PithaNet: A transfer learning-based approach for traditional pitha classification

Pitha, pithe, or peetha are all Bangla words referring to a native and traditional food of Bangladesh as well as some areas of India, especially the parts of India where Bangla is the primary language. Numerous types of pithas exist in the culture and heritage of the Bengali and Bangladeshi people. Pithas are traditionally prepared and offered on important occasions in Bangladesh, such as welcoming a bride grooms, or bride, entertaining guests, or planning a special gathering of family, relatives, or friends. The traditional pitha celebration and pitha culture are no longer widely practiced in modern civilization. Consequently, the younger generation is unfamiliar with our traditional pitha culture. In this study, an effective pitha image classification system is introduced. convolutional neural network (CNN) pre-trained models EfficientNetB6, ResNet50, and VGG16 are used to classify the images of pitha. The dataset of traditional popular pithas is collected from different parts of Bangladesh. In this experiment, EfficientNetB6 and ResNet50 show nearly 90% accuracy. The best classification result was obtained using VGG16 with 92% accuracy. The main motive of this study is to revive the Bengali pitha tradition among young people and people worldwide, which will encourage many other researchers to pursue research in this domain

Node criticality assessment in a blockchain network

Blockchain systems are being rapidly integrated in various technologies, with limited work on the effect of the underlying network topology on the blockchain performance. In this work, we investigate the significance of each network node on the overall blockchain performance. This is assessed by selecting critical nodes according to different criticality metrics, and investigating, using simulations, the degradation in performance incurred upon removing these nodes. The most critical nodes are the ones that incur the greatest degradation in performance. The considered performance metrics are the blockchain size and the packet drop rate. Criticality metrics such as Betweennes Centrality, Closeness Centrality and Degree Centrality are compared. It is found that the Sign Change Spectral Partitioning approach, enhanced with Blockchain Specific traffic flow information, is able to identify critical nodes better in the sense that higher degradation in performance is reported upon their removal

User privacy risk analysis for the Internet of Things

The Internet of Things (IoT) refers to a large network of devices such as sensors and actuators in which diverse types of data is generated and shared. Data can be shared in its raw form or as a result of data processing activities performed by an IoT device (e.g. anonymization, aggregation, etc.). However, sharing such data introduces a multitude of risks which are influenced by data type, data harvesting granularity, user demographics and the device under consideration. In this work, we propose a novel extension to our attack tree risk model [1] to consider user preferences for sharing personal data. We enrich our earlier work by exploring more attacks and complimenting them with a user privacy-risk model. We evaluate this proposed model and identify a range of scenarios which can result in personal information privacy violation and thus provide a model for estimating the potential risk of an IoT ecosystem

Non-Adversarial Video Synthesis with Learned Priors

Most of the existing works in video synthesis focus on generating videos using adversarial learning. Despite their success, these methods often require input reference frame or fail to generate diverse videos from the given data distribution, with little to no uniformity in the quality of videos that can be generated. Different from these methods, we focus on the problem of generating videos from latent noise vectors, without any reference input frames. To this end, we develop a novel approach that jointly optimizes the input latent space, the weights of a recurrent neural network and a generator through non-adversarial learning. Optimizing for the input latent space along with the network weights allows us to generate videos in a controlled environment, i.e., we can faithfully generate all videos the model has seen during the learning process as well as new unseen videos. Extensive experiments on three challenging and diverse datasets well demonstrate that our approach generates superior quality videos compared to the existing state-of-the-art methods.Comment: Accepted to CVPR 202

Trade-Offs in Competitive Transport Operations

One of the goals of developing a transport corridor is to promote socio-economic development by improving connectivity and sustainable transport operations, which largely depends on the operational strategy. Trade-off policies can be important tools for gaining the competitive advantage of road transport corridors, and thus, help facilitate sustainable growth and welfare. This article uses a case-based approach to observe the trade-offs in the first phase of transport infrastructure development, and then, in the second stage, further explores the trade-off variables in the transport operations strategy under the China-Pakistan Economic Corridor (CPEC). The results from the three cases of the parallel route system of the CPEC indicate that trade-off is an easily understandable and applicable method, which can foresee the operational gains or compromises for significant welfare of the regions. The implications of the trade-off are two fold, first is the “importance” of the trade-off, which is related to its impact on operational competitiveness. The other is the “sensitivity” of the trade-off, in terms of the change that will be caused to one variable when changing the other. The trade-off concept can be used for several landlocked transport corridors to achieve a competitive edge in transit trade