Abstract

Personal Health Records (PHRs) allow patients to have full control over their health data. However, storage and sharing of PHRs still remains a difficult but necessary task, especially when health data is one of the major targets of cyber attacks worldwide. Searchable Encryption (SE) is a feasible solution for this problem and can be augmented by Blockchain to address some of its issues, such as verifiability. Therefore, SE using blockchain is a promising technologies to tackle the challenge of PHR storage and sharing. In this survey, we have explored the research works that use SE and blockchain technology for the same. The work starts with an introduction of cloud, searchable encryption and blockchain. Subsequently, we present a literature survey of the corresponding technologies. We then describe SE in detail and how it fits with blockchain. This is followed by description of noteworthy existing solutions for secure storage and sharing of PHRs. Even though there have been a number of surveys related to SE, none of them have surveyed the use of blockchain with SE or use of SE and blockchain in PHR sharing. The work concludes with a comparative study of these existing solutions and future scope in this direction. KEYWORDS: Searchable encryption, Blockchain, Cloud computing, Personal Health Records (PHRs), Storage, Security

Abstract

Secure storage and sharing of Personal Health Records (PHRs) in Internet of Medical Things (IoMT) is one of the significant challenges in the healthcare ecosystem. Due to the high value of personal health information, PHRs are one of the favourite targets of cyber attackers worldwide. Over the years, many solutions have been proposed; however, most solutions are inefficient for practical applications. For instance, several existing schemes rely on the bilinear pairings, which incur high computational costs. To mitigate these issues, we propose a novel PHR-sharing scheme that is dynamic, efficient, and practical. Specifically, we combine searchable symmetric encryption, blockchain technology and a decentralized storage system, known as Inter-Planetary File System (IPFS) to guarantee confidentiality of PHRs, verifiability of search results, and forward security. Moreover, we provide formal security proofs for the proposed scheme. Finally, we have conducted extensive test-bed experiments and the results demonstrate that the proposed scheme can be used in practical scenarios related to IoMT environment.

Abstract

The complexity and fuidity of emotions in the epic of Mahābhārata present before us an interesting case for delving into the phenomenology of emotions. In the rationalist tradition of Kant, emotions are considered as an impediment to moral discernment. The rationalist account of emotions considers it as an animal instinct which needs to be controlled through the exercise of Reason. The paper problematizes the rationalist interpretation of emotions mainly on two counts. First, it ignores the evaluative content of the emotions and considers it as a non-cognitive element. Second, it also overlooks the productive role of various emotions like guilt, shame, remorse in moral deliberation. The paper critically analyzes the episode of Yudhiṣṭhira’s grief in Śānti Parva and argues that the grief of Yudhiṣṭhira cannot be explained as personal loss. Rather, Yudhiṣṭhira’s grief should be understood as a case of moral confict where a moral agent fnds it difcult to justify his moral choices. Yudhiṣṭhira’s analysis of the futility of war and the condemnation of violence should be understood as the evaluative perspective ofered by his emotions. The phenomenological analysis of Yudhiṣṭhira’s grief allows us to understand the signifcance of emotions in constituting the moral perspective on any conficting situation. Hence, emotions cannot be relegated to the domain of irrationality rather they become the site where the truth unveils itself.

Abstract

Fairness in resource allocation is a important problem that has many real life consequences.Althoughmany algorithms that try to achieve envy free allocation,proportionality or min max share wereproposed that tries to encapsulate fairness this does not suce because it was inherently assumedthat agents are not intelligent and there is uniformity in treatment.This is vastly dierent fromreal life where there are many scenarios where agents would actively try to sabotage or reduce theallocation given to their adversaries. Therefore all agents must not be treated the same way.Asseen in economics cartels are where certain players collaborate and try to maximize their interestby undermining competition.This could lead to dangerous consequences and unfair means as seenwith real life examples of apple or google undermining competition by monopoly

Abstract

Abstract Working from home (WFH) is a new reality and norm in today’s work culture. COVID-induced lockdown introduced the concept of WFH for many people. Blurring home and workplace boundaries was a prominent cause of disorientation in people’s lives. Hence, WFH becomes a signifcant phenomenon to explore as it raises the fundamental question of body and space in shaping people’s experiences. To study this, the researchers designed a phenomenological inquiry and examined the lived phenomenon of WFH during the COVID lockdown. Borrowing theoretical concepts from philosophers Martin Heidegger and Merleau-Ponty, they aimed to understand human experiences from an embodied perspective. They interviewed a few adults (age group 27–50) in three urban Indian cities during the frst phase of the COVID-19 lockdown. The participants’ experiences were transcribed, and Interpretative Phenomenological Analysis (IPA) was conducted. The IPA themes highlighted their varied lived experiences and lifeworld, such as distractions in working from home, their changed routines, habits, and social interaction. The fndings are discussed through phenomenological psychology concepts such as embodied cognition, body memory, extended self, shared empathy, intersubjectivity and prerefective bodily intelligence in coping. The study’s contribution is that it advances a methodological understanding by interpreting people’s experiences from a phenomenological view of being-in-the-world in which the individual is not an isolated

