Ultra-Compact Laser Barcode Reader(MS-3 Series)

Compact Industrial Laser Barcode Reader(QX-830 Series)

Industrial Raster Laser Barcode Reader(QX-870 Series)

“Ultra-Rugged” Hand Held Code Reader(HS-360X Series)

Portable Barcode Verification System(LVS-9585 Series)

Portable Barcode Verification System(LVS-9580 Series)

Desktop Barcode Verification System(LVS-9510 Series)

This article describes a real life experience in picking and using the linux operating system, as well as libraries with Open Source Code licenses.

This issue features articles on: Industry Executives sharing their thoughts on 2019, The Great Barcode Chicken Conundrum, The Impact of Labeling, AIM's lifetime achievement award, CEO compensation in the Barcode/RFID/Label industry

Scio, an award-winning nearshore software development company, announced today that they won Software Guru's Top 10 Best Places To Code in Mexico.

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GitHub on Wednesday announced two new security features designed to help developers identify vulnerabilities and potential secrets in their code.

The company announced several new products at its Satellite virtual conference, including ones aimed at helping customers write and consume more secure code.

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Cisco’s Talos threat intelligence and research group revealed on Wednesday that one of its researchers discovered a critical remote code execution vulnerability in the CODESYS Control SoftPLC industrial controller software.

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Also: Math for Programmers!; If #Programming languages had honest slogans #humor; 5 Papers on CNNs Every Data Scientist Should Read; Why Understanding CVEs Is Critical for Data Scientists

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I am at the WordCamp convention in Las Vegas and

The post WordCamp Las Vegas: The WordPress codex appeared first on WPCult.

Yesterday, Cameron Koczon shared a link to the dingbat font, Pictos, by the talented, Drew Wilson. Cameron predicted that dingbats will soon be everywhere. Symbol fonts, yes, I thought. Dingbats? No, thanks. Jason Santa Maria replied:

@FictiveCameron I hope not, dingbat fonts sort of spit in the face of accessibility and semantics at the moment. We need better options.

Jason rightly pointed out the accessibility and semantic problems with dingbats. By mapping icons to letters or numbers in the character map, they are represented on the page by that icon. That’s what Pictos does. For example, by typing an ‘a’ on your keyboard, and setting Pictos as the font-face for that letter, the Pictos anchor icon is displayed.

Other folks suggested SVG and JS might be better, and other more novel workarounds to hide content from assistive technology like screen readers. All interesting, but either not workable in my view, or just a bit awkward.

Falling down with CSS text-replacement

A CSS solution in an article from Pictos creator, Drew Wilson, relies on the fact that most of his icons are mapped to a character that forms part of the common name for that symbol. The article uses the delete icon as an example which is mapped to ‘d’. Using :before and :after pseudo-elements, Drew suggests you can kind-of wrangle the markup into something sort-of semantic. However, it starts to fall down fast. For example, a check mark (tick) is mapped to ‘3’. There’s nothing semantic about that. Clever replacement techniques just hide the evidence. It’s a hack. There’s nothing wrong with a hack here and there (as box model veterans well know) but the ends have to justify the means. The end of this story is not good as a VoiceOver test by Scott at Filament Group shows. In fairness to Drew Wilson, though, he goes on to say if in doubt, do it the old way, using his font to create a background image and deploy with a negative text-indent.

I agreed with Jason, and mentioned a half-formed idea:

@jasonsantamaria that’s exactly what I was thinking. Proper unicode mapping if possible, perhaps?

The conversation continued, and thanks to Jason, helped me refine the idea into this post.

Jon Hicks flagged a common problem for some Windows users where certain Unicode characters are displayed as ‘missing character’ glyphs depending on what character it is. I think most of the problems with dingbats or missing Unicode characters can be solved with web fonts and Unicode.

Rising with Unicode and web fonts

I’d love to be able to use custom icons via optimised web fonts. I want to do so accessibly and semantically, and have optimised font files. This is how it could be done:

1. Map the icons in the font to the existing Unicode code points for those symbols wherever possible.

   Unicode code points already exist for many common symbols. Fonts could be tiny, fast, stand-alone symbol fonts. Existing typefaces could also be extended to contain symbols that match the style of individual widths, variants, slopes, and weights. Imagine a set of Clarendon or Gotham symbols for a moment. Wouldn’t that be a joy to behold?

