Reviewerly/exHarmony huggingface dataset:
A Comparison of Demographic Attributes Detection from Handwriting Based on Traditional and Deep Learning Methods

Analyzing handwritten documents and detecting demographic attributes of writers from handwritten samples has received enormous attention from various fields of research, including psychology, computer science and artificial intelligence. Automatic detection of age, gender, handedness, nationality, and qualification of writers based on handwritten documents has several real-world applications, such as forensics and psychology. This paper proposes two simple but effective methods to detect the demographic information of writers from offline handwritten document images. The proposed methods are based on traditional and deep learning approaches. In the traditional machine learning method, the Rank Transform feature extraction method is used for measuring the intensity in handwriting images. The extracted handcrafted features are then fed into Support Vector Machine based classifiers to predict the demographical attributes of writers. In the deep learning method, a Convolutional Neural Network model based on the ResNet architecture with a fully connected layer, followed by a softmax layer is used to provide probability scores to facilitate demographic information detection. To evaluate the proposed methods and compare the results with the results in the literature, a comprehensive set of experiments was conducted on a frequently used benchmark database, KHATT. Both methods performed relatively well in predicting different demographic attributes. However, considering the settings in our experiments, the results obtained from the traditional model indicated better demographic detection compared to the deep learning models in all the tasks.
A New Optimization Approach to Improve an Ensemble Learning Model: Application to Persian/Arabic Handwritten Character Recognition

Due to the advancement of technology, handwriting recognition has become more important than ever. As a result, several methods for document image recognition have been developed in the literature. This paper presents a new ensemble model based on the Feedforward Neural Networks (FFNN) to accurately recognize Persian and Arabic handwritten characters. As training and optimizing FFNN models have a significant role in obtaining optimal results, two optimization algorithms are integrated into the proposed handwritten recognition method. The Particle Swarm Optimization algorithm is integrated into the proposed model to improve the Neural Networks learning process. The FFNN architectures are further optimized using the League Championship Algorithm. The ensemble model is fed by a set of handcrafted features, including directional and intersection features, extracted from handwritten text. The proposed model is evaluated using three different datasets. Results obtained from the proposed models demonstrate higher accuracies compared to the state-of-the-art models.
Fourier Feature-based CBAM and Vision Transformer for Text Detection in Drone Images

The use of drones for several real-world applications is increasing exponentially, especially for the purpose of monitoring, surveillance, security, etc. Most existing scene text detection methods were developed for normal scene images. This work aims to develop a model for detecting text in drone as well as scene images. To reduce the adverse effects of drone images, we explore the combination of Fourier transform and Convolutional Block Attention Module (CBAM) to enhance the degraded information in the images without affecting high-contrast images. This is because the above combination helps us to extract prominent features which represent text irrespective of degradations. Therefore, the refined features extracted from the Fourier Contouring Network (FCN) are supplied to Vision Transformer, which uses the ResNet50 as a backbone and encoder-decoder for text detection in both drone and scene images. Hence, the model is called Fourier Transform based Transformer. Experimental results on drone datasets and benchmark datasets, namely, Total-Text and ICDAR 2015 of natural scene text detection show the proposed model is effective and outperforms the state-of-the-art models.
Remote Assessment for ALS using Multimodal Dialog Agents: Data Quality, Feasibility and Task Compliance

We investigate the feasibility, task compliance and audiovisual data quality of a multimodal dialog-based solution for remote assessment of Amyotrophic Lateral Sclerosis (ALS). 53 people with ALS and 52 healthy controls interacted with Tina, a cloud-based conversational agent, in performing speech tasks designed to probe various aspects of motor speech function while their audio and video was recorded. We rated a total of 250 recordings for audio/video quality and participant task compliance, along with the relative frequency of different issues observed. We observed excellent compliance (98%) and audio (95.2%) and visual quality rates (84.8%), resulting in an overall yield of 80.8% recordings that were both compliant and of high quality. Furthermore, recording quality and compliance were not affected by level of speech severity and did not differ significantly across end devices. These findings support the utility of dialog systems for remote monitoring of speech in ALS.
Acoustic Word Embeddings for Untranscribed Target Languages with Continued Pretraining and Learned Pooling

