Telanova Blogs


Code Smells, a rise to maturity

The smell of Orchids

Today’s world is full of an increasing amount of program code. Back in 1999 Martin Fowler[1] defined the basis of Code Smells. Smells, being the inherent way humans in nature detect bad, and good things, likewise, Code has a smell, be it bad or good. A bad code smell being code that contains bad programming techniques, duplicate code, ie. poor quality. A new paper called Code Smells, by Peter Kokol, Milan Zorman, Bojan Žlahtič, Grega Žlahtič [2] , has been published.

Kokol’s paper[2] analysed the rise of discussion around code smells. Using bibliometrics to analyse research papers which contain references to code smells, Kokol was able to map and detect the changes in frequency and geographical distribution of papers.

Their results highlighted 337 publications which contained references and of those 70% were related to conference proceedings. Which they concluded may mean that code smells is still in the rising state of maturity.

They plotted the details on a timeline and identified that the largest rises were in 2009, then in 2014. They also identified which countries were using the term the most, and as might be expected USA was top, with almost twice the next country, Italy. Italy contained the individual institution that had produced the most papers, with 19 papers published by the Universita degli Studi di Milano.

The research papers indicated that code smell research was split into 3 themes, smell detection, software refactoring, development & anti-patterns. Of these themes code software development and anti-patterns, was the most popular themes, using anti-patterns and knowledge of software development problems code quality can be increased.

Overall an interesting and highlighting paper that shows that in the future, machine learning, and other analysis tools may be used against software development code to identify if it smells of sulphur or wild orchids.

[1] M. Fowler, Refactoring: Improving the Design of Existing Code., Reading: Addison - Wesley, 1999.

[2] Peter Kokol, Code Smells, 2018


Looking to test the quality of your IT configuration, talk to our consultants about what changes you can make to get that wild orchid smell :

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Take a photo, create the object. 3D from 2D a step closer

Camera on Chair

Edward Smith, Scott Fujimoto, and David Meger from Department of Computer Science, McGill University Montreal, Canada are hoping to make some science fiction become science fact.

Imagine that you really like that chair your grandmother used to sit and rock you to sleep in, on the veranda as the sun set over the hills. Maybe you took a photo of the chair when you got that super new camera.

If you were to drop down to McGill University they may just be able to help you get a 3D creation of that chair so you can own one. Publishing their latest paper, they have factored up what was previously possible by 16. Gaining state-of-the-art super resolution results from their new algorithm the team are showing great signs of taking the world one step closer to getting super 3D resolution images from 2D.

With some striking results it’s amazing how accurate the 3D images are that are produced. From Chairs, to planes to vehicles, a simple 2D image creates a fair representative 3D image. What is equally impressive is that the method and all of the computations were run on a single NVIDIA Titan X GPU.


Did you see that stop sign


Hee Seok Lee and Kang Kim have recently been working with Convolutional Neural Networks in detecting Traffic Signs. To most drivers, a traffic sign is normally fairly clear and identifiable. However, to a computer figuring out where a sign is in a flat image is not so simple.

The system isn’t expected to handle having trees growing over the signs, so any signs that aren’t fully visible, so it is a time to get out the pruning shears. However, CNN is really proving itself when it comes to recognising the shapes at speed.

Using different parameters such as using a lighter base network, ie. deeper neural networks give better results, but to do that requires a higher level of processing power than the rewards. Changing the resolution also allowed recognition of the sames signs at a high frame rate, and cropping the image also assisted in speeding up recognition, ie. far away traffic signs in the centre of the image of a vehicle camera aren’t recognisable, and the normal location for traffic signs will be on the side of the road, so detection can be targeted at the appropriate locations.

In summary the team identified that for the best accuracy at speed, by using the latest architectures, of object detection such as feature pyramid networks and multi scale training, they achieved a 7FPS on a low power mobile platform, ideal for your self driving car, or for alerting drivers to signs that they may have missed!


Counting the people just got a lot more accurate. How many people in this scene


The next US president may well be able to confirm the number of people attending his inauguration, and this down to deep learning and convolutional neural networks.

Thanks to the team, Yuhong Li, Xiaofan Zhang, and Deming Chen from Beijing University of Posts and Telecommunications and the University of Illinois at Urbana-Champaign, their recently published work, CSRNet: Dilated Convolutional Neural Networks for Understanding the Highly Congested Scenes, may revolutionise how accurate and fast crowd counting will be in the future.

The congested scene analysis work is based on 2d images, and can break down an image and calculate where the higher density of crowds are located. This will help organisers in the future to understand how to keep people flowing, and for static situations, how to keep the density low enough to ensure attendee comfort.

The teams work focused on density map creation, and by using dilated CNN on crowd counting for the first time, the system outperforms other state-of-the-art crowd counting solutions. Demonstrating their approach using five public datasets, the overall model, is smaller, more accurate, and easier to train and to deploy.

Using the ShanghaiTech crowd dataset, which is a dataset of 1198 images with a total of 330,165 people, split over 2 areas, one being a highly congested scene, and one being sparse crowd scenes, the method improved the accuracy, dropping the error rate by 7%.

On the other datasets, the CSRnet system continued to outperform in most of the scenes, only bettered in 1 out of 5 sets by CP-CNN method.

The method doesn’t need to be reserved for counting people, it can also be used for counting vehicles, and using the TRANSCOS dataset it outperformed all other methods.

In summary, next time you’re in a photo, there may be a new method of counting how many other people are in the picture with you, with a better accuracy than previously attained. Time to invest in some camouflage.


DDos attacks against QKD networks could be mitigated with SDN

Communication at the speed of a photon

The Latest Technology in Secure Communications is Quantum Key Distribution (QKD) , these rely on single photons traveling between points via an optical channel. Detection of eavesdropping on QKD networks is possible based on the fundamental constraints of quantum mechanics.

However, you may not be able to listen in to QKD traffic, but malicious people can exert a Distributed Denial of Service (DDos) attack. As a QKD detects any disturbance, the key generation between the two points is disrupted and has to be re-established. Naturally DDos can continue to disrupt the communications.

Thanks to collaborative research by the teams at the High Performance Networks group, the Centre for Quantum Photonics at University of Bristol, and British Telecom Research and Innovation they have published their findings on this issue. ( Experimental Demonstration of DDoS Mitigation over a Quantum Key Distribution (QKD) Network Using Software Defined Networking (SDN) Feb 2018 )

Using a Software Defined Network (SDN) application to handle the situation, the SDN was able to monitor the breakdown in communications (key generation) and then automatically selects a different route for the traffic away from the DDoS.

It’s good to see that before technology has become widespread, the research has begun on how malicious attacks might take place and how to protect against them.


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