Evaluation of Federated Learning in Phishing Email Detection.

The use of artificial intelligence (AI) to detect phishing emails is primarily dependent on large-scale centralized datasets, which has opened it up to a myriad of privacy, trust, and legal issues. Moreover, organizations have been loath to share emails, given the risk of leaking commercially sensitive information. Consequently, it has been difficult to obtain sufficient emails to train a global AI model efficiently. Accordingly, privacy-preserving distributed and collaborative machine learning, particularly federated learning (FL), is a desideratum. As it is already prevalent in the healthcare sector, questions remain regarding the effectiveness and efficacy of FL-based phishing detection within the context of multi-organization collaborations. To the best of our knowledge, the work herein was the first to investigate the use of FL in phishing email detection. This study focused on building upon a deep neural network model, particularly recurrent convolutional neural network (RNN) and bidirectional encoder representations from transformers (BERT), for phishing email detection. We analyzed the FL-entangled learning performance in various settings, including (i) a balanced and asymmetrical data distribution among organizations and (ii) scalability. Our results corroborated the comparable performance statistics of FL in phishing email detection to centralized learning for balanced datasets and low organizational counts. Moreover, we observed a variation in performance when increasing the organizational counts. For a fixed total email dataset, the global RNN-based model had a 1.8% accuracy decrease when the organizational counts were increased from 2 to 10. In contrast, BERT accuracy increased by 0.6% when increasing organizational counts from 2 to 5. However, if we increased the overall email dataset by introducing new organizations in the FL framework, the organizational level performance improved by achieving a faster convergence speed. In addition, FL suffered in its overall global model performance due to highly unstable outputs if the email dataset distribution was highly asymmetric.

Structural Characteristics of Two-Sender Index Coding.

This paper studies index coding with two senders. In this setup, source messages are distributed among the senders possibly with common messages. In addition, there are multiple receivers, with each receiver having some messages a priori, known as side-information, and requesting one unique message such that each message is requested by only one receiver. Index coding in this setup is called two-sender unicast index coding (TSUIC). The main goal is to find the shortest aggregate normalized codelength, which is expressed as the optimal broadcast rate. In this work, firstly, for a given TSUIC problem, we form three independent sub-problems each consisting of the only subset of the messages, based on whether the messages are available only in one of the senders or in both senders. Then, we express the optimal broadcast rate of the TSUIC problem as a function of the optimal broadcast rates of those independent sub-problems. In this way, we discover the structural characteristics of TSUIC. For the proofs of our results, we utilize confusion graphs and coding techniques used in single-sender index coding. To adapt the confusion graph technique in TSUIC, we introduce a new graph-coloring approach that is different from the normal graph coloring, which we call two-sender graph coloring, and propose a way of grouping the vertices to analyze the number of colors used. We further determine a class of TSUIC instances where a certain type of side-information can be removed without affecting their optimal broadcast rates. Finally, we generalize the results of a class of TSUIC problems to multiple senders.

Performance and Information Leakage in Splitfed Learning and Multi-Head Split Learning in Healthcare Data and Beyond.

Machine learning (ML) in healthcare data analytics is attracting much attention because of the unprecedented power of ML to extract knowledge that improves the decision-making process. At the same time, laws and ethics codes drafted by countries to govern healthcare data are becoming stringent. Although healthcare practitioners are struggling with an enforced governance framework, we see the emergence of distributed learning-based frameworks disrupting traditional-ML-model development. Splitfed learning (SFL) is one of the recent developments in distributed machine learning that empowers healthcare practitioners to preserve the privacy of input data and enables them to train ML models. However, SFL has some extra communication and computation overheads at the client side due to the requirement of client-side model synchronization. For a resource-constrained client side (hospitals with limited computational powers), removing such conditions is required to gain efficiency in the learning. In this regard, this paper studies SFL without client-side model synchronization. The resulting architecture is known as multi-head split learning (MHSL). At the same time, it is important to investigate information leakage, which indicates how much information is gained by the server related to the raw data directly out of the smashed data-the output of the client-side model portion-passed to it by the client. Our empirical studies examine the Resnet-18 and Conv1-D architecture model on the ECG and HAM-10000 datasets under IID data distribution. The results find that SFL provides 1.81% and 2.36% better accuracy than MHSL on the ECG and HAM-10000 datasets, respectively (for cut-layer value set to 1). Analysis of experimentation with various client-side model portions demonstrates that it has an impact on the overall performance. With an increase in layers in the client-side model portion, SFL performance improves while MHSL performance degrades. Experiment results also demonstrate that information leakage provided by mutual information score values in SFL is more than MHSL for ECG and HAM-10000 datasets by 2×10-5 and 4×10-3, respectively.

Bioactive secondary metabolites in Paris polyphylla Sm. and their biological activities: A review.

Paris polyphylla Sm. is an important medicinal plant used to treat a variety of diseases through traditional medicine systems such as Ayurveda, Tibetan traditional medicines, Chinese traditional medicines, and others around the world. The IUCN red list has designated it as "vulnerable" due to a decline in wild population by over-exploitation, habitat degradation, illegal collection for trade and traditional use. This review paper aims to summarize the bioactive secondary metabolites in Paris polyphylla. Paris saponins or steroidal saponins are the main bioactive chemical constituents from this plant that account for more than 80% of the total compounds. For instance, polyphyllin D, diosgenin, paris saponins I, II, VI, VII, and H are steroidal saponins having anticancer activity comparable to synthetic anticancer medicines. Antioxidant, anticancer, anti-leishmaniasis, antibacterial, antifungal, anthelmintic, antityrosinase, and antiviral effects of extracts and pure compounds were also demonstrated in vivo and in vitro. In conclusion, this review summarizes the bioactive components from the P. polyphylla which will be useful to researchers and scientists, and for the development of potential drugs.

