Reviewerly/exHarmony huggingface dataset:
E-Voting Using Public Blockchain

Abstract: Since then, there have been some ways of voting. Around the world, paper ballots are the most common voting format. Only in the past ten years have electronic voting schemes gained popularity, and they remain unaddressed. E-voting systems have issues with security, reliability, openness, functionality and dependability. Estonia is the innovator in this area and might be regarded as the cutting edge. However, there aren't many alternatives that use blockchain. All of the aforementioned issues can be solved with blockchain, which also offers benefits like immutability and decentralization. The primary issues with blockchainbased technology used for electronic voting are their narrow focus or the lack of testing and comparability. We introduce an electronic voting system built on the blockchain that may be applied to any type of voting. Blockchain uses it to the fullest extent possible and has the capacity to manage all processes. After the voting process has begun, the platform behaves as though it is completely independent and decentralized, with no potential for interference. Although the data are completely transparent, homomorphic encryption protects the voters' identities. The main innovation of our solution is the fully decentralized management of the e-voting platform through blockchain, transparency of the entire process, and privacy and security of the voters thanks to homomorphic encryption.
Design of an app for a module of practices of basic electrical installations

Currently, electrical installations of any kind must comply with minimum safety standards taken into account at the time of their construction and use. This article aims to present the design and construction of a series of practices as well as the design of a mobile application for the improvement of learning in the training of residential electrical installations of the "Mahatma Gandhi" High School of Xilocuautla, Huauchinango, Puebla, through a practice module "ElectriBasic App". The App will serve as a didactic support, since it is a graphic and technical instruction for students who wish to carry out practices related to residential electrical installations. With this module, students will be able to identify, through various previously organized practices, the types of connections and different types of accessory arrangements of a residential electrical installation, as well as identify concepts from basic to intermediate that they do not know within the topics of electricity.
Saliency-Augmented Memory Completion for Continual Learning

Continual Learning (CL) is considered a key step toward next-generation Artificial Intelligence. Among various methods, replay-based approaches that maintain and replay a small episodic memory of previous samples are one of the most successful strategies against catastrophic forgetting. However, since forgetting is inevitable given bounded memory and unbounded tasks, ‘how to forget’ is a problem continual learning must address. Therefore, beyond simply avoiding (catastrophic) forgetting, an under-explored issue is how to reasonably forget while ensuring the merits of human memory, including 1) storage efficiency, 2) generalizability, and 3) some interpretability. To achieve these simultaneously, our paper proposes a new saliency-augmented memory completion framework for continual learning, inspired by recent discoveries in memory completion/separation in cognitive neuroscience. Specifically, we innovatively propose to store the part of the image most important to the tasks in episodic memory by saliency map extraction and memory encoding. When learning new tasks, previous data from memory are inpainted by an adaptive data generation module, which is inspired by how humans “complete” episodic memory. The module's parameters are shared cross all tasks and it can be jointly trained with a continual learning classifier as bilevel optimization. Extensive experiments on several continual learning and image classification benchmarks demonstrate the proposed method's effectiveness and efficiency.
CESED: Exploiting Hyperspherical Predefined Evenly-Distributed Class Centroids for OOD Detection

Out-of-distribution (OOD) detection is critical for ensuring the safe deployment of machine learning models in the open world. Due to the simplicity and intuitiveness of distance- based methods, i.e., samples are detected as OOD if they are relatively far away from the centroids or prototypes of in-distribution (ID) classes, they have attracted widespread attention from researchers in the field of OOD detection. However, prior OOD detection methods directly take off-the- shelf loss functions, like widely used softmax cross-entropy (CE) loss, that suffices for classifying ID samples, but is not optimally designed for OOD detection. In this work, we propose CESED, an improved CE loss applied to the scalable Squared Euclidean Distance vector, which exploits hyper- spherical evenly-distributed class centroids for OOD detection. CESED can promote strong ID-OOD separability because it explicitly encourages maximization of inter-class distances and minimization of intra-class distances. Extensive experiments demonstrate that CESED achieves superior detection performance on a comprehensive suite of benchmark datasets. For the more challenging case where CIFAR-100 is used as ID, our method achieves a 31.98% reduction in average FPR95 and 6.20% reduction in ID test error compared to the baseline method using a softmax confidence score.KeywordsImage ClassificationOut-of-Distribution DetectionHyperspherical EmbeddingCross-Entropy Loss
ML4C: Seeing Causality Through Latent Vicinity

