Reviewerly/exHarmony huggingface dataset:
An efficient encryption method for smart grid data based on improved CBC mode

Cipher-block chaining (CBC) is a widely used encryption mode for smart grid data, however, the chain structure leads to low encryption and decryption efficiency. To overcome this issue, this paper proposes a novel tree-CBC (TCBC) block encryption method. First, the smart grid plaintext is preprocessed according to the security level of the data. Second, the chain structure of the CBC mode is converted into a tree structure, and for the data block with a higher security level, its location is deeper in the encryption tree. Third, since the overall security and encryption time of data encrypted by TCBC depend on the number of forks of the encryption tree, this paper proposes a method to determine the optimal number of forks of TCBC based on the weights of the encryption time and security of the data blocks. The experiment shows that the encryption efficiency of the TCBC mode is improved by nearly 50% compared to the original CBC mode. And as the data size increases, the efficiency improvement becomes more obvious. Furthermore, compared with widely used encryption modes such as CFB, OFB, CTR, and PCBC, the proposed TCBC mode has higher encryption and decryption efficiency.
Hairstyle transfer via manipulating decoupled latent codes of StyleGAN2

Hairstyle transfer aims to combine the hairstyle attributes of the reference images with the face and background of the identity image into one realistic image. This is challenging due to the differences in semantic structure between reference images and identity image, as well as the tangled hairstyle attributes. Existing GAN-based methods tend to simply combine image patches of hair, face, and background, which leads to discordant semantic structures and obvious artifacts in the results. And the GAN inversion-based methods require elaborate orthogonalization of loss gradients with additional calculations to alleviate the entanglement between the complex hairstyle attributes. To address these limitations, we propose a novel framework for hairstyle transfer based on StyleGAN inversion. Specifically, instead of simply combining the image patches, we use StyleGAN’s generative power to get more reasonable target semantic masks. During the alignment with the target masks, background inpainting is performed by StyleGAN rather than auxiliary models, which makes the results more realistic. Furthermore, our method decomposes hairstyle into three attributes: structure, texture, and color to better control the hairstyle transfer process. Besides, we observe that the spatial scales of these attributes match those of the StyleGAN’s latent codes. Then, we decouple the latent code in the hierarchical dimension and use different layers of the latent code to complete the transfer of different hair attributes, which reduces the entanglement between attributes without extra calculations. Correspondingly, our method divides hairstyle transfer into three steps, making the transfer process more controllable. By comparing with state-of-the-art methods, we verify the effectiveness of our method.
Gaussian mixture model sample selection strategy–based active semi-supervised soft sensor for industrial processes

Soft sensors have become reliable tools for estimating difficult-to-measure target variables in modern industrial processes. In order to make full use of labeled and unlabeled samples, an active semi-supervised soft sensor modeling method is proposed, which combines active learning and semi-supervised learning to maximize model performance and minimize the laboratory analysis cost of expanding the labeled sample data set. First, manifold regularization is introduced into the deep extreme learning machine (DELM) algorithm to form a semi-supervised DELM that improves the performance of a model trained with unlabeled samples. Then, considering non-Gaussian processes and the error information between the predicted and true values, an active sample selection strategy based on error Gaussian mixture model is developed. Using this strategy, the most uncertain and representative unlabeled samples are selected for labeling, and thereby expanding the labeled sample data set. Finally, the effectiveness of the proposed method is verified using industrial debutanizer process data.
Hybrid Pelican and Archimedes optimization algorithm fostered energy aware task scheduling in heterogeneous virtualized cloud computing

Abstract Energy reduction is a key issue of virtualized cloud computing (CC) schemes, because it provide significant benefits, like lower operating costs, improving system effectiveness, securing the environment. Energy‐efficient task scheduling is a feasible mode to attain these objectives. Nevertheless, it is difficult to match cloud resources to user requests in a way that improves performance while meeting a user‐defined deadline for energy consumption reduction. Therefore, hybrid Pelican and Archimedes optimization algorithm fostered energy‐aware task scheduling in heterogeneous virtualized cloud computing (TS‐HVCC‐Hyb‐POA‐AOA) is proposed in this article. A hybrid Pelican and Archimedes optimization algorithm (Hyb‐POA‐AOA) is used to lessen the task duration and the consumption of power in the cloud. For evaluation, the cloud environment uses the provided environment hybrid Pelican and Archimedes optimization algorithm for execute a few common workloads at the simulated data center. The performance metrics, like makespan right skewed analysis, makespan left skewed analysis, energy consumption right skewed analysis, energy consumption left skewed analysis, availability and resource utilization is considered. The performance of the proposed TS‐HVCC‐Hyb‐POA‐AOA method provides 23.69%, 29.50%, and 36.78% higher resource utilization; 38.23%, 31.35%, and 26.19% lower consumption of energy left skewed analysis and 34.52%, 30.28%, and 23.54% lower makespan for right skewed analysis compared with existing methods such as; task scheduling in heterogeneous cloud environment with gray wolf optimization algorithm (TS‐HVCC‐GWOA), hybrid whale optimization approach and differential evolution optimization for multiple objective virtual machine programming at cloud services (TS‐HVCC‐WOA), energy and performance planning in TasterkHeterek virtualized cloud computing using particle swarm optimization algorithm (TS‐HVCC‐PSOA).
A comprehensive approach for the evaluation of the impact of blockchain on photovoltaic supply chain using hybrid data analytic method