   There may be a possibility that private code points could be used if a code-point does not exist for a symbol we need. Type designers, iconographers, and foundries might agree a common set of extended symbols. Alternatively, they could be proposed for inclusion in Unicode.

2. Include the font with font-face.

   This assumes ubiquitous support (as any use of dingbats does) — we’re very nearly there. WOFF is coming to Safari and with a bit more campaigning we may even see WOFF on iPad soon.

3. In HTML, reference the Unicode code points in UTF-8 using numeric character references.

   Unicode characters have corresponding numerical references. Named entities may not be rendered by XML parsers. Sean Coates reminded me that in many Cocoa apps in OS X the character map is accessible via a simple CMD+ALT+t shortcut. Ralf Herrmann mentioned that unicode characters ‘…have “speaking” descriptions (like Leftwards Arrow) and fall back nicely to system fonts.’

Limitations

1. Accessibility: Limited Unicode / entity support in assistive devices.

   My friend and colleague, Jon Gibbins’s old tests in JAWS 7 show some of the inconsistencies. It seems some characters are read out, some ignored completely, and some read as a question mark. Not great, but perhaps Jon will post more about this in the future.

   Elizabeth Pyatt at Penn State university did some dingbat tests in screen readers. For real Unicode symbols, there are pronunciation files that increase the character repertoire of screen readers, like this file for phonetic characters. Symbols would benefit from one.

2. Web fonts: font-face not supported.

   If font-face is not supported on certain devices like mobile phones, falling back to system fonts is problematic. Unicode symbols may not be present in any system fonts. If they are, for many designers, they will almost certainly be stylistically suboptimal. It is possible to detect font-face using the Paul Irish technique. Perhaps there could be a way to swap Unicode for images if font-face is not present.

Now, next, and a caveat

I can’t recommend using dingbats like Pictos, but the icons sure are useful as images. Beautifully crafted icon sets as carefully crafted fonts could be very useful for rapidly creating image icons for different resolution devices like the iPhone 4, and iPad.

Perhaps we could try and formulate a standard set of commonly used icons using the Unicode symbols range as a starting point. I’ve struggled to find a better visual list of the existing symbols than this Unicode symbol chart from Johannes Knabe.

Icons in fonts as Unicode symbols needs further testing in assistive devices and using font-face.

Last, but not least, I feel a bit cheeky making these suggestions. A little knowledge is a dangerous thing. Combine it with a bit of imagination, and it can be lethal. I have a limited knowledge about how fonts are created, and about Unicode. The real work would be done by others with deeper knowledge than I. I’d be fascinated to hear from Unicode, accessibility, or font experts to see if this is possible. I hope so. It feels to me like a much more elegant and sustainable solution for scalable icons than dingbat fonts.

For more on Unicode, read this long, but excellent, article recommended by my colleague, Andrei, the architect of Unicode and internationalization support in PHP 6: The Absolute Minimum Every Software Developer Absolutely, Positively Must Know About Unicode and Character Sets.

In a previous blogpost we talked about the Opus codec, which offers very low bitrates. Another codec seeking to achieve even lower bitrates is Codec 2.

Codec 2 is designed for use with speech only, and although the bitrates are impressive the results aren’t as clear as Opus, as you can hear in the following audio examples. However, there is some interesting work being done with Codec 2 in combination with neural network (WaveNets) that is yielding great results.

Layers of a WaveNet neural network.

Background

Codec 2 is an open source codec designed for speech, and aims for compression rates between 700bps and 3200bps (bits per seconds).

The man behind it, David Rowe, is an electronic engineer currently living in South Australia. He started the project in September 2009, with the main aim of improving low-cost radio communication for people living in remote areas of the world. With this in mind, he set out to develop a codec that would significantly reduce file sizes and the bandwidth required when streaming.

Another motivation according to David, was to be free from patented technologies used by closed source codes which he believes “require expensive and awkward licenses and are stifling innovation”. His belief is that this work can be done without requiring the use of patent protected codecs, so all his work is open source.

Potential Applications

Rowe’s perceived applications include VOIP trunking, voice over low bandwidth HF/VHF digital radio, (especially for amateur radio, so as to avoid issues with the use of proprietary codecs), and developing world and remote area communications, including military, police and emergency services.

Why we’re interested here at Auphonic is for its potential for longer podcasts, presentations and audiobooks, allowing for low storage and minimizing the effect of bad network connections.

How it Works

To achieve the lower rates sought, speech has to be reduced into the smallest possible information/data, and this means that the amount of redundant information that is transmitted has to be minimized.

