Reviewerly/exHarmony huggingface dataset:
Constrained Evolutionary Piecemeal Training to Design Convolutional Neural Networks

Neural Architecture Search (NAS), which automates the discovery of efficient neural networks, has demonstrated substantial potential in achieving state of the art performance in a variety of domains such as image classification and language understanding. In most NAS techniques, training of a neural network is considered a separate task or a performance estimation strategy to perform the architecture search. We demonstrate that network architecture and its coefficients can be learned together by unifying concepts of evolutionary search within a population based traditional training process. The consolidation is realised by cleaving the training process into pieces and then put back together in combination with evolution based architecture search operators. We show the competence and versatility of this concept by using datasets from two different domains, CIFAR-10 for image classification and PAMAP2 for human activity recognition. The search is constrained using minimum and maximum bounds on architecture parameters to restrict the size of neural network from becoming too large. Beginning the search from random untrained models, it achieves a fully trained model with a competent architecture, reaching an accuracy of 92.5% and 94.36% on CIFAR-10 and PAMAP2 respectively.
Parallax Free Registration for Augmented Reality Optical See-Through Displays in the Peripersonal Space

Egocentric augmented reality (AR) interfaces are quickly becoming a key asset for assisting high precision activities in the peripersonal space in several application fields. In these applications, accurate and robust registration of computer-generated information to the real scene is hard to achieve with traditional Optical See-Through (OST) displays given that it relies on the accurate calibration of the combined eye-display projection model. The calibration is required to efficiently estimate the projection parameters of the pinhole model that encapsulate the optical features of the display and whose values vary according to the position of the user's eye. In this article, we describe an approach that prevents any parallax-related AR misregistration at a pre-defined working distance in OST displays with infinity focus; our strategy relies on the use of a magnifier placed in front of the OST display, and features a proper parameterization of the virtual rendering camera achieved through a dedicated calibration procedure that accounts for the contribution of the magnifier. We model the registration error due to the viewpoint parallax outside the ideal working distance. Finally, we validate our strategy on a OST display, and we show that sub-millimetric registration accuracy can be achieved for working distances of ±100 mm around the focal length of the magnifier.
Optimal Design of Fuzzy Systems Using Differential Evolution and Harmony Search Algorithms with Dynamic Parameter Adaptation

This paper presents a study of two popular metaheuristics, namely differential evolution (DE) and harmony search (HS), including a proposal for the dynamic modification of parameters of each algorithm. The methods are applied to two cases, finding the optimal design of a fuzzy logic system (FLS) applied to the optimal design of a fuzzy controller and to the optimization of mathematical functions. A fuzzy logic controller (FLC) of the Takagi–Sugeno type is used to find the optimal design in the membership functions (MFs) for the stabilization problem of an autonomous mobile robot following a trajectory. A comparative study of the results for two modified metaheuristic algorithms is presented through analysis of results and statistical tests. Results show that, statistically speaking, optimal fuzzy harmony search (OFHS) is better in comparison to optimal fuzzy differential evaluation (OFDE) for the two presented study cases.
Towards deep clustering of human activities from wearables

Our ability to exploit low-cost wearable sensing modalities for critical human behaviour and activity monitoring applications in health and wellness is reliant on supervised learning regimes; here, deep learning paradigms have proven extremely successful in learning activity representations from annotated data. However, the costly work of gathering and annotating sensory activity datasets is labor intensive, time consuming and not scalable to large volumes of data. While existing unsupervised remedies of deep clustering leverage network architectures and optimization objectives that are tailored for static image datasets, deep architectures to uncover cluster structures from raw sequence data captured by on-body sensors remains largely unexplored. In this paper, we develop an unsupervised end-to-end learning strategy for the fundamental problem of human activity recognition (HAR) from wearables. Through extensive experiments, including comparisons with existing methods, we show the effectiveness of our approach to jointly learn unsupervised representations for sensory data and generate cluster assignments with strong semantic correspondence to distinct human activities.
Improving Cuckoo Search: Incorporating Changes for CEC 2017 and CEC 2020 Benchmark Problems

Cuckoo search (CS) is a highly competitive single objective optimization technique.The algorithm has been widely applied in various diverse application domains and has been found to be efficient in solving various real-life problems.In the present work, we have proposed a new enhanced version of CS algorithm and tested its performance on recently proposed CEC 2017 and CEC 2020 benchmark test problems.The proposed algorithm has been named as CSsin and it employs four major modifications, i) new techniques for global and local search are devised, ii) dual search strategy is followed to enhance exploration and exploitation properties of CS algorithm, iii) a linearly decreasing switch probability has been used to add a balance between local and global search, and iv) linearly decreasing population size is used to reduce the computational burden.Apart from these modifications, the division of iterations has been employed as a further modification.The CSsin algorithm has been tested on IEEE CEC 2017 and CEC 2020 benchmark test problems having various dimension sizes and a comparative study has been performed with respect to stateof-the-art optimization algorithms for single objective bound constraint optimization problems.The results of statistical significance test affirm the competitiveness of the proposed algorithm with respect to state-of-the-art techniques.
Fairness-Efficiency Scheduling for Cloud Computing With Soft Fairness Guarantees

