Forensic Analysis of WhatsApp SQLite Databases on the Unrooted Android Phones

WhatsApp is the most popular instant messaging mobile application all over the world. Originally designed for simple and fast communication, however, its privacy features, such as end-to-end encryption, eased private and unobserved communication for criminals aiming to commit illegal acts. In this paper, a forensic analysis of the artefacts left by the encrypted WhatsApp SQLite databases on unrooted Android devices is presented. In order to provide a complete interpretation of the artefacts, a set of controlled experiments to generate these artefacts were performed. Once generated, their storage location and database structure on the device were identified. Since the data is stored in an encrypted SQLite database, its decryption is first discussed. Then, the methods of analyzing the artefacts are revealed, aiming to understand how they can be correlated to cover all the possible evidence. In the results obtained, it is shown how to reconstruct the list of contacts, the history of exchanged textual and non-textual messages, as well as the details of their contents. Furthermore, this paper shows how to determine the properties of both the broadcast and the group communications in which the user has been involved, as well as how to reconstruct the logs of the voice and video calls. Doi: 10.28991/HIJ-2022-03-02-06 Full Text: PD

Advertising Idea between social meaning and receiver behavior

The identity and value of the product is determine by the process of linking the product with values and social meanings through the means of advertising. In the late 20th century a theory was developed in advertising, based on the theory of meaning as a rule, and this view of the declaration came to say that advertising is not only a conveyor of information but a source of building or Establish the personality and identity of the recipient and society, and thus can expand the entrance of meaning as the basis of individual world life into social contexts. Research problem: 1-        To what extent does the theory of social meaning contribute to the advertising idea? 2-        How does the ideological content of an advertisement contribute to the behavior? Research importance: 1-        Design the ads and create an advertising idea through the theory of meaning as a rule. 2-        Drawing a positive mental image of the advertiser through social meanings in the design of the advertisement. Research goals: 1-        Study the role of the theory of meaning in the design of advertising and its role in the creation of the idea of ​​advertising. 2-        Study the role of the ideological content of the Declaration in drawing a mental image of the declared. Research hypotheses: 1-        The study of social meaning may contribute to the creation of a successful advertising idea. 2-        The meaning of the Declaration may contribute to the drawing of a positive mental image of the recipient from the author. Research Methodology: The researcher will take the approach of description and analysis, through the theoretical framework and analysis of some models to confirm the hypotheses of the researc

Proposal and multicentric validation of a laparoscopic Roux-en-Y gastric bypass surgery ontology.

BACKGROUND Phase and step annotation in surgical videos is a prerequisite for surgical scene understanding and for downstream tasks like intraoperative feedback or assistance. However, most ontologies are applied on small monocentric datasets and lack external validation. To overcome these limitations an ontology for phases and steps of laparoscopic Roux-en-Y gastric bypass (LRYGB) is proposed and validated on a multicentric dataset in terms of inter- and intra-rater reliability (inter-/intra-RR). METHODS The proposed LRYGB ontology consists of 12 phase and 46 step definitions that are hierarchically structured. Two board certified surgeons (raters) with > 10 years of clinical experience applied the proposed ontology on two datasets: (1) StraBypass40 consists of 40 LRYGB videos from Nouvel Hôpital Civil, Strasbourg, France and (2) BernBypass70 consists of 70 LRYGB videos from Inselspital, Bern University Hospital, Bern, Switzerland. To assess inter-RR the two raters' annotations of ten randomly chosen videos from StraBypass40 and BernBypass70 each, were compared. To assess intra-RR ten randomly chosen videos were annotated twice by the same rater and annotations were compared. Inter-RR was calculated using Cohen's kappa. Additionally, for inter- and intra-RR accuracy, precision, recall, F1-score, and application dependent metrics were applied. RESULTS The mean ± SD video duration was 108 ± 33 min and 75 ± 21 min in StraBypass40 and BernBypass70, respectively. The proposed ontology shows an inter-RR of 96.8 ± 2.7% for phases and 85.4 ± 6.0% for steps on StraBypass40 and 94.9 ± 5.8% for phases and 76.1 ± 13.9% for steps on BernBypass70. The overall Cohen's kappa of inter-RR was 95.9 ± 4.3% for phases and 80.8 ± 10.0% for steps. Intra-RR showed an accuracy of 98.4 ± 1.1% for phases and 88.1 ± 8.1% for steps. CONCLUSION The proposed ontology shows an excellent inter- and intra-RR and should therefore be implemented routinely in phase and step annotation of LRYGB