To do this, Codec 2 uses harmonic sinusoidal speech coding. This splits the speech into 10 - 30ms segments, called frames. Each frame is then analysed for the fundamental frequency (or pitch), and the number of harmonics that fit into a 4Khz bandwidth. Further, for each of the harmonics within the 4khz range, the amplitude and phase are recorded.

This information is then coded, and the decoder reconstructs the audio based on this data.

Codec 2 Block diagrams - Encoder (left) And decoder (right)
Figure from Rowtel.

Audio Examples and Comparison with other Codecs

Whilst it all sounds great in theory, how does the reality match up? Let’s have a listen.

Here is a short wav audio file:

Applying Codec 2 (without the WaveNet decoder) at the different rates available, 3200bps, 2400bps,1600bps,1200bps and 700bps, we get:

These examples show significantly reduced file sizes.
Putting that information more meaningfully in terms of how much storage you would need for an hour of audio:

• At 3200bps, 1 hour of audio requires only 1.37MB (this would fit on one old 3½-inch floppy disk!)
• A rate of 2400bps equates to 1.03MB/h
• A rate of 1600bps equates to 0.68MB/h (Or approximately 2 hours of audio on one floppy disk!)
• A rate of 1200bps equates to 0.51MB/h
• A rate of 700bps equates to 0.3MB/h

So great compression, but the result is clearly not natural sounding.

As a comparison here is the same audio as a 8kb/s MP3:

The file size is significantly larger than Codec 2 and the quality is arguably still not useable. You can clearly hear what is sometimes called sizzle - the weird metallic sounds you hear on low quality MP3s.

There is a final codec which is worth comparing, one that that seems to capture the two ideals of usable quality at low bitrates that we want: Opus.
Because of it's convincing low-bitrate performance, Auphonic already offers Opus encoding all the way down to 6 kbps, the lowest bitrate that Opus supports.

Comparing Opus at this 6 kbps rate to the 8kbps MP3 shows a significant improvement - although slightly muffled, it still sounds natural:

Returning to Codec 2, and purely as s a bit of fun, here are some samples of Codec 2 on music! (Note that Codec 2 is not designed for music, it was only ever conceived for use on speech).

I personally couldn’t listen to the MP3 at this rate, so let’s listen to what Codec 2 does!

Codec 2 at different bitrates:

As you can hear, it is not suitable for this application at all!

Codec 2 and WaveNet

As we have heard, despite the impressive bitrates achieved, the end result is not very natural sounding.
However, where it starts to get more interesting is the work done by W. Bastiaan Kleijn from Cornell University Library. He has been using with Codec 2 running at 2400bps on the coding side, but replaced the Codec 2 decoder with a WaveNet deep learning generative model (for more informationsee the paper Wavenet based low rate speech coding).

Here are some samples from the authors:

Comparing to Codec 2 you can hear a significant increase in quality, and if you compare to the original, there is not a significant decrease in quality.

Conclusion

Whilst the (original) Codec 2 project represents very interesting work, it is limited, and the end result is not suited for podcasting. Also as we heard in the audio examples, it can only be used for voice recordings, and not music.

However, Codec 2 in combination with a WaveNet decoder improves the quality a lot and the low bitrate (2400bps) would be extremely interesting for podcasts and audiobooks distribution as well: one hour of audio would require only 1.03MB of storage!

Auphonic will add support for Codec 2 output files when the WaveNet decoder is in a usable form. For now we have just added support for Codec 2 input files.

https://thenextweb.com/growth-quarters/2020/05/08/should-designers-learn-how-to-code-syndication/

Two of the most prominent utilities in web development today are ESLint and Microsoft’s Visual Studio Code. I enjoy using both, and I love the integration between both tools, but warnings from ESLint inside Visual Studio Code aren’t fulfilling — I’d rather lint errors be fixed each time I save. Complete the following steps to […]

The post How to Fix ESLint Errors Upon Save in VS Code appeared first on David Walsh Blog.

The end of this month kicks off the busy summer travel season, which causes many designers to get behind on your design work. But it doesn't have to be that way. Instead, you can use stock graphics to speed up your design work this summer, so you can still enjoy those much-needed getaways and outdoor time. And right now, we have two iStock promo codes that can save you some change...

The post iStock Promo Codes – May 2020 appeared first on Vectips.

