Reviewerly/exHarmony huggingface dataset:
Online Motor Vehicle Sales Data for Supporting Policy in Manufacturing Sector

Data and information on the income of the large and medium trade and the manufacturing sector are essential for the government to make policies. Currently, official statistics containing this information are still carried out conventionally. There has to be a new data source that can be used as a faster and more granular alternative reference. The goal of this research is to investigate a novel technique to generate data on vehicle sales in Indonesia from big data that can support and provide an overview of the manufacturing sector in real-time and may be used as a comparative or complementary to official statistics. This research uses a web scraping method from one of the largest vehicle advertiser sites in Indonesia. Vehicle sales data is collected weekly for the four vehicle types advertised in Indonesia using the HTML structure of the site. The Python programming language&#x0027;s Scrapy module is implemented. Data collection is carried out every week, and 358,451 vehicle advertisements have been collected from January 2019 to June 2021. The findings suggest that vehicle sales data from big data can be used as a comparative or complementary to official statistics, as well as supporting data in the manufacturing sector. By using web scraping techniques, indicators that usually require more time and cost can be done in real-time at a lower budget. This new approach is expected to improve the quality of official statistics in the manufacturing sector.
Neural-based Arabic Morphological Analyzer

Al-Qur 'an is a source and guidance for Muslims. It is a document that is 15 centuries ago and well written in the Arabic language. Many Muslims have to learn Arabic in addition to knowing the meaning of it. One of the most critical aspects of the Arabic language is morphology and identifying the word's morphological description. It is called by morphological analysis task. This task is essential because, from the morphological aspect of a word, it can know the different forms of a word, and from that, it can know its meaning. The Gonzales paper has successfully created a model to identify morphological features (MSD) of Arabic word verb only. This study will focus on adding some other Arabic type of word, which is a noun. Trying to use the current state-of-the-art approach method is neural-based with the recurrent neural network (RNN). RNN can capture more information about the sequence of sub-word like prefix, infix, root, and suffix to make a better msd identifier. The input is a single Arabic word Going through pattern extraction, sub word vectorizing, verb form identification, pronoun and type of word identification, and finally MSD identification process to see the result. This study helps to improve the last gap (morphological analyzer on POS noun) and enhances what can be identified from the previous approach to perform better. This model successfully identifies MSD with 99% accuracy and 97% F1 - score. This method also has been compared with Jabalin Gonzales system using this system data testing and get better accuracy with 99 % and Jabalin got 39% accuracy.
Single Image HDR Reconstruction Using Generative Adversarial Networks

The advances in GANs have paved the way for various methods of high dynamic range (HDR) image reconstruction. In this paper, we use the structural advantages of GAN to infer natural HDR images and reconstruct missing information from a single exposure low dynamic range(LDR) image in an end-to-end fashion, which extends the dynamic range of a given image to generate HDR image. Furthermore, we propose a novel dense feedback model and the feedback mechanism, which can make use of the high-level information to refine the shallow information in the top-down feedback stream through the global feedback and the local feedback connection. The dense connections in the forward-pass enable feature-reuse and comprehensively learn complex nonlinear relationships from LDR to HDR mapping. Experiment results demonstrate proposed method produces superior performance compared to existing state-of-the-art approaches.
Collective Intelligence Coordination and Cooperation in Teamed Part-Time and Full-Time Postgraduates

With the rise of artificial intelligence and highperformance information and computing technologies, as an aftermath of the industry 4.0 mandate, new capabilities have flourished for a more controlled and, to a certain extent, autonomous work-space management. This efficiently links a collection of individuals to exchange information of their inputs (knowledge, expertise, skills, etc.) and outputs (results, outcomes, scores, bonuses, performance, etc.). As an example of this matter, a collective intelligence approach to coordinate the cooperation of teamed part-time (PT) and full-time (FT) postgraduates is presented in this work. The proposition of this behavioral and social computing field as a CI process-of-research and -development is to define PT -FT synergies for the viability of an industrial-like university, where most of the students at the postgraduate level are PT and share academic duties with non-academic tasks. This unprecedented portion of PT students is a challenge to be addressed in the educational process for achieving more productive frameworks to better operate, control, and monitor itself. The cooperation among PT and FT is the key element for a successful overall result, however their coordination must balance the trade-offs and avoid the pitfalls of such CI. This PT-FT cooperation should be in the extent, duration, pace, path, etc., that suits different topics of research and development, students' level of knowledge, or any other feature that can be classified, measured, analyzed, and adjusted. The utilization of social media and work-made platforms are modeled in the PT -FT CI scheme for an optimal individual expertise-platform introduced in this work.
Enhanced Game Theoretical Spectrum Sharing Method Based on Blockchain Consensus

