Reviewerly/exHarmony huggingface dataset:
Introduction to Blockchain and Smart Contract – Principles, Applications, and Security

The popularity of blockchain is on the rise as the technology is touted to become an enabler of many existing and futuristic applications. These applications range from cryptocurrency-based financial applications to secure supply chain management, privacy-protected medical data handling, secure yet transparent e-voting, decentralized Internet of Things (IoT), and fault-tolerant Unmanned Aerial Vehicles (UAVs) towards future 6G communication networks. There are numerous inherent features of blockchain that drive these applications, such as transparency, decentralization, non-repudiation, immutability, pseudonymity, provenance, and lower operational costs. Moreover, the smart contracts further enhance the capabilities of blockchain. Thus, this chapter aims to provide an in-depth understanding of blockchain and smart contracts. In particular, the chapter begins by introducing blockchain as one of the most popular types of Distributed Ledger Technology (DLT). Subsequently, the block structure, transaction process flow, salient features, and the different types of blockchain are presented. Next, the concept of smart contract is discussed along with its salient features and the different blockchain platforms that support smart contracts. Furthermore, the chapter introduces various growing applications of blockchain. Finally, the security aspects of blockchain and smart contracts are briefly discussed since it is an evolving technology.
Adaptive Filtering

In this chapter, an adaptive FIR filter is used to model the behavior of an IIR filter. Let us first examine IIR filtering.
Acapulco

Configuring feature-oriented variability-rich systems is complex because of the large number of features and, potentially, the lack of visibility of the implications on quality attributes when selecting certain features. We present Acapulco as an alternative to the existing tools for automating the configuration process with a focus on mono- and multi-criteria optimization. The soundness of the tool has been proven in a previous publication comparing it to SATIBEA and MODAGAME. The main advantage was obtained through consistency-preserving configuration operators (CPCOs) that guarantee the validity of the configurations during the IBEA genetic algorithm evolution process. We present a new version of Acapulco built on top of FeatureIDE, extensible through the easy integration of objective functions, providing pre-defined reusable objectives, and being able to handle complex feature model constraints.
Validitas Pengembangan Mobile Learning Berbasis Moodle di Sekolah Menengah Kejuruan Pariwisata Aisyiyah Pada Mata Pelajaran Pengetahuan Bahan Makanan

The use of learning media in Aisyiyah Tourism Vocational School is still dominant in using printed books and less using mobile-based learning media, so it is felt to be less effective in learning and makes students passive. So the development of Moodle-based Mobile Learning media is necessary for the learning process. The purpose of this research is the development of Moodle-based Mobile Learning in Aisyiyah Tourism Vocational School in the subject of valid Food Ingredients Knowledge. The development method is R&amp;D for education, with the Four-D development model. Data retrieval was carried out by giving instruments to validators of media experts and material experts, data analysis using Aikens V, the research location was Aisyiyah Tourism Vocational School, West Sumatra, class X-cooking class students. The results of research on Moodle-based Mobile Learning learning media as interactive media from product validity tests based on media validation an average of 0.92 is declared "Valid" and material validation is declared "Valid" an average of 0.87, so Moodle-based Mobile Learning learning media is very feasible to be applied in the learning process at the Aisyiyah Tourism Vocational School.
Predicting the Price of Second-Hand Housing Based on Lambda Architecture and KD Tree

In this paper a system is designed and implemented to predict the price of second-hand housing. This system based on Lambda architecture can execute prediction in both real-time and batch modes so it can give two kinds of different price predictions that reflect current and historical conditions respectively. The kNN related algorithms are used for price prediction. By comparing the performance of brute kNN, kd tree and ball tree, kd tree is selected as the price prediction model of the system. In system implementation the kd tree model is chosen to predict prices in both real-time and batch services. The kd tree model can also recommend housings to user besides price prediction. The experiment shows the effectiveness of our system. Time and space performance of brute kNN, kd tree and ball tree are compared by experiments. And the evaluation metrics of other available maching learning models are compared. The reason of choosing the kd tree model is also explained by the experimental results.
Explicable Behavior Generation

In Chapter 2, among other things, we defined the notion of explicability of a plan and laid out an informal description of explicable planning. In this chapter, we will take a closer look at explicability and discuss some practical methods to facilitate explicable planning. This would include discussion on both planning algorithms specifically designed for generating explicable behavior and how one could design/update the task to make the generation of explicable plans easier.
New Pseudo-Random Key Generator for IoT-security Model Based on a Novel 3D Coupled Map Lattice