Blockchain technology has emerged as a disruptive and transformative innovation, drawing significant interest from organizations for its potential advantages. This study employs an exploratory research approach, utilizing a hybrid data analytics method, to investigate the impact of blockchain on the photovoltaic supply chain. Our research employs a comprehensive methodology, combining both quantitative and qualitative techniques. The research journey begins with the identification of crucial indicators, followed by the collection of data through a carefully designed questionnaire and the utilization of the Delphi method for expert insights. Subsequently, we employ advanced data analytics techniques, specifically the fuzzy Analytic Network Process (ANP) and fuzzy Interpretive Structural Modeling (ISM) methods, to prioritize these indicators and unveil their intricate relationships. Our findings underscore the paramount importance of several key criteria among the twelve identified indicators. These include data security, effective data management, data quality assurance, robust system documentation, and streamlined access protocols. Notably, customer satisfaction emerges as a pivotal factor, exerting substantial influence within our model. Moreover, our analysis underscores that data security stands as the foundational element in this multifaceted framework. This study offers valuable insights for stakeholders in the photovoltaic supply chain, aiding in the strategic adoption of blockchain technology to enhance transparency, efficiency, and overall performance. It serves as a robust framework for organizations seeking to navigate the evolving landscape of sustainable energy supply chains while harnessing the potential of blockchain innovation.
A longitudinal case study on Nexus transformation: Impact on productivity, quality, and motivation

Abstract There have been success stories reported regarding the adoption of agile software development methods in the industry. There also exist observations on their limitations. One of these limitations is scalability since agile methods like Scrum were originally designed for small software teams. Scalable agile frameworks were introduced to address this limitation. We conducted an industrial case study on the adoption of such a framework, called Nexus. Our study involves quantitative and qualitative evaluation based on observations within a product development organization over a period of 12 months. Scrum is used for the development of a product during the first 6 months of this period. Nexus is used in the remaining 6 months. Data are collected throughout the whole period for measuring productivity, quality, and team member motivation. Results suggest a significant increase in productivity and product quality after switching to Nexus. Team motivation was slightly improved as well.
APLIKASI PENGENALAN OBJEK WISATA KEPULAUAN RIAU MENGGUNAKAN METODE SOFTWARE DEVELOPMENT LIFE CYCLE

The Riau Archipelago is a place that has many tourist objects, one of which is in Batam City. Batam City is one of the largest cities in the Riau Archipelago Province, strategically located very close to international waterways, direct widening with the Singapore Strait and the Malaysia Strait. Tourism object is everything that exists in a tourist destination area which is an attraction so that people want to come to visit that place, all places or natural conditions that have tourist resources that are built and developed so that they have an attraction and are cultivated as a place to visit. traveler. However, currently there is no application that helps the public to introduce tourist objects and forms of marketing or marketing tourism objects in Batam City for foreign tourists is very difficult. So with this Android-based application for recognizing tourist objects in the Riau Islands to make it easier or to get to know tourist objects in the Riau Islands, and to make it easier to find locations for tourist objects in the Riau Islands. Its creation in mobile form aims to make it easy to carry anywhere. with this an application will be created using Adobe Animate. This research method uses the SDLC (Software Development Life Cycle) method with the aim of introducing natural tourism objects in the Riau Archipelago and the design uses UML (Unified Modeling Language).
EXPERT SYSTEM MENDIAGNOSA TINGKAT DEPRESI SISWA MENGGUNAKAN METODE CERTAINLY FACTOR

