Reviewerly/exHarmony huggingface dataset:
Deflating Dataset Bias Using Synthetic Data Augmentation

Deep Learning has seen an unprecedented increase in vision applications since the publication of large-scale object recognition datasets and introduction of scalable compute hardware. State-of-the-art methods for most vision tasks for Autonomous Vehicles (AVs) rely on supervised learning and often fail to generalize to domain shifts and/or outliers. Dataset diversity is thus key to successful real-world deployment. No matter how big the size of the dataset, capturing long tails of the distribution pertaining to task-specific environmental factors is impractical. The goal of this paper is to investigate the use of targeted synthetic data augmentation - combining the benefits of gaming engine simulations and sim2real style transfer techniques - for filling gaps in real datasets for vision tasks. Empirical studies on three different computer vision tasks of practical use to AVs -parking slot detection, lane detection and monocular depth estimation - consistently show that having synthetic data in the training mix provides a significant boost in cross-dataset generalization performance as compared to training on real data only, for the same size of the training set.
Patriarchy and Social Media

We explore peer support mechanisms in patriarchal contexts where a woman's movements, social relations and the seeking of support are restricted. We investigate the use of closed Facebook groups as a vital mechanism for access to anonymous peer support in Pakistan for taboo narratives like abortion, sexual harassment, rape, domestic abuse and issues relating to child-rearing and parenting. We discuss how technology is impacting these women's lives and allowing access to a safe, non-judgmental space to share their stories and collectively support other women within the same context. We analyze the posting and support seeking behaviours and the mechanisms for anonymity and privacy within a closed women only Facebook group of 15,000 members. Our study reveals the vital importance of such digital safe spaces and the mechanisms that allow them to remain judgment free and safe for support seeking in stigmatized contexts within vulnerable populations.
Evolving Dynamic S-Boxes Using Fractional-Order Hopfield Neural Network Based Scheme

Static substitution-boxes in fixed structured block ciphers may make the system vulnerable to cryptanalysis. However, key-dependent dynamic substitution-boxes (S-boxes) assume to improve the security and robustness of the whole cryptosystem. This paper proposes to present the construction of key-dependent dynamic S-boxes having high nonlinearity. The proposed scheme involves the evolution of initially generated S-box for improved nonlinearity based on the fractional-order time-delayed Hopfield neural network. The cryptographic performance of the evolved S-box is assessed by using standard security parameters, including nonlinearity, strict avalanche criterion, bits independence criterion, differential uniformity, linear approximation probability, etc. The proposed scheme is able to evolve an S-box having mean nonlinearity of 111.25, strict avalanche criteria value of 0.5007, and differential uniformity of 10. The performance assessments demonstrate that the proposed scheme and S-box have excellent features, and are thus capable of offering high nonlinearity in the cryptosystem. The comparison analysis further confirms the improved security features of anticipated scheme and S-box, as compared to many existing chaos-based and other S-boxes.
A semantic approach to extractive multi-document summarization: Applying sentence expansion for tuning of conceptual densities

Today, due to a vast amount of textual data, automated extractive text summarization is one of the most common and practical techniques for organizing information. Extractive summarization selects the most appropriate sentences from the text and provide a representative summary. The sentences, as individual textual units, usually are too short for major text processing techniques to provide appropriate performance. Hence, it seems vital to bridge the gap between short text units and conventional text processing methods. In this study, we propose a semantic method for implementing an extractive multi-document summarizer system by using a combination of statistical, machine learning based, and graph-based methods. It is a language-independent and unsupervised system. The proposed framework learns the semantic representation of words from a set of given documents via word2vec method. It expands each sentence through an innovative method with the most informative and the least redundant words related to the main topic of sentence. Sentence expansion implicitly performs word sense disambiguation and tunes the conceptual densities towards the central topic of each sentence. Then, it estimates the importance of sentences by using the graph representation of the documents. To identify the most important topics of the documents, we propose an inventive clustering approach. It autonomously determines the number of clusters and their initial centroids, and clusters sentences accordingly. The system selects the best sentences from appropriate clusters for the final summary with respect to information salience, minimum redundancy, and adequate coverage. A set of extensive experiments on DUC2002 and DUC2006 datasets was conducted for investigating the proposed scheme. Experimental results showed that the proposed sentence expansion algorithm and clustering approach could considerably enhance the performance of the summarization system. Also, comparative experiments demonstrated that the proposed framework outperforms most of the state-of-the-art summarizer systems and can impressively assist the task of extractive text summarization.
Demystifying artificial intelligence in pharmacy

