Newer technologies like AI, Machine learning or blockchain will together boost the IT staffing industry creating new jobs. According to ISF report on IT Staffing Industry Landscape, Karnataka, Maharashtra and Delhi NCR are the top 3 states that dominate the IT Flexi Staffing Industry in India. Karnataka dominates the IT Flexi Staffing market standing at $1.05 billion followed by Maharashtra at $0.48 billion and Delhi NCR at $0.23 billion. With the rise of technologies such as cloud computing, artificial intelligence, IoT, Big data and machine learning, the demand is growing for new technology-based services. Mobility and cloud computing each standing at 63 percent are in maximum demand followed by cybersecurity at 53 percent and analytics at 51 percent - they comprise the top technology drivers across organisations for their proficient functioning.

While everywhere else there is a fear of technology taking over people's job, China, the world leader in the digital economy, thinks it has the capacity to bring a change. The latest research from Dentsu Aegis Network (DAN) shows that people in China are the most optimistic about the impact of digital technology and artificial intelligence (AI) on work and life. "The answer may lie in the belief of people in China that their education system is providing them with the tools and knowledge to succeed in the future, however uncertain or disruptive that may be," says Susana Tsui, the chief executive officer of Dentsu Aegis Network China, in an email interview. The report highlights that 68 per cent of people in China think that their formal education (e.g. Tsui gives the credit of formalized education system to the Chinese government.

Microsoft expects to do more artificial intelligence research in Taiwan. Artificial brains threaten to outnumber real ones in Taiwan, as the island's prowess in artificial intelligence (AI) continues to grow. Global players such as Google, IBM and Microsoft have all expressed their intentions of developing either AI R&D centers or similar initiatives in Taiwan. These companies could have selected other tech-savvy locations in Asia like South Korea and Shenzhen, China, but they chose Taiwan. "Taiwan has a lot going for it with AI research," says William Foreman, president of the American Chamber of Commerce in Taipei.

In the past decade, artificial intelligence has embarked its trace in all the leading industries. Artificial intelligence remained a limelight in countries like China and the US. But it is interesting to know that India has also joined the artificially intelligent labeled bandwagon. The report stated that artificial intelligence is accelerating the new digital revolution, the report highlighted three key points. The industries which are extensively using artificial intelligence with their workforce are the most dynamic industries.

Software defined networking (SDN) represents a promising networking architecture that combines central management and network programmability. SDN separates the control plane from the data plane and moves the network management to a central point, called the controller, that can be programmed and used as the brain of the network. Recently, the research community has showed an increased tendency to benefit from the recent advancements in the artificial intelligence (AI) field to provide learning abilities and better decision making in SDN. In this study, we provide a detailed overview of the recent efforts to include AI in SDN. Our study showed that the research efforts focused on three main sub-fields of AI namely: machine learning, meta-heuristics and fuzzy inference systems. Accordingly, in this work we investigate their different application areas and potential use, as well as the improvements achieved by including AI-based techniques in the SDN paradigm.

We develop a Vector Quantized Spectral Clustering (VQSC) algorithm that is a combination of Spectral Clustering (SC) and Vector Quantization (VQ) sampling for grouping Soybean genomes. The inspiration here is to use SC for its accuracy and VQ to make the algorithm computationally cheap (the complexity of SC is cubic in-terms of the input size). Although the combination of SC and VQ is not new, the novelty of our work is in developing the crucial similarity matrix in SC as well as use of k-medoids in VQ, both adapted for the Soybean genome data. We compare our approach with commonly used techniques like UPGMA (Un-weighted Pair Graph Method with Arithmetic Mean) and NJ (Neighbour Joining). Experimental results show that our approach outperforms both these techniques significantly in terms of cluster quality (up to 25% better cluster quality) and time complexity (order of magnitude faster).

It is challenging for stochastic optimizations to handle large-scale sensitive data safely. Recently, Duchi et al. proposed private sampling strategy to solve privacy leakage in stochastic optimizations. However, this strategy leads to robustness degeneration, since this strategy is equal to the noise injection on each gradient, which adversely affects updates of the primal variable. To address this challenge, we introduce a robust stochastic optimization under the framework of local privacy, which is called Privacy-pREserving StochasTIc Gradual lEarning (PRESTIGE). PRESTIGE bridges private updates of the primal variable (by private sampling) with the gradual curriculum learning (CL). Specifically, the noise injection leads to the issue of label noise, but the robust learning process of CL can combat with label noise. Thus, PRESTIGE yields "private but robust" updates of the primal variable on the private curriculum, namely an reordered label sequence provided by CL. In theory, we reveal the convergence rate and maximum complexity of PRESTIGE. Empirical results on six datasets show that, PRESTIGE achieves a good tradeoff between privacy preservation and robustness over baselines.

The automatic digitizing of paper maps is a significant and challenging task for both academia and industry. As an important procedure of map digitizing, the semantic segmentation section mainly relies on manual visual interpretation with low efficiency. In this study, we select urban planning maps as a representative sample and investigate the feasibility of utilizing U-shape fully convolutional based architecture to perform end-to-end map semantic segmentation. The experimental results obtained from the test area in Shibuya district, Tokyo, demonstrate that our proposed method could achieve a very high Jaccard similarity coefficient of 93.63% and an overall accuracy of 99.36%. For implementation on GPGPU and cuDNN, the required processing time for the whole Shibuya district can be less than three minutes. The results indicate the proposed method can serve as a viable tool for urban planning map semantic segmentation task with high accuracy and efficiency.

We propose Neural Entity Reasoner (NE-Reasoner), a framework to introduce global consistency of recognized entities into Neural Reasoner over Named Entity Recognition (NER) task. Given an input sentence, the NE-Reasoner layer can infer over multiple entities to increase the global consistency of output labels, which then be transfered into entities for the input of next layer. NE-Reasoner inherits and develops some features from Neural Reasoner 1) a symbolic memory, allowing it to exchange entities between layers. 2) the specific interaction-pooling mechanism, allowing it to connect each local word to multiple global entities, and 3) the deep architecture, allowing it to bootstrap the recognized entity set from coarse to fine. Like human beings, NE-Reasoner is able to accommodate ambiguous words and Name Entities that rarely or never met before. Despite the symbolic information the model introduced, NE-Reasoner can still be trained effectively in an end-to-end manner via parameter sharing strategy. NE-Reasoner can outperform conventional NER models in most cases on both English and Chinese NER datasets. For example, it achieves state-of-art on CoNLL-2003 English NER dataset.

Britain's chief medical officer has been instructed by health secretary Matt Hancock to draw up official guidelines on time limits for social media use. Mr Hancock said he was "very worried" as a father by growing evidence of the detrimental effect social media was having on the health of young people. So he said he had instructed Dame Sally Davies to prepare official guidance on safe time limits, which would work in a similar way to safe alcohol limits. Facebook mistake leaves 50 million people's accounts exposed Instagram founders resign from social media giant after eight years What are the benefits of giving up social media? Facebook mistake leaves 50 million people's accounts exposed What are the benefits of giving up social media?