Precision health data: Requirements, challenges and existing techniques for data security and privacy.

Precision health leverages information from various sources, including omics, lifestyle, environment, social media, medical records, and medical insurance claims to enable personalized care, prevent and predict illness, and precise treatments. It extensively uses sensing technologies (e.g., electronic health monitoring devices), computations (e.g., machine learning), and communication (e.g., interaction between the health data centers). As health data contain sensitive private information, including the identity of patient and carer and medical conditions of the patient, proper care is required at all times. Leakage of these private information affects the personal life, including bullying, high insurance premium, and loss of job due to the medical history. Thus, the security, privacy of and trust on the information are of utmost importance. Moreover, government legislation and ethics committees demand the security and privacy of healthcare data. Besides, the public, who is the data source, always expects the security, privacy, and trust of their data. Otherwise, they can avoid contributing their data to the precision health system. Consequently, as the public is the targeted beneficiary of the system, the effectiveness of precision health diminishes. Herein, in the light of precision health data security, privacy, ethical and regulatory requirements, finding the best methods and techniques for the utilization of the health data, and thus precision health is essential. In this regard, firstly, this paper explores the regulations, ethical guidelines around the world, and domain-specific needs. Then it presents the requirements and investigates the associated challenges. Secondly, this paper investigates secure and privacy-preserving machine learning methods suitable for the computation of precision health data along with their usage in relevant health projects. Finally, it illustrates the best available techniques for precision health data security and privacy with a conceptual system model that enables compliance, ethics clearance, consent management, medical innovations, and developments in the health domain.

Anxiety and depression among people living in quarantine centers during COVID-19 pandemic: A mixed method study from western Nepal.

BACKGROUND: In response to the COVID-19 pandemic, incoming travelers were quarantined at specific centers in Nepal and major checkpoints in Nepal-India border. Nepal adopted a generic public health approaches to control and quarantine returnee migrants, with little attention towards the quality of quarantine facilities and its aftermath, such as the poor mental health of the returnee migrants. The main objective of this study was to explore the status of anxiety and depression, and factors affecting them among returnee migrants living in institutional quarantine centers of western Nepal. METHODS: A mixed method approach in this study included a quantitative survey and in-depth interviews (IDIs) among respondents in quarantine centers of Karnali province between 21st April and 15th May 2020. Survey questionnaire utilized Beck Depression Inventory (BDI) and Beck Anxiety Inventory (BAI) tools, which were administered among 441 quarantined returnee migrants. IDIs were conducted among 12 participants which included a mix of six quarantined migrants and healthcare workers each from the quarantine centres. Descriptive and inferential analyses were conducted on quantitative data; and thematic analysis was utilized for qualitative data. RESULTS: Mild depression (9.1%; 40/441) and anxiety (16.1%; 71/441) was common among respondents followed by moderate depression and anxiety {depression (3.4%; 15/441), anxiety (4.1%; 18/441)} and severe depression and anxiety {depression (1.1%; 5/441), anxiety (0.7%; 3/441)}. Anxiety and depression were independent of their socio-demographic characteristics. Perceived fear of contracting COVID-19, severity and death were prominent among the respondents. Respondents experienced stigma and discrimination in addition to being at the risk of disease and possible loss of employment and financial responsibilities. In addition, poor (quality and access to) health services, and poor living condition at the quarantine centres adversely affected respondents' mental health. CONCLUSION: Depression and anxiety were high among quarantined population and warrants more research. Institutional quarantine centers of Karnali province of Nepal were in poor conditions which adversely impacted mental health of the respondents. Poor resource allocation for health, hygiene and living conditions can be counterproductive to the population quarantined.

Typhoid vaccine introduction: An evidence-based pilot implementation project in Nepal and Pakistan.

The World Health Organization (WHO) in 2008 recommended the use of currently licensed typhoid vaccines using a high risk or targeted approach. The epidemiology of disease and the vaccine characteristics make school-based vaccination most feasible in reducing typhoid disease burden in many settings. To assess feasibility of school-based typhoid vaccination, two districts in Kathmandu, Nepal and two towns in Karachi, Pakistan were selected for pilot program. Vaccination campaigns were conducted through the departments of health and in partnerships with not-for-profit organizations. In total 257,015 doses of Vi polysaccharide vaccine were given to students in grades 1-10 of participating schools. The vaccination coverage ranged from 39 percent (38,389/99,503) in Gulshan town in Karachi, to 81 percent (62,615/77,341) in Bhaktapur in Kathmandu valley. No serious adverse event was reported post vaccination. The coverage increased for vaccination of the second district in Pakistan as well as in Nepal. There was an initial concern of vaccine safety. However, as the campaign progressed, parents were more comfortable with vaccinating their children in schools. Supported and conducted by departments of health in Pakistan and Nepal, a school-based typhoid vaccination was found to be safe and feasible.