Fairness and efficiency are two important metrics for users in modern data center computing system. Due to the heterogeneous resource demands of CPU, memory, and network I/O for users’ tasks, it cannot achieve the strict 100 percent fairness and the maximum efficiency at the same time. Existing fairness-efficiency schedulers (e.g., Tetris) can balance such a tradeoff elastically by relaxing fairness constraint for improved efficiency using the knob. However, their approaches are <i>unaware</i> of fairness degradation under different knob configurations, which makes several drawbacks. First, it cannot tell how much <i>relaxed</i> fairness can be guaranteed given a knob value. Second, it fails to meet several essential properties such as sharing incentive. To address these issues, we propose a new fairness-efficiency scheduler, <i>QKnober</i> , to balance the fairness and efficiency elastically and flexibly using a tunable fairness knob. QKnober is a <i>fairness-sensitive</i> scheduler that can maximize the system efficiency while guaranteeing the <inline-formula><tex-math notation="LaTeX">$\theta$</tex-math></inline-formula> -soft fairness by modeling the whole allocation as a combination of <i>fairness-oriented</i> allocation and <i>efficiency-oriented</i> allocation. Moreover, QKnober satisfies fairness properties of sharing incentive, envy-freeness and pareto efficiency given a proper knob value. We have implemented QKnober in YARN and evaluated it using both testbed and simulated experiments. The results show that QKnober outperforms its alternatives DRF and Tetris by 31.2 and 4.5 percent, respectively.
Fee-Free Pooled Mining for Countering Pool-Hopping Attack in Blockchain

The pool-hopping attack casts down the expected profits of both the mining pool and honest miners in Blockchain. The mainstream countermeasures, namely PPS (pay-per-share) and PPLNS (pay-per-last-N-share), can hedge pool hopping but need to charge miners some fees when they join in a pool. Obviously, the higher fee charged, the higher cost of joining the pool, the less motivation of a miner to mine in the pool. In this article, we apply the zero-determinant (ZD) theory to design a novel pooled mining which offers an incentive mechanism for motivating miners not to switch in pools strategically by economic means without fee charged. In short, the proposed pooled mining has three unique features: 1) fee-free. No fee is charged if the miner does not hop, 2) wide applicability. It can be employed in both prepaid and postpaid mechanisms, and 3) fairness. Even can dominate the game with any miner, a pool has to cooperate when a miner does not hop among pools, implying that the pool cannot squeeze the honest miners financially. The fairness of our scheme makes it have long-term sustainability. Both theoretical analyses and numerical simulations demonstrate the effectiveness of our scheme.
News Classification for Identifying Traffic Incident Points in a Spanish-Speaking Country: A Real-World Case Study of Class Imbalance Learning

‘El Diario de Juárez’ is a local newspaper in a city of 1.5 million Spanish-speaking inhabitants that publishes texts of which citizens read them on both a website and an RSS (Really Simple Syndication) service. This research applies natural-language-processing and machine-learning algorithms to the news provided by the RSS service in order to classify them based on whether they are about a traffic incident or not, with the final intention of notifying citizens where such accidents occur. The classification process explores the bag-of-words technique with five learners (Classification and Regression Tree (CART), Naïve Bayes, kNN, Random Forest, and Support Vector Machine (SVM)) on a class-imbalanced benchmark; this challenging issue is dealt with via five sampling algorithms: synthetic minority oversampling technique (SMOTE), borderline SMOTE, adaptive synthetic sampling, random oversampling, and random undersampling. Consequently, our final classifier reaches a sensitivity of 0.86 and an area under the precision-recall curve of 0.86, which is an acceptable performance when considering the complexity of analyzing unstructured texts in Spanish.
PublicGarbageNet : A Deep Learning Framework for Public Garbage Classification