In a blog, Opera Software Developer Relations team member Tiffany B. Brown looks at code injection, error throwing and handling and mobile debugging. She notes Opera Dragonfly and its remote debug features provide a way to debug mobile sites from their desktop. Brown mentions WebKit’s recently added remote debugging capabilities, folded into Google Chrome developer Read the rest...

Join me + bestselling author Vishen Lakhiani LIVE Tuesday April 7 at 6:30pm PST. Vishen Lakhiani is one of today’s most influential minds in the fields of education and human consciousness. He is the founder of Mindvalley University and its 2 million-strong student base and creator of the Quests learning platform: a next-generation method of online learning, which attains an unheard-of 60% completion rate on courses, in an industry where 8% is average. Vishen’s book, The Code of the Extraordinary Mind, made the New York Times Business Best Sellers List, and hit the coveted #1 spot on Amazon five times in 2017. Enjoy! FOLLOW VISHEN: instagram | facebook | website Listen to the Podcast coming soon … Subscribe   This podcast is brought to you by CreativeLive. CreativeLive is the world’s largest hub for online creative education in photo/video, art/design, music/audio, craft/maker, money/life and the ability to make a living in any of those disciplines. They are high quality, highly curated classes taught by the world’s top experts — Pulitzer, Oscar, Grammy Award winners, New York Times best selling authors and the best entrepreneurs of our times.

The post The Code of the Extraordinary Mind with Vishen Lakhiani appeared first on Chase Jarvis Photography.

We show that a random puncturing of a code with good distance is list recoverable beyond the Johnson bound. In particular, this implies that there are Reed-Solomon codes that are list recoverable beyond the Johnson bound. It was previously known that there are Reed-Solomon codes that do not have this property. As an immediate corollary to our main theorem, we obtain better degree bounds on unbalanced expanders that come from Reed-Solomon codes.

In encoder-decoder neural models, multiple encoders are in general used to represent the contextual information in addition to the individual sentence. In this paper, we investigate multi-encoder approaches in documentlevel neural machine translation (NMT). Surprisingly, we find that the context encoder does not only encode the surrounding sentences but also behaves as a noise generator. This makes us rethink the real benefits of multi-encoder in context-aware translation - some of the improvements come from robust training. We compare several methods that introduce noise and/or well-tuned dropout setup into the training of these encoders. Experimental results show that noisy training plays an important role in multi-encoder-based NMT, especially when the training data is small. Also, we establish a new state-of-the-art on IWSLT Fr-En task by careful use of noise generation and dropout methods.

We propose Cotatron, a transcription-guided speech encoder for speaker-independent linguistic representation. Cotatron is based on the multispeaker TTS architecture and can be trained with conventional TTS datasets. We train a voice conversion system to reconstruct speech with Cotatron features, which is similar to the previous methods based on Phonetic Posteriorgram (PPG). By training and evaluating our system with 108 speakers from the VCTK dataset, we outperform the previous method in terms of both naturalness and speaker similarity. Our system can also convert speech from speakers that are unseen during training, and utilize ASR to automate the transcription with minimal reduction of the performance. Audio samples are available at https://mindslab-ai.github.io/cotatron, and the code with a pre-trained model will be made available soon.

The de Bruijn graph, its sequences, and their various generalizations, have found many applications in information theory, including many new ones in the last decade. In this paper, motivated by a coding problem for emerging memory technologies, a set of sequences which generalize sequences in the de Bruijn graph are defined. These sequences can be also defined and viewed as constrained sequences. Hence, they will be called constrained de Bruijn sequences and a set of such sequences will be called a constrained de Bruijn code. Several properties and alternative definitions for such codes are examined and they are analyzed as generalized sequences in the de Bruijn graph (and its generalization) and as constrained sequences. Various enumeration techniques are used to compute the total number of sequences for any given set of parameters. A construction method of such codes from the theory of shift-register sequences is proposed. Finally, we show how these constrained de Bruijn sequences and codes can be applied in constructions of codes for correcting synchronization errors in the $ell$-symbol read channel and in the racetrack memory channel. For this purpose, these codes are superior in their size on previously known codes.

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In a wireless communication system, a compact control signaling scheme is provided for signaling the selected retransmission mode and codeword identifier for a codeword retransmission when one of a plurality of codewords being transmitted over two codeword pipes to a receiver fails the transmission and when the base station/transmitter switches from a higher order channel rank to a lower order channel rank, either by including one or more additional signaling bits in the control signal to identify the retransmitted codeword, or by re-using existing control signal information in a way that can be recognized by the subscriber station/receiver to identify the retransmitted codeword. With the compact control signal, the receiver is able to determine which codeword is being retransmitted and to determine the corresponding time-frequency resource allocation for the retransmitted codeword.

