The company has developed a standard framework for Java-based services that provides Holistic Overload Detection and Overload Remediation, aka “Hodor.” It is designed to detect service overload caused by multiple root causes, and to automatically remediate the problem by dropping just enough traffic to allow the service to recover, and then maintaining an optimal traffic level to prevent reentering overload, according to a LinkedIn’s blog post.

The service is designed for a wide range of different types of overload. The most obvious ones revolve around physical resource limits such as CPU and memory exhaustion, and I/O limits for network and disk access. There are also virtual resource limits such as execution threads, pooled DB connections, or semaphore permits.

These limits may be exceeded due to increases in traffic to the service, though they can also be reached when latencies of downstream traffic increase, which can cause the number of concurrent requests being handled in the local service to increase with no change to the incoming request rate as noted in the article.

Hodor’s services include framework overview; detecting CPU overload; shedding requests when overloaded; testing and rollout and more.

To detect vulnerabilities in a repository, the CodeQL engine first builds a database that encodes a special relational representation of the code. On that database it can then execute a series of CodeQL queries, each of which is designed to find a particular type of security problem, according to GitHub’s blog post.

CodeQL uses a machine learning model to extend an existing security query to cover a wider range of frameworks and libraries. The machine learning model is trained to detect problems in code it’s never seen before. Queries that use the model will find results for frameworks and libraries that are not described in the original query.

The rules would replace current interim legislation that allows the voluntary reporting of child sexual abuse material (CSAM) with a legal obligation to recognise, report and remove it.  EU home affairs commissioner Ylva Johansson told Germany’s Welt am Sonntag newspaper:

“I will propose legislation in the coming months that will require companies to detect, report, and remove child sexual abuse.”

The parent company of Facebook, Instagram, and WhatsApp – Meta, would be particularly affected by any change in regulations, added Johansson. The company currently accounts for around 95 per cent of child sexual abuse notifications.

In 2020, internet service providers and social media platforms in the EU filed 22 million reports of child sexual abuse. This is thought to be just a fraction of the actual number of incidents.

According to a 2020 report by the Internet Watch Foundation, France, Latvia, Luxembourg, and the Netherlands appear among the top ten countries hosting child sexual abuse URLs.  Of those, the Netherlands was by far the leader, playing host to over 117,000 CSAM web pages.

In terms of its collision warning system for light rail vehicles, Bosch Engineering relies on components from automotive technology to actively reduce the expensive consequences of accidents or prevent them altogether.

High availability of light rail vehicles

The collision warning system for trams supports drivers in critical situations. When congested traffic or poor visibility conditions occur, the system warns the driver about obstacles on the rails in plenty of time. This lets drivers avoid accidents, or at least significantly limit the effects.

The system consists of a multi-purpose camera, a radar sensor, and a control unit. It monitors the track ahead and any possible obstacles in front of the vehicle in real time. It sends a warning to the driver once it detects a potential collision. Whether the driver responds too late or not at all, the collision warning system can initiate automatic braking.

Detecting critical situations

Life-saving Advanced Driver Assistance Systems’ multi-purpose camera monitors the track ahead and transmits the data in real time to the radar sensor. The radar is independent of weather and light conditions. Its sensor recognizes potential obstacles in front of the vehicle from a distance of up to 80 meters away. Considering the vehicle’s speed, the system uses the information to determine the danger of a collision.

In type 1 of the system, it determines whether the threat, which can be presented by an obstacle that warrants a visual or acoustic warning to the driver. Type 2 of the system sends a signal via relay output to trigger automatic braking, should the driver respond too late or not at all to the system’s warning.

Our capabilities in developing hyperspectral satellites stands them apart from others. The fundamental principle behind hyperspectral imaging is that of imaging spectroscopy. They catch light in several narrow wavelengths with hyperspectral imaging, allowing them to delve into the chemistry of the image they are recording. Instead of telling you whether a section of the farm is green or not, they can tell you what kind of crop grows there.

They already have a significant number of customers from around the world who are waiting for our satellites to launch. Currently, they rely on imagery from planes or drones. In India, they are seeing a lot of interest from the government, with Karnataka, Telangana, and Gujarat all interested in these technologies. The markets range from multibillion dollar companies, large oil and gas companies, and large mining companies to national governments around the world.

Space is a costly field that necessitates financial resources. As of now, Pixxel has raised around $7.3 million in funding. Last year, they raised the largest seed funding of $5 million from established institutional venture capital firms — Lightspeed India and Blume Ventures. Since the launch of IN-SPACe, the regulatory environment in India has improved significantly.

They have seen Digantara raise $2.3 million recently. They have seen Skyroot and Agnikul raise $11 million each as well. Investment is flowing in and all this is possible because of the opening up of Space to private sector.

Moreover, investing in Space-tech startups has now become a viable option for Indian investors. The recent announcement of the seed fund scheme for new-age firms will ensure adequate availability of funds, especially for startups with good ideas across various fields. This will also help in creating a robust ecosystem in smaller towns of the country from where innovative ideas may emerge. Schemes like this are enabling startups grow and provide a safe space to experiment, while a majority of them fail to go beyond the idea stage due to lack of funds for initial deployment and identifying product-market fit. However, fluid policies will and can provide a room for new-age entrepreneurs to access funds that are not available through traditional mode of loans.

