Reviewerly/exHarmony huggingface dataset:
Evolving Optimal Convolutional Neural Networks

Among the different Deep Learning (DL) models, the deep Convolutional Neural Networks (CNNs) have demonstrated impressive performance in a variety of image recognition or classification tasks. Although CNNs do not require feature engineering or manual extraction of features at the input level, yet designing a suitable CNN architecture necessitates considerable expert knowledge involving enormous amount of trial-and-error activities. In this paper we attempt to automatically design a competitive CNN architecture for a given problem while consuming reasonable machine resource(s) based on a modified version of Cartesian Genetic Programming (CGP). As CGP uses only the mutation operator to generate offsprings it typically evolves slowly. We develop a new algorithm which introduces crossover to the standard CGP to generate an optimal CNN architecture. The genotype encoding scheme is changed from integer to floating-point representation for this purpose. The function genes in the nodes of the CGP are chosen as the highly functional modules of CNN. Typically CNNs use convolution and pooling, followed by activation. Rather than using each of them separately as a function gene for a node, we combine them in a novel way to construct highly functional modules. Five types of functions, called ConvBlock, average pooling, max pooling, summation, and concatenation, were considered. We test our method on an image classification dataset CIFAR10, since it is being used as the benchmark for many similar problems. Experiments demonstrate that the proposed scheme converges fast and automatically finds the competitive CNN architecture as compared to state-of-the-art solutions which require thousands of generations or GPUs involving huge computational burden.
A Coarse to Fine Framework for Multi-organ Segmentation in Head and Neck Images

Radiotherapy is widely used in the treatment of head and neck cancer. Due to the harmfulness of radiation, it is necessary to protect our healthy organs during the radiotherapy. Therefore, the accurate delineation of diseased region and surrounding healthy organs is the precondition for doctors to make the radiation plan. In real life, the delineation work is usually done manually. It is time-consuming and requires high professional skill. A fast and accurate organ segmentation method can greatly improve the efficiency of treatment. Most CT image datasets are 3D volumes and each volume can be divided into a series of 2D slice images. For multi-organ segmentation task, how to generate the stable organ features from CT images is still the plagued problem. For 2D framework, which processes the images slice by slice, the network cannot learn the correlation between continuous slices. It will lead to the loss of spatial information. For 3D framework, which processes the images volume by volume, the patch training is commonly used to against the massive increase of network parameters. The 3D patch will limit the maximum reception field of the network. For the organ, which is larger than the patch size, it is easy to lose global information. To solve these incompatible problems, we proposed a coarse to fine framework to take advantage of both 2D framework and 3D framework. The multi-view coarse network is designed to generate the organ probability maps and the coarse segmentation mask in 2D case. The organ volumes are extracted with the probability maps. These organ volumes are sent to the organ-based fine network to refine the mask of each organ in 3D case. Our proposed method is tested on the Head and Neck Automatic Segmentation Challenge datasets in 2015 and predict for 9 different organs. The result show that our framework performs the lowest error range for most organs and three of them achieve the top evaluation results in comparison with existing methods. Contribution-The main contribution of this paper is to propose a novel two-stage, Coarse to Fine, framework for multi-organ segmentation and verify its effectiveness in head and neck CT images.
A Systematic Review on Various Techniques on Image Segmentation

In the present situation, image processing is one of the huge developing fields. It is a strategy which is ordinarily used to improve raw image which is collected from different assets. It is a sort of sign processing. The image segmentation is considered one of the most essential processes in the production of images. Some of these methods use only the histogram of the gray scale, some use spatial information, while others use conceptual approaches of the fuzzy set. In noisy environments, these methods are not appropriate. Some work has been done using the method of MRF, which is robust to noise but involves computing. This paper gives an outline of image processing techniques. The fundamental concern of this paper is to characterize different methods utilized in various periods of image processing.
The Software Development Process – why it has failed us and a different approach going forward

Over time, the problems tackled by software have exponentially grown in scope. At the same time, our development patterns fail to support the complexity of our emerging systems. The traditional pattern, commonly termed the “Waterfall Method”, often results in underperforming systems delivered over cost and over schedule, if delivered at all. We compare traditional approaches with the modern Agile approach. Metrics unequivocally substantiate the superiority of the Agile approach to deliver what the stakeholder actually needs. Central to the hypothesis is stakeholder interaction and willingness to evolve requirements as understanding of the objective system clarifies throughout the development cycle. Also developed is the roadmap to transition an organization for agility.
Pickpocketing Recognition in Still Images

