Reviewerly/exHarmony huggingface dataset:
Application of Ant Colony Optimization (ACO) Algorithm to Optimize Trans Banyumas Bus Routes

The ever-increasing population and high mobility impact the massive number of vehicles that affect the development of public transportation and the determination of effective routes. These factors make it very important to optimize the route because it will impact operational costs and the punctuality of picking up passengers. Determining the optimal route can be categorized as a Traveling Salesman Problem (TSP). TSP is the activity of a salesman to visit each city exactly once and return to his hometown by minimizing the total cost. This study purposed to determine the optimal Trans Banyumas route by applying the Ant Colony Optimization (ACO) algorithm. ACO is an algorithm inspired by the behavior of ant colonies in searching for food by finding the shortest distance between the nest and the food source. The parameter values used in the ACO algorithm significantly affect the quality of the solution. The parameters used in this research are the maximum number of iterations, the number of ants, the pheromone evaporation constant, the pheromone intensity control, and the visibility control value. Based on the test results for the Trans Banyumas Corridor 3 using optimal parameters, the ACO algorithm found the shortest route with a total distance of 29.8 km. The determination of new corridor routes using the ACO algorithm was also successfully carried out, Corridor 4 with a distance of 30.8 km and Corridor 5 about 21.6 km.
Capitalization Feature and Learning Rate for Improving NER Based on RNN BiLSTM-CRF

Entity extraction in the natural language processing research field is still a widely researched topic. It can be a data source for the next NLP stage, such as text summarization, sentiment analysis, chatbot, machine translation, information retrieval, opinion mining, speech recognition, etc. Named Entity Recognition (NER) is the task of detecting named entities on the corpus. The detection process of entities can use various features, one of which is capital letters. Capital letters that appear at the beginning of a sentence indicate the name of a person, place, organization, geolocation, etc. The experiment uses the deep learning approach with Recurrent Neural Network Bidirectional Long Short Term Conditional Random Field (RNN-BiLSTM-CRF). Our comparing three optimization algorithms: Stochastic Gradient Descent (SGD), Adaptive Moment Estimation (Adam), and Adadelta, with the CoNLL2003 dataset. The experiment results using capital letter features showed an increase in the value of F1-Score by 2.9 higher compared to test results that did not use capital letter features. The highest F1-score score was 92.82 in testing using Adam's algorithm, with a 0.001 learning rate.
Fusion of weighted Voronoi diagram and $\mathrm{A}^{\ast}$ algorithm for mobile robot path planning

A* algorithm is one of the most common algorithms in path planning for mobile robots, which can complete global search and seek the lowest cost path. <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathrm{a}^{\ast}$</tex> algorithm has more search ability compared with other algorithms, but its algorithm has long computation time, more search nodes and other drawbacks have always existed. To address these problems, this paper proposes a way to plan paths by fusing weighted Voronoi map and improved <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathrm{A}^{\ast}$</tex> algorithm, which will adopt the concept of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathrm{A}^{\ast}$</tex> algorithm and combine with the idea of map construction, taking the lead to take the weighted Voronoi point as the priority expansion node, and if there is no directly searchable generation value minimum weighted Voronoi node, the weighted Voronoi edge will be the priority path, and so on. Simulation experimental results show that the algorithm can search for shorter paths in complex environments with relatively faster speed, which not only improves the search efficiency, but also improves the safety and accuracy of the paths.
Boosting K-Nearest Neighbour (KNN) Classification using Clustering and AdaBoost Methods

K-Nearest Neighbour (KNN) is a lazy learning algorithm which has been successfully practiced in several real-life machine learning applications. However, it has been titled as lazy learner because it is unable to learn a decision line from historical training data and uses the data itself for classification. KNN discovers the closest neighbors around test instances; At the same time considers the majority voting of the class label to classify the test instances. In the case of noisy datasets, the value of K should be higher. It is still struggling with the time complexity and better accuracy rate in some cases for noisy datasets. In this article, we proposed a technique to boost up the performance of KNN classification employing clustering and boosting techniques. The proposed method automatically determine the value of K based on the current situation of the neighbours instead of setting previous and random values. It also achieves significant accuracy and reduces the time complexity in compare with traditional KNN approach. The proposed method clusters the datasets and finds the majority voting of nearest neighbours from each cluster to classify the test data. We experimented the performance of suggested model on several real benchmark datasets from UCI and KEEL machine learning repositories and found that proposed model gives significant results which are relatively satisfactory in terms of accuracy, precision, f'1-score and Matthew's Correlation Coefficient (MCC).
Hate Speech Classification Implementing NLP and CNN with Machine Learning Algorithm Through Interpretable Explainable AI

