Major challenges in software engineering

Summary The intersection of engineering and neuroscience promises great advances in health care, manufacturing, and communication. While some of thinking machines have mastered specific narrow skills — playing chess, for instance — general-purpose artificial intelligence AI has remained elusive. Part of the problem, some experts now believe, is that artificial brains have been designed without much attention to real ones. Pioneers of artificial intelligence approached thinking the way that aeronautical engineers approached flying without much learning from birds.

Major challenges in software engineering

The network for classfication pre-trained on the ILSVRC classification dataset is modified for bounding box regression. Then the regressor to predict a bouding box is fine-tuned using the features of middle and last convolutional layer [2].

At inference time, the modified greedy-merge techinque [3] for multi-scale prediction is applied on each scale, and the optimal scale is chosen to determin the final predicted box. The Chinese University of Hong Kong 2.

SenseTime Group Limited For the object detection challenge, our submission is based on the combination of two types of models, i. It has higher recall rate with fewer region proposals. Instead of finetuning with the classes once, the models are finetuned for multiple times when the classes are divided into smaller clusters iteratively.

Different clusters share different feature representations. Feature representations gradually adapt to individual classes in this way. For the localization task, class labels are predicted with VGG.

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For each image and each predicted class, candidate regions are proposed by employing a learned saliency map and edge boxes [c] with high recall rate. The classes are grouped in multiple clustered in a hierarchical way.

VGG and GoogleNet are finetuned for multiple times to adapt to different clusters. The fastest publicly available multi-GPU caffe code requires only 6 seconds for 20 iterations when mini-batch size is using GoogleNet using 4 TitanX is our strong support [d].

SenseTime Group Limited For object detection in video, we first employ CNN based on detectors to detect and classify candidate regions on individual frames.

Detectors are based on the combination of two types of models, i. The temporal information is employed propagating detection scores.

Major challenges in software engineering

Score propagation is based on optical flow estimation and CNN based tracking [c]. Spatial and temporal pool along tracks is employed. Video context is also used to rescore candidate regions. Then we extract 12 different regions' CNN features for each proposal, and concatenate them as part of final object representation as the method in [1].

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In detail, region CNN is a layer VGG-version SPPnet modified with some random initialization, a one-level pyramid, very leaky ReLU and a hand-designed two-level label tree for structurally sharing knowledge to defeat class imbalance.

Single model but another three deformation layers are also fused for capturing repeated patterns in just 3 regions each proposal. Semantic segmentation-aware CNN extension in [1] is also used and here segmentation model is a mixed model of deconvnet and CRF.

Second, we use RPN[2] with convolution layers initialized by the pre-trained RCNN above to obtain at least proposals each instance.As the Global Grand Challenges Summit draws nearer, teams of students from schools across the country came to Washington, DC to compete in the Student Day Business Plan Competition.

Scientists have reconnected a paralyzed man's brain to his body by bypassing the damaged spinal cord, marking the first time a paralyzed patient has been able to regain movement in his own body by using signals that originated within the brain. 1. Introduction. Software development has been characterized by harmful disconnects between important activities, such as planning, analysis, design and programming.

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More Lithography/Mask Challenges Experts at the Table, part 2: Options include everything from multi-patterning to high-NA EUV, multi-beam and e-beam—but none of them is perfect. Chad Hawkinson is senior vice president, engineering, product design and technology at IHS Markit.

In this interview, he speaks about the major issues facing engineering executives and pathways to addressing those challenges.

Grand Challenges - 14 Grand Challenges for Engineering