Reviewerly/exHarmony huggingface dataset:
Feature Control as Intrinsic Motivation for Hierarchical Reinforcement Learning

One of the main concerns of deep reinforcement learning (DRL) is the data inefficiency problem, which stems both from an inability to fully utilize data acquired and from naive exploration strategies. In order to alleviate these problems, we propose a DRL algorithm that aims to improve data efficiency via both the utilization of unrewarded experiences and the exploration strategy by combining ideas from unsupervised auxiliary tasks, intrinsic motivation, and hierarchical reinforcement learning (HRL). Our method is based on a simple HRL architecture with a metacontroller and a subcontroller. The subcontroller is intrinsically motivated by the metacontroller to learn to control aspects of the environment, with the intention of giving the agent: 1) a neural representation that is generically useful for tasks that involve manipulation of the environment and 2) the ability to explore the environment in a temporally extended manner through the control of the metacontroller. In this way, we reinterpret the notion of pixel- and feature-control auxiliary tasks as reusable skills that can be learned via an intrinsic reward. We evaluate our method on a number of Atari 2600 games. We found that it outperforms the baseline in several environments and significantly improves performance in one of the hardest games-Montezuma's revenge-for which the ability to utilize sparse data is key. We found that the inclusion of intrinsic reward is crucial for the improvement in the performance and that most of the benefit seems to be derived from the representations learned during training.
Optimized Blockchain Model for Internet of Things based Healthcare Applications

There continues to be a recent push to taking the cryptocurrency based ledger system known as Blockchain and applying its techniques to non-financial applications. One of the main areas for application remains Internet of Things (IoT) as we see many areas of improvement as we move into an age of smart cities. In this paper, we examine an initial look at applying the key aspects of Blockchain to a health application network where patients health data can be used to create alerts important to authenticated healthcare providers in a secure and private manner. This paper also presents the benefits and also practical obstacles of the blockchain-based security approaches in IoT.
Densely Connected Hierarchical Network for Image Denoising

Recently, deep convolutional neural networks have been applied in numerous image processing researches and have exhibited drastically improved performances. In this study, we introduce a densely connected hierarchical image denoising network (DHDN), which exceeds the performances of state-of-the-art image denoising solutions. Our proposed network improves the image denoising performance by applying the hierarchical architecture of the modified U-Net; this makes our network to use a larger number of parameters than other methods. In addition, we induce feature reuse and solve the vanishing-gradient problem by applying dense connectivity and residual learning to our convolution blocks and network. Finally, we successfully apply the model ensemble and self-ensemble methods; this enable us to improve the performance of the proposed network. The performance of the proposed network is validated by winning the second place in the NTRIE 2019 real image denoising challenge sRGB track and the third place in the raw-RGB track. Additional experimental results on additive white Gaussian noise removal also establishes that the proposed network outperforms conventional methods; this is notwithstanding the fact that the proposed network handles a wide range of noise levels with a single set of trained parameters.
Shift R-CNN: Deep Monocular 3D Object Detection With Closed-Form Geometric Constraints

We propose Shift R-CNN, a hybrid model for monocular 3D object detection, which combines deep learning with the power of geometry. We adapt a Faster R-CNN network for regressing initial 2D and 3D object properties and combine it with a least squares solution for the inverse 2D to 3D geometric mapping problem, using the camera projection matrix. The closed-form solution of the mathematical system, along with the initial output of the adapted Faster R-CNN are then passed through a final ShiftNet network that refines the result using our newly proposed Volume Displacement Loss. Our novel, geometrically constrained deep learning approach to monocular 3D object detection obtains top results on KITTI 3D Object Detection Benchmark [5], being the best among all monocular methods that do not use any pre-trained network for depth estimation.
FlowPic: Encrypted Internet Traffic Classification is as Easy as Image Recognition

Identifying the type of a network flow or a specific application has many advantages, but become harder in recent years due to the use of encryption, e.g., by VPN and Tor. Current solutions rely mostly on handcrafted features and then apply supervised learning techniques for the classification. We introduce a novel approach for encrypted Internet traffic classification by transforming basic flow data into a picture, a FlowPic, and then using known image classification deep learning techniques, Convolutional Neural Networks (CNNs), to identify the flow category (browsing, chat, video, etc.) and the application in use. We show using the UNB ISCX datasets that our approach can classify traffic with high accuracy. We can identify a category with very high accuracy even for VPN and Tor traffic. We classified with high success VPN traffic when the training was done for a non-VPN traffic. Our categorization can identify with good success new applications that were not part of the training phase. We can also use the same CNN to classify applications with an accuracy of 99.7%. Overall, our approach achieves significant better performance than previous work, and can handle classification problems that were not studied in the past.
High-Performance CNN Accelerator on FPGA Using Unified Winograd-GEMM Architecture