The main challenge the researchers needed to solve was to design the Artificial Intelligence for this complex task that would fit in the tight computational and memory constraints of the tiny drones. They solved this challenge by means of bio-inspired navigation and search strategies. The scientific article has now been made public on the ArXiv article server, and it will be presented at the IROS robotics conference later this year. The work forms an important step in the intelligence of small robots and will allow finding gas leaks more efficiently and without the risk of human lives in real-world environments.

Autonomous gas source localization

Autonomous gas source localization is a complex task. For one, artificial gas sensors are currently less capable than animal noses in detecting small amounts of gas and staying sensitive to quick changes in gas concentration. Moreover, the environment in which the gas spreads can be complex. Consequently, much of the research in this area has focused on single robots that search for a gas source in rather small, obstacle-free environments in which the source is easier to find.

Swarms of tiny drones

Guido de Croon, Full Professor at the Micro Air Vehicle laboratory of TU Delft, says:

“We are convinced that swarms of tiny drones are a promising avenue for autonomous gas source localization. The drones’ tiny size makes them very safe to any humans and property still in the building, while their flying capability will allow them to eventually search for the source in three dimensions. Moreover, their small size allows them to fly in narrow indoor areas. Finally, having a swarm of these drones allows them to localize a gas source quicker, while escaping local maxima of gas concentration in order to find the true source.”

However, these properties also make it very hard to instill the drones with the necessary artificial intelligence for autonomous gas source localization. The onboard sensing and processing is extremely limited, excluding the type of AI algorithms that make self-driving cars autonomous. Moreover, operating in a swarm brings its own challenges, since the drones need to be aware of each other for collision avoidance and collaboration.

Bio-inspired Artificial Intelligence

Bart Duisterhof, who performed the research for obtaining his MSc thesis at TU Delft says:

“Actually, in nature there are ample examples of successful navigation and odor source localization within strict resource constraints. Just think of how fruitflies with their tiny brains of 100,000 neurons infallibly locate the bananas in your kitchen in the summer. They do this by elegantly combining simple behaviors such as flying upwind or orthogonally to the wind depending on whether they sense the odor. Although we could not directly copy these behaviors due to the absence of airflow sensors on our robots, we have instilled our robots with similarly simple behaviors to tackle the task.”

In particular, the tiny drones implement a new “bug” algorithm for their navigation, termed “Sniffy Bug.” As long as no drone has sensed any gas, the drones spread out as much as possible over the environment, while avoiding obstacles and each other. If one of the drones senses gas at its location, it communicates this to the others. From that point on, the drones will collaborate with each other to find the gas source as soon as possible. Specifically, the swarm then performs a search for maximal gas concentration with an algorithm termed “particle swarm optimization” (PSO), with each drone being a “particle.” This algorithm was originally modelled after the social behavior and motion of bird flocks. It has each drone moving based on its own perceived highest gas concentration location, the swarm’s highest location, and an inertia in its current moving direction. As a search strategy, PSO has the advantage that it only requires measuring the gas concentration, and not the gas concentration gradient or wind direction. Furthermore, it allows the swarm to ignore local maxima that may occur in complex environments.

According to Patrick Carey, senior director of market analysis and strategy of the Software Integrity Group at Synopsys, application security is often defined by siloed solutions: static application security testing (SAST), software composition analysis (SCA), dynamic application security testing (DAST), and interactive application security testing (IAST). But these silos conflict with the way developers build, test and fix software. Carey said:

“They don’t care which analysis techniques are used. They just want to quickly identify the issues that pose the highest risk.”

Application security testing needs to not only happen earlier in the application life cycle, it needs to be executed more intelligently. Carrey added:

“As development, security, and operations converge we see these silos being knocked down, with security testing being delivered as an intelligent, integrated system of services that knows which tests to run when, and can identify the highest priority issues.”

The next generation of application security test automation

As software development has picked up speed, organizations have deployed automation to keep up, but many are having trouble working out the security testing aspect of it. Current application security testing tools tend to scan everything all the time, overwhelming and overloading teams with too much information.

If you look at all the tools within a CI pipeline, there are tools from multiple vendors, including open-source tools that are able to work separately, but together in an automated fashion while integrating with other systems like ticketing tools.

“Application security really needs to make that shift in the same manner to be more more fine-grained, more service-oriented, more modular and more automated,” said Carey.

Intelligent orchestration and correlation is a new approach being used to manage security tests, reduce the overwhelming amount of information and let developers focus on what really matters: the application. While the use of orchestration and correlation solutions are not uncommon on the IT operations side for things like network security and runtime security, they are just beginning to cross into the application development and security side of things, Carey explained.

Expanding on intelligent orchestration and correlation

To add to its intelligent orchestration and correlation initiative, Synopsys recently announced it acquired the application security orchestration and correlation solution Code Dx. According to the company, Code Dx complements the Intelligent Orchestration solution released last year. Intelligent Orchestration simplifies and streamlines security testing in CI pipelines by determining and initiating the appropriate tests to run based on predefined policies, application risk profiles, and code changes.