Human activity recognition (HAR) is a challenging topic in the computer vision field. Pickpocketing is a type of human criminal actions. It needs extensive research and development for detection. This paper researches how it’s possible of pickpocketing recognition in still images. This paper takes consideration both of classification and detection. We develop our models from state-of-art pre-trained models: VGG16, ResNet50, ResNet101, and ResNet152. Moreover, we also include a convolutional block attention module (CBAM [27]) in the model. The attention mechanism enhances model performances by focusing on informative features. For classification, the highest accuracy (89%) is ResNet152 with CBAM [27] (ResNet152+CBAM). We also examine pickpocketing detection on RetinaNet [14] and YOLOv.3 [34]. The mean average precision (mAP) of pickpocketing detection is consistent with Redmon et al. [34]. RetinaNet’s precision (80 mAP) is higher than YOLOv.3 (78 mAP), but YOLOv.3 is much faster detection. ResNet152+CBAM model detection on RetinaNet approach provides the highest mAP. However, it is much slower detection than YOLOv.3 (only 10 ms). This paper proves that It is possible to implement pickpocketing on still images in a reliable time and with outstanding accuracy. This proposed model possibly apply to the other HAR tasks.
Challenges in Certifying Small-Scale (IoT) Hardware Random Number Generators

Abstract This chapter focuses on the testing and certification of Random Number Generators (RNG). Statistical testing is required to identify whether sequences produced by RNG demonstrate non-random characteristics. These can include structures within their output, repetition of sequences, and any other form of predictability. Certification of computer security systems draws on such evaluations to determine whether a given RNG implementation contributes to a secure, robust security system. Recently, small-scale hardware RNGs have been targeted at IoT devices, especially those requiring security. This, however, introduces new technical challenges; low computational resources for post-processing and evaluation of on-board RNGs being just two examples. Can we rely on the current suite of statistical tests? What other challenges are encountered when evaluating RNG?
Understanding Effects of Architecture Design to Invariance and Complexity in Deep Neural Networks

This article studies the role of architecture design, i.e. choice of the number of nodes at each hidden layer, in deep neural networks (DNNs). We give a theoretical explanation that invariance and complexity of a DNN are determined by the design of its architecture. To be more specific, for DNNs with the rectified linear activation function, we prove that the variations of gradients become the largest when the bottleneck layer, the layer with the fewest nodes, changes its activation pattern and the gap of the variations of gradients of the bottleneck layer to those of the other layers is getting larger as the differences of node counts between the bottleneck layer and the other layers become larger. That is, most large variations of the function made by a DNN are concentrated on the decision boundaries corresponding to the activation patterns of the bottleneck layer. An important implication is that invariance and complexity can be improved simultaneously, which is a key to success of DNNs for many artificial intelligence domains, by designing the highest layer as the bottleneck layer. Various experiments are carried out to validate our theoretical result and its by-products.
Homeostasis-Inspired Continual Learning: Learning to Control Structural Regularization

Learning continually without forgetting might be one of the ultimate goals for building artificial intelligence (AI). However, unless there are enough resources equipped, forgetting knowledge acquired in the past is inevitable. Then, we can naturally pose a fundamental question about how to control what knowledge and how much of it to forget to improve the overall accuracy. To give a clear answer to it, we propose a novel trainable network termed homeostatic meta-model. The proposed neuromorphic framework is a natural extension of the conventional concept Synaptic Plasticity (SP) for further optimizing the accuracy of continual learning. In the preceding works on SP and its variations, though they seek important network parameters for structural regularization, they care less about the intensity of regularization (IoR). Per contra, this work reveals that a careful selection of IoR during continual training can remarkably improve the accuracy of tasks. The proposed method balances IoR between newly learned knowledge and the previously-acquired ones rather than biasing it to a specific task or evenly balancing. To obtain effective and optimal IoRs for the real-time continual learning circumstances, we propose a homeostasis-inspired meta learning architecture. The proposed meta-model automatically controls the IoRs by capturing important parameters from the previous tasks and the current learning direction. We provide experimental results considering various types of continual learning tasks showing that the proposed method notably outperforms the conventional methods in terms of learning accuracy and knowledge forgetting. We also show that the proposed method is relatively stable and robust compared to the existing SP-based methods. Furthermore, the IoR generated by our model interestingly appears to be proactively controlled within a specific range, which resembles a negative feedback mechanism of homeostasis in synapses.
Object Detection on Aerial Image by Using High-Resolutuion Network

To detect trespassing in images captured by drones for water conservancy facilities inspection, this paper proposes a method that adapts High-Resolution Net to reserve high resolution features for improving detecting results. To detect trespassing target with small scale, this method parallels low-resolution and high-resolution conventical feature maps to reserve high-resolution features, besides that multi-scale fusions are conducted to enhance feature maps with different resolutions. Compare to Faster R-CNN, proposed method achieves 1.7% higher mAP on small targets.
Keynote I: Quantum Annealing and Its Application to Real World