The limited spectrum resources need to provide safe and efficient spectrum service for the intensive users. Malicious spectrum work nodes will affect the normal operation of the entire system. Using the blockchain model, consensus algorithm Praft based on optimized Raft is to solve the consensus problem in Byzantine environment. Message digital signatures give the spectrum node some fault tolerance and tamper resistance. Spectrum sharing among spectrum nodes is carried out in combination with game theory. The existing game theoretical algorithm does not consider the influence of spectrum occupancy of primary users and cognitive users on primary users&#x0027; utility and enthusiasm at the same time. We elicits a reinforcement factor and analyzes the effect of the reinforcement factor on strategy performance. This scheme optimizes the previous strategy so that the profits of spectrum nodes are improved and a good Nash equilibrium is shown, while Praft solves the Byzantine problem left by Raft.
*-Autonomous Envelopes and Conservativity

We prove 2-categorical conservativity for any {0,T}-free fragment of MALL over its corresponding intuitionistic version: that is, that the universal map from a closed symmetric monoidal category to the *-autonomous category that it freely generates is fully faithful, and similarly for other doctrines. This implies that linear logics and graphical calculi for *-autonomous categories can also be interpreted canonically in closed symmetric monoidal categories. In particular, every closed symmetric monoidal category can be fully embedded in a *-autonomous category, preserving both tensor products and internal-homs. In fact, we prove this directly first with a Yoneda-style embedding (an enhanced "Hyland envelope" that can be regarded as a polycategorical form of Day convolution), and deduce 2-conservativity afterwards from Hyland-Schalk double gluing and a technique of Lafont. The same is true for other fragments of *-autonomous structure, such as linear distributivity, and the embedding can be enhanced to preserve any desired family of nonempty limits and colimits.
Design of Robot Vision Servo Control System Based on Image

Abstract Visual servo is a closed-loop control system of robot, which takes the image information obtained by visual sensor as feedback. Generally speaking, visual servo plays an important role in robot control, which is one of the main research directions in the field of robot control and plays a decisive role in the development of intelligent robots. In order to make the robot competent for more complex tasks and work more intelligently, autonomously and reliably, it is necessary not only to improve the control system of the robot, but also to obtain more and better information about the working environment of the robot. This paper introduces the principle and basic realization method of robot visual servo based on image, and expounds the problems and solutions in image feature extraction and visual servo controller design. In order to further expand the application field of robots and improve the operation performance of robots, robots must have higher intelligence level and stronger adaptability to the environment, so as to manufacture intelligent robots that can replace human labor.
Segmentation of Enhalus acoroides seagrass from underwater images using the Mask R-CNN method

Abstract Seagrass is a Spermatophyta plant that has many roles, including as a primary producer in the food chain in the waters. Monitoring of seagrass meadows and conditions needs to be done in order to achieve a healthy marine ecosystem. The steps taken in monitoring seagrass are by detecting and segmenting it. The purpose of the study is to implement and get information about the performance of the Mask R-CNN algorithm in detecting and segmenting the Enhalus acoroides . The dataset consists of 500 Enhalus acoroides images that had gone through a color correction and labelling process. The training process was performed with the configuration of 0.001 learning rate, batch size of 4 and some image augmentation was used to avoid overfitting. The optimum weight value was obtained after conducting the learning process with 100 epochs. A confusion matrix was used to evaluate detection performance, and linear regression was used to evaluate the segmentation produced by the model. The model evaluation results showed an accuracy value of 0.9246, a precision value of 0.9507, a recall value of 0.9712 and a correlation coefficient value of 0.8771. The value indicates that the model can detect and segment the seagrass Enhalus acoroides well and accurately.
Protection and Authentication of Dubai Digital Elevation Model using Hybrid Watermarking Technique

Nowadays, with the availability of digital images and models at no cost on the World Wide Web, the need to provide copyright protection of multimedia data arises. Hence, digital watermarking products have been in high demand. Digital watermarking essentially embeds information into data in such a way that data usage is not affected, and it simultaneously protects and authenticates the data. This research paper deals with the development and evaluation of a watermarking technique for protection and authentication of Dubai Digital Elevation Model (DEM) provided by United States Geological Survey (USGS). The technique uses a hybrid combination of Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT), and it is implemented for the protection of DEM by embedding the ownership information in hybrid DCT-DWT domain and for checking the integrity of the elevation model by embedding hash-key information in the spatial domain. The proposed watermarking technique causes minimal distortion to the DEM and the performance is assessed by using Peak Singal-to-Noise Ratio (PSNR), Wavelet Signal-to-Noise Ratio (WSNR), and Structural Similarity Index Measurement (SSIM). The results show promising performance with strong robustness of watermark information ownership for many intentional and non-intentional attacks, in addition to precise detection of localized modified areas on tampered DEM.
Self-supervised Fine-grained Image Classification via Progressive Global Disturbance