Adolescents’ use of online food delivery applications and perceptions of healthy food options and food safety: a cross-sectional study in the United Arab Emirates

IntroductionThis cross-sectional study aimed to assess Online food delivery applications (OFDA) usage trends among adolescent users in the United Arab Emirates (UAE), focusing on their perceptions of healthy food options and food safety (n = 532).MethodsSociodemographic information, frequency of OFDA use, factors affecting food choices, and perceptions of healthy food and food safety were investigated. A total perception score was calculated for each participant;ResultsMost participants used OFDAs weekly (65.4%), favoring fast food (85.7%). Factors like appearance and price drove food choices (65.0%), while taste and cost hindered healthy food orders (29.7 and 28.2%). Younger and frequent users had lower scores for perceiving healthy food, while seeking healthy options was associated with higher scores (p < 0.05). Females and those seeking healthy food showed higher food safety scores (p < 0.05).DiscussionThe study suggests tailored interventions to promote healthier choices and improve food safety perceptions among adolescents using OFDAs in the UAE

Federated Benchmarking of Medical Artificial Intelligence With MedPerf

Medical artificial intelligence (AI) has tremendous potential to advance healthcare by supporting and contributing to the evidence-based practice of medicine, personalizing patient treatment, reducing costs, and improving both healthcare provider and patient experience. Unlocking this potential requires systematic, quantitative evaluation of the performance of medical AI models on large-scale, heterogeneous data capturing diverse patient populations. Here, to meet this need, we introduce MedPerf, an open platform for benchmarking AI models in the medical domain. MedPerf focuses on enabling federated evaluation of AI models, by securely distributing them to different facilities, such as healthcare organizations. This process of bringing the model to the data empowers each facility to assess and verify the performance of AI models in an efficient and human-supervised process, while prioritizing privacy. We describe the current challenges healthcare and AI communities face, the need for an open platform, the design philosophy of MedPerf, its current implementation status and real-world deployment, our roadmap and, importantly, the use of MedPerf with multiple international institutions within cloud-based technology and on-premises scenarios. Finally, we welcome new contributions by researchers and organizations to further strengthen MedPerf as an open benchmarking platform

Burnout among surgeons before and during the SARS-CoV-2 pandemic: an international survey

Background: SARS-CoV-2 pandemic has had many significant impacts within the surgical realm, and surgeons have been obligated to reconsider almost every aspect of daily clinical practice. Methods: This is a cross-sectional study reported in compliance with the CHERRIES guidelines and conducted through an online platform from June 14th to July 15th, 2020. The primary outcome was the burden of burnout during the pandemic indicated by the validated Shirom-Melamed Burnout Measure. Results: Nine hundred fifty-four surgeons completed the survey. The median length of practice was 10 years; 78.2% included were male with a median age of 37 years old, 39.5% were consultants, 68.9% were general surgeons, and 55.7% were affiliated with an academic institution. Overall, there was a significant increase in the mean burnout score during the pandemic; longer years of practice and older age were significantly associated with less burnout. There were significant reductions in the median number of outpatient visits, operated cases, on-call hours, emergency visits, and research work, so, 48.2% of respondents felt that the training resources were insufficient. The majority (81.3%) of respondents reported that their hospitals were included in the management of COVID-19, 66.5% felt their roles had been minimized; 41% were asked to assist in non-surgical medical practices, and 37.6% of respondents were included in COVID-19 management. Conclusions: There was a significant burnout among trainees. Almost all aspects of clinical and research activities were affected with a significant reduction in the volume of research, outpatient clinic visits, surgical procedures, on-call hours, and emergency cases hindering the training. Trial registration: The study was registered on clicaltrials.gov "NCT04433286" on 16/06/2020

A Review of Medical Federated Learning: Applications in Oncology and Cancer Research

