Reviewerly/exHarmony huggingface dataset:
Modular Neural Networks for Low-Power Image Classification on Embedded Devices

Embedded devices are generally small, battery-powered computers with limited hardware resources. It is difficult to run deep neural networks (DNNs) on these devices, because DNNs perform millions of operations and consume significant amounts of energy. Prior research has shown that a considerable number of a DNN’s memory accesses and computation are redundant when performing tasks like image classification. To reduce this redundancy and thereby reduce the energy consumption of DNNs, we introduce the Modular Neural Network Tree architecture. Instead of using one large DNN for the classifier, this architecture uses multiple smaller DNNs (called modules ) to progressively classify images into groups of categories based on a novel visual similarity metric. Once a group of categories is selected by a module, another module then continues to distinguish among the similar categories within the selected group. This process is repeated over multiple modules until we are left with a single category. The computation needed to distinguish dissimilar groups is avoided, thus reducing redundant operations, memory accesses, and energy. Experimental results using several image datasets reveal the effectiveness of our proposed solution to reduce memory requirements by 50% to 99%, inference time by 55% to 95%, energy consumption by 52% to 94%, and the number of operations by 15% to 99% when compared with existing DNN architectures, running on two different embedded systems: Raspberry Pi 3 and Raspberry Pi Zero.
Integration of recurrent convolutional neural network and optimal encryption scheme for intrusion detection with secure data storage in the cloud

Abstract Data communication security is growing day after day with the proliferation of cloud computing. It is primarily because of the few security constraints and challenges occurring in the cloud environment during data transmission. Existing research has shown that the intrusion detection system (IDS) centered on the cloud is more complicated. In this article, we address the above issues by proposing an attention‐based recurrent convolutional neural network (RCNN). This proposed RCNN is used to detect whether the text data are intrusion or nonintrusion. The nonintrusion text information is then used for further processing and encrypted using a two‐way encryption scheme. We introduce the elliptical curve cryptography (ECC) approach to increase the security‐level performance of nonintrusion data. Moreover, the integration of ECC with the modified flower pollination algorithm (MFP‐ECC) creates the two‐way encryption scheme, and it is used to produce an optimal private key. The encrypted data are then stored in a cloud environment by steganography and the data with the sensitive information are replaced by some other text, thus providing security to the data at rest. The proposed MFP‐ECC approach shows maximum breaking time results and can also withstand different classical attacks when compared with other methods. As a result, the proposed intrusion detection and secure data storage mechanism is highly secured and it is never affected by any kinds of conspiracy attacks.
Evaluating and analyzing the energy efficiency of CNN inference on high‐performance GPU

Summary Convolutional neural network (CNN) inference usually runs on high‐performance graphic processing units (GPUs). Since GPU is a high power consumption unit, that makes the energy consumption increases sharply due to the deep learning tasks. The energy efficiency of CNN inference is not only related to the software and hardware configurations, but also closely related to the application requirements of inference tasks. However, it is not clear on GPUs at present. In this paper, we conduct a comprehensive study on the model‐level and layer‐level energy efficiency of popular CNN models. The results point out several opportunities for further optimization. We also analyze the parameter settings (i.e., batch size, dynamic voltage and frequency scaling) and propose a revenue model to allow an optimal trade‐off between energy efficiency and latency. Compared with the default settings, the optimal settings can improve revenue by up to 15.31 × . We obtain the following main findings: (i) GPUs do not exploit the parallelism from the model depth and small convolution kernels, resulting in low energy efficiency. (ii) Convolutional layers are the most energy‐consuming CNN layers. However, due to the cache, the power consumption of all layers is relatively balanced. (iii) The energy efficiency of TensorRT is 1.53 × than that of TensorFlow.
Critical intellectuals in postdigital times

This article starts with a brief analysis of what it means to be an intellectual within the US tradition of critical pedagogy. Pointing toward important socio-technological transformations which have taken place in the past few decades, the article situates the concept of the intellectual into the contemporary postdigital context. The article looks into two main areas of intellectual work which seem to have undergone significant transformations—automation and post-truth. It develops possible responses to recent challenges in these areas and shows that contemporary intellectuals working in the tradition of critical pedagogy need to take technology seriously. Heading toward the conclusion, the article promotes Greta Thunberg as an important example of a postdigital intellectual. Our analyses show that critical pedagogy has an important role in the development of contemporary intellectual work. Aiming at constantly transforming challenges, however, old theories require constant reconceptualization in and for our postdigital context. Within this mash-up of questions and answers old and new, we identify a starting point for this reconceptualization in the notion of critical praxis.
Maximally entangled and gigahertz-clocked on-demand photon pair source