Intelligent garbage classification is an important technique for garbage harmless, reducing and recycling treatment. In this paper, we propose a public garbage classification algorithm based on the CNN architecture, namely PublicGarbageNet. The proposed algorithm is a multi-task classification algorithm in which one task identifies four major categories of domestic garbage, and the other task achieves recognition of 10 subclasses garbage. The two classification tasks are related to each other, and the joint loss function is helpful to improve the accuracy of garbage recognition. Considering the fact that the existing garbage datasets are incomplete in classes and small in quantity, we constructed a new pubic garbage dataset including 10 subclasses and a total of 10624 images. In order to obtain better performance, systematically studies such as backbone optimization selection, data augmentation, learning rate optimization, and label smoothing have been made, and finally the accuracy of the optimized model reaches 96.35%.
Intelligent system of English composition scoring model based on improved machine learning algorithm

The online English teaching system has certain requirements for the intelligent scoring system, and the most difficult stage of intelligent scoring in the English test is to score the English composition through the intelligent model. In order to improve the intelligence of English composition scoring, based on machine learning algorithms, this study combines intelligent image recognition technology to improve machine learning algorithms, and proposes an improved MSER-based character candidate region extraction algorithm and a convolutional neural network-based pseudo-character region filtering algorithm. In addition, in order to verify whether the algorithm model proposed in this paper meets the requirements of the group text, that is, to verify the feasibility of the algorithm, the performance of the model proposed in this study is analyzed through design experiments. Moreover, the basic conditions for composition scoring are input into the model as a constraint model. The research results show that the algorithm proposed in this paper has a certain practical effect, and it can be applied to the English assessment system and the online assessment system of the homework evaluation system algorithm system.
Single image super-resolution with attention-based densely connected module

Benefited from the abundant features provided by the dense connection block, the densely connected-based super-resolution network has achieved superior performance in the single image super-resolution (SISR) task. However, the abundant features also introduce redundant and conflicting information, resulting in longer training time and unsatisfied image reconstruction results. To solve this problem, we propose an attention-based densely connected module (DAM). DAM consists of two parts: channel attention module (CAM) and dense connection block (DB). CAM is placed at the front of each DB and gives different weights of each channel from received features for suppressing redundant responses. Based on DAM, we propose an Attention-based Densely Connected Network (ADSRNet) for SISR, and explore the effectiveness of DAM on other densely connected-based super-resolution networks. Extensive experiments are performed on commonly-used super-resolution benchmarks. Qualitative and quantitative results demonstrate the effectiveness of our method.
Beyond Covariance: SICE and Kernel Based Visual Feature Representation

The past several years have witnessed increasing research interest on covariance-based feature representation. Originally proposed as a region descriptor, it has now been used as a general representation in various recognition tasks, demonstrating promising performance. However, covariance matrix has some inherent shortcomings such as singularity in the case of small sample, limited capability in modeling complicated feature relationship, and a single, fixed form of representation. To achieve better recognition performance, this paper argues that more capable and flexible symmetric positive definite (SPD)-matrix-based representation shall be explored, and this is attempted in this work by exploiting prior knowledge of data and nonlinear representation. Specifically, to better deal with the issues of small number of feature vectors and high feature dimensionality, we propose to exploit the structure sparsity of visual features and exemplify sparse inverse covariance estimate as a new feature representation. Furthermore, to effectively model complicated feature relationship, we propose to directly compute kernel matrix over feature dimensions, leading to a robust, flexible and open framework of SPD-matrix-based representation. Through theoretical analysis and experimental study, the proposed two representations well demonstrate their advantages over the covariance counterpart in skeletal human action recognition, image set classification and object classification tasks.
UPIC

The Sussex-Huawei Locomotion-Transportation (SHL) Challenge 2020 was an open competition of recognizing eight different activities that had been performed by three individual users and participants of this competition were tasked to classify these eight different activities with modes of locomotion and transportation. This year's data was recorded with a smartphone which was located in four different body positions. The primary challenge was to make a user-invariant as well as position-invariant classification model. The train set consisted of data from only user-1 with all positions whereas the test set consisted of data from user 2 and 3 with unspeicified sensor position. Moreover, a small validation with the same charecteristics of the test set was given to validate the classifier. In this paper, we have described our (Team Red Circle) approach in which we have used previous year's challenge data as well as this year's provided data to make our training dataset and validation set that have helped us to make our model generative. In our approach, we have extracted various types of features to make our model user independent and position invariant, we have applied Random Forest classifier which is a classical machine learning algorithm and achieved 92.69% accuracy on our customized train set and 77.04% accuracy on our customized validation set.
Optimizing DNN computation graph using graph substitutions

