Reviewerly/exHarmony huggingface dataset:
Evolving Efficient Deep Neural Networks for Real-time Object Recognition

While Deep Neural Networks (DNNs) achieve state-of-the-art performance in many fields, e.g., object recognition, they rely on deep networks with millions or even billions of parameters. Accelerating DNNs by reducing the parameters of DNNs is crucial for real-time object recognition. This paper presents an evolutionary approach to evolve efficient DNNs that can be run on Low-Performance Computing Hardware (LPCH) for real-time object recognition with the fastest possible speed and an accuracy of more than 95%. This approach achieves the goal by means of two design choices. First, NeuroEvolution of Augmenting Topologies (NEAT) is applied to evolve both weights and topology of DNNs starting from simple initial topology, which reduces the number of parameters of DNNs from millions to thousands. Second, we propose the novel fitness functions to further select the evolved DNNs for lower computation time, while maintaining high accuracy. We test the approach to evolve the efficient DNNs on the well-known benchmark MNIST dataset and the self-defined modular robots dataset. Furthermore, com-pared with most current studies, we not only evolve DNNs on the datasets but also implement the best evolved DNN on LPCH to recognize objects real-time in the real world. The experimental results show that the best evolved DNN recognizes the modular robots on a microcomputer, Raspberry Pi 3, with an accuracy of 95.6% and a speed of 5.3 fps. This work can be extended to achieve efficient DNNs for other real-time tasks. We published the source code <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> that was used to evolve the efficient DNNs, and the video <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> that the best evolved DNN was run on a Raspberry Pi 3 to recognize two modular robots simultaneously in the real world.
Virtual Big Data for GAN Based Data Augmentation

Researchers deal with the class imbalanced problem in many real-world applications and GAN based data augmentation is considered as an efficient approach to address this problem. GANs need a huge training data to generate efficient augmented data. However, the required sufficient training data is not available in many research areas. In this paper, we introduce a new concept called virtual big data to address this problem. We prove that, virtual big data can provide the GANs sufficient training data to generate efficient augmented data with less mode collapse and vanishing generator gradients problems. We show that, the curse of dimensionality which is considered as a negative factor in machine learning can play a positive role to solve vanishing generator gradients via making discriminator less perfect. First, we transform the training data from n dimensional space into m dimensional space where, m = c * n and c is concatenation factor. To do so, c different training instances are selected and concatenated to each other to form a c * n dimensional instance. Increasing the dimension of training data from n to c * n is key to increase the number of training instances from N to C(N, c). Transformed training data are called virtual big data since they differ original training instances in terms of size and dimension. Our experiments show that, V-GAN, a GAN trained by virtual big data can outperform standard GANs when it comes to deal with extremely scarce training data. Furthermore, V-GAN can outperform traditional oversampling techniques in terms of precision, F1 score and Area Under Curve (AUC) score.
Blockchain applications for healthcare

Healthcare systems control and monitor the health of patients with the help of advanced technologies. The advancement of these systems needs to incorporate an unequivocal spotlight on making these systems efficient. Blockchain and their inherent combination of consensus algorithms, distributed data storage, and secure protocols can be utilized to build robustness and reliability in these systems. Blockchain is the underlying technology behind bitcoins and it provides a de-centralized framework to validate transactions and ensure that they cannot be modified. By distributing the role of information validation across the network peers, blockchain eliminates the risks associated with a centralized architecture. It is the most secure validation mechanism that is efficient, and enables the provision of financial services, thereby giving users more freedom and power. This emerging technology provides internet users the capability to create value and authenticate the digital information. It has the capability to revolutionize a diverse set of business applications ranging from sharing economy to data management and prediction markets. In this paper, we present a survey of blockchain applications in healthcare. Healthcare systems play a very crucial role in people's life and there are diverse ways by which it can benefit from the blockchain technology and have been discussed in the chapter. The survey results demonstrate that Blockchain has distinct advantages for healthcare applications as compared to other applications. The benefits of blockchain can be further amplified by using a light-weight distributed ledger system like IOTA.
Asymmetric coupling between two quantum emitters

