Improving Face Recognition from Caption Supervision with Multi-Granular Contextual Feature Aggregation

We introduce caption-guided face recognition (CGFR) as a new framework to improve the performance of commercial-off-the-shelf (COTS) face recognition (FR) systems. In contrast to combining soft biometrics (eg., facial marks, gender, and age) with face images, in this work, we use facial descriptions provided by face examiners as a piece of auxiliary information. However, due to the heterogeneity of the modalities, improving the performance by directly fusing the textual and facial features is very challenging, as both lie in different embedding spaces. In this paper, we propose a contextual feature aggregation module (CFAM) that addresses this issue by effectively exploiting the fine-grained word-region interaction and global image-caption association. Specifically, CFAM adopts a self-attention and a cross-attention scheme for improving the intra-modality and inter-modality relationship between the image and textual features, respectively. Additionally, we design a textual feature refinement module (TFRM) that refines the textual features of the pre-trained BERT encoder by updating the contextual embeddings. This module enhances the discriminative power of textual features with a cross-modal projection loss and realigns the word and caption embeddings with visual features by incorporating a visual-semantic alignment loss. We implemented the proposed CGFR framework on two face recognition models (ArcFace and AdaFace) and evaluated its performance on the Multi-Modal CelebA-HQ dataset. Our framework significantly improves the performance of ArcFace in both 1:1 verification and 1:N identification protocol.Comment: This article has been accepted for publication in the IEEE International Joint Conference on Biometrics (IJCB), 202

Forecasting Temperature in the Coastal Area of Bay of Bengal-An Application of Box-Jenkins Seasonal ARIMA Model

Temperature is one of the most vital elements of the climate system and forecasting of the temperature helps the stakeholders those who are depends on it directly or indirectly to prepare  in advance. Country like Bangladesh whose economy mostly geared up by the agricultural product need to know the upcoming pattern of temperature beforehand to take necessary actions. This study has been conducted on the monthly maximum and minimum temperature data (1949-2012) from the second largest and port city of Bangladesh, Chittagong. Non-parametric Mann-Kendall test has been adopted to identify the trend of the series and found that though the maximum temperature is increasing but not significantly but the minimum temperature is increasing significantly. The anomaly plot is just portrait the ups and downs of minimum and maximum temperature and found minimum temperature is increasing from last two decades whereas the maximum temperature has abrupt changes with increase and decrease. The linear trend analysis shows the climate line for maximum and minimum temperature are 35.67 and 10.23 degree Celsius respectively and the rate for significant increase of minimum temperature is 0.07 degree Celsius. The forecasting Seasonal ARIMA model for maximum temperature is SARIMA (1, 1, 1) (2, 0, 0) [12] and for minimum temperature is SARIMA (1, 1, 1) (1, 0, 1) [12]. The resulted outcomes indicate the increasing pattern of temperature in upcoming days in this area of Bangladesh. Keywords: temperature, Seasonal ARIMA, forecasting, climate, Chittagon

Boron Nitride nanotube reinforced Titanium matrix composite

Boron nitride nanotube reinforcement at titanium matrix composite increased the strength of the composite both at room and high temperature. At higher sintering temperature, nanotube reacts with titanium first forming TiB2 transition phase at the interface and then in-situ formed TiB phases in the matrix, which is also responsible for enhanced mechanical properties

Brain Cancer Segmentation Using YOLOv5 Deep Neural Network

An expansion of aberrant brain cells is referred to as a brain tumor. The brain's architecture is extremely intricate, with several regions controlling various nervous system processes. Any portion of the brain or skull can develop a brain tumor, including the brain's protective coating, the base of the skull, the brainstem, the sinuses, the nasal cavity, and many other places. Over the past ten years, numerous developments in the field of computer-aided brain tumor diagnosis have been made. Recently, instance segmentation has attracted a lot of interest in numerous computer vision applications. It seeks to assign various IDs to various scene objects, even if they are members of the same class. Typically, a two-stage pipeline is used to perform instance segmentation. This study shows brain cancer segmentation using YOLOv5. Yolo takes dataset as picture format and corresponding text file. You Only Look Once (YOLO) is a viral and widely used algorithm. YOLO is famous for its object recognition properties. You Only Look Once (YOLO) is a popular algorithm that has gone viral. YOLO is well known for its ability to identify objects. YOLO V2, V3, V4, and V5 are some of the YOLO latest versions that experts have published in recent years. Early brain tumor detection is one of the most important jobs that neurologists and radiologists have. However, it can be difficult and error-prone to manually identify and segment brain tumors from Magnetic Resonance Imaging (MRI) data. For making an early diagnosis of the condition, an automated brain tumor detection system is necessary. The model of the research paper has three classes. They are respectively Meningioma, Pituitary, Glioma. The results show that, our model achieves competitive accuracy, in terms of runtime usage of M2 10 core GPU