Acoustic word embeddings are typically created by training a pooling function using pairs of word-like units.For unsupervised systems, these are mined using k-nearest neighbor (KNN) search, which is slow.Recently, mean-pooled representations from a pre-trained self-supervised English model were suggested as a promising alternative, but their performance on target languages was not fully competitive.Here, we explore improvements to both approaches: we use continued pre-training to adapt the self-supervised model to the target language, and we use a multilingual phone recognizer (MPR) to mine phone n-gram pairs for training the pooling function.Evaluating on four languages, we show that both methods outperform a recent approach on word discrimination.Moreover, the MPR method is orders of magnitude faster than KNN, and is highly data efficient.We also show a small improvement from performing learned pooling on top of the continued pre-trained representations.
Peningkatan Kompetensi Peneliti Yayasan Kakikota Banajrmasin Dalam Melakukan Pre-Proccesing Data Hasil Survei, Analisis Data Kategorik, Dan Pembuatan Peta Tematik

Banjarmasin KAKIKOTA Foundation is one of the NGOs based in South Kalimantan that works on urban issues in Banjarmasin City. Banjarmasin KAKIKOTA Foundation has conducted several social stuides on phenomena in Banjarmasin City through several surveys. However, due to the lack of knowledge about data processing, the researchers of the Banjarmasin KAKIKOTA Foundation experienced difficulties in analyzing survey results data. Therefore, Program Studi Statistika, FMIPA, Universitas Lambung Mangkurat (ULM) provides assistance in the form of statistical training to researcher of the Banjarmasin KAKIKOTA Foundation in order to improve the researcher’s competency and technical skills in analyzing research data. The method used in this community service was training. The training was divided into three subthemes, namely: 1) Data preprocessing, 2) Categorical data analysis, and 3) Thematic map making. Based on the evaluation results, this training was very useful for the researcher of the Banjarmasin KAKIKOTA Foundation and is expected to carry out further training in 2023. Keywords: Statistical Training; NGO; Banjarmasin KAKIKOTA Foundation; Research
Yin-yang pair algorithm based on cubic chaos and lens imaging learning

In order to solve the problem that the Yin-Yang Pair Algorithm (YYPO) tends to converge prematurely and has low convergence accuracy, a Yin-Yang Pair Optimization Algorithm (CYYPO) that combines Cubic chaotic mapping and lens imaging learning strategies is proposed. The algorithm uses Cubic chaotic mapping to initialize the two points of yin and yang, and uses the strategy of lens imaging learning method to design a new forward and reverse search segmentation method, which is tested by CEC2013 standard test function, and the results show that compared with the most basic YYPO and other five intelligent optimization algorithms, CYYPO has higher computational accuracy and more outstanding performance advantages.
A fog-assisted revocable and auditable access control system

Fog computing is a cutting-edge distributed computing architecture that optimizes resource allocation by fully utilizing the resources within the computing network. However, data security is a major concern in fog computing. The implementation of existing Ciphertext-Policy Attribute-based Encryption (CP-ABE) schemes in the fog computing environment faces challenges such as communication blockage, insufficient security, and poor performance. To tackle these challenges, this paper introduces a novel Fog-assisted Revocable and Auditable Access Control System (FRAACS), which incorporates fog-assisted key generation and decryption, user revocation, and auditing mechanisms. The comparative analysis of the proposed scheme's functionality and performance shows that it is a viable, secure, and fitting scheme for the fog computing environment.
Learning CenterNet model for wild animal detection

The detection of animal species as a kind of target detection, the application of artificial intelligence techniques to the field of image processing helps us to study animals more intelligently. In this paper, we use CenterNet-based algorithm to achieve target detection of animals. firstly, we perform data enhancement on the used dataset to achieve multi-scene coverage. secondly, we use CenterNet algorithm to train the model on the dataset to improve the accuracy of recognition. finally, we deploy it to the camera for animal recognition in the actual target scene. The experimental results are analyzed and the trained animal detection system recognizes an average accuracy of 90%-97%. This experiment demonstrates the utility of this research in animal detection in the field of image processing.
Insulator detection based on improved YOLOv5s