In cryptography fields, one concern is the consideration of the capacity of the resource, especially, in environments that run IoT devices.This paper presents a lightweight and comprehensive security system, which involves a novel Pseudo-Random Number Generator (PRNG) based on a novel 3D Coupled Map Lattice system (3D-CML) as a chaotic system that is more practical in cryptography, and encryption algorithms depend on two security levels, firstly, permuting the plain image using a standard 2D Henon chaotic map, then, encrypt it by a One-Time Symmetric Key (OTSK).The bifurcation diagram of the 3D-CML shows that the chaotic parameters have been extended in their ranges, this feature has a positive effect on the key-space size which its size equal to 2^373.From another aspect, with fewer iteration configurations, all random sequences that are produced by the proposed PRNG have passed all statistical tests of the NIST suite, in turn, this configuration would lower the run-time, hence decreasing the computational effort as a response to the requirements of limited-resources environments, such as IoT.Several assessment metrics, such as Mutual Information, Gray Difference Degree, Histogram, Chi-square, Correlation Coefficient, Entropy, and more, confirmed that the proposed algorithms can be robust in dissolving the internal characteristics in the original image for producing an encryption image, resulting in strong resistance against any cyberattacks.
CEP Rule Extraction Framework Based on Evolutionary Algorithm

Complex Event Processing (CEP) is an effective method to find the time and causality relationship between various events in the stream data. Its purpose is to match the low-level events in the event stream into complex events according to a certain pattern. CEP has a wide range of applications in the Internet of Things, cloud computing, finance and cyber security. Currently, in CEP design, event matching rules are mainly formulated by domain experts according to their professional knowledge and subjective judgment. However, with the increase of the complexity of event flow data, it is increasingly difficult to formulate rules. To solve this problem, a CEP rule extraction framework based on an evolutionary algorithm is proposed in this study to realize automatic learning of CEP rules, and test data are used for verification, and high-precision experimental results are obtained.
Smart City Theories and Models

The theories and models of smart city make up the systemic approach that governs its readiness. This contains the in depth analysis of the concepts of the smart city as it relates to the environment as wells as the energy present. Several theories are modelled into the smart city system to guide its implementation and consideration among the construction professionals in the constitution industry. Subsequently, frameworks are not neglected in terms of their relationship between the outcome of the theories and models propounded and conceptual integration in human, technology and institution. Inferences are made from these theories and models, and thus decision-making are dependent on the visibility envisaged.
Design and Implementation of Geological Data Management Platform based on WebGIS

China Geological Survey (CGS) has accumulated a large number of geological data in its geological survey activities, but the relevant geological data exists in a variety of forms and data formats, with low utilization and difficulty in sharing, which is not conducive to the rapid inquiry and mining and utilization of geological data. Relying on different types of geological data produced by CGS, this paper uses ArcGIS Server, Web Service, Ajax, Memcached, and other computers and GIS technologies to complete the digitization, integration and includes standardized management of different types of geological data such as article reports, geological maps, geological vector data and geological remote sensing image data. In this paper, a WebGIS-based geological data management and display platform is developed to achieve a one-stop service for geological data collection, storage, query and visualization, which can quickly respond to scientific researchers to view geological data and multidimensional statistical analysis needs. The experiment proves that the platform can effectively improve the information technology level of geological data management and the working efficiency of researchers.
PRIMARY SCHOOL TEACHERS’ SOFT SKILLS DEVELOPMENT WHILE TEACHING PHILOLOGICAL DISCIPLINES

The article is devoted to the topical issue of primary school teacher training in higher educational institutions. The purpose of the research is to investigate the process of developing primary school teachers’ soft skills in the process of teaching philological disciplines.&#x0D; After a comparative analysis of the definitions of the category ‘soft skills’ proposed by Ukrainian and foreign researchers, the authors have emphasized that soft skills are especially crucial for primary school teachers. Researchers have noted that to form and develop soft skills effectively during lectures and practical classes it is necessary to use different ways of interaction, namely games, case studies, colloquia, and individual and group projects.
Lambda:Omicron - A new prediction model to track maneuvering objects

This paper deals with motion modeling for 2-dimensional tracking of a maneuvering object. Specifically, a new class of nonlinear dynamic motion models, called Lambda:Omicron, is introduced with the purpose of accurately modeling maneuvers (regarded as variations of speed and turning rate) of the moving object. These models rely on the unicycle navigation model, suitably augmented with two chains of integrators to account for the unknown speed and turning rate command inputs. Quasi-exact time-discretization of the continuous-time Lambda:Omicron models is also carried out to allow their exploitation in nonlinear recursive filters. Simulation experiments are presented to show the effectiveness of the proposed models as compared to state-of-the-art linear and nonlinear motion models for tracking of strongly maneuvering objects.
Faceless: A Cross-Platform Private Payment scheme for Human-Readable Identifiers

