Reviewerly/exHarmony huggingface dataset:
Learning Enriched Feature Descriptor for Image Matching and Visual Measurement

Recent feature descriptor research has witnessed tremendous progress with the development of the deep neural network. However, most existing descriptors solely focus on learning strong discriminativeness with deep invariant features, neglecting their representation ability and rich hierarchical clues hidden in images, which could further establish high-quality matches via implicit hierarchical comparisons in rich representative descriptor space. In this paper, we consider both the discriminative and representation ability of feature descriptors to enrich the descriptor space with a novel representative learning framework. On the one hand, we introduce Histogram of Oriented Gradient (HOG) as a prior term to guide our descriptor to learn a powerful representation and robustness in a self-supervised manner. on the other hand, we present an adaptive triplet loss, which penalizes the triplet loss according to the descriptor matching distances in order to encourage our descriptor to learn strong discriminativeness. Moreover, to fully use the information encapsulated in images and boost the representation ability, we propose a novel HIerarchical Feature Transformer Network (HIFT), which derives dense descriptions from the semantic and cross-scale enhanced hierarchical features in a local-to-global manner. Extensive experiments on popular feature matching and visual localization benchmarks show that HIFT achieves highly competitive performance compared with the state-of-the-art methods. Applications on visual measurement tasks of visual 3D reconstruction and ego-motion estimation also demonstrate the high generalization ability of our method. Our model is avaliable at https://github.com/Ray2OUC/HIFT.
KDANet: Handwritten Character Recognition for Bangla Language using Deep Learning

Character recognition is the numerical conversion of images in typed, handwritten, or printed format which allows a computer to recognize them. Bangla is one of the most complex languages as it has so many characters and digits. Moreover, the Bangla language has about 300 composite characters. That is why the extraction of characters from images is more di cult for Bangla compared to other languages. Deep learning has recently developed good capabilities for extracting high-level features from an image kernel. These systems learn more accurate and inclusive features from large-scale training datasets than earlier feature extraction techniques. This paper introduces a custom deep learning model to recognize handwritten Bangla characters and compares it with popular deep learning models that recognize handwritten characters. BanglaLekha Isolated dataset has been used to train and compare these models. Our proposed model KDANet was trained on 72,500 images containing primary characters and obtained an accuracy of 98.10% on the BanglaLekha Isolated dataset with 98.12% f1-score.
A Breast Cancer Detection Model using a Tuned SVM Classifier

Breast cancer has become a common disease that affects women all over the world. Early detection and diagnosis of the breast cancer is crucial for an effective medication and treatment. But, detection of breast cancer at the primary stage is challenging due to the ambiguity of the mammograms. Many researchers have explored Machine learning (ML) based model to detect breast cancer. Most of the developed models have not been clinically effective. To address this, in this paper, we propose an optimized SVM based model for the prediction of breast cancer where Bayesian search method is applied to discover the best hyper-parameters of the SVM classifier. Performance of the model with default hyper-parameter for the SVM is compared to the performance with tuned hyper-parameter. The comparison shows that performance is significantly improved when the tuned hyper-parameter is used for training SVM classifier. Our findings show that SVM’s performance with default parameters is 96% whereas the maximum accuracy level 98% is obtained using tuned hyper-parameter.
Bengali Crime News Classification Based on Newspaper Headlines using NLP

In our daily lives, newspapers and online news portals have become ubiquitous. These provide us with information on global events. Of all the news available in newspapers, crime news is the most significant. People read this kind of news with sincerity and considerable curiosity. We read a lot of Bangla newspapers and news sources, but we didn’t find any news on crime that was categorized. Perhaps categorizing the Bangla crime news would be helpful for the readers. Therefore, we decided to work on Bengali crime news classification, which will have a big influence in the Bengali community. However, categorizing crime news from daily newspaper headlines is not an easy task for a human. In this paper, we introduced a practical model to automatically annotate crime news from Bengali newspaper headlines in 6 predetermined crimes. In order to accomplish this goal, we have used TF-IDF for extracting features with 8 different machine learning and language classifier models (SVM, Decision Tree,Random Forest, LSTM, Bi-LSTM, BERT etc) and got best result by Sagor Sarkar’s Bangla-Bert-Base. The experimental result with 6293 training and 1574 testing samples shows 90.15% accuracy. This research output and dataset can be utilized by enthusiasts for further research purposes like subsetting crimes, crime status or judgment analysis etc. Our dataset will be available upon request @https://tinyurl.com/5n7wwaek.
User Product Recommendation System Using KNN-Means and Singular Value Decomposition

