Reviewerly/exHarmony huggingface dataset:
Analysis of Identification of Food Commodity Farmers’ Needs in utilizing Smart Farming Technology

Abstract Smart farming technology (SFT) is currently widely used by farmers around the world. Especially in Asia, the use of SFT is increasing. Before introducing SFT to farmers, it is necessary to research what kind of technology farmers need based on the commodities they cultivate. The research objectives are: (1) To find out the interest in the types of Smart Farming technology (SFT) for food commodity farmers in Aceh Province; and (2) To identify differences in interest between types of Smart Farming (SFT) technology by food commodity farmers in Aceh Province. This research was conducted in 2 districts of Aceh province, namely Bener Meriah and North Aceh. The research was carried out in January 2022. The method of determining the sample in this study uses a non-probability sampling method with quota sampling as a sampling technique. This study uses descriptive statistical methods, cross tabulation test analysis and the Kruskal Wallis test. The result is interest of food commodity farmers for each type of SFT is very varied. Corn and potato farmers are interested in 3 types of SFT Autonomous Machines, Recording/Mapping and FMIS/Apps. Paddy farmers are interested in 4 types of SFT, namely Autonomous Machines, Recording/Mapping, Tractor GPS/Connected Tools, and FMIS/Apps. The SFT in demand has significant differences for each food commodity farmer, such as Recording/Mapping and Tractor GPS/Connected Tools. For Autonomous Machines and FMIS/Apps, there are no significant differences in the interest of each food commodity farmer.
Does human capital mediate the relationship between digital competence and service innovation capability? Evidence from the UAE during covid-19 pandemic

Purpose The purpose of this study is to investigate the relationship between digital competence and the capability to innovate in the service sector and examine if this relationship is mediated by human capital in the context of COVID-19 pandemic. Design/methodology/approach Using a cross-sectional survey, data were collected from 188 service sector professionals in the United Arab Emirates. Data were analyzed using partial least square-based structural equation modeling. Findings The findings of partial least square based structural equation modeling analysis indicate that there is a significant positive relationship between digital competence and the capacity to innovate in the service sector, which is partially mediated by human capital. Originality/value This study contributes to knowledge by offering an understanding of the relationship between digital competence and innovation capability, especially in uncertain situations. This study also notes the importance of human capital as a strategic resource for innovation.
A TEDE Algorithm Studies the Effect of Dataset Grouping on Supervised Learning Accuracy

Datasets are the basis for research on deep learning methods in computer vision. The impact of the percentage of training sets in a dataset on the performance of neural network models needs to be further explored. In this paper, a twice equal difference enumeration (TEDE) algorithm is proposed to investigate the effect of different training set percentages in the dataset on the performance of the network model, and the optimal training set percentage is determined. By selecting the Pascal VOC dataset and dividing it into six different datasets from largest to smallest, and then dividing each dataset into the datasets to be analyzed according to five different training set percentages, the YOLOv5 convolutional neural network is used to train and test the 30 datasets to determine the optimal neural network model corresponding to the training set percentages. Finally, tests were conducted using the Udacity Self-Driving dataset with a self-made Tire Tread Defects (TTD) dataset. The results show that the network model performance is superior when the training set accounts for between 85% and 90% of the overall dataset. The results of dataset partitioning obtained by the TEDE algorithm can provide a reference for deep learning research.
Feature Extracted Deep Neural Collaborative Filtering for E-Book Service Recommendations

The electronic publication market is growing along with the electronic commerce market. Electronic publishing companies use recommendation systems to increase sales to recommend various services to consumers. However, due to data sparsity, the recommendation systems have low accuracy. Also, previous deep neural collaborative filtering models utilize various variables of datasets such as user information, author information, and book information, and these models have the disadvantage of requiring significant computing resources and training time for their training. To address this issue, we propose a deep neural collaborative filtering model with feature extraction that uses minimal data such as user number, book number, and rating information. The proposed model comprises an input layer for inputting and embedding the product and user data, a feature extraction layer for extracting the features through data correlation analysis between the embedded user and product data, a multilayer perceptron, and an output layer. To improve the performance of the proposed model, Bayesian optimization was used to determine hyperparameters. To evaluate the deep neural collaborative filtering model with feature extraction, a comparative analysis experiment was conducted with currently used collaborative filtering models. The goodbooks-10k public dataset was used, and the results of the experiment show that the low accuracy caused by data sparsity was considerably improved.
An efficient novel paradigm for object detection through web camera using deep learning (YOLOv5’s object detection model)