We present a 1 GHz-clocked, maximally entangled and on-demand photon pair source based on droplet etched GaAs quantum dots using two-photon excitation. By employing these GaP microlens-enhanced devices in conjunction with their substantial brightness, raw entanglement fidelities of up to $0.95\ifmmode\pm\else\textpm\fi{}0.01$ and postselected photon indistinguishabilities of up to $0.93\ifmmode\pm\else\textpm\fi{}0.01$, the suitability for quantum repeater based long range quantum entanglement distribution schemes is shown. Comprehensive investigations of a complete set of polarization selective two-photon correlations facilitate an innovative method to determine the extraction and excitation efficiencies directly, as opposed to commonly employed indirect techniques. Additionally, time-resolved analysis of Hong-Ou-Mandel interference traces reveal an alternative approach to the investigation of pure photon dephasing.
Towards Requirements Specification for Machine-learned Perception Based on Human Performance

The application of machine learning (ML) based perception algorithms in safety-critical systems such as autonomous vehicles have raised major safety concerns due to the apparent risks to human lives. Yet assuring the safety of such systems is a challenging task, in a large part because ML components (MLCs) rarely have clearly specified requirements. Instead, they learn their intended tasks from the training data. One of the most well-studied properties that ensure the safety of MLCs is the robustness against small changes in images. But the range of changes considered small has not been systematically defined. In this paper, we propose an approach for specifying and testing requirements for robustness based on human perception. With this approach, the MLCs are required to be robust to changes that fall within the range defined based on human perception performance studies. We demonstrate the approach on a state-of-the-art object detector.
Improved-StoryGAN for sequential images visualization

Story visualization is a novel and challenging topic that intersects computer vision and natural language processing, which needs to generate sequential images based on a story. It is related to text-to-image generation and video generation. Apart from ensuring the quality of the results, the synthesized images of story visualization are supposed to be consistent with each other and reflect the input story. In order to improve the performance of generated sequential images, we have developed the baseline model StoryGAN. Firstly, we use Dilated Convolution in the discriminators to expand the receptive field of the convolution kernel in the feature maps, thus enhancing the quality of the generated sequential images. In addition, Weighted Activation Degree (WAD) is introduced in the discriminators to provide a robust evaluation in view of similarity between the generated images and the target story, which results in enhancement on the consistency between the generated images and the target story. Last but not least, Bi-GRU stores the historical and future information of each sentence to effectively extract the textual features. What’s more, in order to make full use of the features of the long story features, Gated Convolution is used to replace the original MLP in the Initial State Encoder to improve the consistence between the generated sequential images. Experimental results and visual sequential images demonstrate the outperformance of the model we develop, compared with the other models.
AiRound and CV-BrCT: Novel Multiview Datasets for Scene Classification

It is undeniable that aerial/satellite images can provide useful information for a large variety of tasks. But, since these images are always taken from above, some applications can benefit from complementary information provided by other perspective views of the scene, such as ground-level images. Despite a large number of public repositories for both georeferenced photographs and aerial images, there is a lack of benchmark datasets that allow the development of approaches that exploit the benefits and complementarity of aerial/ground imagery. In this article, we present two new publicly available datasets named AiRound and CV-BrCT. The first one contains triplets of images from the same geographic coordinate with different perspectives of view extracted from various places around the world. Each triplet is composed of an aerial RGB image, a ground-level perspective image, and a Sentinel-2 sample. The second dataset contains pairs of aerial and street-level images extracted from southeast Brazil. We design an extensive set of experiments concerning multiview scene classification, using early and late fusion. Such experiments were conducted to show that image classification can be enhanced using multiview data.
EduBlock: Securing Educational Documents using Blockchain Technology

Bitcoin and Cryptocurrencies are the most popular and the earliest applications of Blockchain. A Blockchain is a public ledger that is distributed and decentralized and it is used to carry out transactions online across the entire network of computer systems. It is a recent technology adopted for data security. The non-modifiable property of the blockchain helps to overcome the problem of document forgery. According to researches, about one million students graduate each year and the document issuing authorities have seemed to be compromised for the security credentials of student data. Due to a lack of effective anti-forge mechanism, forged documents often get unnoticed. The use of physical copies of documents creates a huge overhead as it involves manual verification, paper storage, and manual auditing. Various agencies fail to verify the authenticity of the documents and create loopholes. In this paper, in an attempt to solve the above problem, we design a multi-node private blockchain network using the Ethereum framework and create an off-chain storage i.e. private Interplanetary File System (IPFS) to store the documents. We also evaluate the performance of Ethereum blockchain by analysing the impact of various parameters such as varying difficulty level, load, network size, and consensus algorithms.
ENHANCING home security through visual CRYPTOGRAPHY