Abstract Purpose To provide pharmacists and other clinicians with a basic understanding of the underlying principles and practical applications of artificial intelligence (AI) in the medication-use process. Summary “Artificial intelligence” is a general term used to describe the theory and development of computer systems to perform tasks that normally would require human cognition, such as perception, language understanding, reasoning, learning, planning, and problem solving. Following the fundamental theorem of informatics, a better term for AI would be “augmented intelligence,” or leveraging the strengths of computers and the strengths of clinicians together to obtain improved outcomes for patients. Understanding the vocabulary of and methods used in AI will help clinicians productively communicate with data scientists to collaborate on developing models that augment patient care. This primer includes discussion of approaches to identifying problems in practice that could benefit from application of AI and those that would not, as well as methods of training, validating, implementing, evaluating, and maintaining AI models. Some key limitations of AI related to the medication-use process are also discussed. Conclusion As medication-use domain experts, pharmacists play a key role in developing and evaluating AI in healthcare. An understanding of the core concepts of AI is necessary to engage in collaboration with data scientists and critically evaluating its place in patient care, especially as clinical practice continues to evolve and develop.
Blockchain for E-Health-Care Systems: Easier Said Than Done

Blockchain as a distributed ledger technology can be very effective in providing access control and big data management in health-care systems. Because implementing or migrating to a pure blockchain solution is an extremely challenging task, several design and implementation dynamics should be considered.
AutoEmbedder: A semi-supervised DNN embedding system for clustering

Clustering is widely used in unsupervised learning method that deals with unlabeled data. Deep clustering has become a popular study area that relates clustering with Deep Neural Network (DNN) architecture. Deep clustering method downsamples high dimensional data, which may also relate clustering loss. Deep clustering is also introduced in semi-supervised learning (SSL). Most SSL methods depend on pairwise constraint information, which is a matrix containing knowledge if data pairs can be in the same cluster or not. This paper introduces a novel embedding system named AutoEmbedder, that downsamples higher dimensional data to clusterable embedding points. To the best of our knowledge, this is the first research endeavor that relates to traditional classifier DNN architecture with a pairwise loss reduction technique. The training process is semi-supervised and uses Siamese network architecture to compute pairwise constraint loss in the feature learning phase. The AutoEmbedder outperforms most of the existing DNN based semi-supervised methods tested on famous datasets.
Single-Photon-Memory Two-Step Quantum Secure Direct Communication Relying on Einstein-Podolsky-Rosen Pairs

Quantum secure direct communication (QSDC) is an important branch of quantum communication that is capable of directly transmitting secret messages over a quantum channel. It may be viewed as a concrete realization of Wyner's wiretap channel theory, which ensures the reliable and secure communication of information in the presence of noise and eavesdropping. Hence it is a fully-fledged quantum-communications protocol, which does not require a separate secret key negotiation phase. By contrast, its quantum key distribution (QKD) counterpart represents a secret key-negotiation protocol, which has to be followed up by a separate classical communication session. The essential difference between these two modes of quantum communication lies in the employment of a block-based data transmission technique, proposed by Long and Liu in 2000. However, the original block-based data transmission requires quantum memory, which is not widely available at the time of writing. Recently, this difficulty has been overcome by using classical coding theory, which has been successfully applied to the single-qubit DL04 QSDC. Here we will present a single-photon-memory QSDC protocol based on entangled pairs of photons. We commence by comparing QSDC to QKD, followed by an example of the single-photon QSDC and single-photon QKD protocol. Then we continue by modifying the so-called two-step QSDC protocol designed for deterministic QKD by reducing the number of qubits in a block into a single one, in which Alice prepares Einstein-Podolsky-Rosen (EPR) photon pairs and partitions them into two parts: the so-called pioneer qubit and the follow-up qubit. The pioneer photon is transferred first to Bob, while the follow-up photon is used either for performing encoding or for eavesdropping detection. Bob extracts the candidate key by combining the two particles of the EPR pair to perform Bell-basis measurement. Then the protocol is transformed into a single-photon-memory QSDC using coding theory. Our theoretical analysis shows that the resultant protocol is robust to individual attacks. Additionally, a high communication efficiency is achieved.
GPU-Accelerated Real-Time Stereo Estimation With Binary Neural Network