Object detection, a fundamental duty in computer vision that has a wide range of practical applications, they are surveillance, robotics, and autonomous driving. Recent developments of deep learning have got gradual improvemenrts in detection accuracy and speed. One of the most popular and effective deep learning models for object detection is YOLOv5. In this discussion, we an object detection model through YOLOv5 and its implementation for object detection tasks. We discuss the model’s architecture, training process, and evaluation metrics. Furthermore, we present experimental results on popular object detection benchmarks to demonstrate the efficacy and efficiency of YOLOv5 in detecting various objects in complex scenes. Our experiments states that YOLOv5 out performs other state of the art object detection models case of accuracy of detected image and speed of detection, making it a promising approach for real-world applications. Our work contributes to the growing body of research on deep learning-based object detection and provides valuable insights into the capabilities and limitations of YOLOv5. By improving accuracy, speed of object detection models, we have enabled a wide range of applications that can benefit society in countless ways.
Design of 3d animation color rendering system based on image enhancement algorithm and machine learning

At present, this paper points out the color problem of 3D animation, comprehensively studies the mechanism of machine image quality degradation and the related image enhancement methods integrating multiple scene variables, and improving the image enhancement method is an important problem to be solved in the industry. One or more image quality evaluation indicators are used to extract some features of the image, maintain the consistency with the subjective perception of the human eye, build an appropriate image quality evaluation machine model, and use machine learning technology to analyze image improvement algorithm data to achieve image quality evaluation results. With the change of lighting conditions in complex scenes, the gray distribution, clarity and color reproducibility of the captured image will have a great impact. Therefore, this paper mainly takes these three attributes as the key parameters to evaluate the image quality. At this stage, computer learning technology has developed rapidly. Therefore, based on computer learning technology, 3D animation has brought great changes. It can continuously improve the rendering quality of animation, and also increase the complexity of animation program development. The 3D animation rendering engine, including cross platform, image rendering, memory management, resource management and other related technologies, provides a simple and easy-to-use interface for developers to develop graphical programs, and greatly improves the work efficiency of developers. At the same time, some mainstream 3D color systems are analyzing the architecture and modules to design and develop a set of architecture suitable for the engine. The 3D animation color rendering system designed in this paper is mainly divided into multiple modules according to different functions. In addition, this paper uses the cache memory management strategy in the memory management module, which significantly reduces the number of dynamic memory allocation, avoids the generation of memory fragments, and improves the efficiency of memory use.
Sentiments Analysis of Covid-19 Vaccine Tweets Using Machine Learning and Vader Lexicon Method

The novel Coronavirus disease of 2019 (COVID-19) has subsequently named Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) have tormented the lives of millions of people worldwide. Effective and safe vaccination might curtail the pandemic. This study aims to apply the VADER lexicon, TextBlob and machine learning approach: to analyze and detect the ongoing sentiments during the affliction of the Covid-19 pandemic on Twitter, to understand public reaction worldwide towards vaccine and concerns about the effectiveness of the vaccine. Over 200000 tweets vaccine-related using hashtags #CovidVaccine #Vaccines #CornavirusVaccine were retrieved from 18 August 2020 to 20 July 2021. Data analysis conducted by VADER lexicon method to predict sentiments polarity, counts and sentiment distribution, TextBlob to determine the subjectivity and polarity, and also compared with two other models such as Random Forest (RF) and Logistic Regression (LR). The results determine sentiments that public have a positive stance towards a vaccine follows by neutral and negative. Machine learning classification models performed better than the VADER lexicon method on vaccine Tweets. It is anticipated this study aims to help the government in long run, to make policies and a better environment for people suffering from negative thoughts during the ongoing pandemic.
Tracking of Multiple Static and Dynamic Targets for 4D Automotive Millimeter-Wave Radar Point Cloud in Urban Environments

This paper presents a target tracking algorithm based on 4D millimeter-wave radar point cloud information for autonomous driving applications, which addresses the limitations of traditional 2 + 1D radar systems by using higher resolution target point cloud information that enables more accurate motion state estimation and target contour information. The proposed algorithm includes several steps, starting with the estimation of the ego vehicle’s velocity information using the radial velocity information of the millimeter-wave radar point cloud. Different clustering suggestions are then obtained using a density-based clustering method, and correlation regions of the targets are obtained based on these clustering suggestions. The binary Bayesian filtering method is then used to determine whether the targets are dynamic or static targets based on their distribution characteristics. For dynamic targets, Kalman filtering is used to estimate and update the state of the target using trajectory and velocity information, while for static targets, the rolling ball method is used to estimate and update the shape contour boundary of the target. Unassociated measurements are estimated for the contour and initialized for the trajectory, and unassociated trajectory targets are selectively retained and deleted. The effectiveness of the proposed method is verified using real data. Overall, the proposed target tracking algorithm based on 4D millimeter-wave radar point cloud information has the potential to improve the accuracy and reliability of target tracking in autonomous driving applications, providing more comprehensive motion state and target contour information for better decision making.
Containerization and its Architectures: A Study