Deep neural networks have revolutionized a variety of applications in varying domains like autonomous vehicles, weather forecasting, cancer detection, surveillance, traffic management, and so on. The convolutional neural network (CNN) is the state-of-the-art technique for many machine learning tasks in the image and video processing domains. Deployment of CNNs on embedded systems with lower processing power and smaller power budget is a challenging task. Recent studies have shown the effectiveness of field-programmable gate array (FPGA) as a hardware accelerator for the CNNs that can deliver high performance at low power budgets. Majority of computations in CNNs involve 2-D convolution. Winograd minimal filtering-based algorithm is the most efficient technique for calculating convolution for smaller filter sizes. CNNs also consist of fully connected layers that are computed using general element-wise matrix multiplication (GEMM). In this article, we propose a unified architecture named UniWiG, where both Winograd-based convolution and GEMM can be accelerated using the same set of processing elements. This approach leads to efficient utilization of FPGA hardware resources while computing all layers in the CNN. The proposed architecture shows performance improvement in the range of $1.4\times $ to $4.02\times $ with only 13% additional FPGA resources with respect to the baseline GEMM-based architecture. We have mapped popular CNN models like AlexNet and VGG-16 onto the proposed accelerator and the measured performance compares favorably with other state-of-the-art implementations. We have also analyzed the vulnerability of the accelerator to the side-channel attacks. Preliminary investigations show that the UniWiG architecture is more robust to memory side-channel attacks than direct convolution-based techniques.
A Fuzzy Logic Based Machine Learning Tool for Supporting Big Data Business Analytics in Complex Artificial Intelligence Environments

Business analytics use techniques from data science, data mining, artificial intelligence (especially, machine learning), mathematics and statistics to gain insights and understanding on the performance of business processes. The gained insights and knowledge help driving the business planning. As employees play important roles in the business process, having a tool to classify and predict their wage levels is desirable. Such classification and prediction enables the public or private sector to offer competitive wages for recruiting and retaining employees. In this paper, we present a tool for classifying and predicting wage levels. It incorporates fuzzy logic into a machine-learning tool to support business analytics on big data. Evaluation results show the applicability of our tool for classification and prediction of wages levels in the business world, which in turn supports business analytics in complex artificial intelligence environments.
Optimization of quantum circuit mapping using gate transformation and commutation

This paper addresses quantum circuit mapping for Noisy Intermediate-Scale Quantum (NISQ) computers. Since NISQ computers constrain two-qubit operations to limited couplings, an input circuit must be transformed into an equivalent output circuit obeying the constraints. The transformation often requires additional gates that can affect the accuracy of running the circuit. On the basis of a previous work on quantum circuit mapping that leverages gate commutation rules, this paper presents algorithms that utilize both transformation and commutation rules. Results of experiments on a standard benchmark dataset demonstrate the algorithms with more rules can find even better circuit mappings than the previously known best algorithms.
Research on Text Classification Based on CNN and LSTM

With the rapid development of deep learning technology, CNN and LSTM have become two of the most popular neural networks. This paper combines CNN and LSTM or its variant and makes a slight change. It proposes a text classification model named NA-CNN-LSTM or NA-CNN-COIF-LSTM, which has no activation function in CNN. The experimental results on the subjective and objective text categorization dataset [1] show that the proposed model has better performance than the standard CNN or LSTM.
DEVDAN: Deep evolving denoising autoencoder

The Denoising Autoencoder (DAE) enhances the flexibility of data stream method in exploiting unlabeled samples. Nonetheless, the feasibility of DAE for data stream analytic deserves in-depth study because it characterizes a fixed network capacity which cannot adapt to rapidly changing environments. Deep evolving denoising autoencoder (DEVDAN), is proposed in this paper. It features an open structure in the generative phase and the discriminative phase where the hidden units can be automatically added and discarded on the fly. The generative phase refines the predictive performance of discriminative model exploiting unlabeled data. Furthermore, DEVDAN is free of the problem-specific threshold and works fully in the single-pass learning fashion. We show that DEVDAN can find competitive network architecture compared with state-of-the-art methods on the classification task using ten prominent datasets simulated under the prequential test-then-train protocol.
A Quantitative Comparison of Overlapping and Non-Overlapping Sliding Windows for Human Activity Recognition Using Inertial Sensors