Home security systems in the recent times have gained greater importance due to increasing threat in the society. Biometrics deals with automated approaches of recognizing a user or verifying the user identity based on behavioral or physiological features. Visual cryptography is a scheme of secret sharing where a secret image is encrypted into shares which disclose no data independently about the original secret image. As the template of biometric are stored in centralized database due to the threats of security the template of biometric may be changed by attacker. If the template of biometric is changed then the authorized user will not be permitted to access the resource. To manage this problem the schemes of visual cryptography can be used to secure the face recognition. Visual cryptography offers huge ways for supporting such needs of security as well as additional authentication layer. To manage this problem the visual cryptography schemes can be used to secure digital biometric information privacy. In this approach the face or private image is dithered in two varied host images that is sheets and are stored in separate servers of data so as to assure that the original image can get extracted only by accessing both sheets together at a time and a single sheet will not be capable to show any data of private image. The main aim of the study is to propose an algorithm which is a combination of CVC and Siamese network. This research implements visual cryptography for face images in a biometric application. The Siamese network is essential to solve one shot learning by representation of learning feature that are compared to verification tasks. In this research face authentication helps in accomplishing robustness by locating face image from an n input image. This research explores the availability of using visual cryptography for securing the privacy to biometric data. The results of the proposed approach provide an accuracy of 93% which is found to be superior when compared with that of the approaches that are already in practice.
Vehicle and Person Re-Identification With Support Neighbor Loss

One of the key tasks for an intelligent visual surveillance system is to automatically re-identify objects of interest, e.g., persons or vehicles, from nonoverlapping camera views. This demand incurs the vast investigation of person re-identification (re-ID) and vehicle re-ID techniques, especially those deep learning-based ones. While most recent algorithms focus on designing new convolutional neural networks, less attention is paid to the loss functions, which are of vital roles as well. Triplet loss and softmax loss are the two losses that are extensively used, both of which, however, have limitations. Triplet loss optimizes the model to produce features with which samples from the same class have higher similarity than those from different classes. The problem of triplet loss is that the number of triplets to be constructed grows cubically with training samples, which causes scalability issue, unstable performance, and slow convergence. Softmax loss has favorable scalable property and is widely used for large-scale classification problems. However, since Softmax loss only aims to separate well training classes, its performance for re-ID tasks is not desirable because the model is tested to measure the similarity of samples from unseen classes. We propose the support neighbor (SN) loss, which avoids the limitations of the abovementioned two losses. Unlike triplet loss that is calculated based on triplets, SN loss is derived from <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> -nearest neighbors (SNs) of anchor samples. The SNs of an anchor are unique, containing more valuable contextual information and neighborhood structure of the anchor, and thus contribute to more stable performance and reliable embedding from image space to feature space. Based on the SNs, a softmax-like separation term and a squeeze term are proposed, which encourage interclass separation and intraclass compactness, respectively. Experiments show that SN loss surpasses triplet and softmax losses with the same backbone network and reaches the state-of-the-art performance for both person and vehicle re-ID using a ResNet50 backbone when combined with training tricks.
Revealing Perceptual Proxies with Adversarial Examples

Data visualizations convert numbers into visual marks so that our visual system can extract data from an image instead of raw numbers. Clearly, the visual system does not compute these values as a computer would, as an arithmetic mean or a correlation. Instead, it extracts these patterns using perceptual proxies; heuristic shortcuts of the visual marks, such as a center of mass or a shape envelope. Understanding which proxies people use would lead to more effective visualizations. We present the results of a series of crowdsourced experiments that measure how powerfully a set of candidate proxies can explain human performance when comparing the mean and range of pairs of data series presented as bar charts. We generated datasets where the correct answer-the series with the larger arithmetic mean or range-was pitted against an “adversarial” series that should be seen as larger if the viewer uses a particular candidate proxy. We used both Bayesian logistic regression models and a robust Bayesian mixed-effects linear model to measure how strongly each adversarial proxy could drive viewers to answer incorrectly and whether different individuals may use different proxies. Finally, we attempt to construct adversarial datasets from scratch, using an iterative crowdsourcing procedure to perform black-box optimization.
Pop-up Print