The payment of most popular cryptocurrencies such as Bitcoin or Ethereum is based on blockchain addresses, which are usually two random strings that are not easily memorialized or managed by human beings. On the other hand, traditional payment is usually based on human-readable identifiers (HRI). We believe that the decentralized identifiers such as Ethereum naming service (ENS) combined with the users’ existing various online identities such as mobile phone numbers, social media accounts, etc, will constitute the main HRI layer for the payment network of Web 3.0, and most payments in the Web 3.0 world will happen between the HRI accounts.This work presents a cross-platform private payment scheme for HRIs, that allows a user to manage multiple HRIs with one single secret key. Our payment scheme guarantees the confidentiality of the payment amounts and the anonymity of users involved in the payment. We also provide a mechanism for verifiable HRI, which paves the way for many interesting applications such as electronic invoicing, regulation-compliant DeFi, etc.
K Nearest Neighbor Imputation Performance on Missing Value Data Graduate User Satisfaction

A missing value is a common problem of most data processing in scientific research, which results in a lack of accuracy of research results. Several methods have been applied as a missing value solution, such as deleting all data that have a missing value, or replacing missing values with statistical estimates using one calculated value such as, mean, median, min, max, and most frequent methods. Maximum likelihood and expectancy maximization, and machine learning methods such as K Nearest Neighbor (KNN). This research uses KNN Imputation to predict the missing value. The data used is data from a questionnaire survey of graduate user satisfaction levels with seven assessment criteria, namely ethics, expertise in the field of science (main competence), foreign language skills, foreign language skills, use of information technology, communication skills, cooperation, and self-development. The results of testing imputation predictions using KNNI on user satisfaction level data for STMIK PPKIA Tarakanita Rahmawati graduates from 2018 to 2021. Where using the five k closest neighbors, namely 1, 5, 10, 15, and 20, the error value of the k nearest neighbors is 5 in RMSE is 0, 316 while the error value using MAPE is 3,33 %, both values are smaller than the value of k other nearest neighbors. K nearest neighbor 5 is the best imputation prediction result, both calculated by RMSE and MAPE, even in MAPE the error value is below 10%, which means it is very good.&#x0D;
Challenges for education post pandemic by COVID 19

The COVID 19 pandemic brought with it a radical change in everything that humanity has always been used to. Education is one of the most marked areas since it had to adjust to the online class model to continue teaching the population. This represents a great challenge for educational systems, guidance for teachers and directors as well as for institutions and officials is an issue that must be addressed in the midst of the post-pandemic crisis, taking into account organizational plans and the needs of students by level and field of study through the use of digital formats including varied tasks and jobs that place COVID-19 in a global and historical context where it can be considered that there is a before and after developing study plans designed in based on what was experienced as a result of the pandemic.
Research on Improvement of College Teachers’ Teaching Abilities in the Artificial Intelligence Era

Artificial intelligence is deeply contributing to the reforms and innovations in teaching and learning, bringing opportunities as well as challenges to the teachers in colleges and universities. This paper highlights the important impact of AI technologies on the field of higher education and discusses the potential challenges faced by college teachers in the era of AI in terms of the teaching methods, knowledge delivery, changes in the roles and functions of the teachers, and attempts to elaborate three capacities that college teachers should possess to deal with these challenges, namely, the personalized professional teaching capacity, modern technology application capacity, and scholarship of teaching capacity. These three capacities serve as the core factors that constitute the college teachers’ teaching abilities as well. Finally, this paper suggests feasible paths for the future improvement of college teachers’ teaching abilities and the effective integration of AI into teaching from the university and college level and the individual teacher level, respectively.
Garbage In, Flowers Out: Noisy Training Data Help Generative Models at Test Time

Despite important progress, conversational systems often generate dialogues that sound unnatural to humans. We conjecture that the reason lies in the different training and testing conditions: agents are trained in a controlled “lab” setting but tested in the “wild”. During training, they learn to utter a sentence given the ground-truth dialogue history generated by human annotators. On the other hand, during testing, the agents must interact with each other, and hence deal with noisy data. We propose to fill this gap between the training and testing environments by training the model with mixed batches containing both samples of human and machine-generated dialogues. We assess the validity of the proposed method on GuessWhat?!, a visual referential game. We show that our method improves the linguistic quality of the generated dialogues, and it leads to higher accuracy of the guessing task; simple perturbations of the ground-truth dialogue history that mimic machine-generated data do not account for a similar improvement. Finally, we run a human evaluation experiment on a sample of machine-machine dialogues to complement the quantitative analysis. This experiment shows that also human annotators successfully exploit dialogues generated by a model trained with mixed batches to solve the task. Hence, the mixed-batch training does not cause a language drift. Moreover, we find that the new training regime allows human annotators to be significantly more confident when selecting the target object, showing that the generated dialogues are informative.
Practical routes in the UX of AI, or sharing more beaten paths