In recent years, data innovation has progressed quickly, and online media has gone through a problematic transformation. Destinations like Facebook, Twitter, and Instagram where clients can communicate their thoughts through messages, photographs, and recordings have become famous. Sadly, be that as it may, it has turned into a favorable place for disdain discourse, affronts, cyberbullying, and mysterious dangers. There are many examinations around here, however, none have given an adequate degree of exactness. This article proposes a Convolutional Neural Network (CNN) and Natural Language Processing (NLP) amalgamation strategy that characterizes malicious and non-malicious remarks at a beginning phase and groups them into six classifications utilizing Wikipedia's talk page edits gathered by Kaggle. The proposed engineering changes over words into words by utilizing NLP strategies like tokenization and stemming, and word embedding as well as data preprocessing methods that transform words into vectors. In the subsequent advance, the handled informational index gathered in the initial step is applied through 5 distinct classifiers (XGBoost, Random Forest, Decision Tree, AdaBoost, and Gradient Boosting classifier) and an explainable artificial intelligence (XAI) strategy (LIME). In the last stage, all classifiers were joined to accomplish a malicious comment classification accuracy of 99.75, which is higher than the past work.
Distributed Attestation Revocation in Self-Sovereign Identity

Self-Sovereign Identity (SSI) aspires to create a standardised identity layer for the Internet by placing citizens at the centre of their data, thereby weakening the grip of big tech on current digital identities. However, as millions of both physical and digital identities are lost annually, it is also necessary for SSIs to possibly be revoked to prevent misuse. Previous attempts at designing a revocation mechanism typically violate the principles of SSI by relying on central trusted components. This lack of a distributed revocation mechanism hampers the development of SSI. In this paper, we address this limitation and present the first fully distributed SSI revocation mechanism that does not rely on specialised trusted nodes. Our novel gossip-based propagation algorithm disseminates revocations throughout the network and provides nodes with a proof of revocation that enables offline verification of revocations. We demonstrate through simulations that our protocol adequately scales to national levels.
Moving Object Detection and Speed Estimation by Digital Image Processing

Abstract Intelligent network systems and security surveillance are used for smart city development. Manual and heritage technology is used in surveillance systems to detect speeds higher than usual in developing countries. This process can be automated with ease by leveraging the potential of image processing. Moving object detection is one of the essential tasks in image processing because of its prominent role in many real-world applications. Vehicle speed detection can be achieved by employing image and video processing methods. In this work, a unique approach is proposed for moving object detection and speed estimation based on an integrated approach in video sequence frames as opposed to the most commonly used RADAR and LIDAR devices for traffic law enforcement. Video data is collected and analyzed for speed in real-time without any sensor calibrations,thereby it removes any external hardware dependency requirements. Moving vehicles are segmented out by using frame-subtraction and masking techniques. The proposed algorithm tracks the time taken by the car to cover a predetermined fixed distance in order to calculate its speed.
A multi-head adjacent attention-based pyramid layered model for nested named entity recognition

Abstract Named entity recognition (NER) is one of the widely studied natural language processing tasks in recent years. Conventional solutions treat the NER as a sequence labeling problem, but these approaches cannot handle nested NER. This is due to the fact that nested NER refers to the case where one entity contains another entity and it is not feasible to tag each token with a single tag. The pyramid model stacks L flat NER layers for prediction, which subtly enumerates all spans with length less than or equal to L . However, the original model introduces a block consisting of a convolutional layer and a bidirectional long short-term memory (Bi-LSTM) layer as the decoder, which does not consider the dependency between adjacent inputs and the Bi-LSTM cannot perform parallel computation on sequential inputs. For the purpose of improving performance and reducing the forward computation, we propose a Multi-Head Adjacent Attention-based Pyramid Layered model. In addition, when constructing a pyramid structure for span representation, the information of the intermediate words has more proportion than words on the two sides. To address this imbalance in the span representation, we fuse the output of the attention layer with the features of head and tail words when doing classification. We conducted experiments on nested NER datasets such as GENIA, SciERC, and ADE to validate the effectiveness of our proposed model.
Memory-Based Cross-Image Contexts for Weakly Supervised Semantic Segmentation

Weakly supervised semantic segmentation (WSSS) trains segmentation models by only weak labels, aiming to save the burden of expensive pixel-level annotations. This paper tackles the WSSS problem of utilizing image-level labels as the weak supervision. Previous approaches address this problem by focusing on generating better pseudo-masks from weak labels to train the segmentation model. However, they generally only consider every single image and overlook the potential cross-image contexts. We emphasize that the cross-image contexts among a group of images can provide complementary information for each other to obtain better pseudo-masks. To effectively employ cross-image contexts, we develop an end-to-end cross-image context module containing a memory bank mechanism and a transformer-based cross-image attention module. The former extracts cross-image contexts online from the feature encodings of input images and stores them as the memory. The latter mines useful information from the memorized contexts to provide the original queries with additional information for better pseudo-mask generation. We conduct detailed experiments on the Pascal VOC 2012 and the COCO dataset to demonstrate the advantage of utilizing cross-image contexts. Besides, state-of-the-art performance is also achieved. Codes are available at https://github.com/js-fan/MCIC.git.
Content-Aware Brightness Solving and Error Mitigation in Large-Scale Multi-Projection Mapping