Containerization is a technique for lightweight virtualization of programs in cloud computing, which leads to the widespread use of cloud computing. It has a positive impact on both the development and deployment of software. Containers can be divided into two groups based on their setup. The Application Container and the System Container are two types of containers. A container is a user-space that is contained within another container, while a system container is a user-space that is contained within another container. This study compares and contrasts several container architectures and their organization in micro-hosting environments for containers.
AN ADAPTIVE ELLIPTICAL CURVE CRYPTOGRAPHY-RIVEST-SHAMIR-ADLEMAN-BASED ENCRYPTION FOR IoT HEALTHCARE SECURITY MODEL WITH BLOCKCHAIN TECHNOLOGY

The healthcare systems follow the path of client-server architecture in a centralized manner for managing the patient’s health information with the secure storage process. Medical data can be preserved in every medical institution that remains safely in silos. It could be able to access without proper authorization and cannot be transferred. At the same time, while handling massive data, the clinics or hospitals are fragile enough to govern the patient’s health information as the system is confined with such security constraints. Rather than other methodologies, blockchain plays a pivotal part in the medical industry for storing medical data in a secure manner. Since the blockchain represents a reliable and scalable decentralized network, it can conquer certain challenges of classical methods. Furthermore, the Internet of Things (IoT) is an emerging process of fetching data by wearing some sensors and devices. Through these intelligent elements, the healthcare data can be shared and enriched with the data quality for effective healthcare services. Nevertheless, this connection becomes vulnerable to data privacy and security because the data management relies on open service networks. In order to overcome these effects, a novel IoT-derived blockchain model is proposed for storing and authenticating purposes. Initially, medical data are collected from different benchmark sources and subjected to the data encryption phase. Then, encryption is performed with the help of Adaptive Elliptical Curve Cryptography and Rivest-Shamir-Adleman (A-ECC-RSA), where the key optimization is done by utilizing Enhanced Sand Cat Swarm Optimization (ESCSO). Further, encrypted data are stored in the blockchain and finally authentication access is performed with the help of Adaptive Bidirectional Long Short-Term Memory (ABiLSTM) using biometric information, where the BiLSTM parameters are optimally selected by developed ESCSO. Thus, the developed model will attain an effective security rate than conventional models that can be established through several experiments on the proposed model.
OMCD: Offensive Moroccan Comments Dataset

Offensive content, such as verbal attacks, demeaning comments, or hate speech, has become widespread on social media. Automatic detection of this content is considered an important and challenging task. Although several research works have been proposed to address this challenge for high-resource languages, research on detecting offensive content in Dialectal Arabic (DA) remains under-explored. Recently, the detection of offensive language in DA has gained increasing interest among researchers in Natural Language Processing (NLP). However, only a limited number of annotated datasets have been introduced for single or multiple coarse-grained dialects. In this paper, we introduce Offensive Moroccan Comments Dataset (OMCD), the first dataset for offensive language detection for the Moroccan dialect. First, we present the data collection steps, the statistical analysis, and the annotation guidelines of the introduced dataset. Then, we evaluate several state-of-the-art Machine Learning (ML) and Deep Learning (DL) based models on the OMCD dataset. Finally, we highlight the impact of emojis on the evaluated models for offensive language detection.
The use of blockchain technology to build smart cities: creating public value in Kuwait

Purpose The purpose of this study is to understand the public value of the government of Kuwait using blockchain technology to develop the capabilities of smart cities. Design/methodology/approach Research was conducted in Kuwait, where the increased use of blockchain technology has been evidenced in both the private and public sectors. A total of seven IT managers were interviewed to gauge their responses to blockchain and its use in Kuwait ministries. Findings Blockchain technology offers many benefits for the development of smart cities in Kuwait. This is a statement that received almost mutual agreement amongst all the IT managers interviewed. However, as regards wider acceptance, the majority mentioned that a framework is necessary to better articulate the public value of using blockchain in smart cities in Kuwait. Originality/value This paper develops research hypotheses and a framework for articulating the public value of blockchain technology for smart cities in Kuwait.
Starting to register DOIs for all blog posts included in the Rogue Scholar