This forum provides a space to engage with the challenges of designing for intelligent algorithmic experiences. We invite articles that tackle the tensions between research and practice when integrating AI and UX design. We welcome interdisciplinary debate, artful critique, forward-looking research, case studies of AI in practice, and speculative design explorations. --- Juho Kim and Henriette Cramer, Editors
A Generalized Labeled Multi-Bernoulli Filter Based on Track-before-Detect Measurement Model for Multiple-Weak-Target State Estimate Using Belief Propagation

In this paper, we propose the specific recursion formula for the generalized labeled multi-Bernoulli filter based on the track-before-detect strategy (GLMB-TBD) using a belief propagation algorithm. The proposed method aims to track multiple weak targets with superior performance. Compared to the Murty algorithm-based and Gibbs sampling-based implementation of GLMB-TBD filter, the proposed algorithm improves the tracking accuracy of multiple weak targets without pruning operation to preserve the relevant association information. The superior performance in tracking accuracy of the algorithm is validated for simulated scenarios using OSPA(2) metric. More importantly, the simulation results demonstrate that the proposed algorithm outputs both the Gibbs sampling-based version and Murty algorithm-based version in computational cost due to linear complex in the number of both Bernoulli components and measurements.
Fast Quaternion Product Units for Learning Disentangled Representations in $\mathbb {SO}(3)$

Real-world 3D structured data like point clouds and skeletons often can be represented as data in a 3D rotation group (denoted as <inline-formula><tex-math notation="LaTeX">$\mathbb {SO}(3)$</tex-math></inline-formula>). However, most existing neural networks are tailored for the data in the Euclidean space, which makes the 3D rotation data not closed under their algebraic operations and leads to sub-optimal performance in 3D-related learning tasks. To resolve the issues caused by the above mismatching between data and model, we propose a novel non-real neuron model called <i>quaternion product unit</i> (QPU) to represent data on 3D rotation groups. The proposed QPU leverages quaternion algebra and the law of the 3D rotation group, representing 3D rotation data as quaternions and merging them via a weighted chain of Hamilton products. We demonstrate that the QPU mathematically maintains the <inline-formula><tex-math notation="LaTeX">$\mathbb {SO}(3)$</tex-math></inline-formula> structure of the 3D rotation data during the inference process and disentangles the 3D representations into &#x201C;rotation-invariant&#x201D; features and &#x201C;rotation-equivariant&#x201D; features, respectively. Moreover, we design a fast QPU to accelerate the computation of QPU. The fast QPU applies a tree-structured data indexing process, and accordingly, leverages the power of parallel computing, which reduces the computational complexity of QPU in a single thread from <inline-formula><tex-math notation="LaTeX">$\mathcal {O}(N)$</tex-math></inline-formula> to <inline-formula><tex-math notation="LaTeX">$\mathcal {O}(\log N)$</tex-math></inline-formula>. Taking the fast QPU as a basic module, we develop a series of quaternion neural networks (QNNs), including quaternion multi-layer perceptron (QMLP), quaternion message passing (QMP), and so on. In addition, we make the QNNs compatible with conventional real-valued neural networks and applicable for both skeletons and point clouds. Experiments on synthetic and real-world 3D tasks show that the QNNs based on our fast QPUs are superior to state-of-the-art real-valued models, especially in the scenarios requiring the robustness to random rotations. The code of this work is available at <uri>https://github.com/SuferQin/Fast-QPU</uri>.
Mobile based Inventory Management System with QR code

This work proposes an inventory management system that manages the stock details of a system with various agents and factors to make up an inventory. The ever-evolving era of automation is always thriving to replace the conventional way of manual efforts. Nowadays it is extremely important to have an easy access to data in a visually representable form via an app for a user-friendly system. Managing the data manually tends to be enormous and existing systems fail to maintain every inventory in the shipment process accurately. This article focuses on developing an Inventory management system of multiple organizations in a secure manner by having two authentication mechanisms when a user logs in. It mainly stores data with the help of Quick Response (QR) code and keeps an eye on the various activities. The goal is to maintain an error-free record with a track of all the activities of every employee in an organization.
A transfer-learning fault diagnosis method considering nearest neighbor feature constraints

Abstract Aiming at the problem of low diagnostic accuracy of fault diagnosis models due to changes in actual operating conditions, a novel fault diagnosis method based on transfer learning considering nearest neighbor feature constraints is proposed. First, nearest neighbor samples are considered to measure data features. In addition, a nearest neighbor feature constraint strategy is designed to improve the feature extraction performance of the network. Second, a multiple-alignment strategy of nearest neighbor samples is proposed to enhance the domain adaptation performance of the network model utilizing multiple alignments. Then, a loss function dynamic weight strategy is used to improve the convergence of the loss function during model training. Finally, the experimental verification is carried out on the public data set of the Western Reserve University and the private data set. The experimental results show that the proposed method exhibits superior transfer performance with reliability and stability compared to the existing methods.