We study a prototypical model of two coupled two-level systems, where the competition between coherent and dissipative coupling gives rise to a rich phenomenology. In particular, we analyze the case of asymmetric coupling, as well as the limiting case of chiral (or one-way) coupling. We investigate various quantum optical properties of the system, including its steady state populations, power spectrum, and second-order correlation functions, and outline the characteristic features which emerge in each quantity as one sweeps through the nontrivial landscape of effective complex couplings. Most importantly, we reveal instances of population trapping, unexpected spectral features and strong photon correlations.
PcapGAN: Packet Capture File Generator by Style-Based Generative Adversarial Networks

After the advent of GAN technology, many varied models have been studied and applied to various fields such as image and audio. However, in the field of cyber data, which has the same issue of data shortage, the research on data augmentation is insufficient. To solve this problem, we propose PcapGAN that can augment pcap data, a kind of network data. The proposed model includes an encoder, a data generator, and a decoder. The encoder subdivides network data into four parts. The generator generates new data for each part of the data. The decoder combines the generated data into realistic network data. We demonstrate the similarity between the generated data and original data, and validation of the generated data by increased performance of intrusion detection algorithms.
“Breaking Free” from the Frame: International Human Rights and the <i>New York Times’</i> 360-Degree Video Journalism

This article conducts a qualitative textual analysis of the New York Times' 360-degree news reports that focus on international human rights issues, posing the following research questions: (1) How do the form and content of the New York Times' 360-degree videos potentially help to construct the vividness and interactivity that virtual reality scholars say will contribute to a greater sense of telepresence? (2) In what ways do the form and content of the New York Times' 360-degree video news reports reflect the tension between traditional notions of journalistic authority on the one hand, and the need to engage—on both ethical and economic levels—with news audiences on the other hand? The article will show that the news industry's deep ambivalence toward giving up control of the journalistic narrative in the digital age is coded into the visual and aural structures of the videos, raising questions about the celebratory discourse on agency and interpersonal engagement with distant suffering.
Applicability of Machine Learning Methods on Mobile App Effort Estimation: Validation and Performance Evaluation

Software cost estimation is one of the most crucial tasks in a software development life cycle. Some well-proven methods and techniques have been developed for effort estimation in case of classical software. Mobile applications (apps) are different from conventional software by their nature, size and operational environment; therefore, the established estimation models for traditional desktop or web applications may not be suitable for mobile app development. The objective of this paper is to propose a framework for mobile app project estimation. The research methodology adopted in this work is based on selecting different features of mobile apps from the SAMOA dataset. These features are later used as input vectors to the selected machine learning (ML) techniques. The results of this research experiment are measured in mean absolute residual (MAR). The experimental outcomes are then followed by the proposition of a framework to recommend an ML algorithm as the best match for superior effort estimation of a project in question. This framework uses the Mamdani-type fuzzy inference method to address the ambiguities in the decision-making process. The outcome of this work will particularly help mobile app estimators, development professionals, and industry at large to determine the required efforts in the projects accurately.
Deep Reinforcement Learning-Based Topology Optimization for Self-Organized Wireless Sensor Networks

Wireless sensor networks (WSNs) are the foundation of the Internet of Things (IoT), and in the era of the fifth generation of wireless communication networks, they are envisioned to be truly biquitous, reliable, scalable, and energy efficient. To this end, topology control is an important mechanism to realize self-organized WSNs that are capable of adapting to the dynamics of the environment. Topology optimization is combinatorial in nature, and generally is NP-hard to solve. Most existing algorithms leverage heuristic rules to reduce the number of search candidates so as to obtain a suboptimal solution in a certain sense. In this paper, we propose a deep reinforcement learning-based topology optimization algorithm, a unified search framework, for self-organized energy-efficient WSNs. Specifically, the proposed algorithm uses a deep neural network to guide a Monte Carlo tree search to roll out simulations, and the results from the tree search reinforce the learning of the neural network. In addition, the proposed algorithm is an anytime algorithm that keeps improving the solution with an increasing amount of computing resources. Various simulations show that the proposed algorithm achieves better performance as compared to heuristic solutions, and is capable of adapting to environment and network changes without restarting the algorithm from scratch.
Sclera color enhances gaze perception in humans

