Database Info

Sneak peek into my new roundup: ALL remote control solutions (VNC,TSC,LogMeIn,z2 etc)

It will take some 2-3 days at least to completely finish my forthcoming article, which discusses and COMPARES all full (that is, I don’t discuss plain media controllers OR mail / PIM synchronizer client – these will be discussed in a later roundup) remote control Pocket PC – to – PC solutions. In the meantime, if a “plain” comparison / benchmark / feature chart isn’t a problem, feel free to check out THIS CHART. It’s already FULL of never-before-published information on, for example, the bandwidth usage, the compatibility, the advantages and disadvantages etc. of each and every desktop PC-from-Pocket PC remote control solution.
(I post this announcement right now because I receive 2-3 related questions a day (the last one for example here), which certainly shows there’s very high demand for any kind of information on this subject, even (still) without a fully-fledged article.
If you find the chart insufficient or not clear enough, stay tuned: the FULL roundup will definitely be published next week
Also, any kind of feedback (including flames) is welcome. Note that you don't need to elaborate / comment on chart cells marked “will be filled in later” – they will be filled in.

Wow, that's some comprehensive work! Excellent...
V

Hi Minniesys,
You know I track your blogs religiously. Not sure I get your conclusions here tho. In my VERY HUMBLE opinion logmein is slow and cumbersome (connection can take 5 mins!), whilst the built in TSC (notably absent) is stunningly fast - particuarly with VJ's full-screen (which, for some strange reason, I'm battling to get working on my latest JJ altho I used it successfully a year ago). I'm guessing that TSC has firewall issues for some users?

craigiecraigie4 said:
Hi Minniesys,
You know I track your blogs religiously. Not sure I get your conclusions here tho.
Click to expand...
Click to collapse
No problem - at this early stage of the article (it's just the comparison chat that I've published), this is natural. Here, we're comparing apples and oranges - LogMeIn and TSC are meant for completely different things, in radcally different networking environments. That is, both are excellent technologies. I'll elaborate on all these stuff in the article.
craigiecraigie4 said:
In my VERY HUMBLE opinion logmein is slow and cumbersome (connection can take 5 mins!)
Click to expand...
Click to collapse
Strange you have problems like that - in my tests, connecting didn't take more time than with the alternate Web-based technologies. Neither has anyone complained about slow connection speeds. Are you trying to use LogMeIn trhough a HTTP gateway?

Updated version posted.
Dunno when I publish the final article - at least 2-3 days more. (Still working on correctly filling in the chart). In the meatime, comments are surely welcome

[Q] [APP][IDEA] A Reference app for Organic Chemistry

Hi
Organic chemistry is a branch of chemistry dealing with compounds and reactions between compounds containing mostly carbon, hydrogen, oxygen and nitrogen. These reactions are extremely diverse and most proceed in several steps, as a result they are organized into schemes called reaction mechanisms. Although new mechanisms are discovered all the time, there are hundreds of mechanisms that are already well known and useful in an everyday chemistry setting. For an organic chemist, a quick way to find a particular mechanism is extremely valuable.
Unfortunately, there is no quick and easy way to do this on a mobile platform. Wikipedia does describe a lot of these mechanisms, but the most useful way of presenting the information would be something along the lines of a website called Name Reaction. It presents reaction mechanisms in a clear and to-the-point fashion, but it requires an internet connection and only lists a fraction of what is out there.
The closest thing to the app I'm describing is an app called formulae in Google Play. It does a number of things, including reaction mechanisms, but isn't particularly user-friendly, and it doesn't present the information in a clear and easy to find way.
My idea is therefore to create an app that has an searchable offline database of reaction mechanisms presented in a crisp, clear way that would allow chemists to look up the information they need quickly and efficiently. This is not an app that would make it to the top list in any app store, but I know that there is a market for something like this.
Since my coding experience is quite limited, I need help to make this project happen. I can provide the science data if someone is willing to help out with the coding and design.
Does this sound interesting to anyone?

[Q] Data compression usage

Recently, I put together a benchmark for general-purpose lossless data compression algorithms (think zilb and LZMA, not HEVC and MP3). While I was doing so it occurred to me that the standard corpora aren't very representative of the type of data people actually use data compression for these days.
To address this I'm putting together a new corpus and I would like to make sure it includes data relevant for mobile developers. Since I have virtually no experience with mobile development I was hoping some of the developers around here could tell me what kind of data they are compressing (or would like to in the future) so I can include something like it in the corpus, especially the differences from desktop usage.
Note that, in addition to developers using benchmarks run against this data to help decide what codec(s) to consider for their project, the data from this corpus will also be used by people writing compression codecs to help tune their algorithms and implementations—in other words, if the corpus includes data which is representative of what mobile apps use it will likely result in better compression (higher ratio, faster, lower memory…) for mobile apps.
I can't post links here yet (this is my first post), but for more details see the project page at <https://github.com/nemequ/squash-corpus>. There is an item in the issue tracker for "Data from a mobile app", but it's pretty vague—that's what I'm here trying to pin down, especially content that doesn't really fit into one of the other issues.