The YOLO series is a single-stage detection model, and the insulator is in a complex environment, which makes the detection accuracy generally low. At the same time, due to the large number of insulator image data, the training time of data is too long, resulting in low model training efficiency, slow detection speed and other problems. Based on YOLOv5s, this paper proposes an improved YOLOv5s insulator detection algorithm, YOLOv5c, to achieve high precision and real-time rapid detection. The classical YOLOv5s model is selected as the basis of this algorithm. Add CBAM attention mechanism to backbone network so that important features can be extracted adaptively to improve feature detection ability and detection accuracy. Add a weighted bidirectional feature pyramid network (BiFPN) to the Neck network for bidirectional cross scale connectivity and weighted feature fusion, so as to improve the efficiency of model training. The experimental results show that compared with the original algorithm, YOLOv5c has an average detection accuracy improvement of 1%, reaching 99.5%, and the training efficiency of the model is also improved by about double compared with the original model.
Research on UAV path planning based on Voronoi map construction and improved ant colony algorithm

The traditional ant colony algorithm is prone to problems such as long search time and many turning points in the path when searching on the grid map. In this paper, a map modeling method is proposed, which combines Voronoi diagram to construct feasible paths and threat circle to describe obstacle areas. At the same time, the initial pheromone concentration of nodes is set differently, and the path is optimized by the point-by-point method. The experimental results show that the proposed method can effectively improve the ant colony algorithm and make it more suitable for UAV flight in battlefield environment.
A particle swarm optimization algorithm with dynamic weights

Particle swarm optimization algorithm is a stochastic global optimization algorithm, inertia weight is one of its important parameters, in order to improve its easy to fall into the defects of local optimization and precocious maturity, this paper proposes a new dynamic weight particle swarm optimization algorithm. The new algorithm designs a dynamic weight based on the global optimal solution and its own optimal solution, which makes the velocity change of particles more reasonable, applies it to the classical test function, accelerates the convergence speed, and effectively improves the difficulty of falling into local optimum, and improves the global search ability.
An integrated network based on 2D/3D feature correlations for benign-malignant tumor classification and uncertainty estimation in digital breast tomosynthesis

Abstract Objective. Classification of benign and malignant tumors is important for the early diagnosis of breast cancer. Over the last decade, digital breast tomosynthesis (DBT) has gradually become an effective imaging modality for breast cancer diagnosis due to its ability to generate three-dimensional (3D) visualizations. However, computer-aided diagnosis (CAD) systems based on 3D images require high computational costs and time. Furthermore, there is considerable redundant information in 3D images. Most CAD systems are designed based on 2D images, which may lose the spatial depth information of tumors. In this study, we propose a 2D/3D integrated network for the diagnosis of benign and malignant breast tumors. Approach. We introduce a correlation strategy to describe feature correlations between slices in 3D volumes, corresponding to the tissue relationship and spatial depth features of tumors. The correlation strategy can be used to extract spatial features with little computational cost. In the prediction stage, 3D spatial correlation features and 2D features are both used for classification. Main results. Experimental results demonstrate that our proposed framework achieves higher accuracy and reliability than pure 2D or 3D models. Our framework has a high area under the curve of 0.88 and accuracy of 0.82. The parameter size of the feature extractor in our framework is only 35% of that of the 3D models. In reliability evaluations, our proposed model is more reliable than pure 2D or 3D models because of its effective and nonredundant features. Significance. This study successfully combines 3D spatial correlation features and 2D features for the diagnosis of benign and malignant breast tumors in DBT. In addition to high accuracy and low computational cost, our model is more reliable and can output uncertainty value. From this point of view, the proposed method has the potential to be applied in clinic.
Wireless microseismic monitoring technology based on cloud service platform

With the deepening of the depth of open-pit mining, the country's open-pit mining gradually into the deep depression mining stage, the number of high and steep rock slopes increased, landslide accidents frequently occur. Landslide accident has become a major hidden danger in open-pit mine safety production. Microseismic monitoring technology has been widely recognized by the industry as an effective slope monitoring and early warning means covering the whole mine. In this paper, a wireless microseismic monitoring scheme is formed through wireless bridge technology and solar power supply technology, which is successfully implemented in an open-pit mine slope monitoring. The data processing and analysis results are uploaded to the cloud service platform in real time. The wireless microseismic monitoring system based on cloud service platform runs stably, successfully solving the problem of long-term stable operation of the open-pit slope monitoring system and ensuring the safety of mine production.
Natural scene text recognition based on artificial intelligence machine learning

The text of physical scene images often contains a lot of accurate and advanced semantic information. Due to the rapid development of mobile networks and computer vision technology, this information has been widely used in applications such as geographic location, license plate identification, and unmanned driving. Therefore, this article mainly investigates the natural scene text recognition of artificial intelligence machine learning, and understands the relevant basic theories of natural scene text recognition on the basis of literature data, and then renews the natural scene text recognition using artificial intelligence machine learning, and test the quoted text recognition algorithm. The conclusion of the test is that the recognition accuracy of the algorithm in this paper is 83.7%, so the natural scene recognition model designed in this paper is effective.
Research on generating text summaries based on improved transformer

