Short Run Transit Route Planning Decision Support System Using a Deep Learning-Based Weighted Graph

Public transport routing plays a crucial role in transit network design, ensuring a satisfactory level of service for passengers. However, current routing solutions rely on traditional operational research heuristics, which can be time-consuming to implement and lack the ability to provide quick solutions. Here, we propose a novel deep learning-based methodology for a decision support system that enables public transport (PT) planners to identify short-term route improvements rapidly. By seamlessly adjusting specific sections of routes between two stops during specific times of the day, our method effectively reduces times and enhances PT services. Leveraging diverse data sources such as GTFS and smart card data, we extract features and model the transportation network as a directed graph. Using self-supervision, we train a deep learning model for predicting lateness values for road segments. These lateness values are then utilized as edge weights in the transportation graph, enabling efficient path searching. Through evaluating the method on Tel Aviv, we are able to reduce times on more than 9\% of the routes. The improved routes included both intraurban and suburban routes showcasing a fact highlighting the model's versatility. The findings emphasize the potential of our data-driven decision support system to enhance public transport and city logistics, promoting greater efficiency and reliability in PT services.

Interruptions detection in video conferences

In recent years, video conferencing (VC) popularity has skyrocketed for a wide range of activities. As a result, the number of VC users surged sharply. The sharp increase in VC usage has been accompanied by various newly emerging privacy and security challenges. VC meetings became a target for various security attacks, such as Zoombombing. Other VC-related challenges also emerged. For example, during COVID lockdowns, educators had to teach in online environments struggling with keeping students engaged for extended periods. In parallel, the amount of available VC videos has grown exponentially. Thus, users and companies are limited in finding abnormal segments in VC meetings within the converging volumes of data. Such abnormal events that affect most meeting participants may be indicators of interesting points in time, including security attacks or other changes in meeting climate, like someone joining a meeting or sharing a dramatic content. Here, we present a novel algorithm for detecting abnormal events in VC data. We curated VC publicly available recordings, including meetings with interruptions. We analyzed the videos using our algorithm, extracting time windows where abnormal occurrences were detected. Our algorithm is a pipeline that combines multiple methods in several steps to detect users' faces in each video frame, track face locations during the meeting and generate vector representations of a facial expression for each face in each frame. Vector representations are used to monitor changes in facial expressions throughout the meeting for each participant. The overall change in meeting climate is quantified using those parameters across all participants, and translating them into event anomaly detection. This is the first open pipeline for automatically detecting anomaly events in VC meetings. Our model detects abnormal events with 92.3% precision over the collected dataset.

Co-Membership-based Generic Anomalous Communities Detection

Nowadays, detecting anomalous communities in networks is an essential task in research, as it helps discover insights into community-structured networks. Most of the existing methods leverage either information regarding attributes of vertices or the topological structure of communities. In this study, we introduce the Co-Membership-based Generic Anomalous Communities Detection Algorithm (referred as to CMMAC), a novel and generic method that utilizes the information of vertices co-membership in multiple communities. CMMAC is domain-free and almost unaffected by communities' sizes and densities. Specifically, we train a classifier to predict the probability of each vertex in a community being a member of the community. We then rank the communities by the aggregated membership probabilities of each community's vertices. The lowest-ranked communities are considered to be anomalous. Furthermore, we present an algorithm for generating a community-structured random network enabling the infusion of anomalous communities to facilitate research in the field. We utilized it to generate two datasets, composed of thousands of labeled anomaly-infused networks, and published them. We experimented extensively on thousands of simulated, and real-world networks, infused with artificial anomalies. CMMAC outperformed other existing methods in a range of settings. Additionally, we demonstrated that CMMAC can identify abnormal communities in real-world unlabeled networks in different domains, such as Reddit and Wikipedia.

Automatic Large Scale Detection of Red Palm Weevil Infestation using Aerial and Street View Images

The spread of the Red Palm Weevil has dramatically affected date growers, homeowners and governments, forcing them to deal with a constant threat to their palm trees. Early detection of palm tree infestation has been proven to be critical in order to allow treatment that may save trees from irreversible damage, and is most commonly performed by local physical access for individual tree monitoring. Here, we present a novel method for surveillance of Red Palm Weevil infested palm trees utilizing state-of-the-art deep learning algorithms, with aerial and street-level imagery data. To detect infested palm trees we analyzed over 100,000 aerial and street-images, mapping the location of palm trees in urban areas. Using this procedure, we discovered and verified infested palm trees at various locations.