Visual Aesthetics: Judging a photo’s quality using AI techniques

Visual aesthetics has been shown to critically affect a variety of constructs such as perceived usability, satisfaction, and pleasure. However, visual aesthetics is also a subjective concept and therefore, presents its unique challenges in training a machine learning algorithm to learn such subjectiveness.
Given the importance of visual aesthetics in human-computer interaction, it is vital that machines adequately assess the concept of visual aesthetics. Machine learning, especially deep learning techniques have already shown great promise on tasks with well-defined goals such as identifying objects in images or translating from one language to another. However, quantification of image aesthetics has been one of the most persistent problems in image processing and computer vision.
We decided to build a deep learning system that can automatically analyze and score an image for aesthetic quality with high accuracy. Please check out our demo to check your photo’s aesthetic score.
About the Research
We came up with a novel Deep Convolutional Neural Network which can be trained to recognize an image’s aesthetic quality. We also came up with multiple hacks while training the algorithm to increase the accuracy.
In our paper published on arxiv, we have proposed a new neural network architecture which can model the data efficiently by taking both low level and high-level features into account. It is a variant of DenseNets which has a skip connection at the end of every dense block. Besides this, we also propose training techniques that can increase the accuracy with which the algorithm trains. These methods are to train on LAB color space and to use similar images in a minibatch to train the algorithm, which we call coherent learning. Using these techniques, we get an accuracy of 78.7% of the AVA2 dataset. The state of the art accuracy on the AVA2 dataset is 85.6% which uses a deep Convolutional Neural Network with pretrained weights on the imagenet dataset. The best accuracy on the AVA2 dataset using handcrafted features is 68.55%. We also show that adding more data to our training set (from AVA dataset not included in AVA2) increases its accuracy to 81.48% on AVA2 Test Set, hence showing the model gets better with more data.
Use-cases of Visual Aesthetics
App developers of social media sites can help their users decide which photo will suit best for their profile image. We all have faced anxiety while uploading photos on social media sites or changing our display pic. With our API integration, app developers can help their users look good, always!
Smart Machine Learning algorithms can help you put your best photo on dating apps
Ok, now this use-case may not appeal to the zen, non-materialistic folks among us but to be honest, dating leads to the most social anxiety. Dating landscape keeps changing as well and therefore, if you are active on dating apps, it’s important to choose your best photos to improve your chances for right swipes!
Dating App developers can easily integrate our APIs to help their users upload their best photos; the visual aesthetics model can also be fine-tuned if the developers want to optimise it on their data set.
Recently Google has launched Pixel 2 and Pixel 2 XL which has a portrait mode. This phone offers the portrait mode even though it lacks the second lens that many other phones have. For example, the iPhone X, Galaxy Note 8, OnePlus 5… all these phones offer the portrait mode because they use data from two lenses. One lens captures the image, the other one captures the depth information, apart from providing some focal range magic for the blurred background. However, Pixel phone uses AI to give HDR+ images to users which are comparable to pictures clicked by a DSLR camera.
Similarly, mobile manufacturers can augment the capabilities of their native camera by integrating the visual aesthetic APIs to let their users know in real-time the quality of their photo even before taking a snap! This will enable your users to share their photos with confidence and you will end up creating a great differentiator for your brand at no additional hardware cost.
Virality in online content
visual aesthetics
Content is king, and it has become ever more difficult to write compelling content that resonates with your audience. However, the best content these days often have great images to complement them, and therefore, you’ve got to include something that will keep eyes moving down the page.
BuzzSumo did an analysis that covered over 1 million articles and found that the ones that had images every 75-100 words had more social shares. Using our visual aesthetics tool, you can quickly check how appealing your images are and accordingly, improve the virality of your blog post.
In this blog post, we have covered some of the use-cases of our visual aesthetics API. When machines become more competent than humans to judge such subjective content, it opens up a lot of possibilities to exploit which were not feasible yet. You can read more blogs on Visual Analytics here.
ParallelDots AI APIs are a deep learning powered web service by ParallelDots Inc, that can comprehend a huge amount of unstructured text and visual content to empower your products. You can check out some of our Visual Analytics APIs and write to us at [email protected].

Crital Kernnel bug in most versions before 4.20.11

Haven't seen mobile specifically mentioned anywhere but given the nature of this, it may very well be. Short version is .. well, everything going back at to least 2.6.10. If it is indeed this bad it would be one of the worst I've ever seen in terms of scope.
https://nvd.nist.gov/vuln/detail/CVE-2019-8912
http://www.securityfocus.com/bid/107063
> In the Linux kernel through 4.20.11, af_alg_release() in crypto/af_alg.c neglects to set a NULL value for a certain structure member, which leads to a use-after-free in sockfs_setattr.
Impact
CVSS v3.0 Severity and Metrics:
Base Score: 9.8 CRITICAL
Vector: AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H (V3 legend)
Impact Score: 5.9
Exploitability Score: 3.9
Attack Vector (AV): Network
Attack Complexity (AC): Low
Privileges Required (PR): None
User Interaction (UI): None
Scope (S): Unchanged
Confidentiality (C): High
Integrity (I): High
Availability (A): High