Word level Bangla Sign Language Dataset for Continuous BSL Recognition

An robust sign language recognition system can greatly alleviate communication barriers, particularly for people who struggle with verbal communication. This is crucial for human growth and progress as it enables the expression of thoughts, feelings, and ideas. However, sign recognition is a complex task that faces numerous challenges such as same gesture patterns for multiple signs, lighting, clothing, carrying conditions, and the presence of large poses, as well as illumination discrepancies across different views. Additionally, the absence of an extensive Bangla sign language video dataset makes it even more challenging to operate recognition systems, particularly when utilizing deep learning techniques. In order to address this issue, firstly, we created a large-scale dataset called the MVBSL-W50, which comprises 50 isolated words across 13 categories. Secondly, we developed an attention-based Bi-GRU model that captures the temporal dynamics of pose information for individuals communicating through sign language. The proposed model utilizes human pose information, which has shown to be successful in analyzing sign language patterns. By focusing solely on movement information and disregarding body appearance and environmental factors, the model is simplified and can achieve a speedier performance. The accuracy of the model is reported to be 85.64%

Modeling of Potato Shelf Life on Evaporative Cooling Storage

A model of evaporative cooling storage system was designed to increase potato shelf life for improving potato storage system. Two cultivars of potato ‘Diamant’ (100 gm and 51 gm per tuber) and ‘LalPakri (23 gm and 11 gm per tuber) were placed on four shelves of the bin. Each shelf holds 240 kg of potato from 23 march 2013 to December 2013. Potato spoilage, sprouting, shrinkage, moisture content, vitamin C and total sugar content of potato were measured. Experimental results revealed that potato spoilage progressively increased from April to November and sprouting of potato gradually increased from June to October, but stopped in November. The cumulative spoilage and sprouting were much lower in the improved bin compared to traditional farmer’s practices. Shrinkage of potato was found higher in farmer’s practice than that of storage bin from October to November. Moisture content of potato was higher during May and reduced gradually to the lowest value during November in both of practices. No significant difference was found in two practices on vitamin-C content. Sugar content of ‘Diamant; potato was lower in the storage bin during November. According to data analysis and regression curve storage bin model was more appropriate for both cultivars than farmer practice and significantly more appropriate for ‘LalPakri’ potato

Enhancing Data Security for Cloud Computing Applications through Distributed Blockchain-based SDN Architecture in IoT Networks

Blockchain (BC) and Software Defined Networking (SDN) are some of the most prominent emerging technologies in recent research. These technologies provide security, integrity, as well as confidentiality in their respective applications. Cloud computing has also been a popular comprehensive technology for several years. Confidential information is often shared with the cloud infrastructure to give customers access to remote resources, such as computation and storage operations. However, cloud computing also presents substantial security threats, issues, and challenges. Therefore, to overcome these difficulties, we propose integrating Blockchain and SDN in the cloud computing platform. In this research, we introduce the architecture to better secure clouds. Moreover, we leverage a distributed Blockchain approach to convey security, confidentiality, privacy, integrity, adaptability, and scalability in the proposed architecture. BC provides a distributed or decentralized and efficient environment for users. Also, we present an SDN approach to improving the reliability, stability, and load balancing capabilities of the cloud infrastructure. Finally, we provide an experimental evaluation of the performance of our SDN and BC-based implementation using different parameters, also monitoring some attacks in the system and proving its efficacy.Comment: 12 Pages 16 Figures 3 Table

Adoption of Digital Payment Systems in Microcredit Operations: Challenges & Opportunities in the Context of Bangladesh

This study explores the integration of digital payment systems into microcredit operations within the context of Bangladesh. The primary objective was to find the transformative potential of mobile banking technology and digital payment systems in reshaping microcredit distribution, disbursement, and repayment processes. Employing a qualitative research approach, the study examined the impact of mobile banking technologies and digital payment systems on microcredit operations. For the analysis, data was collected through indepth interviews with seven participants working in various roles in various organizations within the microcredit sector, including Branch Manager, Branch Manager, Regional Manager, Regional Manager, Assistant Director, Area Manager, and Senior Manager. The selection of participants was based on their expertise and experience in microcredit operations and their involvement in the adoption and implementation of digital payment systems. All the participants agreed to the fact that the integration digital payment systems indeed can be a game changer for microcredit operation in the context of Bangladesh. Content analysis methods were employed to analyze the interview data, identifying recurring themes and patterns. The findings reveal that the adoption of digital payment systems has a significant impact on microcredit operations, streamlining the distribution of microcredits, eliminating administrative bottlenecks, and ensuring timely disbursements. Additionally, digital payment systems had led to substantial improvements in operational efficiency, particularly in recordkeeping and reconciliation, which has enhanced internal operational processes. These findings align with existing scholarly literature, confirming the transformative potential of digital payment systems in revolutionizing microcredit operations as well as provide valuable insights for practitioners and their managers, highlighting the critical role of digital payment systems in enhancing operational efficiency and elevating customer service. While the study's findings about the impactful possibilities of digital payment systems in microcredit operations within the context of Bangladesh, it is essential to recognize and address the potential limitations regarding their applicability to other contexts. The findings may be restricted to the current timeframe, as technological advancements and evolving microcredit practices may render them less applicable in the future. Additionally, cultural factors such as general behavior, norms and values may have greater influence in the adoption and utilization of digital payment systems in microcredit operations across different cultures. Furthermore, industryspecific characteristics, including the regulatory environment, competitive landscape, and target clientele, may impact the success of digital payment system adoption in industries beyond microcredit. Notwithstanding these contextual limitations, the overall outcome of that particular study offer useful insights into the transformative potential of digital payment systems in microcredit operations, paving the way for further research to explore the broader applicability