Deep learning has achieved great success in various real-world applications. As deep neural networks (DNNs) are getting larger, the inference and training cost of DNNs increases significantly. Since one round of inference or one iteration in the training phase of a DNN is typically modeled as a computation graph, existing works propose to optimize computation graphs by performing a sequence of functionally equivalent graph substitutions, leading to higher inference and training efficiency. In this work, we formally define the Optimizing Computation Graph using Graph Substitutions (OCGGS) problem, and prove it to be NP-hard and Poly-APX-complete. We develop two exact and efficient methods to the OCGGS problem. The pruning-based algorithm eliminates the examination of redundant graph substitution sequences, and the dynamic programming with pruning algorithm makes use of the explored graph substitutions. To further speed up the search process, we propose a sampling heuristic which is effective to optimize complex computation graphs with polynomial time and space complexity. Extensive experiments on various DNN architectures and sizes are conducted to verify the effectiveness and efficiency of our proposed solutions compared with existing techniques.
A data augmentation method for human action recognition using dense joint motion images

With the development of deep learning and neural network techniques, human action recognition has made great progress in recent years. However, it remains challenging to analyse temporal information and identify human actions with few training samples. In this paper, an effective motion image called a dense joint motion image (DJMI) was proposed to transform an action to an image. Our method was compared with state-of-the-art methods, and its contributions are mainly reflected in three characteristics. First, in contrast to the current classic joint trajectory map (JTM), every pixel of the DJMI is useful and contains essential spatio-temporal information. Thus, the input parameters of the deep neural network (DNN) are reduced by an order of magnitude, and the efficiency of action recognition is improved. Second, each frame of an action video is encoded as an independent slice of the DJMI, which avoids the information loss caused by action trajectory overlap. Third, by using DJMIs, proven algorithms for graphics and images can be used to generate training samples. Compared with the original image, the generated DJMIs contain new and different spatio-temporal information, which enables DNNs to be trained well on very few samples. Our method was evaluated on three benchmark datasets, namely, Florence-3D, UTKinect-Action3D and MSR Action3D. The results showed that our method achieved a recognition speed of 37 fps with competitive accuracy on these datasets. The time efficiency and few-shot learning capability of our method enable it to be used in real-time surveillance.
An efficient variance estimator of AUC and its applications to binary classification

Abstract The area under the ROC (receiver operating characteristic) curve, AUC, is one of the most commonly used measures to evaluate the performance of a binary classifier. Due to sampling variation, the model with the largest observed AUC score is not necessarily optimal, so it is crucial to assess the variation of AUC estimate. We extend the proposal by Wang and Lindsay and devise an unbiased variance estimator of AUC estimate that is of a two‐sample U‐statistic form. The proposal can be easily generalized to estimate the variance of a K ‐sample U‐statistic ( K ≥ 2 ). To make our developed variance estimator more applicable, we employ a partition‐resampling scheme that is computationally efficient. Simulation studies suggest that the developed AUC variance estimator yields much better or comparable performance to jackknife and bootstrap variance estimators, and computational times that are about 10 to 30 times faster than the times of its counterparts. In practice, the proposal can be used in the one‐standard‐error rule for model selection, or to construct an asymptotic confidence interval of AUC in binary classification. In addition to conducting simulation studies, we illustrate its practical applications using two real datasets in medical sciences.
Context-aware network for RGB-D salient object detection

Convolutional neural networks (CNNs) have shown unprecedented success in object representation and detection. Nevertheless, CNNs lack the capability to model context dependencies among objects, which are crucial for salient object detection. As the long short-term memory (LSTM) is advantageous in propagating information, in this paper, we propose two variant LSTM units for the exploration of contextual dependencies. By incorporating these units, we present a context-aware network (CAN) to detect salient objects in RGB-D images. The proposed model consists of three components: feature extraction, context fusion of multiple modalities and context-dependent deconvolution. The first component is responsible for extracting hierarchical features in color and depth images using CNNs, respectively. The second component fuses high-level features by a variant LSTM to model multi-modal spatial dependencies in contexts. The third component, embedded with another variant LSTM, models local hierarchical context dependencies of the fused features at multi-scales. Experimental results on two public benchmark datasets show that the proposed CAN can achieve state-of-the-art performance for RGB-D stereoscopic salient object detection.
Enhancing information technology for value added across economic sectors in Sub-Saharan Africa✰

This study investigates how enhancing information and communication technology (ICT) affects value added across sectors in 25 countries in Sub-Saharan Africa using data for the period 1980–2014. The empirical evidence is based on the Generalised Method of Moments. The following findings are established. First, the enhancement of mobile phone and internet penetrations respectively have net negative effects on value added to the agricultural and manufacturing sectors. Second, enhancing ICT (i.e. mobile phone penetration and internet penetration) overwhelmingly has positive net effects on value added to the service sector. From an extended analysis, enhancing ICT in the agricultural and manufacturing sectors should exceed certain thresholds for value added, notably: 114.375 of mobile phone penetration per 100 people for added value in the agricultural sector and 22.625 of internet penetration per 100 people for added value in the manufacturing sector.