In the course of our regular lives, each and every one of us frequently makes use of the e-commerce websites in order to purchase a variety of goods. These results in a rising diversity of consumer demand, which makes it difficult for a retail company to deliver the appropriate products in accordance to the tastes of its customers. These e-commerce websites make use of a variety of recommendation systems in order to give the consumer a satisfying experience when they are shopping online. A technique that may be used to overcome this obstacle is called a recommender system. With the help of product recommendation, it is possible to meet the requirements of customers, which assists in sustaining loyal clients while also attracting new customers. In this project, we propose a system for product based recommendation by using hybrid machine learning algorithms which consists of the combination of KNN-Means and Singular Value Decomposition for the purpose of improving the overall excellence of our rating and recommendation system, that improvises the disadvantages of collaborative filtering on their own. This method has the benefit that there is no necessity for the development of a brand new algorithm in order to calculate the forecasts. The trials demonstrate that our strategy can provide superior outcomes, and our integrated model provides an exceptionally high level of accurate predictions.
Semantic Topic Extraction from Bangla News Corpus Using LDA and BERT-LDA

In order to infer topics from unstructured text data, topic modeling techniques is extensively employed in the field of Natural Language Processing. Latent Dirichlet Allocation (LDA), a popular technique in topic modeling, can be used for the automatic identification of topics from a vast sample of textual documents. The LDA-based topic models, however, may not always yield good outcomes on their own. One of the most efficient unsupervised machine learning methods, clustering, is often employed in applications like topic modeling and information extraction from unstructured textual data. In our study, a hybrid clustering based approach using Bidirectional Encoder Representations from Transformers (BERT) and LDA for large Bangla textual dataset has been thoroughly investigated. The BERT has done the contextual embedding with LDA. The experiments on this hybrid model are carried out to show the efficiency of clustering similar topics from a noble dataset of Bangla news articles. The outcomes of the experiments demonstrate that clustering with BERT-LDA model would aid in the inference of more coherent topics. The maximum coherence value of 0.63 has been found for our noble dataset using LDA and for BERT-LDA model, the value is 0.66.
A Blockchain-Based Secure Payment System for Vehicle Fuel Filling Station

South Asia countries like Bangladesh, India, and Pakistan have a large number of fuel filling stations that use centralized payment transaction systems. In some cases, this fuel filling station uses the hand cash payment system which is not secured and time-consuming. Each transaction takes more than five minutes to process. For that reason, in some cases, customers face the huge hassle of standing in a long line and waiting for their turn. Not only that, there are high possibilities of fraud activities and robbery being occur for large amounts of the payment transaction. To solve this problem we propose a blockchain-based payment transaction method for fuel filling stations. Here we use the decentralized open ledger infrastructure and proof-of-work to approve each transaction block. Every transaction between the customer and the filling station authority is completed through a digital wallet which is fully secured, fast, and transparent. Comparing to the bank payment transaction system our proposed method is decentralized and has low transaction fees applied in every transaction. This transaction process is free from third-party involvement and all transactions are immutable. For that reason no issues of customer trust and safe from fraud activities in a large number of payment transactions. Our proposed payment transaction method can play an important part to handle large amounts of transactions and provide transaction security for increasing the number of fuel filling stations in South Asia's most populated country.
BoMaCNet: A Convolutional Neural Network Model to Detect Bone Marrow Cell Cytology

Bone Marrow is responsible for the creation of all the different types of blood cells in the human body and helps us to identify various types of bone marrow cell disorders. Therefore it is necessary to correctly identify and classify the different types of cells. Conducting different pathological and blood tests may take some time. Applying a Deep Neural Network (DNN) for blood cell detection allows us to quickly classify the call types, which further enables us to identify multiple types of blood cells simultaneously from the same sample. Not only does this save us the time needed for cell classification but also removes the possibility of human error as an automated system can deliver more precise and instantaneous results than a hematologist or pathologist. Machine Learning algorithms are capable of solving these problems quite easily. With that in mind, we propose a CNN-based architecture named BoMaCNet, which is capable of detecting and classifying bone marrow cell images quickly and accurately. Our CNN model takes 96000 images in total, which are then split into training, testing, and validation. Six common types of bone marrow cells (Artefact, Blast, Erythroblast, Lymphocyte, Segmented Neutrophil and Promyelocyte) are chosen for this research. Our entire data set was split into three parts 80% was kept for training, 10% was kept for validation and 10% was used for testing. For testing, 1600 instances of each label were used. Our model was able to produce the highest by far results on the used dataset by achieving an overall accuracy of 95.71%. With 95.71% accuracy in training and 93.06% accuracy in validation along with achieving an impressive mean average F-1 score of 0.93, we were able to achieve exceptional results.
VR Glove: A Virtual Input System for Controlling VR with Enhanced Usability and High Accuracy