Depth estimation from stereo images is essential to many applications such as robotics and autonomous vehicles, most of which ask for the real-time response, high energy and storage efficiency. Recent work has shown deep neural networks (DNN) perform extremely well for stereo estimation. However, these state-of-the-art DNN based algorithms are challenging to be deployed into real-world applications due to the high computational complexities of DNNs. Most of them are too slow for real-time inference and require several seconds of GPU computation to process image frames. In this article, we address the problem of fast stereo estimation and propose an efficient and light-weighted stereo matching system, called StereoBit, to produce a disparity map in a real-time manner while achieving close to state-of-the-art accuracy. To achieve this goal, we propose a binary neural network to generate weighted Hamming distance for an efficient similarity join in stereo estimation. In addition, we propose a novel approximation approach to derive StereoBit network directly from the well-trained network with the cosine similarity. Our approximation strategies enable a significant speedup while maintaining almost the same accuracy compared to the network with the cosine similarity. Furthermore, we present an optimization framework for fully exploiting the computing power of StereoBit. The framework provides a significant speedup of stereo estimation routines, and at the same time, reduces the memory usage for storing parameters. The effectiveness of StereoBit is evaluated by comprehensive experiments. StereoBit can achieve 60 frames per second on an NVIDIA TITAN Xp GPU on KITTI 2012 benchmark while achieving 3-pixel non-occluded stereo error 3.56 percent.
Recurrent-neural-network-based unscented Kalman filter for estimating and compensating the random drift of MEMS gyroscopes in real time

The presence of the stochastic errors in MEMS (Micro Electro Mechanical Systems) gyroscopes makes the improvement of the measurement precision challenging. This paper addresses a novel method to estimate and compensate the random drift of MEMS gyroscopes in real time, combining unscented Kalman filter (UKF) with recurrent neural network (RNN). In the proposed method, the random drift is regarded as a generalized nonlinear autoregressive moving average (NARMA) model, and its optimal predictor is realized by a dynamic RNN. To compensate the random drift in real time, the RNN model is brought into the framework of UKF, for establishing the state equation of the improved UKF. The novelty of this paper is that a strategy is presented to guarantee the validity of the combination of UKF and RNN. The effectiveness and superiorities of the proposed method are verified by experiments.
Constructing bi-plots for random forest: Tutorial

Current technological developments have allowed for a significant increase and availability of data. Consequently, this has opened enormous opportunities for the machine learning and data science field, translating into the development of new algorithms in a wide range of applications in medical, biomedical, daily-life, and national security areas. Ensemble techniques are among the pillars of the machine learning field, and they can be defined as approaches in which multiple, complex, independent/uncorrelated, predictive models are subsequently combined by either averaging or voting to yield a higher model performance. Random forest (RF), a popular ensemble method, has been successfully applied in various domains due to its ability to build predictive models with high certainty and little necessity of model optimization. RF provides both a predictive model and an estimation of the variable importance. However, the estimation of the variable importance is based on thousands of trees, and therefore, it does not specify which variable is important for which sample group. The present study demonstrates an approach based on the pseudo-sample principle that allows for construction of bi-plots (i.e. spin plots) associated with RF models. The pseudo-sample principle for RF. is explained and demonstrated by using two simulated datasets, and three different types of real data, which include political sciences, food chemistry and the human microbiome data. The pseudo-sample bi-plots, associated with RF and its unsupervised version, allow for a versatile visualization of multivariate models, and the variable importance and the relation among them.
A novel retrieval strategy for case-based reasoning based on attitudinal Choquet integral

Retrieval is a very important stage in the case-based reasoning (CBR) process because it is the critical foundation for the success of the CBR system. Its goal is to retrieve valuable cases that can be employed as references to solve the target problem. The most commonly employed methods in the retrieval process today is similarity-based weighted average operators, which have been criticized for not considering the interactions among features. In this paper, we develop a novel retrieval strategy for CBR based on the attitudinal Choquet integral (ACI), which can capture (a) the features interaction, (b) relative features importance, and (c) the attitudinal character of decision maker. The core of the retrieval strategy is to define a global similarity which aggregates local similarity and feature similarity through ACI. In addition, to ensure the availability of data in the case base, we present a method of filling in missing data. The novel retrieval strategy and filling method are validated through two simulation experiments on several real data sets. The superiority of the developed approaches in terms of retrieval capability and filling efficiency can be demonstrated by approaching an average recognition rate of 82% and a filling accuracy of over 90%.
Revisiting Sparsity Invariant Convolution: A Network for Image Guided Depth Completion

The limitation of LiDAR (Light Detection And Ranging) sensor causes the general sparsity of produced depth measurement. However, the sparse representation of the world is insufficient for applications such as 3D reconstruction. Thus, depth completion is an important computer vision task in which a synchronized RGB image is commonly available. In this paper, we propose a deep neural network to tackle this image guided depth completion problem. By revisiting the sparsity invariant convolution and revealing how it can be used in a novel approach, we propose three mask aware operations to process, downscale, and fuse sparse features. These operations explicitly consider the observation mask of its corresponding feature map. In addition, the structure of this network follows a novel scheme in which data from image and depth domain are processed by these proposed operations independently. Our proposed model achieves state-of-the-art performance on the KITTI depth completion benchmark. Furthermore, it presents a strong robustness for bearing input sparsity under different densities and patterns.
Power Efficient Tiny Yolo CNN Using Reduced Hardware Resources Based on Booth Multiplier and WALLACE Tree Adders

