Chakraborty, Suchetana

The Hyper-Kamiokande project is a 258-kton Water Cherenkov together with a 1.3-MW high-intensity neutrino beam from the Japan Proton Accelerator Research Complex (J-PARC). The inner detector with 186-kton fiducial volume is viewed by 20-inch photomultiplier tubes (PMTs) and multi-PMT modules, and thereby provides state-of-the-art of Cherenkov ring reconstruction with thresholds in the range of few MeVs. The project is expected to lead to precision neutrino oscillation studies, especially neutrino CP violation, nucleon decay searches, and low energy neutrino astronomy. In 2020, the project was officially approved and construction of the far detector was started at Kamioka. In 2021, the excavation of the access tunnel and initial mass production of the newly developed 20-inch PMTs was also started. In this paper, we present a basic overview of the project and the latest updates on the construction status of the project, which is expected to commence operation in 2027.

Hyper-Kamiokande is a multi-purpose next generation neutrino experiment. The detector is a two-layered cylindrical shape ultra-pure water tank, with its height of 64 m and diameter of 71 m. The inner detector will be surrounded by tens of thousands of twenty-inch photosensors and multi-PMT modules to detect water Cherenkov radiation due to the charged particles and provide our fiducial volume of 188 kt. This detection technique is established by Kamiokande and Super-Kamiokande. As the successor of these experiments, Hyper-K will be located deep underground, 600 m below Mt. Tochibora at Kamioka in Japan to reduce cosmic-ray backgrounds. Besides our physics program with accelerator neutrino, atmospheric neutrino and proton decay, neutrino astrophysics is an important research topic for Hyper-K. With its fruitful physics research programs, Hyper-K will play a critical role in the next neutrino physics frontier. It will also provide important information via astrophysical neutrino measurements, i.e., solar neutrino, supernova burst neutrinos and supernova relic neutrino. Here, we will discuss the physics potential of Hyper-K neutrino astrophysics.

Next-generation smart city applications, attributed to the power of the Internet of Things (IoT) and Cyber–Physical Systems (CPS), significantly rely on sensing data quality. With an exponential increase in intelligent applications for urban development and enterprises offering sensing-as-a-service these days, it is imperative that a shared sensing infrastructure could thwart the better utilization of resources. However, a shared sensing infrastructure that leverages low-cost sensing devices for a cost-effective solution remains unexplored territory. A significant research effort is still needed to make edge-based data shaping solutions more reliable, feature-rich, and cost-effective while addressing the associated challenges in sharing the sensing infrastructure among multiple collocated services with diverse Quality of Service (QoS) requirements. Towards this, we propose UniPreCIS, a novel edge-based data preprocessing solution that accounts for the inherent characteristics of low-cost ambient sensors and their exhibited measurement dynamics concerning application-specific QoS. UniPreCIS aims to identify and select quality data sources by performing sensor ranking and selection that dynamically adapts to the change in sensor attributes. Finally, multimodal data preprocessing is performed in a unified manner to meet heterogeneous application QoS and, at the same time, reduce the resource consumption footprint for the resource-constrained network edge. We study the effectiveness of UniPreCIS on a real-world testbed deployed on our campus. As observed, the processing time and memory utilization of the stakeholder services have been reduced in the proposed approach while achieving up to 90% accuracy, which is arguably significant compared to state-of-the-art sensing techniques.

Identification and characterization of human crowd formulation have been a topic of immense interest in recent times due to its applicability in a wide range of smart-city applications covering infrastructure automation to targeted advertising. The core idea is to extract the dynamics and associated behavioural patterns of mass gatherings within an environment through a continuous remote monitoring of the crowd. In general, the existing approaches heavily rely on computer vision and image processing based algorithmic tools and techniques to address this problem or mandate the crowd entities to carry a smartphone with them. However, considering the ubiquitous design goals of futuristic smart applications, camera and smartphone driven active sensing is not suitable to honour users' right to privacy by requiring an active user participation. In this work, we introduce a novel approach towards measuring the spatiotemporal significance of an object in terms of the curious crowd it has attracted over the others. The proposed approach utilizes a set of passive sensors and Wireless signal properties for the necessary estimation. We validate the idea using a room-scale testbed with rigorous experimentation in a real-world scenario. The low cost solution has minimal invasive footprints towards privacy and is capable to reach beyond 90% of accuracy for this measurement.

We investigate how the evolutionary network is associated among Human, Chimpanzee and Mouse with regards to their genomic information. We provide a quantitative description of genomic evolution through indexes based on fractals and mathematical morphology. These indexes are carefully chosen and reveal quantitatively the similarity or differences among the structure of the concerned sequences. They also reveal how the sequences have evolved over the course of time. We have considered olfactory receptors (ORs) as our case study. These ORs do function in different species with the subtle differences in between the structures of DNA sequences. Such differences are quantified in this paper.