Supervised Causal Learning (SCL) aims to learn causal relations from observational data by accessing previously seen datasets associated with ground truth causal relations. This paper presents a first attempt at addressing a fundamental question: What are the benefits from supervision and how does it benefit? Starting from seeing that SCL is not better than random guessing if the learning target is non-identifiable a priori, we propose a two-phase paradigm for SCL by explicitly considering structure identifiability. Following this paradigm, we tackle the problem of SCL on discrete data and propose ML4C. The core of ML4C is a binary classifier with a novel learning target: it classifies whether an Unshielded Triple (UT) is a v-structure or not. Specifically, starting from an input dataset with the corresponding skeleton provided, ML4C orients each UT once it is classified as a v-structure. These v-structures are together used to construct the final output. To address the fundamental question of SCL, we propose a principled method for ML4C featurization: we exploit the vicinity of a given UT (i.e., the neighbors of UT in the skeleton), and derive features by considering the conditional dependencies and structural entanglement within the vicinity. We further prove that ML4C is asymptotically correct. Thorough experiments conducted on benchmark datasets demonstrate that ML4C remarkably outperforms other state-of-the-art algorithms in terms of accuracy, reliability, robustness and tolerance. In summary, ML4C shows promising results on validating the effectiveness of supervision for causal learning. Our codes are publicly available at https://github.com/microsoft/ML4C.KeywordsCausal discoverysupervised causal learningidentifiabilitylearnability
jsunrUse of GISand RSCombined with Artificial Intelligence Plate for Detecting and Analyzing of Aquatic Heavy Metals Threats for Environmental Sustainability of Green Ports

With the technological development globally, Geographic Information System(GIS)and Remote Sensing (RS)has been used and reformed in providing reliable and useful data in the field of environmental studies advancement. There has been needed forGIS and RS in terms of biological and chemical elements in the aquatic environments to assess the concentration of trace metals in the tissue of aquatic species around the world. However, there is still inadequate information and exploration on the subject matter which calls for the use of artificial intelligence plates in acquiring data.There is a need for the use of the invented plate in green port sustainability in order to save time, money, life, less labour and availability of more multiple accurate data information that will have improved sustainability. The primary aim of this concept paper focused on using GIS &amp; RS combined with artificial intelligence plate in Green port environmental sustainability to monitor, acquire data and identify biologicaland chemical elements data online for analysis. This aim was achieved by the application of qualitative research, that is synthetic literature review. Through the review, we are able to find out that artificial intelligence plates will be useful. Therefore, it requests for innovation of an artificial intelligent plate that will detect, analyze and recommend the concentration of heavy metals and other anthropogenic elements in the water bodies and species tissues without necessarily involving laboratory processes. In conclusion, to provide a theory from fusing past and present circumstances in order to forecast the future for coastal areas, GIS &amp; RS applications and artificial intelligence plates were suggested. This review gives instances of GIS applications' used in the stages of assessment, conservation, maintenance, sustainability, and protection of the environment of marine areas. This study suggests the use of a digital plate that can be used with GIS &amp; RS in monitoring, spatiotemporal distribution and identifying the concentration of biological and chemical elements in the environmental sustainability of port institutions and industrial organizations.
Demonstration of production and conceptualization of a continuous beam of X-Rays electromagnetic radiation, from Nikola Tesla’s single node tube, with subsequent characterization of the pulsating occult sound frequency, of the inner tube discharge gas, using Artificial Intelligence software, and elaboration of a plan of virtual energy

The life work of Serbian scientist and inventor Nikola Tesla has produced important innovations not only in the field of telecommunications and wireless transmission but also in the fields of electrical distribution systems, fluid propulsion and signaling systems. Such is the amount of his work, especially from the point of view of scientific weight, that his resonance will be felt for many decades in the future, having himself been a precursor. One of the experiences of the Serbian inventor, not well known to most people, is his work on X-rays. His experiments led to the definition, at the same time as German physicist Wilhelm Conrad Roentgen published his discovery on "particularly penetrating rays", in 1895, a form of radiation that could pass through soft tissue, highlighting electron-dense structures such as bones. Of great importance is his work which included not only the production of X-rays through the then known Crookes tubes, with anti-cathode, but also on a new version of the X-ray tubes, the one on which this single node study will be focused. of reduced size and even simpler constitution, compared to the aforementioned Crookes tubes with red-hot cathode rays where, X-rays were produced thanks to the impact of fast electrons (cathode rays) on a heavy metal structure, such as Tungsten, Palladium, or Platinum. Beyond the demonstration of the production of X-Rays, through the Tesla Tube, we have demonstrated the possibility of producing audio frequencies, having the same frequency as the X-Rays, thus defining them, Virtual Energy.
A General-Purpose Transferable Predictor for Neural Architecture Search

