Evaluating state-of-the-art vision-language models for video recognition on real world dataset

Abstract. One of the main challenges in Computer Vision is the training of custom models from scratch. This process is highly computer-intensive, time-consuming, and requires vast amount of labeled datasets to achieve reasonable results. Recently, various foundation models trained using self-supervised learning techniques have been proposed, claiming to achieve good results for downstream tasks after fine-tuning. This document aims to discuss the results obtained by three multi-class video recognition methods based on such vision-language foundation models using a dataset that closely corresponds to real-world. The primary objective of this work was to investigate the number of instances required by these models to provide competitive results. Three models, namely VideoMAE, X-CLIP, and Text4Vis, are chosen for the evaluation in this study. Their performance is assessed using YT8M dataset which include YouTube videos captured in uncontrolled environments, closely resembling real-world settings. Notably, Text4Vis stood out by achieving an impressive weighted F1-score of 0.87 after fine-tuning with just 1142 videos. The results of X-CLIP are also competitive with Text4Vis \cite{Text4Vis}, while VideoMAE exhibits comparatively lower performance

Design of Event-Triggered Asynchronous H∞ Filter for Switched Systems Using the Sampled-Data Approach

The design of networked switched systems with event-based communication is attractive due to its potential to save bandwidth and energy. However, ensuring the stability and performance of networked systems with event-triggered communication and asynchronous switching is challenging due to their time-varying nature. This paper presents a novel sampled-data approach to design event-triggered asynchronous H∞ filters for networked switched systems. Unlike most existing event-based filtering results, which either design the event-triggering scheme only or co-design the event-triggering condition and the filter, we consider that the event-triggering policy is predefined and synthesize the filter. We model the estimation error system as an event-triggered switched system with time delay and non-uniform sampling. By implementing a delay-dependent multiple Lyapunov method, we derive sufficient conditions to ensure the global asymptotic stability of the filtering error system and an H∞ performance level. The efficacy of the proposed design technique and the superiority of the filter performance is illustrated by numerical examples and by comparing the performance with a recent result

Characterization of Interictal Epileptiform Discharges with Time-Resolved Cortical Current Maps Using the Helmholtz–Hodge Decomposition

Source estimates performed using a single equivalent current dipole (ECD) model for interictal epileptiform discharges (IEDs) which appear unifocal have proven highly accurate in neocortical epilepsies, falling within millimeters of that demonstrated by electrocorticography. Despite this success, the single ECD solution is limited, best describing sources which are temporally stable. Adapted from the field of optics, optical flow analysis of distributed source models of MEG or EEG data has been proposed as a means to estimate the current motion field of cortical activity, or “cortical flow.” The motion field so defined can be used to identify dynamic features of interest such as patterns of directional flow, current sources, and sinks. The Helmholtz–Hodge Decomposition (HHD) is a technique frequently applied in fluid dynamics to separate a flow pattern into three components: (1) a non-rotational scalar potential U describing sinks and sources, (2) a non-diverging scalar potential A accounting for vortices, and (3) an harmonic vector field H. As IEDs seem likely to represent periods of highly correlated directional flow of cortical currents, the U component of the HHD suggests itself as a way to characterize spikes in terms of current sources and sinks. In a series of patients with refractory epilepsy who were studied with magnetoencephalography as part of their evaluation for possible resective surgery, spike localization with ECD was compared to HHD applied to an optical flow analysis of the same spike. Reasonable anatomic correlation between the two techniques was seen in the majority of patients, suggesting that this method may offer an additional means of characterization of epileptic discharges

Examination of Restriction of Free Speech under International Covenant on Civil and Political Rights (ICCPR) in Reference to Prevention of Electronic Crimes Act 2016, Pakistan

This study aimed to explore the regime for the restriction of freedom of speech under ICCPR. Besides, it assesses the standards and level of freedom of speech restriction under technology law in Pakistan, PECA-2016. A through document analysis of ICCPR and cyber law depicts that at the standards for freedom of speech is far below then the criteria given in an Article 19(3). Furthermore, it appeals to the policy makers and legislators to bring the restriction of freedom of speech in technology law Pakistan at par with that of ICCPR. Nevertheless, it should be amended or repealed to improve the standards for the freedom of speech in the technology law in Pakistan (PECA-2016

A Unique Training Strategy to Enhance Language Models Capabilities for Health Mention Detection from Social Media Content

An ever-increasing amount of social media content requires advanced AI-based computer programs capable of extracting useful information. Specifically, the extraction of health-related content from social media is useful for the development of diverse types of applications including disease spread, mortality rate prediction, and finding the impact of diverse types of drugs on diverse types of diseases. Language models are competent in extracting the syntactic and semantics of text. However, they face a hard time extracting similar patterns from social media texts. The primary reason for this shortfall lies in the non-standardized writing style commonly employed by social media users. Following the need for an optimal language model competent in extracting useful patterns from social media text, the key goal of this paper is to train language models in such a way that they learn to derive generalized patterns. The key goal is achieved through the incorporation of random weighted perturbation and contrastive learning strategies. On top of a unique training strategy, a meta predictor is proposed that reaps the benefits of 5 different language models for discriminating posts of social media text into non-health and health-related classes. Comprehensive experimentation across 3 public benchmark datasets reveals that the proposed training strategy improves the performance of the language models up to 3.87%, in terms of F1-score, as compared to their performance with traditional training. Furthermore, the proposed meta predictor outperforms existing health mention classification predictors across all 3 benchmark datasets

USING HEALTH INSURANCE DATABASES FOR EPIDEMIOLOGICAL RESEARCH: A SCOPING REVIEW

OBJECTIVE: This scoping review aimed to appraise the existing literature on using the claims databases for epidemiological studies and to draw inferences for using data from Pakistan’s health insurance databases. METHODS: We conducted a scoping review of literature focusing on health insurance databases, querying MEDLINE, EMBASE and Google Scholar. We used the frameworks proposed by the Joanna Briggs Institute and Arksy and O’Malley for mapping our results. RESULTS:  There was a considerable chronological increase in studies published using data from health insurance databases. Most of the studies in our search were from economically developed countries. Most of the studies (n=84) focussed on chronic non-communicable diseases, while a limited number (n=09) focussed on communicable (infectious) diseases. Our findings suggest that insurance databases could be utilised to study rare diseases, prospects of prolonged follow-up, and minimal research costs. This is especially important for countries like Pakistan, having limited resources to conduct regular, population-level epidemiological studies. Several methodological approaches (for instance, disease, pharmacy or intervention classification codes) were presented in these studies to extract epidemiological data from the insurance database. CONCLUSION: Health insurance databases are utilised as sources for epidemiological studies, predominantly for chronic illnesses, in economically developed countries. Methodological approaches described in these papers could be used to extract data for epidemiological research from health insurance databases in Pakistan. This could be especially useful for following the patterns of infectious disease in the country