Recently, self-supervised learning has shown to surpass the supervised counterpart for finetuning downstream applications such as ImageNet classification. However, in the area of fine-grained image recognition, self-supervised learning still lags far behind supervised learning. In this work, we study the self-supervised learning for fine-grained visual recognition, and propose an effective method called PGD, which consist of two mutually coordinated operations: Global Disturbance and Progressive Multi-granularity Learning. For the problem of small inter-class variance, Global Disturbance forces the neural network to pay more attention to the local discriminative areas by destroying global semantics but retaining local semantics. For the problem of high intra-class variance, progressive multi-granularity learning integrates different granularity information to locate the discriminative regions more accurately. Extensive experiments show that whether on large-scale datasets or small-scale datasets, PGD can obtain consistency gains by transfering to fine-grained recognition downstream tasks. In addition, we demonstrate the possibility of getting rid of weights pretrained on large-scale dataset. Specifically, we conduct sufficient experiments on three standard small-scall fine-grained classification datasets (CUB200-2011, Aircraft, Car), and the results show that PGD can reach or exceed the results of ImageNet supervised learning without using any additional data or annotations, which further proves the effectiveness of our method, and also shows that self-supervised learning has great potential in the domain of fine-grained image classification.
Improved YOLOv3 Road Multi-target Detection Method

Traditional object detection algorithms have problems of low efficiency and high error rate in traffic object detection. So, this study proposes an improved algorithm based on YOLOv3. Firstly, the K-means++ algorithm was used to improve the extraction of the central point of clustering prior box, and a more appropriate prior box was selected. Meanwhile, the loss function was replaced with CIoU loss. Besides, the CIoU algorithm was used for optimization. Experiments were conducted to detect pedestrians and vehicles on a self-made mixed dataset. The experimental results indicate that the improved YOLOv3 algorithm can effectively reduce the object’s missed and false detection rate. The improved algorithm achieved an average accuracy of 92.79% on the mixed data, and its accuracy and recall rate are 2.6% and 1.79% higher than those of the original YOLOv3 algorithm.
Exploring the Feasibility of an Automated Essay Scoring Model Based on LSTM

In the present study, the feasibility of an automated essay scoring of English was explored using Long-Short Term Memory (LSTM), a type of Recurrent Neural Network (RNN). LSTM is a deep learning model proposed to overcome the problem of long-term dependence of the existing RNN. In this study, an automated essay scoring model based on LSTM was adopted to score English essay data extracted from the open huge repository of data ‘kaggle,’ and the performance of the model was validated. Unlike multiple-choice scoring data which consisted of binary (true/false) data, essay scoring data had multiple facets, thus the data used for the deep learning model was constructed within a multinomial classification to order to predict scores of those essay data. For its validation, the six indices of ‘accuracy,’ ‘precision,’ ‘recall’, ‘F1-measure,’ ‘kappa,’ and ‘correlation coefficient’ were used. As a result, it turned out that the LSTM model could predict students
New Generative Image Model for Variational Autoencoders Based on Counts Partition

The article substantiates a generative image model based on a sample of random counts and its representation using compressed (coded) lattice partitioning. The basics of the model proposed is a concept of the ideal image. To avoid the problems with cumbersome descriptions, the ideal image is reduced to a sample of counts of a fixed (controlled) size. It is shown that the corresponding statistical description can be factorized into the product of the distributions of individual counts, which fits well with naive Bayesian algorithms and other machine learning approaches. Guided by this association in the image and likeness of the well-known K–means algorithm, the method of iterative partitioning - maximization of counts was synthesized. The main goal of the method is to calculate the maximum likelihood parameters of sample probability distribution and to use these parameters as a code for some intermediate representation of the input data. The method specific feature is the use of lattice neighbourhood trick for partitioning counts, that allows to control both the computation time and some characteristics of the resulting code. The final of the article is devoted to the algorithmic details of the proposed partition-maximization method are considered and several iterations related characteristics, including the convergence property, convergence criterion and asymptotic efficiency of the resulting estimates are discussed. Most of those characteristics are illustrated by the results of numerical simulations.
Detecting and Inoculating against PTSD in Online Forums and Platforms