Despite recent advancements in 3D printing technology, which allows users to rapidly produce 3D objects, printing tall and/or large objects still consumes more time and large amount of support material. In order to address these problems, we propose Pop-up Print, a method to 3D print an object in a compact "folded" state and then unfold it after printing to achieve the final artifact. Using this method, we can reduce the object's print height and volume, which directly affects the printing time and support material consumption. In addition, thanks to the reversibility of folding/unfolding, we can reversibly minimize the printed object's volume when unused for storage or transportation, and expand it only in use. To achieve Pop-up Print, we first conducted an experiment using selected printed sample objects with several parameters, in order to determine suitable crease patterns that make both the unfolded and folded state mechanically stable. Based on this result, we developed an interactive design tool to convert 3D models - such as a Stanford Bunny or a Huffman's cone - to the folded shape. Our design tool allows users to decide non-intuitive parameters that may affect the form's mechanical stability, while maintaining both functional crease patterns and the object's original form factor. Finally, we demonstrate the feasibility of our method through several examples of folded objects.
A Weighted Text Representation framework for Sentiment Analysis of Medical Drug Reviews

The steady growth of the Internet has increased the amount of user-generated data on the web. Patients are now commonly posting their reviews after consuming the medicines to express themselves and create public awareness. Sentiment analysis can significantly contribute to the medical field by analyzing these public reviews and study the effectiveness or popularity of several medicines. Hence, in this paper, we propose an effective framework based on the weighted word representation technique and adding linguistic constraints to model the contextually similar words. We also train five popular classifiers for the same, namely: SVM, Decision Tree, Random Forests, Naïve Bayes, and K-Nearest Neighbor. Extensive experiments were performed on the drug review dataset, which is crawled from online pharmaceutical review websites. The results demonstrate that the proposed framework has outperformed various state-of-the-art by showing an accuracy of 94.6% and an F1 score of 90.2%, respectively. This shows that the framework has effectively captured the sentiments expressed by the people on different drugs.
Slice of Light: Transparent and Integrative Transition Among Realities in a Multi-HMD-User Environment

This work presents Slice of Light, a visualization design created to enhance transparency and integrative transition between realities of Head-Mounted Display (HMD) users sharing the same physical environment. Targeted at reality-guests, Slice of Light's design enables guests to view other HMD users' interactions contextualized in their own virtual environments while allowing the guests to navigate among these virtual environments. In this paper, we detail our visualization design and the implementation. We demonstrate Slice of Light with a block-world construction scenario that involves a multi-HMD-user environment. VR developer and HCI expert participants were recruited to evaluate the scenario, and responded positively to Slice of Light. We discuss their feedback, our design insights, and the limitations of this work.
Design of SIW Filters in D-band Using Invertible Neural Nets

Inverse design of microwave circuits and electronic systems is challenging due to the ambiguous mapping relationship from output response to input design parameters. In this paper, we apply invertible neural networks (INN) for addressing inverse design problems, where geometrical parameters are estimated given the desired performance. In the proposed approach, the bi-directional inference processes are learnt using an INN. During the inverse process, the posterior distribution of the design parameters are reproduced based on the target response, which is especially advantageous in cases where similar design performances can be achieved with different parameter combinations. The effectiveness of the method is demonstrated using an inverse design example of substrate integrated waveguide (SIW) filter in D-band.
System, Design and Experimental Validation of Autonomous Vehicle in an Unconstrained Environment

In recent years, technological advancements have made a promising impact on the development of autonomous vehicles. The evolution of electric vehicles, development of state-of-the-art sensors, and advances in artificial intelligence have provided necessary tools for the academia and industry to develop the prototypes of autonomous vehicles that enhance the road safety and traffic efficiency. The increase in the deployment of sensors for the autonomous vehicle, make it less cost-effective to be utilized by the consumer. This work focuses on the development of full-stack autonomous vehicle using the limited amount of sensors suite. The architecture aspect of the autonomous vehicle is categorized into four layers that include sensor layer, perception layer, planning layer and control layer. In the sensor layer, the integration of exteroceptive and proprioceptive sensors on the autonomous vehicle are presented. The perception of the environment in term localization and detection using exteroceptive sensors are included in the perception layer. In the planning layer, algorithms for mission and motion planning are illustrated by incorporating the route information, velocity replanning and obstacle avoidance. The control layer constitutes lateral and longitudinal control for the autonomous vehicle. For the verification of the proposed system, the autonomous vehicle is tested in an unconstrained environment. The experimentation results show the efficacy of each module, including localization, object detection, mission and motion planning, obstacle avoidance, velocity replanning, lateral and longitudinal control. Further, in order to demonstrate the experimental validation and the application aspect of the autonomous vehicle, the proposed system is tested as an autonomous taxi service.