Virtual Reality (VR) is one of the pioneering technologies in the current decade. An increasing number of users are migrating to the virtual world as time progresses. This has led to users desiring more intuitive and natural control for their inputs in the digital world, surpassing the need for traditional input systems such as mouse and keyboard. VR based natural inputs systems are scarce, and the available ones are expensive. Again, very few of them translates the normal hand movements into the virtual control inputs. Therefore, the objective is to design and develop a wearable input system for controlling VR with enhanced usability and high accuracy. To attain this objective, user requirements were firstly elicited through semi-structured interviews. Then, a cost-effective and usable wearable system (VR Glove) was developed based on the revealed requirements for controlling VR. Finally, the system was evaluated with 20 test-participants (novice and expert); and it was found that the VR Glove was usable both to the novice and expert users, though the system was more usable to experts than the novice users. Participants were comfortable with the working mechanism of the proposed VR Glove system and also found the system very responsive.
Towards Communication Efficient and Fair Federated Personalized Sequential Recommendation

Federated recommendations leverage the federated learning (FL) techniques to make privacy-preserving recommendations. Though recent success in the federated recommender system, several vital challenges remain to be addressed: (i) The majority of federated recommendation models only consider the model performance and the privacy-preserving ability, while ignoring the optimization of the communication process; (ii) Most of the federated recommenders are designed for heterogeneous systems, causing unfairness problems during the federation process; (iii) The personalization techniques have been less explored in many federated recommender systems. In this paper, we propose a Communication efficient and Fair personalized Federated Sequential Recommendation algorithm (CF-FedSR) to tackle these challenges. CF-FedSR introduces a communication-efficient scheme that employs adaptive client selection and clustering-based sampling to accelerate the training process. A fairness-aware model aggregation algorithm that can adaptively capture the data and performance imbalance among different clients to address the unfairness problems is proposed. The personalization module assists clients in making personalized recommendations and boosts the recommendation performance via local fine-tuning and model adaption. Extensive experimental results show the effectiveness and efficiency of our proposed method.
Blind Dehazed Image Quality Assessment: A Deep CNN-Based Approach

Research on image dehazing has made the need for a suitable dehazed image quality assessment (DIQA) method even more urgent. The performance of existing DIQA methods heavily relies on handcrafted haze-related features. Since hazy images with uneven haze density distributions will result in uneven quality distributions after dehazing, the manually extracted feature expression is neither accurate nor robust. In this paper, we design a deep CNN-based DIQA method without a handcrafted feature requirement. Specifically, we propose a blind dehazed image quality assessment model (BDQM), which consists of three components: image preprocessing, a haze-related feature extraction network (HFNet), and an improved regression network (IRNet). In HFNet, we design a perceptual information enhancement (PIE) module to learn powerful feature representations and enhance network capability according to channel attention, multiscale convolution and residual concatenation. IRNet aims to aggregate all patch information for the quality prediction of the whole image, where the effect of inhomogeneous distortion from the dehazing procedure is attenuated via a specifically designed patch attention (PA) mechanism. Experimental results on benchmark datasets demonstrate the effectiveness and superiority of the proposed network architecture over state-of-the-art methods.
A Hybrid Method of Feature Extraction for Signatures Verification Using CNN and HOG a Multi-Classification Approach

The offline signature verification system’s feature extraction stage is regarded as crucial and has a big impact on how well these systems perform because the quantity and calibre of the features that are extracted determine how well these systems can distinguish between authentic and fake signatures. In this study, we introduced a hybrid method for extracting features from signature images, wherein Convolutional Neural Network (CNN) and Histogram of Oriented Gradients (HOG) were used, followed by the feature selection algorithm (Decision Trees) to identify the key features, and finally the CNN and HOG methods were combined. Three classifiers were employed to evaluate the hybrid method’s efficacy (long short-term memory, support vector machine, and K-nearest Neighbor). The experimental findings indicated that our suggested model executed quite satisfactorily in terms of efficiency and predictive ability, obtaining accuracy of (95.4%, 95.2%, and 92.7%) with UTSig dataset, and (93.7%, 94.1%, and 91.3%) with CEDAR dataset. This accuracy is deemed to be a high significance, particularly given that we checked skilled forged signatures that are more difficult to recognize than other forms of forged signatures like (simple or opposite-hand).
A Noncontact Fall Detection Method for Bedside Application With a MEMS Infrared Sensor and a Radar Sensor