Abstract

The default approach to deal with the enormous size and lim- ited accessibility of many Web and social media networks is to sample one or more subnetworks from a conceptually unbounded unknown network. Clearly, the extracted subnet- works will crucially depend on the sampling scheme. Mo- tivated by studies of homophily and opinion formation, we propose a variant of snowball sampling designed to priori- tize inclusion of entire cohesive communities rather than any kind of representativeness, breadth, or depth of coverage. The method is illustrated on a concrete example, and experiments on synthetic networks suggest that it behaves as desired.

Abstract

The burdensome impact of a skewed judges-to-cases ratio on the judicial system manifests in an overwhelming backlog of pending cases alongside an ongoing influx of new ones. To tackle this issue and expedite the judicial process, the proposition of an automated system capable of suggesting case outcomes based on factual evidence and precedent from past cases gains significance. This research paper centres on developing a graph neural network-based model to address the Legal Judgment Prediction (LJP) problem, recognizing the intrinsic graph structure of judicial cases and making it a binary node classification problem. We explored various embeddings as model features, while nodes such as time nodes and judicial acts were added and pruned to evaluate the model's performance. The study is done while considering the ethical dimension of fairness in these predictions, considering gender and name biases. A link prediction task is also conducted to assess the model's proficiency in anticipating connections between two specified nodes. By harnessing the capabilities of graph neural networks and incorporating fairness analyses, this research aims to contribute insights towards streamlining the adjudication process, enhancing judicial efficiency, and fostering a more equitable legal landscape, ultimately alleviating the strain imposed by mounting case backlogs. Our best-performing model with XLNet pre-trained embeddings as its features gives the macro F1 score of 75% for the LJP task. For link prediction, the same set of features is the best performing giving ROC of more than 80%

Abstract

Criticality, observed during second-order phase transitions, is an emergent phenomenon. The brain operates near criticality where complex systems exhibit high correlations. As a system approaches criticality, it develops “domain”-like regions with competing phases and increased spatio-temporal correlations that diverge. The dynamics of these domains depend on the system’s proximity to criticality. This study explores the differences in the proximity to criticality of Alzheimer’s-afflicted and cognitively normal brains through the use of a spin-lattice model derived from resting-state fMRI data and investigates the type of criticality found in the human brain - whether it is of the Ising class or something more complex. The autocorrelation relaxation time in both groups displays a stretched exponential nature, indicating closer alignment with the criticality of the spin-glass class rather than the Ising class. Longer relaxation times observed in cognitively normal subjects suggest increased proximity to the phase boundary. The weak distinction observed in the spatial characteristics related to proximity to criticality might once more point to a spin-glass scenario, necessitating nuanced order parameters to distinguish between phase-ordering in Alzheimer’s and cognitively normal brains.

Abstract

Generative AI has seen remarkable growth over the past few years, with diffusion models being state-of-the-art for image generation. This study investigates the use of diffusion models in generating artificial data generation for electronic circuits for enhancing the accuracy of subsequent machine learning models in tasks such as performance assessment, design, and testing when training data is usually known to be very limited. We utilize simulations in the HSPICE design environment with 22nm CMOS technology nodes to obtain representative real training data for our proposed diffusion model. Our results demonstrate the close resemblance of synthetic data using diffusion model to real data. We validate the quality of generated data, and demonstrate that data augmentation certainly effective in predictive analysis of VLSI design for digital circuits.

Abstract

Efficient and reliable non-invasive methods for micro-organism detection have significant implications in various fields such as medical diagnostics, environmental monitoring, and food safety. Existing detection techniques, such as culture-based methods, polymerase chain reaction (PCR) have limitations including time-consuming processes, expensive equipment requirements and invasive sampling procedures. These drawbacks emphasize the need for alternate detection techniques that are non-invasive, rapid, sensitive, specific, and user-friendly. For this purpose, in this research, we present a portable integrated system for micro-organism detection, which is based on electrochemical impedance spectroscopy (EIS). The proposed system includes Zinc Oxide (ZnO) nanorods based interdigitized electrode (IDE) sensor and an EIS circuit. This work also presents the considerations and methodology for the fabrication of ZnO nanorods with high-yield. To validate the efficacy of the proposed methods, experimental results through scanning electron microscope are also presented. Finally, measurement results for detection of a micro-organism (yeast) is also presented to demonstrate the utility and effectiveness of the proposed methods and system.