Understanding and modelling the performance of neural architectures is key to Neural Architecture Search (NAS). Performance predictors have seen widespread use in low-cost NAS and achieve high ranking correlations between predicted and ground truth performance in several NAS benchmarks. However, existing predictors are often designed based on network encodings specific to a predefined search space and are therefore not generalizable to other search spaces or new architecture families. In this paper, we propose a general-purpose neural predictor for NAS that can transfer across search spaces, by representing any given candidate Convolutional Neural Network (CNN) with a Computation Graph (CG) that consists of primitive operators. We further combine our CG network representation with Contrastive Learning (CL) and propose a graph representation learning procedure that leverages the structural information of unlabeled architectures from multiple families to train CG embeddings for our performance predictor. Experimental results on NAS-Bench-101, 201 and 301 demonstrate the efficacy of our scheme as we achieve strong positive Spearman Rank Correlation Coefficient (SRCC) on every search space, outperforming several Zero-Cost Proxies, including Synflow and Jacov, which are also generalizable predictors across search spaces. Moreover, when using our proposed general-purpose predictor in an evolutionary neural architecture search algorithm, we can find highperformance architectures on NAS-Bench-101 and find a MobileNetV3 architecture that attains 79.2% top-1 accuracy on ImageNet.
CCTV Based Observation for Face Masked Detector

The utilization of CCTV observation is presently required withinside the general public and individual areas to ensure assurance against psychological oppression and burglary. Normal articulations are utilized to demonstrate a major arrangement of development credits caught in a video. Video surveillance is a famous machine wherein vital scenes aren't captured with the aid of using human intervention. The essential intention is to mechanically discover masked human beings in much less time. In this paper, the device includes 4 distinct steps: calculating the gap variety of someone from the camera, detecting eyes or eyes, and detecting components of the face consisting of the mouth and face. The overall performance of the Viola-Jones set of rules runs on loads of real-time inputs. Experimental critiques display that the Viola-Jones set of rules is advanced in phrases of time consumption. Viola-Jones algorithm is a unique approach to this problem, it creates a transparent, complex and simple way to make real-time implementations advantageous and practical. Analysis of Viola-Jones set of rules compliance at the take a look at video music offers an affordable evaluation for extra upgrades in masked face detection performance.&#x0D;
Analisis Peta Persebaran dan Pembandingan Hasil Forecasting Ketersediaan Beras di Kabupaten Tulang Bawang Provinsi Lampung

The availability of rice as a staple food commodity is one of the essential aspects in showing food security in an region, and the government must guarantee its availability. Tulang bawang regency is one of the rice barns in Lampung province, with a total population of 427,000 people. This study aims to map the condition of rice availability in Tulang bawang Regency from 2016-to 2020. In addition, this study also aims to analyze the forecasting of rice production, consumption, and availability in Tulang bawang regency. This study uses secondary data from the Food Security Office and the Statistics Center Office of Tulang bawang regency from 2016-to 2022. This research uses mapping analysis processed with microsoft excel and forecasting analysis with linear regression method. The mapping results show that the availability of rice in all sub-districts in Tulang bawang regency is moving towards a surplus from 2016-to 2020, and fewer sub-districts are experiencing a rice availability deficit. Forecasting results on production, consumption, population, and rice availability have residuals with actual data even though the movement trends are both positive.
Aplikasi Portal Berita Berbasis Website (Studi Kasus: Lintasberita1.Com)

Since its establishment on September 25, 2018, lintasberita1.com has been active in reporting for the Palangka Raya city area of Central Kalimantan Province. Currently, lintasberita1.com still uses WordPress as a blog engine for news content. The use of WordPress has limitations in that the customization that can be done is limited to what is provided by the application developer itself. Another problem occurs in the news reporting process for field journalists who still use the WhatsApp application. This process is time-consuming for editors to take news and edit it on the WordPress administrator's site for publishing. Ad management constraints also often occur because they have to manually input data. Based on these problems, the purpose of this study is to design and build a website-based news portal lintasberita1 that can accommodate user needs. The waterfall software development method was used in this study. This method has several stages, namely requirement definition, system and software design, implementation and unit testing, integration and system testing, and operation and maintenance.. This research produces a website application that can provide information on the latest news in Central Kalimantan Province, and improve the news processing process to be systematic and integrated.
Aplikasi Tanda Tangan Digital Pada Platform Android