The sliding window technique is widely used to segment inertial sensor signals, i.e., accelerometers and gyroscopes, for activity recognition. In this technique, the sensor signals are partitioned into fix sized time windows which can be of two types: (1) non-overlapping windows, in which time windows do not intersect, and (2) overlapping windows, in which they do. There is a generalized idea about the positive impact of using overlapping sliding windows on the performance of recognition systems in Human Activity Recognition. In this paper, we analyze the impact of overlapping sliding windows on the performance of Human Activity Recognition systems with different evaluation techniques, namely, subject-dependent cross validation and subject-independent cross validation. Our results show that the performance improvements regarding overlapping windowing reported in the literature seem to be associated with the underlying limitations of subject-dependent cross validation. Furthermore, we do not observe any performance gain from the use of such technique in conjunction with subject-independent cross validation. We conclude that when using subject-independent cross validation, non-overlapping sliding windows reach the same performance as sliding windows. This result has significant implications on the resource usage for training the human activity recognition systems.
Cover the Violence: A Novel Deep-Learning-Based Approach Towards Violence-Detection in Movies

Movies have become one of the major sources of entertainment in the current era, which are based on diverse ideas. Action movies have received the most attention in last few years, which contain violent scenes, because it is one of the undesirable features for some individuals that is used to create charm and fantasy. However, these violent scenes have had a negative impact on kids, and they are not comfortable even for mature age people. The best way to stop under aged people from watching violent scenes in movies is to eliminate these scenes. In this paper, we proposed a violence detection scheme for movies that is comprised of three steps. First, the entire movie is segmented into shots, and then a representative frame from each shot is selected based on the level of saliency. Next, these selected frames are passed from a light-weight deep learning model, which is fine-tuned using a transfer learning approach to classify violence and non-violence shots in a movie. Finally, all the non-violence scenes are merged in a sequence to generate a violence-free movie that can be watched by children and as well violence paranoid people. The proposed model is evaluated on three violence benchmark datasets, and it is experimentally proved that the proposed scheme provides a fast and accurate detection of violent scenes in movies compared to the state-of-the-art methods.
An Annotation Saved is an Annotation Earned: Using Fully Synthetic Training for Object Detection

Deep learning methods typically require vast amounts of training data to reach their full potential. While some publicly available datasets exists, domain specific data always needs to be collected and manually labeled, an expensive, time consuming and error prone process. Training with synthetic data is therefore very lucrative, as dataset creation and labeling comes for free. We propose a novel method for creating purely synthetic training data for object detection. We leverage a large dataset of 3D background models and densely render them using full domain randomization. This yields background images with realistic shapes and texture on top of which we render the objects of interest. During training, the data generation process follows a curriculum strategy guaranteeing that all foreground models are presented to the network equally under all possible poses and conditions with increasing complexity. As a result, we entirely control the underlying statistics and we create optimal training samples at every stage of training. Using a challenging evaluation dataset with 64 retail objects, we demonstrate that our approach enables the training of detectors that compete favorably with models trained on real data while being at least two orders of magnitude more time and cost effective with respect to data annotation. Finally, our approach performs significantly better on the YCB-Video Dataset than DOPE - a state-of-the-art method in learning from synthetic data.
Emerging evolutionary algorithm integrated with kernel principal component analysis for modeling the performance of a water treatment plant

Providing a robust and reliable model is essential for hydro-environmental and public health engineering perspectives, including water treatment plants (WTPs). The current research develops an emerging evolutionary data-intelligence model: extreme learning machine (ELM) integrated with kernel principal component analysis (KPCA) to predict the performance of the Tamburawa WTP in Kano, Nigeria. A traditional feed-forward neural network (FFNN) and a classical linear autoregressive (AR) models were also employed to compare the predictive performance. For this purpose, different input data with the corresponding treated pH, turbidity, total dissolve solids, and hardness as the target variables obtained from the WTP were used. The predictive models are evaluated based on the three numerical indices, namely Nash-Sutcliffe (NC), root mean squared error (RMSE) and mean absolute percentage error (MAPE). To examine the similarities and differences between the observed and predicted values, a two-dimension graphical diagram (i.e., Taylor diagram) was also utilized. The predictive results revealed the potential of KPCA-ELM, which exhibited a high level of accuracy in comparison to the single models for all the considered variables with a slight exception in terms of pH prediction. Two different model combination were built for each single (FFNN, ELM, and AR) model and KPCA algorithms (KPCA-FFNN, KPCA-ELM, and KPCA-AR). The results also depicted that both ELM and FFNN models demonstrated prediction skill and therefore, can serve as reliable models. The outcomes may contribute to the aforementioned modeling of the treated parameters and provides a reference benchmark for wastewater management and control in the Tamburawa WTP.
Blockchain-Enabled Decentralized Trust Management and Secure Usage Control of IoT Big Data