Gaze perception is an essential behavior that allows individuals to determine where others are directing their attention but we know relatively little about the ways in which eye morphology influences it. We therefore tested whether eyes with conspicuous morphology have evolved to facilitate gaze perception. During a visual search task, we recorded the eye movements of human participants (Homo sapiens) as they searched for faces with directed gaze within arrays of faces with averted gaze or the reverse; the faces were large and upright, small and upright, or large and inverted. The faces had sclera that were conspicuous (white or colored lighter than the iris color) or inconspicuous (colored the same or darker than the iris color). We found that participants were fastest and most accurate in finding the faces with conspicuous sclera versus inconspicuous sclera. Our results demonstrate that eyes with conspicuous morphology facilitate gaze perception in humans.
A Wide ResNet-Based Approach for Age and Gender Estimation in Face Images

Age and gender prediction from facial images with high accuracy is of immense importance in various fields viz. social media, retail business, and academic research. In this paper, we make age prediction with an optimized model for efficient training by wide residual networks (ResNet) and efficient gradient optimization on loss function for better test accuracy. Our model has been evaluated and tested on IMDb-WIKI and APPA-REAL dataset and it performed well in evaluation compared to traditional deep Convolutional Neural Networks such as VGG-16, naive SVM classifiers with feature edge parameters, etc. The final accuracy achieved for our model is 96.269% with the wide ResNet architecture along with augmentation and erasing techniques on images. Significant reduction in the Mean Apparent Error (1.73) on apparent images and Mean Apparent Error (1.65) on Real images of the APPA-REAL dataset has been achieved with respect to traditional VGG-16 model.
Surfing on Fitness Landscapes: A Boost on Optimization by Fourier Surrogate Modeling

Surfing in rough waters is not always as fun as wave riding the "big one". Similarly, in optimization problems, fitness landscapes with a huge number of local optima make the search for the global optimum a hard and generally annoying game. Computational Intelligence optimization metaheuristics use a set of individuals that "surf" across the fitness landscape, sharing and exploiting pieces of information about local fitness values in a joint effort to find out the global optimum. In this context, we designed surF, a novel surrogate modeling technique that leverages the discrete Fourier transform to generate a smoother, and possibly easier to explore, fitness landscape. The rationale behind this idea is that filtering out the high frequencies of the fitness function and keeping only its partial information (i.e., the low frequencies) can actually be beneficial in the optimization process. We prove our theory by combining surF with a settings free variant of Particle Swarm Optimization (PSO) based on Fuzzy Logic, called Fuzzy Self-Tuning PSO. Specifically, we introduce a new algorithm, named F3ST-PSO, which performs a preliminary exploration on the surrogate model followed by a second optimization using the actual fitness function. We show that F3ST-PSO can lead to improved performances, notably using the same budget of fitness evaluations.
Learning Behavior Analysis in Classroom Based on Deep Learning

In this work, we study learning behavior analysis for automatic evaluation of the classroom teaching. We define five classroom learning behaviors including listen, fatigue, hand-up, sideways and read-write, and construct a class-room learning behavior dataset named as ActRec-Classroom, which includes five categories with 5,126 images in total. With the aid of convolutional neural network (CNN), we propose a classroom learning behavior analysis system framework. Firstly, Faster R-CNN is used to detect human body. Then OpenPose is used to extract key points of human skeleton, faces and fingers. Finally, a CNN based classifier is designed for action recognition. Extensive experiments validate the proposed system. The validation accuracy reaches 92.86% on average, and it meets the need of learning behavior analysis in the real classroom teaching environment.
Perspectives on the origin, derivation, meaning, and significance of the isotropic sky model

The original and most common method to transpose horizontal to tilted diffuse irradiance is based on the Isotropic Sky Model (ISM). Various aspects of the ISM are explored here, including the connection between the ideal isotropic sky radiance distribution and the corresponding diffuse irradiance on a tilted collector. By completing an extensive literature review of the ISM, its origin and history are clearly established, and the methods of derivation are discussed. The oldest known derivations of the ISM are identified. In particular, the landmark contribution of Liu and Jordan (1960) is found not to be the original source of the ISM transposition formula, even though it is usually credited for it. The ISM significance is discussed in the present context of solar energy applications, where the accuracy of irradiance calculations is critical. A detailed review of the methods of derivation, including boundary conditions, is proposed to explain differing results and remove all sources of confusion that have permeated the literature. Lastly, the use of anisotropic models is advocated to avoid unnecessary systematic errors.
A Graph Fourier Transform Based Method for Missing Temperature Data Detection