Depression is a mood disorder characterized by symptoms such as moodiness, lethargy, lack of passion, feelings of worthlessness, deep frustration, hopelessness, thoughts of death and suicidal thoughts, depression is one of the mental health conditions, many students still have difficulty determining which stage and how severe the depression they are experiencing, Identifying factors that affect the level of depression experienced by students by applying the Certainty Factor (CF) method to the expert system to diagnose the level of student depression, Certainty Factor is a method for dealing with uncertainty in rule-based systems by performing calculations, after the system runs well, the implementation is carried out on students by registering first then logging in after that answering questions from data on symptoms that cause depression based on their respective levels.
PERANCANGAN GAME EDUKASI PEMBELAJARAN BAHASA PEMROGRAMAN JAVA BERBASIS ANDROID

Digital education is currently very developed in the field of technology which has learning facilities with educational games so that lessons will be easier to understand and more interesting. while studying the Java programming language is known as learning that teaches the basics of programming languages based on android. while learning at BATAM ADVENT VOCATIONAL SCHOOL uses guidebooks that can be obtained by teachers from the internet using traditional methods making it difficult for students to learn Java, because the teacher only delivers lessons in front of the class and students only listen to the lessons delivered by the teacher. The method used to design educational games for learning the Java programming language based on Android is the Game Development Life Cycle (GDLC) and Unified Modeling Language (UML) methods, making games using Unity. The research is educational games for learning the Java programming language based on Android which can be used as learning media at ADVENT BATAM Vocational Schools, to add insight and knowledge to students about Java programming.
RANCANG BANGUN SISTEM E-ADMINISTRASI BERBASIS CODEIGNITER FRAMEWORK DENGAN NOTIFIKASI WHATSAPP

e-administration in citizens is a process that always exists in the administrative process using an electronic system, especially in the process of administering citizen data collection. With the development of technology that is growing rapidly from era to era which is increasingly sophisticated with its role in creating, storing, conveying, and disseminating information, therefore various jobs, especially in the administration of citizens, become easier and more efficient.The purpose of this research is to design an e-administration system based on the codeigniter framework with whatsapp notifications to be applied to Griya Pratama Housing. The final result that will be achieved by the author is the Design of an E-Administration System based on CodeIgniter Framework with Whatsapp Notification designed to facilitate the administration process, especially in the process of collecting data on residents, the relevant RT no longer has to visit residents' homes one by one and also in the procurement of activity agendas, one of which is mutual cooperation activities or other activity agendas, only need to notify them in the features that have been provided in the system on the website.
RANCANGAN SISTEM ABSENSI SISWA OTOMATIS BERBASIS SMART QR CARD MENGGUNAKAN ALGORITMA BCRYPT

Various problems due to shortages in attendance are still implementing the system manual method so that the teacher feels that it has reduced flexibility in attendance. Following up on the problem, system changes must occur in order to support the attendance program. The system that was built applies some of the technology (internet of things) with a combination of hardware that functions to scan student cards of the Smart Quik Response Card type and web-based software for processing machine data with a MySQL database. The method of choice for software design is to apply bcrypt as password security at login to overcome breaches in attendance data. The results of this study built an automatic attendance system based on a smart quik response card. Based on testing by applying the blackbox method to accurately assess the attendance system that has been built, it gets a compatible score or runs smoothly for each trial feature. After experimenting with the implementation at the research location, it can be concluded that the process of absenteeism that occurs at school aims to make teachers more efficient and facilitate administration in using the application system to become disciplined and timely students.
Blockchain Privacy and Regulatory Compliance: Towards a Practical Equilibrium

In this paper we study Privacy Pools, a novel smart contract-based privacy-enhancing protocol. We discuss the pros and cons of this protocol, and show how it could be employed to create a separating equilibrium between honest and dishonest users. The core idea of the proposal is to allow users to publish a zero-knowledge proof, demonstrating that their funds (do not) originate from known (un-)lawful sources, without publicly revealing their entire transaction graph. This is achieved by proving membership in custom association sets that satisfy certain properties, required by regulation or social consensus. The proposal may be a first step towards a future where people could prove regulatory compliance without having to reveal their entire transaction history.
Book Publishing and Print Disabilities

Abstract There is a community of people in Lithuania who experience difficulties in reading printed books. When ebooks are scarce they are left without proper access to an important information source. Comparing the Lithuanian results with data from other contexts, we find that the availability of ebooks in Lithuania is scarcer than in other developed countries: they are from two to three times less available. Computer use and access to internet use in Lithuania over the past decade has increased by 20 per cent; however, access to ebooks grew at a slower pace. Analysis of the most popular books indicates that relatively few of them are available in ebook format, which suggests deliberate choices to promote printed books by restraining the release of ebook versions. From 2010 to 2019, on average, only 14 per cent of printed titles published were available in one of the ebook formats.
MPCNet: Compressed multi-view video restoration via motion-parallax complementation network