With an exponential increase in intelligent data-driven applications in recent times, ensuring the reliability of this massive amount of data poses a great challenge. Fortunately, the advent of blockchain technologies could enable enforcing as well as managing trust in a transparent and robust manner. A trust value associated with a user or data can help prevent fraud and improve user experience. The same user or data source (an IoT device) can be a part of multiple blockchains considering the many-to-one mapping of running applications to the user (or devices) in a practical scenario. In such an ecosystem, it is important to preserve the hard-earned trust of a user across different chains. However, limited interoperability among multiple blockchains restricts the reusability and transitivity of trust realization, which would otherwise be helpful in reducing the overhead to a great extent. This naturally raises an interesting question: 'How to enable a seamless flow of trust from one chain to the other without compromising the privacy of the data that would conform to the specific characteristics of individual blockchain and preserve the integrity of trust value?' In this paper, we design a novel architecture called CrossTrustchain to facilitate the interoperability of trust across different chains to address the stated problem. We also look at how the multivariate trust model can be interoperated between different chains. Finally, we conduct performance benchmarking for a banking use case scenario on Hyperledger Caliper and presented interesting results to open up newer possibilities for further research in this direction.

In this demonstration, we present the working principles and key features of a unique mobile application named LegalHelper. The application is developed to address the existing challenges in terms of reduced interpretability and low perception of the legal documents as commonly faced by the general public. The core software is built up by leveraging the power of cutting-edge technologies such as Blockchain, Computer Vision, and Natural Language Processing. The main components to be demonstrated include Prominent Language Translator, Legal Maxim Library, and Contract Digitizer.

With the advent of new technologies in both hardware and software, we are in the need of a new type of application that requires huge computation power and minimal delay. Applications such as face recognition, augmented reality, virtual reality, automated vehicles, industrial IoT, etc. belong to this category. Cloud computing technology is one of the candidates to satisfy the computation requirement of resource-intensive applications running in UEs (User Equipment) as it has ample computational capacity, but the latency requirement for these applications cannot be satisfied by the cloud due to the propagation delay between UEs and the cloud. To solve the latency issues for the delay-sensitive applications a new network paradigm has emerged recently known as Multi-Access Edge Computing (MEC) (also known as mobile edge computing) in which computation can be done at the network edge of UE devices. To execute the resource-intensive tasks of UEs in the MEC servers hosted in the network edge, a UE device has to offload some of the tasks to MEC servers. Few survey papers talk about task offloading in MEC, but most of them do not have in-depth analysis and classification exclusive to MEC task offloading. In this paper, we are providing a comprehensive survey on the task offloading scheme for MEC proposed by many researchers. We will also discuss issues, challenges, and future research direction in the area of task offloading to MEC servers.

This paper presents a framework called enGauge that leverages ear-based inertial sensing to continuously monitor listener focus levels in online meetings and provide feedback to the speaker about audience engagement. This allows for the identification of the onset of several neurodevelopmental disorders, including attention deficit hyperactivity disorder (ADHD), and can help to improve the effectiveness of online meetings by allowing speakers to adjust their speaking pace and style based on audience engagement. We explore a contrastive learning-based approach coupled with a judicial selection of anchor events from the meeting contents to model the system. enGauge can detect patterns or shifts in behavior and focus levels of passive listeners to accurately identify changes in focus. Results from a user study with 38 participants showed an overall F1-score of 0.89 for detecting passive listeners' focus levels. Our study suggests that ear-based inertial sensing has the potential to be a valuable tool for the early detection and monitoring of several neurodevelopmental disorders among individuals.

With ubiquitous adaptation of IoT infrastructure, the smart applications these days are not confined within a single realm. Be it the boundary of a home environment or a city-scale infrastructure, multiple smart applications and associated services tend to serve the users by sharing a common sensing platform. Although the very purpose of this shared infrastructure motivates from the reduced installation and maintenance expenses, it offers a great deal in enabling faster cross-domain inference too. For instance, the same data reflecting room temperature and CO2 density can be taken into consideration by occupancy estimation service to feed the HVAC system, surveillance system as well as smart resource management system concurrently. Towards faster decision-making and automation it is imperative that the sensory data be pre-processed at the network edge. Now, the existing simulation platforms cannot account for the real-world dynamics affecting the performance of such edge-based sensing and data processing solutions for a shared IoT infrastructure. This work aims to document the practical lessons learnt while developing a testbed to evaluate the performance of such edge-based solutions to support ambient living. Along with the detailed description of system architecture, requirement analysis, design and development arrangement, we also provide a use case demonstration to unfold the inherent challenges and potential solution ideas to setup and conduct experiments on such a testbed environment.