In this paper, a graph Fourier transform (GFT) based method for missing temperature data detection problem is presented. First, the frequency contents of GFT of temperature data are analyzed. If there is missing temperature data occurring in the sensor station, the high-frequency content increases in the spectral domain. Then, the energy ratio between high-frequency band and low-frequency band is used to develop a detector for detecting whether temperature data misses or not. The threshold value of detector is determined by computing the maximum energy ratio value of the regular daily data without missing temperature data. Finally, the real-world data measured from the weather sensor stations are used to evaluate the performance of the proposed detection method.
Boosting Standard Classification Architectures Through a Ranking Regularizer

We employ triplet loss as a feature embedding regularizer to boost classification performance. Standard architectures, like ResNet and Inception, are extended to support both losses with minimal hyper-parameter tuning. This promotes generality while fine-tuning pretrained networks. Triplet loss is a powerful surrogate for recently proposed embedding regularizers. Yet, it is avoided due to large batch-size requirement and high computational cost. Through our experiments, we re-assess these assumptions.During inference, our network supports both classification and embedding tasks without any computational overhead. Quantitative evaluation highlights a steady improvement on five fine-grained recognition datasets. Further evaluation on an imbalanced video dataset achieves significant improvement. Triplet loss brings feature embedding capabilities like nearest neighbor to classification models. Code available at http://bit.ly/2LNYEqL.
Innovative approach of distance learning in the form of online courses

This article discusses the role of the online form of education in today's conditions of informationalization of society, the importance of online education as one of the forms of basic and additional education is deduced, the role of the interaction of traditional professional education and its innovative online forms is shown. It also analyzes the advantages and disadvantages of the distance view of the educational process in comparison with the traditional education system, the potential difficulties and positive aspects of using online learning.
Enhancing ICT for productivity in sub-Saharan Africa: Thresholds for complementary policies

The purpose of this research is to investigate the relevance of enhancing information and communication technology (ICT) on dynamics of total factor productivity (TFP) in 25 sub-Saharan African countries using data covering the period 1980–2014. The empirical evidence is based on the Generalised Method of Moments. The following main findings are established. First, while enhancing ICT overwhelmingly has net positive effects on productivity, the corresponding marginal effects are negative. Second, an extended analysis is performed to establish thresholds for complementary policies. These thresholds are: 100% mobile phone penetration for TFP; between 101.214% and 101.419% mobile phone penetration for welfare TFP and 15% internet penetration for welfare real TFP. It follows that approximately 100% mobile penetration and 15% internet penetration are thresholds at which ICT should be complemented with other macroeconomic policies for favorable outcomes on productivity dynamics. Other policy implications are discussed.
In Storage, Yet on Display

This paper suggests that humans value robotic systems as signals - costly, visible commitments that can secure access to preferred resources. This contrasts with HRI research, design, engineering and deployment, which have focused on robots' instrumental value - namely how designing and interacting with them may produce more or less productivity. Drawing on a multiyear ethnography of a "failed" robotic telepresence deployment in a teaching hospital, this paper shows that robots' signaling value can significantly outweigh - and even contravene - any practical utility that they may provide through use. This analysis further suggests that - unlike nontechnological organizational signals - robots' signaling value is highly contingent on the observability of their use. Considering robots' signaling value in complex social systems such as organizations promises improved robotic systems development and deployment techniques and improved prediction regarding human-robot interaction.
Improving neighborhood construction with Apollonius region algorithm based on density for clustering

With the rapid rate of information flow today, local identification of similar data points has gained greater significance for information processing in various branches of sciences. Geometric methods are especially useful because of their accuracy in locating similar neighborhood points by means of geometric structures. Geometric methods are not accurate enough for large scale data sets. Because of the persistent serious challenges in data point analysis, we have used a geometric method in which the Apollonius circle is been utilized to achieve high local accuracy. This paper proposes a neighborhood construction algorithm, termed Neighborhood Construction with Apollonius Region Density (NCARD). The neighbors of data points are determined not only by using geometric structures but also by means of density information. For efficient clustering, in comparison with the previous methods, the proposed algorithm can work better for high dimensional data; it is also able to identify local outlier data. Moreover, after locating similar data points with Apollonius circle, we will extract density and relationship among the points leading to a unique and rather accurate neighborhood. The proposed algorithm is more accurate than the state-of-the-art and well-known algorithms up to almost 8–13% in real and artificial data sets.