The performance in restoring compressed multi-view video (MVV) of the existing learning-based methods is limited because they only utilize information of temporally adjacent frames or parallax neighboring views. However, the compression artifacts caused by multi-view coding (MVC) may be related to the reference errors of intra-frame, inter-frame, and inter-view. In this paper, with delicately utilizing the stereo information from both temporal and parallax domains, a motion-parallax complementation network (MPCNet) is proposed to restore the quality of compressed MVV more efficiently. First, we introduce a motion-parallax complementation strategy consisting of a coarse stage and a fine stage. By mutually compensating the feature extracted from multiple domains, useful multi-frame information can be efficiently preserved and aggregated step by step. Second, an attention-based feature filtering and modulation module (AFFM) is proposed, which provides an efficient fusion method for two features by suppressing misleading information. By deploying it in most submodules of the proposed approach, the representational ability of MPCNet can be improved, resulting in a more substantial restoration performance. Experimental results prove the effectiveness of MPCNet by an average increase of 1.978 dB in PSNR, and 0.0282 in MS-SSIM. The BD-rate reduction can reach 47.342% on average. The subjective quality is greatly improved and lots of compression distortions are eliminated. Meanwhile, this work also benefits the accuracy improvement for high-level vision tasks, e.g., mIoU of semantic segmentation and mAP of object detection achieve 0.352 and 51.71, respectively. Quantitative and qualitative analyses demonstrate that MPCNet outperforms state-of-the-art approaches.
The effect of an event-triggered controller on non-strict feedback system based on the single input interval type-2 fuzzy method

Non-Strict Feedback (NSF) physical systems are broader than Strict-Feedback (SF) systems. The system state variables depend on their system functions, unlike SF systems that the system variables do not depend on their system function. Consequently, NSF physical systems are challenging to control. To address this control challenge in NSF physical systems, this research proposed an Event-Triggered (ET) adaptive backstepping control method based on Single Input Interval Type-2 Fuzzy (SIIT-2F) for NSF system with input delay. The effect of the method is examined on a third order system using transient and steady state performance as a metric. The ET mechanism prompts due to violation of a predefined condition (mismatch between the actuator and the controller), uncertainty of the system dynamics was handled by the Radial Basis Function Neural Network (RBFNN). The input delay was handled by a developed auxiliary system, and the SIIT-2F was used to minimize error between state variables and auxiliary states of the adaptive backstepping controller. The results of this work were compared with adaptive neural control for NSF method. The method has an average percentage performance improvement of23% peak overshoot for state variables; and 47% for adaptive variables, the average performance improvement for the absolute peak undershoot are 50%, and 60% respectively for the state and auxiliary variables. In addition, the settling time average performance improvement is 11%, 21%, and 10% for state, auxiliary, and adaptive variables, respectively. The results of this study underline that the ET based SIIT-2F controller improved the performance of NSF systems compared to the adaptive neural control method.
HSFA: A novel firefly algorithm based on a hierarchical strategy

The firefly algorithm (FA) is a well-known metaheuristic algorithm that has demonstrated effectiveness and superiority in a variety of optimization tasks. To alleviate the oscillation caused by fireflies during the search process and improve the solution quality of the FA, this paper proposes an improved FA based on the hierarchy strategy (HSFA). In the HSFA, the firefly population was separated into elite and non-elite groups based on fitness values, and distinct attractive models were used for each group to improve the performance of the HSFA from the perspective of exploration and exploitation capabilities. This enhances the convergence speed and solution quality of the algorithm. The HSFA was run on two sets of standard benchmark functions, and its performance was evaluated using two practical engineering design problems. The experimental results demonstrate that the HSFA outperforms state-of-the-art optimization algorithms.
Nested ensemble selection: An effective hybrid feature selection method

It has been shown that while feature selection algorithms are able to distinguish between relevant and irrelevant features, they fail to differentiate between relevant and redundant and correlated features. To address this issue, we propose a highly effective approach, called Nested Ensemble Selection (NES), that is based on a combination of filter and wrapper methods. The proposed feature selection algorithm differs from the existing filter-wrapper hybrid methods in its simplicity and efficiency as well as precision. The new algorithm is able to separate the relevant variables from the irrelevant as well as the redundant and correlated features. Furthermore, we provide a robust heuristic for identifying the optimal number of selected features which remains one of the greatest challenges in feature selection. Numerical experiments on synthetic and real-life data demonstrate the effectiveness of the proposed method. The NES algorithm achieves perfect precision on the synthetic data and near optimal accuracy on the real-life data. The proposed method is compared against several popular algorithms including mRMR, Boruta, genetic, recursive feature elimination, Lasso, and Elastic Net. The results show that NES significantly outperforms the benchmarks algorithms especially on multi-class datasets.