A signature or initial is a handwriting that is sometimes given a certain style of writing from a person's name or other identification mark. Basically, the signature has a function as written evidence that shows the fulfillment of the terms of the "agreement". Along with the development of digital technology and the emergence of the internet, digital signatures are growing. Now digital signatures are increasingly in demand by the public and are increasingly used by individuals and companies to state institutions for formal purposes involving digital documents. This application was developed using the Extreme Programming methodology. Application development begins with making plans to find the needs of users. Then do the design of the application system that will be made. Followed by design, program implementation and application testing.The results of this study are applications that can create, store, and insert digital signatures in the form of QR Codes or hand drawings into PDF files and save them, and can scan QR Codes either through the camera or take QR Code images from the gallery.
Using the LARA platform to crowdsource a multilingual, multimodal Little Prince

We describe an ongoing project, in which an informally organised international consortium is using the open source LARA platform to create multimodal annotated editions of Antoine de Saint-Exupéry’s Le petit prince in multiple languages, so far French, English, Italian, Icelandic, Irish, Japanese, Polish, Farsi and Mandarin. LARA versions of the book include integrated audio and translations and an automatically generated lemma-based concordance, and are freely available online. We describe the methods used to construct the various versions. In some cases, work for a given language was simply divided by type, typically with one person adding translations and another recording audio. In other languages, we experimented with crowdsourcing methods, splitting the text into chapter-sized units and using the LARA platform to distribute these to multiple annotators, then combining the results at the end. Finally, we report an initial classroom study, where the French version was used by intermediate-level Australian students of French.
Methodologies for Enhancing Data Integrity and Security in Distributed Cloud Computing with Techniques to Implement Security Solutions

Usually, cloud infrastructure is used individually by businesses, whereas the hybrid cloud would be a blend of two or many kinds of clouds. Because as clouds become increasingly common, safety issues also expanding. Because of such cybersecurity threats, numerous experts suggested procedures as well as ways to assure internet confidentiality. Providers of cloud-based services were accountable for the complete safety of cloud information. Nevertheless, since the clouds are accessible (easily accessible over the World wide web), much research has been conducted on cloud storage cybersecurity. This paper describes methods for enhancing security and reliability in decentralized cloud-based solutions, as well as suggests a few security solution methods of implementation.
Metric Learning as a Service With Covariance Embedding

Metric learning as a service (MLaaS) represents one of the main learning streams to handle complex datasets in service computing research communities and industries. A common approach for dealing with high-dimensional and complex datasets is employing a feature embedding algorithm to compress data through dimension reduction while optimizing intra-class distance. To create generalizable MLaaS for high-performance artificial intelligence applications with high-dimensional Big Data, a robust and meaningful embedding space representation by efficiently optimizing both intra-class and inter-class relationships is required. We developed a novel MLaaS methodology that incorporates covariance to signify the direction of the linear relationship between data points in an embedding space. Our covariance-based feature embedding architecture introduces three different yet complementary mapping functions: inner-class mapping, intra-class with semi-inter-class mapping, and intra- and inter-class mapping. Unlike conventional metric learning, our covariance-embedding-enhanced approach is more expressive and explainable for computing similar or dissimilar measures and can capture positive, negative, or neutral relationships. Our MLaaS framework ensures efficient, composable, and extensible metric learning by supporting the selection of dimension reduction and data compression methods. Experiments conducted using various benchmark datasets demonstrate that the proposed model can obtain higher-quality, more separable, and more expressive embedding representations than existing models.
Image inpainting algorithm based on image decomposition model and patch global structural information

With the improvement of image processing tools and the flexibility of digital image editing, automatic image inpainting has found important applications in computer vision and has also become an important and challenging topic of research in image processing. Through the analysis of the exemplar-based image inpainting method, we found the calculation of priority of image patches to fill is beneficial to the textures than the geometric structure, while the geometric structure is more important to the filling than the textures, resulting in the problem of texture extension or the discontinuity of the geometric structure in the image. The image decomposition model is applied to decompose the image to in paint into two components: texture and cartoon, which are then filled respectively. We improve the calculation of the priority of the texture component and the cartoon component, which make it suitable to the image character. In order to solve the mismatch problem, we propose a measurement based on global structure information of the target patch and the candidate patch in this paper. The mean and variance of the pixels in the image patch representing the global structure of image patches added to the measurement formula can improve the accuracy of the best matching patch to fill the missing region. We compared our model with the Criminisi algorithm and its improved one, through applying the algorithm to the synthetic images and the natural image. We also compute the PNSF and SSIM values between the original images and the inpainted images to verify the effectiveness of the proposed algorithm. The experimental results show that the proposed algorithm can get more perceptually aware in painted results than the Criminisi algorithm and its improved algorithm.