Bug-Free, Efficient Tor Network Inching Towards Completion
November 30, 2016
The development team behind the Tor Project recently announced the release of Tor 0.2.9.5 that is almost bug-free, stable and secure.
Softpedia in a release titled New Tor “The Onion Router” Anonymity Network Stable Branch Getting Closer says:
Tor 0.2.9.5 Alpha comes three weeks after the release of the 0.2.9.4 Alpha build to add a large number of improvements and bug fixes that have been reported by users since then or discovered by the Tor Project’s hard working development team. Also, this release gets us closer to the new major update of The Onion Router anonymity network.
Numerous bugs and loopholes were being reported in Tor Network that facilitated backdoor entry to snooping parties on Tor users. With this release, it seems those security loopholes have been plugged.
The development team is also encouraging users to test the network further to make it completely bug-free:
If you want to help the Tor Project devs polish the final release of the Tor 0.2.9 series, you can download Tor 0.2.9.5 Alpha right now from our website and install it on your GNU/Linux distribution, or just fetch it from the repositories of the respective OS. Please try to keep in mind, though, that this is a pre-release version, not to be used in production environments.
Though it will always be a cat and mouse game between privacy advocates and those who want to know what goes on behind the veiled network, it would be interesting to see who will stay ahead of the race.
Vishal Ingole, November 30, 2016
Sponsored by ArnoldIT.com, publisher of the CyberOSINT monograph
The FBI Uses Its Hacking Powers for Good
March 4, 2016
In a victory for basic human decency, Engadget informs us, the “FBI Hacked the Dark Web to Bust 1,500 Pedophiles.” Citing an article at Vice Motherboard, writer Jessica Conditt describes how the feds identified their suspects through a site called (brace yourself) “Playpen,” which was launched in August 2014. We learn:
Motherboard broke down the FBI’s hacking process as follows: The bureau seized the server running Playpen in February 2015, but didn’t shut it down immediately. Instead, the FBI took “unprecedented” measures and ran the site via its own servers from February 20th to March 4th, at the same time deploying a hacking tool known internally as a network investigative technique. The NIT identified at least 1,300 IP addresses belonging to visitors of the site.
“Basically, if you visited the homepage and started to sign up for a membership, or started to log in, the warrant authorized deployment of the NIT,” a public defender for one of the accused told Motherboard. He said he expected at least 1,500 court cases to stem from this one investigation, and called the operation an “extraordinary expansion of government surveillance and its use of illegal search methods on a massive scale,” Motherboard reported.
Check out this article at Wired to learn more about the “network investigative technique” (NIT). This is more evidence that, if motivated, the FBI is perfectly capable of leveraging the Dark Web to its advantage. Good to know.
Cynthia Murrell, March 4, 2016
Sponsored by ArnoldIT.com, publisher of the CyberOSINT monograph
Beyond LinkedIn
October 26, 2015
Though LinkedIn remains the largest professional networking site, it may be time to augment its hobnobbing potential with one or more others. Search Engine Journal gives us many to choose from in “12 Professional Networking Alternatives to LinkedIn.” Like LinkedIn, some are free, but others offer special features for a fee. Some even focus on local connections. Reporter Albert Costill writes:
“While LinkedIn has proven to be an incredible assist for anyone looking to make professional connections or find employment, there have been some concerns. For starters, the company has been forced to reduce the number of emails it sends out because of complaints. There have also been allegations of the company hacking into member’s emails and a concern that activity on LinkedIn groups are declining.
“That doesn’t mean that you should give up on LinkedIn. Despite any concerns with the network, it still remains one of the best locations to network professionally. It just means that in addition to LinkedIn you should also start networking on other professional sites to cast that wide net that was previously mentioned. I previously shared eight alternatives to LinkedIn like Twylah, Opprtunity, PartnerUp, VisualCV, Meetup, Zerply, AngelList, and BranchOut, but here are twelve more networking sites that you should also consider using in no particular order.”
So between Costill’s lists, there are 20 sites to check out. A few notable entries from this second list: Makerbase is specifically for software creators, and is free to any Twitter users; LunchMeet connects LinkedIn users who would like to network over lunch; Plaxo automatically keeps your cloud-based contact list up-to-date; and the European Xing is the place to go for a job overseas. See the article for many more network-boosting options.
Cynthia Murrell, October 26, 2015
Sponsored by ArnoldIT.com, publisher of the CyberOSINT monograph
Deep Learning System Surprises Researchers
June 24, 2015
Researchers were surprised when their scene-classification AI performed some independent study, we learn from Kurzweil’s article, “MIT Deep-Learning System Autonomously Learns to Identify Objects.”