AbstractMachine learning has revolutionized every facet of human life, while also becoming more accessible and ubiquitous. Its prevalence has had a powerful impact in healthcare, with numerous applications and intelligent systems achieving clinical level expertise. However, building robust and generalizable systems relies on training algorithms in a centralized fashion using large, heterogeneous datasets. In medicine, these datasets are time consuming to annotate and difficult to collect centrally due to privacy concerns. Recently, Federated Learning has been proposed as a distributed learning technique to alleviate many of these privacy concerns by providing a decentralized training paradigm for models using large, distributed data. This new approach has become the defacto way of building machine learning models in multiple industries (e.g. edge computing, smartphones). Due to its strong potential, Federated Learning is also becoming a popular training method in healthcare, where patient privacy is of paramount concern. In this paper we performed an extensive literature review to identify state-of-the-art Federated Learning applications for cancer research and clinical oncology analysis. Our objective is to provide readers with an overview of the evolving Federated Learning landscape, with a focus on applications and algorithms in oncology space. Moreover, we hope that this review will help readers to identify potential needs and future directions for research and development.</jats:p

Federated Cycling (FedCy): Semi-supervised Federated Learning of Surgical Phases

Recent advancements in deep learning methods bring computer-assistance a step closer to fulfilling promises of safer surgical procedures. However, the generalizability of such methods is often dependent on training on diverse datasets from multiple medical institutions, which is a restrictive requirement considering the sensitive nature of medical data. Recently proposed collaborative learning methods such as Federated Learning (FL) allow for training on remote datasets without the need to explicitly share data. Even so, data annotation still represents a bottleneck, particularly in medicine and surgery where clinical expertise is often required. With these constraints in mind, we propose FedCy, a federated semi-supervised learning (FSSL) method that combines FL and self-supervised learning to exploit a decentralized dataset of both labeled and unlabeled videos, thereby improving performance on the task of surgical phase recognition. By leveraging temporal patterns in the labeled data, FedCy helps guide unsupervised training on unlabeled data towards learning task-specific features for phase recognition. We demonstrate significant performance gains over state-of-the-art FSSL methods on the task of automatic recognition of surgical phases using a newly collected multi-institutional dataset of laparoscopic cholecystectomy videos. Furthermore, we demonstrate that our approach also learns more generalizable features when tested on data from an unseen domain.Comment: 11 pages, 4 figure

Forensic analysis of private browsing mechanisms: Tracing internet activities

Forensic analysts are more than ever facing challenges upon conducting their deep investigative analysis on digital devices due to the technological progression. Of these are the difficulties present upon analyzing web browser artefacts as this became more complicated when web browser companies introduced private browsing mode, a feature aiming to protect users&rsquo; data upon opening a private browsing session, by leaving no traces of data on the local device used. Aiming to investigate whether the claims of web browser companies are true concerning the protection private browsing provides to the users and whether it really doesn&rsquo;t leave any browsing data behind, the most popular desktop browsers in Windows were analyzed after surfing them regularly and privately. The results shown in this paper suggest that the privacy provided varies among different companies since evidence might be recovered from some of the browsers but not from others

Role of Adiponectin in Endoscopic Gastritis

Endoscopic gastritis is a term used when there is an inflammatory change in the gastric mucosa like color and/or structure that was noticed by endoscope. Is to assesses the effect of these factors and association of adiponectin with these factors. This is a case-controlled study. The study consists from 100 subjects. Eighty of them had gastritis by endoscopy Forty of them were H. pylori positive and the rest were H. pylori negative. The rest twenty persons were healthy control group. Demographic information’s were taken like age, sex and others by questionnaire. Endoscopy and lipid profile were done for them. Adiponectin was significantly lower ( P=0.001) in gastritis patients whether infected (8.783±0.968) with H pylori or not (8.278 ±0.838) when compared with control group (9.119±0.1593) (Table-1-). Regarding lipid profile , there was a significant in all parameters of lipid profile in gastritis patients than healthy group (Table-1-). Analysis of correlation between adiponectin and BMI and weight demonstrated a negative correlation with gastritis with h pylori infection (r= -0.068 and r=0.356 respectively). This study shows that adiponectin had an important role in gastritis especially when there is an h pylori infection. Its level had a negative correlation with BMI and lipid profile