTP and MQTT protocols to determine advantages and disadvantages of the protocols, several examples are given where a particular protocol could be used in the development of mobile/IoT applications. While talking about MQTT and HTTP protocols, HTTP is one of the globally most used protocols for communication on the World Wide Web (Internet), when MQTT is for sure most used for connecting more devices in the networking called IoT (Internet of Things). Main goal is to show that MQTT can be also used in the mobile application, for example as notification service (server is notifying mobile devices).
Semantics-Based Analysis for Multi-source Remote Sensing Image Retrieval

With the continuous increasing diversity and volume of the remote sensing data, the urgent need of efficient approaches for multi-source remote sensing data retrieval has attracted a lot of attention in the remote sensing field. However, there are limitations to existing remote sensing image retrieval approaches, such as high use threshold caused by excessive reliance on users’ domain knowledge, and imperfect multi-dimensional retrieval ability caused by insufficient data feature utilization. To address these problems, we propose an approach of semantics-based analysis for multi-source remote sensing image retrieval (SBARSIR), to make multi-source remote sensing data more accessible. In more detail, a convolutional neural network (CNN) is exploited for semantic feature extraction, and the global and partial classification results are integrated to improve the accuracy on remote sensing images with differences resolutions. Then, we cross-encode the visual semantic, temporal, and spatial features, and establish the mapping between features and indexing structures to provide retrieval capabilities. Extensive experiments have been carried out on several typical public remote sensing data sets to validate the effectiveness of the SBARSIR proposed. It turns out that the SBARSIR proposed outperforms the main stream approaches under the examined conditions.
Mathematical Word Problem Solver System Using Deep Learning

Since the introduction of the idea of Math Word Problem(MWP) solver, platforms like Intelligent tutoring systemand computer based training have gained much popularity and hence the development of MWP proves to be anessential part. As many types of word problems exist we primarily tend to have our focus on arithmetic singleoperation word problems of elementary grade level. We add our contribution to this open research by working onan already existing model which uses word cues and patterns to understand the semantics of the word problem.Hence we also add our ideas to introduce the model of inference so as to make the system in-sync with basic worldknowledge.
Pelatihan Kecerdasan Artifisial (KA) kepada Guru SD di Kabupaten Blora Jawa Tengah untuk Peningkatan Kemampuan di Bidang Digital

In this era of digitalization, the Government is trying from an early age to introduce programming concepts and Artificial Intelligence, which in the future can be used to support the learning process of elementary school education. This training aims to provide new knowledge about how coding programs are included in the subject matter of elementary school children. The teachers are given lessons on basic things suitable to be applied in class and immediately put into practice their projects. With this method, teachers learn to adapt to the new model, where the material will be taught to students in class. Activities are carried out offline at the Arra Cepu Hotel. Stages of Training through Presentations, Videos, and Quizzes. In this activity, the teachers gain knowledge and skills in coding programs for beginners; pre-test and post-test will be used as a measure to assess it. In the future, teachers will experiment with coding lessons with students. An early introduction to programming and artificial intelligence for elementary school teachers is expected to positively impact students, mainly in their ease of receiving and understanding a lesson.
Clear, easy, plain, and simple as keywords for text simplification

In this paper, we distinguish between four interconnected notions that recur in the literature on text simplification: clarity, easiness, plainness, and simplicity. While plain language and easy language have both been the subject of standardization efforts, there are few attempts to define text clarity and text simplicity. Indeed, in the definition of plain language, clarity has been favored at the expense of simplicity but is employed as a self-evident notion. Meanwhile, text simplicity suffers from a negative connotation and is more likely to be defined by its antonym, text complexity. In our analysis, we examine the current definitions of plain language and easy language and discuss common definitions of text clarity and text complexity. We propose a model of text simplification that can clarify the transition from specialized texts to plain language texts, and easy language texts. It is our contention that text simplification should be placed in a more general framework of discursive ergonomics.
Consecutive quadratic residues in Beatty sequences

Let [Formula: see text] be an odd prime. We study the distribution of consecutive quadratic residues modulo [Formula: see text] in non-homogeneous Beatty sequences [Formula: see text], where [Formula: see text] and [Formula: see text] is irrational. Furthermore, we study the pseudorandom properties of sequences formed by the Legendre symbol and Beatty sequences. In addition, we give upper bounds for certain character sums over [Formula: see text].