The Rogue Scholar archive of scholarly blogs has grown to 34 blogs with about 420 blog posts. In order to implement DOI registration for these blog posts, I needed two things:Content and metadata, ideally without requiring blogs to implement anything special.A way to track the DOIs that have been registeredInitial work on DOI registration for blog posts focussed on exposing the relevant metadata on the blog landing page, using schema.org and/or HTML meta tags. While this approach worked well...
Micro-expression action unit recognition based on dynamic image and spatial pyramid

Most of the existing studies have focused on the expression recognition of micro-expressions, while little research has been done on how to recognize the action units of micro-expressions. This is due to the low intensity of facial action units, which are not easy to be recognized. We proposed a micro-expression action unit recognition algorithm based on dynamic image and spatial pyramids to address this problem. First, the video is passed through the dynamic image generation module to generate a dynamic image and extract the motion information contained in all frames. Then, given the subtle movement properties of micro-expressions, different levels of semantic features are obtained through spatial pyramids. It is also known that micro-expressions appear in the small range and are concentrated in the local area of the face, so the regional feature network and attention mechanism are used for the image features of each layer. Finally, our models are trained separately due to the weak correlation between action units. Experiments on CASME and CAS(ME) $$^2$$ datasets verify that our proposed algorithm has shown better action unit recognition performance compared with other advanced methods.
Faster FFT-based Wildcard Pattern Matching

We study the problem of pattern matching with wildcards, which naturally occurs in the SQL expression like. It consists in finding the occurrences of a pattern P, |P| = m, in a text T, |T| = n, where the pattern possibly contains wildcards, i.e., special characters that can match any letter of the alphabet. The naive algorithm to this problem achieves O(nm) since in O(m) we need to check at each position of T whether a match is possible. For this purpose, several algorithms have been proposed, the simplest one being a deterministic FFT-based algorithm where pattern matching is interpreted in algebraic form, i.e., P = T iff (P-T)^2 = 0. This naturally leads to an O(n log n) algorithm via FFT, as we can evaluate the binomial and search for zero-valued coefficients. Clifford et al. introduce a trick to achieve O(n log m): Instead of matching the entire text to the pattern, the text is divided into n / m overlapping slices of length 2m, which are then matched to the pattern in O(m log m). The total time complexity is then O((n / m) m log m) = O(n log m). We mention that other works, especially in pattern matching with errors, rely on this trick. However, the O-expression hides in this case a factor of 4, assuming m = 2^k. This is because FFT-based matching between strings of length m and 2m, respectively, actually requires 4m log 4m steps, since the result is of size 3m - 1 and FFT requires a power of two as the size. We argue that this trick incurs redundancy, and show how it can be discarded to achieve a twice as fast O(n log m) algorithm without compromise. Furthermore, we show by experiments that the proposed algorithm approaches the theoretical improvement.
Demonstration of ThalamusDB: Answering Complex SQL Queries with Natural Language Predicates on Multi-Modal Data

ThalamusDB supports SQL queries with natural language predicates on multi-modal data. Our data model extends the relational model and integrates multi-modal data, including visual, audio, and text data, as columns. Users can write SQL queries including predicates on multi-modal data, described in natural language. In this demonstration, we show how ThalamusDB enables users to query multi-modal data. Visitors can write their own SQL queries on two real-world data sets gathered from Craigslist and YouTube.
Fast and Robust Unsupervised Dimensionality Reduction with Adaptive Bipartite Graphs

Recent unsupervised dimension reduction algorithms use similarity graphs between data point pairs to preserve local structure while reducing dimension. However, the time complexity of these methods is proportional to the square of the number of samples, which limits their application to large-scale datasets. Moreover, the square Euclidean calculation criterion between sample point pairs will magnify the bad influence of outliers on the graph. In addition, these methods only preserve the local structure while losing other important structural information. To this end, we propose a fast adaptive unsupervised projection model termed Fast and Robust Unsupervised Dimensionality Reduction with Adaptive Bipartite Graph (FRUDR-ABG), which uses a few anchor points and sample points to build a bipartite graph to preserve the local geometric structure of the data to reduce the running time and improve efficiency. We propose a criterion based on the l2,1 norm to calculate the distance between anchor points and data points to reduce the negative influence of outliers on graph construction. A practical strategy is also proposed to realize joint learning of global and local structures. According to the characteristics of graph construction and dimensionality reduction adaptive learning in the algorithm, we design an iterative reweighting method to solve the model. Experimental results on several benchmark datasets show that FRUDR-ABG has higher efficiency and recognition performance than existing unsupervised dimensionality reduction methods.