Summary form only given, as follows. The complete presentation was not made available for publication as part of the conference proceedings. Quantum annealing is a generic solver of combinatorial optimization problem and is implemented by a hardware known as the D-Wave quantum annealer. In this talk, we introduce future directions of its application, while showing several practical applications, namely the control of the automated guided vehicles in factory and evacuation system from disaster after big earthquake etc.
Efficiently Coevolving Deep Neural Networks and Data Augmentations

Designing large deep learning neural networks by hand requires tuning large sets of method parameters, requiring trial and error testing and domain specific knowledge. Neuroevolution methods such as CoDeepNeat (CDN), based on Neuroevolution of Augmenting Topotogies (NEAT), apply evolutionary algorithms to automate deep neural network parameter optimisation. This paper presents and demonstrates various novel beneficial extensions to the CDN method, including new genotypic speciation mechanisms, special mappings in deep neural network encodings, as well as evolving Data Augmentation schemes. Results indicate that these CDN method variants yield significant task-performance benefits over the benchmark CDN method when evaluated on a popular public image recognition data-set.
Fast and Safe Path Planning Method for an Autonomous Smart Walker

Autonomous Smart walkers are assistive devices that promote locomotion assistance and social interaction for people with lower limb impairments and provide a safe navigation. The Probabilistic Foam method (PFM) is a sampling-based path planner that uses structures called bubbles to compute obstacle-free paths with high clearance. These bubbles propagate through the free space and ensure safe regions, meeting the safety requirements in maneuvering. The Goal-biased Probabilistic Foam (GBPF) is a variant of the PFM that improves the propagation strategy to obtain shorter paths in reduced time. In this paper, we propose a new variant of the GBPF, called Improved Goal-biased probabilistic Foam (IGBPF), to improve the planning execution time. In addition, we present the modelling of a new bubble for a smart walker, using information from the workspace. Some simulated experiments were performed using PFM, GBPF, and IGBPF to plan safe paths for the smart walker robot considering two different maps, where our approach achieved satisfactory results related to safety, execution time and path length.
Facial Features in Stylized Images Typically Used for Face Recognition and Comparison of Impression Between Stylized Images and AI Images

In a caricature, a person’s character is often exaggerated. While the stylized picture may not look exactly like the model, it can make one feel familiar and attached to it. In this research, we compare our impressions between stylized images and the images created with AI. In addition, the tendency people have in recognizing the characteristics of a human face to create a stylized image is also investigated.
Automatic Classification of Low-Angle Fuze-Quick Craters Using Deep Learning

Crater Analysis is a technique used for precautionary and investigative purpose, especially in confirming suspected locations of hostile fire and detecting the presence of enemy ammunition. Specifically, investigators have manually examined terrain disturbances due to blast craters, which are evidently caused by artillery fire. Although this manual process has been helpful in the identification of possible origins of fire, it is tedious and prone to errors. With the recent advancements in the fields of computer vision and machine learning (ML), this work investigates the automation of low-angle fuze-quick craters (LaFCs) classification using satellite imagery of the Ukranian border obtained from google earth. Due to limited volume of available satellite images (of LaFCs), we hereby propose the use of off-the-shelf ImageNet pretrained models (Resnet50, Resnet101 and ResNet152) for the automatic classification of the LaFCs. Thus, learned filters from these deep neural networks are used to extract features from the satellite images. These features are then fed into support vector machine (SVM) for classification. Promising results are obtained from our experiments, showing that it is possible to use ML to identify LaFCs. To the best of our knowledge, this is the first time classification of these type of craters has been investigated.
Program Recommendation System for Students or Coder through View Histories and Feedback Systems

Now a day's most of the students/coders tries to get help of social media for most of their problems. So the recommendation system is also growing very fast for students/coders also. So these days social media also takes helps of recommendation system to catch the attention of every user. Some drawbacks are there in the existing recommendation systems and insufficient support to the current situations. These issues we try to by proposing the Hybrid FB (Feed Back) technique takes help of current MI system. Existing system does not provide accuracy and flexibility so to address this issue we proposed Hybrid FB technique. The methods used by existing recommendation systems are failed to achieve the accuracy and flexibility and proper recommendations. To overcome these problems recently MI method extends ROSE but it doesn't consider satisfaction of the end user which gives scope for improvement in accuracy as per end users requirement. Here we are presenting HFB (Hybrid FB) method in which we are improving the accuracy by taking relevant feedback over the time again and again and also maintains log of feedbacks based on end users. Our system uses this feedback because of which it can generate more precise recommendation next time for same query with less time for others.
Rethinking the Rotation Invariance of Local Convolutional Features for Content-Based Image Retrieval