With the rapid development of economy, science, and technology, the aging issues become more and more serious. People aged above 65 have a risk of 28%–35% to fall. Among them, bedside falls happen most frequently. Therefore, the capability to detect fall events of the elderly is very important. In this article, a novel noncontact fall detector based on a MEMS low-resolution infrared sensor and a low-cost radar sensor is developed to detect bedside fall. Besides, IR image processing algorithms based on the adaptive filter, successive approximation, double boundary scans, and mathematical morphology processing are proposed in detail. Partition processing algorithm is used to suppress the influence of residual or existed heat sources on the bed or ground. Then, the statistical features of the center, area, temperature and duration, as well as stable flag and fall action flag, are extracted for fall recognition. Finally, a three-layer radial basis function neural network is applied to distinguish the fall events from the nonfall events. Considering the influence factors of ambient temperature, brightness, gender, dressing, fall posture, fall location, and scenario, a total of 640 tests are conducted and 5-fold cross validation is used to evaluate the classification performance. Experimental results indicate that the averages of the recall, precision, F1-Score, and detection accuracy are measured to be 91.25%, 94.76%, 92.97%, and 93.13%, respectively, which demonstrates that the proposed fall detection method is effective. Besides, the detection accuracy decreases from 96.88% to 85.94% as the ambient temperature rises. Hence, this noncontact fall detector can be widely applied for bedside fall detection at home, which is low cost, nonwearable, unobtrusive, noninvasive, and privacy preserved.
Dynamic Construction of Outlier Detector Ensembles With Bisecting K-Means Clustering

Outlier detection (OD) is a key problem, for which numerous solutions have been proposed.To deal with the difficulties associated with outlier detection across various domains and data characteristics, ensembles of outlier detectors have recently been employed to improve the performance of individual outlier detectors.In this paper, we follow an ensemble outlier detection approach in which good outlier detectors are selected through an enhanced clustering-based dynamic selection (CBDS) method.In this method, a bisecting K-means clustering algorithm is employed to partition the input data into clusters where every cluster defines a local region of competence.Among the initial pool of detectors, the outputs of the detectors with the most competent local performance were combined through four possible schemes to produce the final OD results.Experimental evaluation and comparison of our method were carried out against four variants of locally selective combination in parallel (LSCP) outlier ensembles.The CBDS-based schemes compare well with the LSCP-based ones on 16 public benchmark datasets and incur considerably lower computational costs.The CBDS method consistently achieved superior average scores of the area under the curve (AUC) of the receiver operating characteristic (ROC), and particularly outperformed the LSCP method on nine of the 16 datasets in terms of the AUC score.In addition, while the CBDS and LSCP methods have similar computational costs on small datasets, the CBDS method achieves significant time savings compared with the LSCP method on large datasets.
A 65 nm 12.92-nJ/Inference Mixed-Signal Neuromorphic Processor for Image Classification

Spiking neural networks are a promising candidate for next-generation machine learning and are suitable for powerconstrained edge devices. In this paper, we present a mixed-signal neuromorphic processor that efficienctly implements an echo state network (ESN) and achieves high accuracy without a costly on-chip training process. The design employs a charge-domain computation circuit that efficiently realizes a leaky integrate and fire neuron. Combined with optimizing sparse connections, the proposed algorithm-hardware co-design approach results in a highly energy-efficient operation while delivering high accuracy. Fabricated in a 65nm LP process, the processor is measured to achieve 95.35% MNIST classification accuracy, which closely matches the software model, and energy efficiency of 12.92nJ per classification.
Base Station Dataset-Assisted Broadband Over-the-Air Aggregation for Communication-Efficient Federated Learning

This paper proposes an over-the-air aggregation framework for federated learning (FL) in broadband wireless networks where not only edge devices but also a base station (BS) has its own local dataset. The proposed framework leverages the BS dataset to improve communication efficiency of FL by reducing the number of channel uses required for the model convergence as well as avoiding the signaling overhead incurred by power scale coordination among edge devices. We analyze the convergence to a stationary point without convexity assumption on the objective function. The analysis result reveals that the utilization of BS dataset improves the convergence rate and the update distortion caused by the limited power budget is a crucial factor hindering the model convergence. To facilitate the convergence, we develop an optimized power control method by solving the distortion minimization problem without assumptions on power scale coordination and global CSI at BS. Our simulation results validate that BS dataset is beneficial to reducing the number of channel uses for the model convergence and the developed power control method outperforms the conventional method in terms of both convergence rate and converged test accuracy. Furthermore, we identify some scenarios where the compression of local update can be helpful to reduce communication resources for model training.