A logical operations functional block for an execution unit of a processor includes a first input data link for a first operand and a second input data link for a second operand. The execution unit includes a register connected to an error correction code detection unit. The logical operations functional block includes a look-up table configured to receive an error correction code syndrome from the error correction code detection unit. The logical operations functional block also includes a multiplexer configured to receive an output signal from the look-up table at a first input and the first operand at a second input, wherein an output of the multiplexer is coupled to the first input data link of a logical functional unit.

Embodiments of the present disclosure describe device, methods, computer-readable media and system configurations for decoding codewords using a side channel. In various embodiments, a memory controller may be configured to determine that m of n die of non-volatile memory (“NVM”) have failed iterative decoding. In various embodiments, the memory controller may be further configured to generate a side channel from n-m non-failed die and the m failed die other than a first failed die. In various embodiments, the memory controller may be further configured to reconstruct, using iterative decoding, a codeword stored on the first failed die of the m failed die based on the generated side channel and on soft input to an attempt to iteratively decode data stored on the first failed die. In various embodiments, the iterative decoding may include low-density parity-check decoding. Other embodiments may be described and/or claimed.

A method for decoding and checking a tail-biting convolutional code is provided. The method fully utilizes structural features of the tail-biting convolutional code to re-sort Log-Likelihood Ratio (LLR) values input into a decoder, and by reconstructing a derivative generator polynomial of a convolutional code, allows the decoder to output in serial according to a normal ordering of information bits during backtracking, that is, a first bit of an information sequence is first decoded successfully. Thus, CRC checking may be activated as soon as possible, so that part of the backtracking process and the CRC checking may be performed in parallel, thereby achieving the objective of reducing a processing time delay in decoding and checking the tail-biting convolutional code.

A method of generating a code sequence in a wireless communication system is disclosed. More specifically, the method includes recognizing a desired length of the code sequence, generating a code sequence having a length different from the desired length, and modifying the length of the generated code sequence to equal the desired length. Here, the step of modifying includes discarding at least one element of the generated code sequence or inserting at least one null element to the generated code sequence.

A method of processing an electronic file by identifying portions of content data in the electronic file and determining if each portion of content data is passive content data having a fixed purpose or active content data having an associated function. If a portion is passive content data, then a determination is made as to whether the portion of passive content data is to be re-generated. If a portion is active content data, then the portion is analyzed to determine whether the portion of active content data is to be re-generated. A re-generated electronic file is then created from the portions of content data which are determined to be re-generated.

A system for protecting an electronic device against malware includes a memory, an operating system configured to execute on the electronic device, and a below-operating-system security agent. The below-operating-system security agent is configured to trap an attempted access of a resource of the electronic device, access one or more security rules to determine whether the attempted access is indicative of malware, and operate at a level below all of the operating systems of the electronic device accessing the memory. The attempted access includes attempting to write instructions to the memory and attempting to execute the instructions.

Augmented processor hardware contains a microcode interpreter. When encrypted microcode is included in a message from a service, the microcode may be passed to the microcode interpreter. Based on decryption and execution of the microcode taking place at the processor hardware, extended functionality may be realized.

Mechanisms are provided for offloading a workload from a main thread to an assist thread. The mechanisms receive, in a fetch unit of a processor of the data processing system, a branch-to-assist-thread instruction of a main thread. The branch-to-assist-thread instruction informs hardware of the processor to look for an already spawned idle thread to be used as an assist thread. Hardware implemented pervasive thread control logic determines if one or more already spawned idle threads are available for use as an assist thread. The hardware implemented pervasive thread control logic selects an idle thread from the one or more already spawned idle threads if it is determined that one or more already spawned idle threads are available for use as an assist thread, to thereby provide the assist thread. In addition, the hardware implemented pervasive thread control logic offloads a portion of a workload of the main thread to the assist thread.

Systems and methods for static code scheduling are disclosed. A method can include receiving an intermediate representation of source code, building a directed acyclic graph (DAG) for the intermediate representation, and creating chains of dependent instructions from the DAG for cluster formation. The chains are merged into clusters and each node in the DAG is marked with an identifier of a cluster it is part of to generate a marked instruction DAG. Instruction DAG scheduling is then performed using information about the clusters to generate an ordered intermediate representation of the source code.