Recently, local features computed using convolutional neural networks (CNNs) show good performance to image retrieval. The local convolutional features obtained by the CNNs (LC features) are designed to be translation invariant, however, they are inherently sensitive to rotation perturbations. This leads to miss-judgements in retrieval tasks. In this work, our objective is to enhance the robustness of LC features against image rotation. To do this, we conduct a thorough experimental evaluation of three candidate anti-rotation strategies (in-model data augmentation, in-model feature augmentation, and post-model feature augmentation), over two kinds of rotation attack (dataset attack and query attack). In the training procedure, we implement a data augmentation protocol and network augmentation method. In the test procedure, we develop a local transformed convolutional (LTC) feature extraction method, and evaluate it over different network configurations. We end up a series of good practices with steady quantitative supports, which lead to the best strategy for computing LC features with high rotation invariance in image retrieval.
Voice-Based Gender Identification Using qPSO Neural Network

Gender identification is a classic problem. AJha, Ruchi considerableSaxena, Anvita amountSingh, Jodh of research has been carried out in this field. Gender identification has many applications in various fields, especially in marketing, online researchKhanna, Ashish andGupta, Deepak media industry. This work proposes a novel method of quantum-inspired Particle Swarm Optimised neural network for voice-based genderJain, Prerna identification. ToReddy Allugunti, Viswanatha the best of our knowledge, the above-mentioned approach has been presented for the first time in this paper. In the presented work, statistical extracted features are fed into a qPSO optimised feed-forward four-layered neural network. The data set was cleaned and preprocessed using principal component analysis and was then fed into the network, which gave us an optimal accuracy of 91.15%. The proposed approach performed significantly better than the classical Particle Swarm Optimisation approach.
Remittances and manufacturing sector growth in sub-Saharan Africa

This paper analyzes the link between remittances and the growth of manufacturing value-added (MVA) and explores whether the quality of institutions and exchange rate policy matter for the dynamics of the relationship in sub-Saharan African (SSA) countries. The findings suggest that when the level of financial development is accounted for, remittances have a positive impact on MVA. The standalone effect of remittances on MVA is, however, negative. The results also suggest that there is a positive influence of exchange rate flexibility on the dynamics between remittances and MVA. Furthermore, improvement in the business environment seems to matter for the performance of the manufacturing sector, but it is not significant to the dynamics between remittances and MVA. The development of the financial sector, on that account, emerges as an important factor influencing the impact of remittances on the growth of the manufacturing sector in SSA countries.
Architecture and Algorithm of Formation control of Multi-unmanned vehicles Based on Swarm intelligence

Abstract The main contents of this dissertation are the application of swarm intelligence to multi-unmanned vehicle system and the realization of cooperative control of multi-unmanned vehicle system. Firstly, this thesis introduces the research background and significance of multi-unmanned vehicle system and swarm intelligence. Secondly, we introduce the research contents of cooperative control of multi-unmanned vehicle system. Then the research status of cooperative control of multi-unmanned vehicle system in the domestic and overseas are introduced. Finally, this dissertation summarizes the mainstream types of architecture and algorithm of the multi-unmanned vehicle system.
Next syllables prediction system in Dzongkha using long short-term memory

Dzongkha typing is time-consuming. A word in Dzongkha is formed by either a single syllable or multiple syllables. A single syllable རྒྱུ (property) and multiple syllabic word ས་སྨུག་སྤུབས་སྤུབསཔ (cloudy) require 6 and 22 keypresses respectively. Similarly, most of the syllables and words require several keypresses. To date, the study on syllable prediction has not been done. Moreover, the lack of text corpus poses a challenge. The purpose of this study was to develop the next syllables prediction system to reduce keystrokes and typing-time. The proposed system takes a single syllable and predicts the next top five probable syllables. The best suitable syllable is selected to form a word and subsequently, a word predicts the next plausible syllables. The corpus was curated with different genres collected from the Dzongkha Development Commission of Bhutan and Kuensel online. The dataset consisted of 31,199 sentences and 222,844 syllables. Using the n-gram method, 195,998 sequences were generated from the dataset and comprised of 2,929 unique syllables. The text sequences were converted into vectors using the word embedding and trained with the variants of Recurrent Neural Networks. The single-layer Long Short-Term Memory with 128 memory cells obtained the best training accuracy of 78.33%.