With the advent of the information age, a large number of information has emerged, and people need to deal with more and more information. In this case, text summarization technology came into being. As a Text summarization model, Transformer has the problem of obtaining key text information and out of vocabulary, which is difficult to effectively solve and resulted in the accuracy of the generated summary not achieving the desired effect, an improved transformer model was proposed, The model constructs an Aggregation module between the encoder and decoder to make the generated abstract more appropriate to the original content ,and the pointer network is introduced at the decoding end of the model to complete the decoding of the text summary, The function of pointer network that allows the unlisted words in the vocabulary to be copied directly from the source text is used to generate a text summary, which improves the flexibility of summary generation, Using the News2016zh dataset to test the performance of the model. In the experimental results, the improved Transformer model performs better than the benchmark model on ROUGE evaluation indicators.
Explainability and the Epistemic Division of Labour in Adjudication

The ‘black box’ quality of contemporary algorithmic tools raises concerns related to their use in court because of the law’s emphasis on explanations, transparency, and public reasons. We argue that the problems of explainability associated with contemporary algorithmic tools are, from a legal perspective, neither sui generis nor irreconcilable with existing norms. We distinguish between the types of explanations required by fact-finders and those required from judges. We conclude that apparent tensions can be reconciled by attending to the epistemic division of labour between the legal and scientific communities, contextualizing expert evidence appropriately, and distinguishing between explanation as reconstruction and as justification.
ECC-CS: a multi-label data classification algorithm for class imbalance based on cost-sensitive learning

Multi-label learning has attracted much attention due to its numerous applications in the real world. Although many multilabel learning algorithms have been proposed, most of them neglect the class imbalance problem that exists in multi-label data. Even when some studies focus on this problem, they often fail to explore label correlations. To address these two issues simultaneously, a cost-sensitive-based class imbalance multi-label data classification method called ECC-CS (Ensemble classifier chains - Cost sensitive) is proposed. In brief, ECC-CS combines the popular Ensemble Classifier Chain (ECC) algorithm with cost-sensitive learning techniques. Therefore, the ECC-CS algorithm simultaneously inherits the merit of label correlation exploration held by ECC and the merit of addressing the class imbalance problem owned by cost-sensitive learning techniques. Specifically, in ECC-CS, the cost-sensitive learning technique runs on each branch of ECC. 12 benchmark multi-label datasets were used to compare the proposed ECC-CS algorithm with several traditional class imbalance multi-label learning algorithms. The experimental results have indicated the effectiveness and superiority of the proposed ECC-CS algorithm.
Object detection based on image pyramid feature fusion and shared detection head

In the object detection, the processing of feature fusion and the structure of detection head have an important impact on the detection performance. The current detectors often use the detection pipeline of ‘backbone-feature fusion network-head’. We first propose the Feature Fusion (FF), which constructs a lightweight branching network based on the image pyramid and fuses its extracted features with those of the backbone network, providing a new idea for the focus of feature fusion. In addition, we design the Shared Detector Head (SDH). The main purpose of SDH is to reduce the inconsistency of predictions on feature maps between classification and regression tasks, enhance the interaction between the two, and enhance the detection performance. Our experiments on MS COCO2017 and PASCAL VOC0712 datasets support the above analysis. Based on the above improvements, our approach achieves 0.8% mAP improvements on MS COCO2017. The above experiments prove the effectiveness of our approach.
Emotion Recognition on Partial Faces with Histogram of Oriented Gradients and Local Binary Patterns

Facial expression is a way to improve the ability of communication. If machines can understand the emotion, their responses should be improved. Many researchers can teach the machine to learn emotions from facial images. Sometimes, the face in an image may not be complete, especially when the masks are used in pandemic. Some parts of the human face cannot be reached. However, if the important features can be extracted from the remaining parts, the machine should be able to learn. In this paper, Histogram of Oriented Gradient (HOG) and Local Binary Pattern (LBP) are considered to extract features from the upper-frontal facial images. The concatenation of these features is also studied. The experimental results show the combined features provide the most stability performance with the highest averaged accuracy at 68.39% on Support Vector Machine.