Convolutional Neural Network (CNN) has attained high accuracy and it has been widely employed in image recognition tasks. In recent times, deep learning-based modern applications are evolving and it poses a challenge in research and development of hardware implementation. Therefore, hardware optimization for efficient accelerator design of CNN remains a challenging task. A key component of the accelerator design is a processing element (PE) that implements the convolution operation. To reduce the amount of hardware resources and power consumption, this article provides a new processing element design as an alternate solution for hardware implementation. Modified BOOTH encoding (MBE) multiplier and WALLACE tree-based adders are proposed to replace bulky MAC units and typical adder tree respectively. The proposed CNN accelerator design is tested on Zynq-706 FPGA board which achieves a throughput of 87.03 GOP/s for Tiny-YOLO-v2 architecture. The proposed design allows to reduce hardware costs by 24.5% achieving a power efficiency of 61.64 GOP/s/W that outperforms the previous designs.
Fast hyperparameter tuning using Bayesian optimization with directional derivatives

In this paper we develop a Bayesian optimization based hyperparameter tuning framework inspired by statistical learning theory for classifiers. We utilize two key facts from PAC learning theory; the generalization bound will be higher for a small subset of data compared to the whole, and the highest accuracy for a small subset of data can be achieved with a simple model. We initially tune the hyperparameters on a small subset of training data using Bayesian optimization. While tuning the hyperparameters on the whole training data, we leverage the insights from the learning theory to seek more complex models. We realize this by using directional derivative signs strategically placed in the hyperparameter search space to seek a more complex model than the one obtained with small data. We demonstrate the performance of our method on the tasks of tuning the hyperparameters of several machine learning algorithms.
Hybrid Adaptive Lossless Image Compression Based on Discrete Wavelet Transform

A new hybrid transform for lossless image compression exploiting a discrete wavelet transform (DWT) and prediction is the main new contribution of this paper. Simple prediction is generally considered ineffective in conjunction with DWT but we applied it to subbands of DWT modified using reversible denoising and lifting steps (RDLSs) with step skipping. The new transform was constructed in an image-adaptive way using heuristics and entropy estimation. For a large and diverse test set consisting of 499 photographic and 247 non-photographic (screen content) images, we found that RDLS with step skipping allowed effectively combining DWT with prediction. Using prediction, we nearly doubled the JPEG 2000 compression ratio improvements that could be obtained using RDLS with step skipping. Because for some images it might be better to apply prediction instead of DWT, we proposed compression schemes with various tradeoffs, which are practical contributions of this study. Compared with unmodified JPEG 2000, one scheme improved the compression ratios of photographic and non-photographic images, on average, by 1.2% and 30.9%, respectively, at the cost of increasing the compression time by 2% and introducing only minimal modifications to JPEG 2000. Greater ratio improvements, exceeding 2% and 32%, respectively, are attainable at a greater cost.
“The first mile problem”: Deriving an endogenous demand for auditing in blockchain-based business processes

In this paper, we model the business process within which blockchain applications operate in order to extract an endogenous demand for auditing in that environment. We begin by undertaking a case study of the pharmaceutical drug industry supply chain, exploring both the proposed blockchain initiatives in it initiated by the FDA and startup companies, and the problems in the supply chain that blockchain cannot address. Drawing on this analysis, we derive an endogenous demand for auditing to overcome what we label the "first mile problem" (FMP) with blockchains: ensuring that the data stored on the blockchain distributed ledger is isomorphic with the real life data that it purports to represent. The first mile problem arises only when the blockchain is used to store data about physical items, especially ones involving a service component, rather than being native digital, as is the case with the genesis blockchain application, bitcoin. We show that unless it is feasible to store a "digital twin" of the item (Roberts, 2017), there is a role for auditors, with the training in professional skepticism, to help alleviate the first mile problem. There is no guarantee, however, that this new demand for auditing will be met by the traditional financial statement auditors.
Data augmentation with conditional GAN for automatic modulation classification

Deep learning has great potential for automatic modulation classification (AMC). However, its performance largely hinges upon the availability of sufficient high-quality labeled data. In this paper, we propose data augmentation with conditional generative adversarial network (CGAN) for convolutional neural network (CNN) based AMC, which provides an effective solution to the limited data problem. We present the design of the proposed CGAN based data augmentation method, and validate its performance with a public dataset. The experiment results show that CNN-based modulation classification can greatly benefit from the proposed data augmentation approach with greatly improved accuracy.