With the fast development of Internet-of-Things (IoT) technologies, IoT big data and its applications are getting more and more useful. However, traditional IoT data management is fragile and vulnerable. Once the gathered data are untrusted or the stored data are tampered with deliberately from the internal users or attacked by an external hacker, then the tampered data have a serious problem to be utilized. To solve the problems of trust and security of IoT big data management, in this article, we propose a permissioned blockchain-based decentralized trust management and secure usage control scheme of IoT big data (called BlockBDM), upon which all the data operations and management, such as data gathering, invoking, transfer, storage, and usage, are processed over the blockchain smart contract. To encourage the IoT client to supply high-quality content, in our scheme, we design public-blockchain-based tokens reward mechanism for the high-quality data supply contribution. All the data processing and usage procedure can be recorded in a cryptography-signed and Merkle tree-based transaction(s) and block(s) with high-level security in a global and distributed ledger with tamper resistance. For data utilization and consumption, we propose secure usage control for digital rights management and token-based data consumption approach of high-value data from being violated or spread without any limitation. We implemented the BlockBDM scheme based on public and permissioned blockchain for IoT big data management. Finally, a large amount of evaluation manifests that the proposed BlockBDM scheme is feasible, secure, and scalable for decentralized trust management of IoT big data.
Efficient Multiphoton Sampling of Molecular Vibronic Spectra on a Superconducting Bosonic Processor

The efficient simulation of quantum systems is a primary motivating factor for developing controllable quantum machines. For addressing systems with underlying bosonic structure, it is advantageous to utilize a naturally bosonic platform. Optical photons passing through linear networks may be configured to perform quantum simulation tasks, but the efficient preparation and detection of multiphoton quantum states of light in linear optical systems are challenging. Here, we experimentally implement a boson sampling protocol for simulating molecular vibronic spectra [Nature Photonics $\textbf{9}$, 615 (2015)] in a two-mode superconducting device. In addition to enacting the requisite set of Gaussian operations across both modes, we fulfill the scalability requirement by demonstrating, for the first time in any platform, a high-fidelity single-shot photon number resolving detection scheme capable of resolving up to 15 photons per mode. Furthermore, we exercise the capability of synthesizing non-Gaussian input states to simulate spectra of molecular ensembles in vibrational excited states. We show the re-programmability of our implementation by extracting the spectra of photoelectron processes in H$_2$O, O$_3$, NO$_2$, and SO$_2$. The capabilities highlighted in this work establish the superconducting architecture as a promising platform for bosonic simulations, and by combining them with tools such as Kerr interactions and engineered dissipation, enable the simulation of a wider class of bosonic systems.
Multi-objective feature selection based on artificial bee colony: An acceleration approach with variable sample size

Due to the need to repeatedly call a classifier to evaluate individuals in the population, existing evolutionary feature selection algorithms have the disadvantage of high computational cost. In view of it, this paper studies a multi-objective feature selection framework based on sample reduction strategy and evolutionary algorithm, significantly reducing the computational cost of algorithm without affecting optimal results. In the framework, a selection strategy of representative samples, called K-means clustering based differential selection, and a ladder-like sample utilization strategy are proposed to reduce the size of samples used in the evolutionary process. Moreover, a fast multi-objective evolutionary feature selection algorithm, called FMABC-FS, is proposed by embedding an improved artificial bee colony algorithm based on the particle update model into the framework. By applying FMABC-FS to several typical UCI datasets, and comparing with three multi-objective feature selection algorithms, experimental results show that the proposed variable sample size strategy is more suitable to FMABC-FS, and FMABC-FS can obtain better feature subsets with much less running time than those comparison algorithms.
Neural Machine Translation With GRU-Gated Attention Model

Neural machine translation (NMT) heavily relies on context vectors generated by an attention network to predict target words. In practice, we observe that the context vectors for different target words are quite similar to one another and translations with such nondiscriminatory context vectors tend to be degenerative. We ascribe this similarity to the invariant source representations that lack dynamics across decoding steps. In this article, we propose a novel gated recurrent unit (GRU)-gated attention model (GAtt) for NMT. By updating the source representations with the previous decoder state via a GRU, GAtt enables translation-sensitive source representations that then contribute to discriminative context vectors. We further propose a variant of GAtt by swapping the input order of the source representations and the previous decoder state to the GRU. Experiments on the NIST Chinese-English, WMT14 English-German, and WMT17 English-German translation tasks show that the two GAtt models achieve significant improvements over the vanilla attention-based NMT. Further analyses on the attention weights and context vectors demonstrate the effectiveness of GAtt in enhancing the discriminating capacity of representations and handling the challenging issue of overtranslation.