Posttraumatic stress disorder (PTSD) has significantly impacted the mental health of individuals on a global scale. The linguistic patterns and emotional expressions of PTSD are evident via online behaviors. In this study, we look at both the English and Arabic languages to detect PTSD, and to also devise an intervention for those that are experiencing this form of long-term trauma. This research aims to detect PTSD patterns online, with the ultimate purpose of inoculating against PTSD in online forums and platforms. Through the interdisciplinary combination of social media analytics and tech-assisted behavior change, intervention tools can be applied online to detect and inoculate against PTSD. Emojis are a form of expression that vary according to language, and more specifically, dialect. This research aims to create posttraumatic stress disorder specific emoticons, and eventually globally release a curated glossary.
Improving full-FORCE with dynamical data coupling and multilayer architecture

Training Deep Recurrent Neural Networks (Deep- RNNs) using Back Propagation Through Time (BPTT) has shown tremendous success in improving benchmark performance and solving real-world problems. However, the non-locality of loss functions for deep networks and the requirement of parallel computing hardware such as GPUs to propel learning make mapping gradient-based RNNs onto neuromorphic devices challenging. As a result, gradient-free alternatives like reservoir computing have emerged as more realistic and bio-plausible options to train RNNs on neuromorphic substrates. This study improves the stability of a target-based method called full-FORCE for training RNNs by dynamically coupling targets with the network. We show that this coupling has the same stabilizing effect as when learning online. We also extend full-FORCE to multiple layers while permitting online local weight updates for each layer. The proposed network outperforms the original version on tasks including pattern generation and interval matching. It can also classify eye states using electroencephalogram (EEG) recordings with higher accuracy than full-FORCE (0.886±0.030 versus 0.772±0.052), showing potential in biomedical applications such as real-time computation on brain-interfacing devices.
Organization of distance competitions in video conference form with parallel demonstration of results

The article presents the experience of conducting online contests as part of extracurricular activities. This experience can be useful in organizing distance competitions under the constraints of a pandemic, with remote locations of schools, as well as for involving children with disabilities in competitions. During the competition, for the objectivity of its results, the participants are given the opportunity to evaluate and self-evaluate. The article describes in detail the technology of preparing materials for the competition, which will allow everyone to independently develop their own version of the competition. To summarize the results of the competition, a method is presented to consolidate data from several Google forms in one document.The material will be useful for teachers, methodologists, heads of educational institutions of all types, students of higher and secondary pedagogical educational institutions.
Dialectical and activity-based foundations for the modern original definition of science

The article is devoted to the actual problem of uncertainty in science. The work done not only contributes to the removal/solution of this modern problem, but also contributes to the theoretical understanding of the need for qualitative changes in the sphere of scientific activity, the most organic combination of theory with practice to form a new form of science, guaranteed to remove the global dangers and uncertainties of scientific and technological development. The author presents the basis for the formation of a modern, general definition of science, the absence of which indicates the presence of the above problem. The aim of the research is to give a modern general definition of science, making a serious step, if not to the final solution, then to a generally meaningful solution of this problem. In the authors opinion, all substantive elements necessary for forming a modern general definition of science have already been identified, but they are not structured. The methodological approach has a high importance in solving this problem, and the emphasis should be placed on dialectics, rather than formal logic. In dialectical methodology, it is necessary to refer to the theory of dialectical development from the abstract to the concrete, as well as the general theory of activity. The specificity of science is defined in the consistent disclosure of the content of science as knowledge, as individual-collective, internalist-externalist activity and as a sphere of social life. As a result of the analysis of these aspects the author comes to the initial, general and actual definition of science.
Non-Equidistant Location of Objects in Space

Application of the developed method of constructing a two-dimensional arrangement of objects made it possible to find variants of their optimal arrangement (by the criterion of different distance vectors). Variants of two-dimensional object locations are constructed. This has extended the possibilities of optimizing the location of objects according to the established criteria.
Casual Discovery for Complex Mission Planning with Subtask Dependencies

Discovering causal structure among a set of previously-unseen subtasks which have inner dependencies is a fundamental problem in real-world environments. Unlike existing hierarchical multitask RL approaches that focus on task descriptions such as natural language or formal language that describe what the agent should do at a high level, we develop a Casual Inference Solver with Subtask Graph (CISG), which performs complex reasoning with subtasks, properties and relationships among them to find the optimal subtask to execute. To overcome the difficulty of efficient exploration, upper confidence bound (UCB) is adopted to produce the intrinsic reward. Meanwhile, a novel inductive logic programming (ILP), which utilizes the classification and regression tree (CART) to infer the precondition function for subtasks with Gini impurity, is utilized to pretrain our CISG agent to achieve the optimal subtask to execute. Our experimental results on StarCraft II domains show that our method is able to perform complex reasoning to accurately find the casual relationship among subtasks and make a mission planning more efficiently than existing hierarchical RL methods.