Projection mapping with inexpensive hardware often suffers from calibration errors that lead to visually compromised results. In this paper, we classify common errors that lead to typical visual artifacts. Based on this classification, we present the first content-aware brightness solver. It is tailored for high GPU performance, yet efficiently hides the most common calibration artifacts. Moreover, it is specifically designed to handle both single and larger networked projection mapping setups with minimal latency.
Temperature Data Denoising Based on Directed Laplacian Matrix and Heat Kernel Smoothing

In this paper, a temperature data denoising method using directed Laplacian matrix (DLM) and heat kernel smoothing (HKS) is presented. First, the temperature data collected from sensor network is represented as the directed graph signal. Then, the adjacency matrix and degree matrix of directed graph is used to define the DLM. And, directed graph Fourier transform is defined by the eigen-decomposition of DLM. Next, the HKS filter is employed to reduce the noise superimposed on the temperature data. Using the Taylor series expansion, the HKS filter can be approximated by a polynomial digraph filter to get a distributed implementation in vertex domain. Finally, the performance of proposed denoising method is evaluated by the real-word temperature data to show its effectiveness.
On Galois connections between polytomous knowledge structures and polytomous attributions

Polytomous knowledge structure theory (abbr. polytomous KST) was introduced by Stefanutti et al. (2020) and further results on polytomous KST were obtained by Heller (2021). As the interesting work, this paper discusses Galois connections in polytomous KST. In this paper, two derivations between polytomous knowledge structures and polytomous attributions are presented. In addition, this paper gives an explicit characterization to introduce the completeness of polytomous attributions and defines the concept of a complete polytomous knowledge structure by the property that such a polytomous knowledge structure is derived from a complete polytomous attribution. This paper establishes a Galois connection between the collection K of all polytomous knowledge structures and the collection F of all polytomous attributions, where the closed elements are respectively in K the complete polytomous knowledge structures, and in F the complete polytomous attributions. Furthermore, this Galois connection induces a one-to-one correspondence between the two sets of closed elements. Moreover, this Galois connection can also induce a Galois connection between the collection of all granular polytomous knowledge structures and the collection of all granular polytomous attributions.
Evaluation of models for prediction of diffuse solar radiation and comparison with satellite values

The diffuse radiation data are one of the important parameters required in solar energy projects. However, this parameter has not been measured in everywhere of Turkey. In the present study, models in the open literature for the determination of horizontal diffuse radiation with clearness index are investigated and, new models are developed for thirteen provinces over the Eastern Anatolia Region (EAR) of Turkey where the diffuse radiation values are not existing. In the monthly average, these models have a relation between the diffuse coefficient ( H d / H o ) and the diffuse fraction ( H d / H ) with the daily clearness index . The most appropriate models based on NASA – Surface meteorology and Solar Energy (NASA-SSE) values are determined. For this, the diffuse radiation data taken from the NASA-SSE and calculated from the models are compared for the locations mentioned in the study. The validation of the correlations obtained to predict the diffuse radiation over the EAR for which experimental data are not available is firstly tested by using the NASA-SSE satellite data. The performances of the models are indicated with the statistical test methods as mean absolute bias error (MABE), root mean square error (RMSE), the mean bias error (MBE), and mean relative error (MRE). The results indicate that some models are reasonably well to forecast the diffuse radiation. In addition, the diffuse radiation predictions of the new correlations obtained in the study are compared with satellite data. When the diffuse radiation values taken from the NASA-SSE and calculated from the models are compared, it is seen that the Model 4 for Ağrı, Bingöl, Bitlis, Elazığ, Erzincan, Hakkari, Iğdır, Kars, and Muş, Model 14 for Erzurum, Model 20 for Malatya and Tunceli, and Model 11 for Van indicate the best performance. According to the RMSE values of twenty models in the literature and the NASA-SSE, the best result among all provinces is obtained from the Model 11 with 0.2385 MJ m −2 day −1 for Van. • Diffuse radiation measurements are in limited periods and are very rare. • Diffuse radiation models are given as function of clearness index. • Models can be used in locations without the diffuse solar radiation data. • The values calculated from models are compared with satellite data. • The best model in the literature for each province is found.
Information bottleneck and selective noise supervision for zero-shot learning