At last December’s International Conference on Learning Representations, a research team from MIT demonstrated that their scene-recognition software was 25-33 percent more accurate than its leading predecessor. They also presented a paper describing the object-identification tactic their software chose to adopt; perhaps this is what gave it the edge. The paper’s lead author, and MIT computer science/ engineering associate professor, Antonio Torralba ponders the development:
“Deep learning works very well, but it’s very hard to understand why it works — what is the internal representation that the network is building. It could be that the representations for scenes are parts of scenes that don’t make any sense, like corners or pieces of objects. But it could be that it’s objects: To know that something is a bedroom, you need to see the bed; to know that something is a conference room, you need to see a table and chairs. That’s what we found, that the network is really finding these objects.”
Researchers being researchers, the team is investigating their own software’s initiative. The article tells us:
“In ongoing work, the researchers are starting from scratch and retraining their network on the same data sets, to see if it consistently converges on the same objects, or whether it can randomly evolve in different directions that still produce good predictions. They’re also exploring whether object detection and scene detection can feed back into each other, to improve the performance of both. ‘But we want to do that in a way that doesn’t force the network to do something that it doesn’t want to do,’ Torralba says.”
Very respectful. See the article for a few more details on this ambitious AI, or check out the researchers’ open-access paper here.
Cynthia Murrell, June 24, 2015
Sponsored by ArnoldIT.com, publisher of the CyberOSINT monograph
MIT Discover Object Recognition
June 23, 2015
MIT did not discover object recognition, but researchers did teach a deep-learning system designed to recognize and classify scenes can also be used to recognize individual objects. Kurzweil describes the exciting development in the article, “MIT Deep-Learning System Autonomously Learns To Identify Objects.” The MIT researchers realized that deep-learning could be used for object identification, when they were training a machine to identify scenes. They complied a library of seven million entries categorized by scenes, when they learned that object recognition and scene-recognition had the possibility of working in tandem.
“ ‘Deep learning works very well, but it’s very hard to understand why it works — what is the internal representation that the network is building,’ says Antonio Torralba, an associate professor of computer science and engineering at MIT and a senior author on the new paper.”
When the deep-learning network was processing scenes, it was fifty percent accurate compared to a human’s eighty percent accuracy. While the network was busy identifying scenes, at the same time it was learning how to recognize objects as well. The researchers are still trying to work out the kinks in the deep-learning process and have decided to start over. They are retraining their networks on the same data sets, but taking a new approach to see how scene and object recognition tie in together or if they go in different directions.
Deep-leaning networks have major ramifications, including the improvement for many industries. However, will deep-learning be applied to basic search? Image search still does not work well when you search by an actual image.
Whitney Grace, June 23, 2015
Sponsored by ArnoldIT.com, publisher of the CyberOSINT monograph
Video and Image Search In the News
June 17, 2015
There’s been much activity around video and image search lately. Is it all public-relations hype, or is there really progress to celebrate? Here are a few examples that we’ve noticed recently.
Fast Company reports on real-time video-stream search service Dextro in, “This Startup’s Side Project Scans Every Periscope Video to Help You Find the Best Streams.” Writer Rose Pastore tells us:
“Dextro’s new tool, called Stream, launches today as a mobile-optimized site that sorts Periscope videos by their content: Cats, computers, swimming pools, and talking heads, to name a few popular categories. The system does not analyze stream text titles, which are often non-descriptive; instead, it groups videos based only on how its algorithms interpret the visual scene being filmed. Dextro already uses this technology to analyze pre-recorded videos for companies … but this is the first time the two-year-old startup has applied its algorithms to live streams.”
Meanwhile, ScienceDaily reveals an interesting development in, “System Designed to Label Visual Scenes Turns Out to Detect Particular Objects Too.” While working on their very successful scene-classification tool, researchers at MIT discovered a side effect. The article explains that, at an upcoming conference:
“The researchers will present a new paper demonstrating that, en route to learning how to recognize scenes, their system also learned how to recognize objects. The work implies that at the very least, scene-recognition and object-recognition systems could work in concert. But it also holds out the possibility that they could prove to be mutually reinforcing.”
Then we have an article from MIT’s Technology Review, “The Machine Vision Algorithm Beating Art Historians at Their Own Game.” Yes, even in the highly-nuanced field of art history, the AI seems to have become the master. We learn:
“The challenge of analyzing paintings, recognizing their artists, and identifying their style and content has always been beyond the capability of even the most advanced algorithms. That is now changing thanks to recent advances in machine learning based on approaches such as deep convolutional neural networks. In just a few years, computer scientists have created machines capable of matching and sometimes outperforming humans in all kinds of pattern recognition tasks.”
Each of these articles is an interesting read, so check them out for more information. It may be a good time to work in the area of image and video search.
Cynthia Murrell, June 17, 2015
Sponsored by ArnoldIT.com, publisher of the CyberOSINT monograph
-
- Subscribe to Beyond Search
Feature archive
News archive
-