Zero-shot learning (ZSL) aims to recognize novel classes by transferring semantic knowledge from seen classes to unseen classes. Though many ZSL methods rely on a direct mapping between the visual and the semantic space, the calibration deviation and hubness problem limit the generalization capability to unseen classes. Recently emerged generative ZSL methods generate unseen image features to transform ZSL into a supervised classification problem. However, most generative models still suffer from the seen-unseen bias problem as only seen data is used for training. To address these issues, we propose a novel bidirectional embedding based generative model with a tight visual-semantic coupling constraint. We learn a unified latent space that calibrates the embedded parametric distributions of both visual and semantic spaces. Since the embedding from high-dimensional visual features comprises much non-semantic information, the alignment of visual and semantic in latent space would inevitably be deviated. Therefore, we introduce an information bottleneck constraint to ZSL for the first time to preserve essential attribute information during the mapping. Specifically, we utilize the uncertainty estimation and the wake-sleep procedure to alleviate the feature noises and improve model abstraction capability. In addition, our method can be easily extended to the transductive ZSL setting by generating labels for unseen images. We then introduce a robust self-training loss to solve this label-noise problem. Extensive experimental results show that our method outperforms the state-of-the-art methods in different ZSL settings on most benchmark datasets.
Optimization two-qubit quantum gate by two optical control methods in molecular pendular states

Implementation of quantum gates are important for quantum computations in physical system made of polar molecules. We investigate the feasibility of implementing gates based on pendular states of the molecular system by two different quantum optical control methods. Firstly, the Multi-Target optimal control theory and the Multi-Constraint optimal control theory are described for optimizing control fields and accomplish the optimization of quantum gates. Numerical results show that the controlled NOT gate (CNOT) can be realized under the control of above methods with high fidelities (0.975 and 0.999) respectively. In addition, in order to examine the dependence of the fidelity on energy difference in the same molecular system, the SWAP gate in the molecular system is also optimized with high fidelity (0.999) by the Multi-Constraint optimal control theory with the zero-area and constant-fluence constraints.
Boosting sparrow search algorithm for multi-strategy-assist engineering optimization problems

An improved optimization algorithm, namely, multi-strategy-sparrow search algorithm (MSSSA), is proposed to solve highly non-linear optimization problems. In MSSSA, a circle map is utilized to improve the quality of the population. Moreover, the adaptive survival escape strategy (ASES) is proposed to enhance the survival ability of sparrows. In the producer stage, the craziness factor integrated with ASES is introduced to enhance the search accuracy and survival ability. In the scout stage, the ASES facilitates sparrows successful escape from danger. Besides, opposition-based learning or Gaussian–Chachy variation helps optimal individuals escape from local solutions. The performance of the MSSSA is investigated on the well-known 23 basic functions and CEC2014 test suite. Furthermore, the MSSSA is applied to optimize the real-life engineering optimization problems. The results show that the algorithm presents excellent feasibility and practicality compared with other state-of-the-art optimization algorithms.
User Authentication Method Based on Keystroke Dynamics and Mouse Dynamics with Scene-Irrelated Features in Hybrid Scenes

In order to improve user authentication accuracy based on keystroke dynamics and mouse dynamics in hybrid scenes and to consider the user operation changes in different scenes that aggravate user status changes and make it difficult to simulate user behaviors, we present a user authentication method entitled SIURUA. SIURUA uses scene-irrelated features and user-related features for user identification. First, features are extracted based on keystroke data and mouse movement data. Next, scene-irrelated features that have a low correlation with scenes are obtained. Finally, scene-irrelated features are fused with user-related features to ensure the integrity of the features. Experimental results show that the proposed method has the advantage of improving user authentication accuracy in hybrid scenes, with an accuracy of 84% obtained in the experiment.
Nonlinear Integrity Algorithm for Blockchain Based Supply Chain Databases

With an ever-growing global supply chain, supply chain management(SCM) is becoming more complex and competitive because of continuous development in global supply chain. In recent years, the blockchain has grown with the promise of becoming a “ trust machine “. However, this technological innovation can foster trust and transparency in many other sectors. In the industrial sector, for example, it could have interesting applications in production ecosystems and in particular in improving supply chain performance, offering the opportunity to increase speed and quality with potential cost savings ranging from 12 to 50%. the computation power required to setup and maintain a blockchain is expensive because of Computing hash for each block added to the blockchain and validating blocks. In this paper we proposed a nonlinear Integrity algorithm which works efficiently in terms of run-time and hash-bit variation with dynamic hash size and data size when compared it with traditional hashing algorithms like whirlpool, SHA, MD5.