Reviewerly/exHarmony huggingface dataset:
Cloud-Assisted Multiview Video Summarization Using CNN and Bidirectional LSTM

The massive amount of video data produced by surveillance networks in industries instigate various challenges in exploring these videos for many applications, such as video summarization (VS), analysis, indexing, and retrieval. The task of multiview video summarization (MVS) is very challenging due to the gigantic size of data, redundancy, overlapping in views, light variations, and interview correlations. To address these challenges, various low-level features and clustering-based soft computing techniques are proposed that cannot fully exploit MVS. In this article, we achieve MVS by integrating deep neural network based soft computing techniques in a two-tier framework. The first online tier performs target-appearance-based shots segmentation and stores them in a lookup table that is transmitted to cloud for further processing. The second tier extracts deep features from each frame of a sequence in the lookup table and pass them to deep bidirectional long short-term memory (DB-LSTM) to acquire probabilities of informativeness and generates a summary. Experimental evaluation on benchmark dataset and industrial surveillance data from YouTube confirms the better performance of our system compared to the state-of-the-art MVS methods.
A heuristic-based approach for optimizing a small independent solar and wind hybrid power scheme incorporating load forecasting

Small independent hybrid power schemes (IHPSs) are promising alternatives for load supply for remote areas. In IHPS optimization, load data are generally the main input. In this paper, forecasting strategies are proposed for load related parameters and tested on real data. Also, an efficient method based on the heuristic procedure (tabu search) is presented for optimization of an IHPS based on solar and wind energy along with a battery. The effect of using forecast load information instead of past information on the IHPS performance is investigated. In the optimization, there are three main decision variables: number of batteries, surface area of the PV system, and wind turbine swept area. The optimization is done to satisfy continually the load demand and to minimize the IHPS life cycle cost while respecting relevant limitations. To ensure the scheme's reliability, the probability of loss of power supply is determined. The performance of the proposed algorithm-based load forecasting approach is compared with the harmony search algorithm-based load forecasting and simulated annealing algorithm-based load forecasting. The simulation results clearly demonstrate the advantages of utilizing load forecasting in an IHPS optimization problem, and confirm that the tabu search method earnings more promising results than the harmony search and simulated annealing methods.
Echo State Network-Based Backstepping Adaptive Iterative Learning Control for Strict-Feedback Systems: An Error-Tracking Approach

In this article, an echo state network (ESN)-based backstepping adaptive iterative learning control scheme is proposed for nonlinear strict-feedback systems performing the same operation repeatedly over a finite-time interval. Different from most of the output tracking approaches, an error-tracking approach is presented using the backstepping technique, such that the tracking error can follow a prespecified error trajectory without any requirement on the initial value of system states. Then, a novel Lyapunov function is constructed to deal with the unknown state-dependent gain function of the controller design. The uncertain nonlinearities are approximated by employing ESNs with simple feedback structures, and the weight update laws are developed by combining the parameter adaptation in the time domain and iteration domain. Moreover, the proposed control scheme is further extended to handle the strict-feedback systems with input saturations. Through the Lyapunov-like synthesis, the closed-loop stability and error convergence of the proposed error-tracking control scheme are analyzed in the presence of the approximation errors. Numerical simulations are provided to verify the effectiveness of the proposed scheme.
MudraChain: Blockchain-based framework for automated cheque clearance in financial institutions

Currently, the burden on the cheque clearing houses in financial institutions is increasing day-by-day, which necessitates the upgrading of the existing cheque truncation system (CTS). It is a manual process which uses Magnetic Ink Character Recognition (MICR), where cheques have been scanned and sent to the clearing house for further processing. The limitations of existing CTS are — illegal duplication of cheque images, invisible ink usage, visibility issues in beneficiary name, and amount on the cheque. To handle the aforementioned issues of the existing CTS, blockchain has emerged as a new technology which is a distributed ledger that is timestamped and immutable. Being immutable, forgeries related to images of cheques during clearance cycles are not allowed. This provides trust and consensus among all participating entities in the network. Motivated by the above discussion, in this paper, we propose a framework named MudraChain for automated cheque clearance, where clearance operations are handled by the blockchain network, instead of existing CTS. It includes: (i) A multi-level authentication scheme to make the blockchain-based framework secure and tamper-proof among participating financial stakeholders, (ii) A quick-response (QR) code generation algorithm which performs digital signing of a cheque, and (iii) A novel two-factor authentication protocol to generate a time based one-time password (TOTP) for secure funds transfer. The obtained results are examined against state-of-the-art approaches to indicate the supremacy of the proposed framework. Thus, MudraChain allows a seamless flow of clearance operation via blockchain for the payer and the payee without any intermediaries. Finally, it addresses the requirements of building a secure application for cheque clearance in view of decentralized blockchain 4.0 applications.
Targeted Adversarial Examples for Black Box Audio Systems

The application of deep recurrent networks to audio transcription has led to impressive gains in automatic speech recognition (ASR) systems. Many have demonstrated that small adversarial perturbations can fool deep neural networks into incorrectly predicting a specified target with high confidence. Current work on fooling ASR systems have focused on white-box attacks, in which the model architecture and parameters are known. In this paper, we adopt a black-box approach to adversarial generation, combining the approaches of both genetic algorithms and gradient estimation to solve the task. We achieve a 89.25% targeted attack similarity, with 35% targeted attack success rate, after 3000 generations while maintaining 94.6% audio file similarity.
Convolutional Recurrent Neural Networks for Text Classification

Text classification is an important task in natural language processing with wide applications. Traditional text classification methods manually extract the features which are later fed into the classifier for training. Recent researchers have employed convolutional neural networks or recurrent neural networks for text classification motivated by the noticeable success of deep learning. However, most of their models are based on single network. In this paper, we introduce a convolutional recurrent neural network for text classification, which enjoys both the advantages of convolutional neural networks for extracting local features from text and also those of recurrent neural networks (LSTM) in memory to connect the extracted features. We conduct extensive experiments on two Chinese data sets and five English data sets, and compare our method with several other classification methods. Experimental results show that the proposed method can achieve better accuracy on text classification tasks.
Nexus

We address the problem of serving Deep Neural Networks (DNNs) efficiently from a cluster of GPUs. In order to realize the promise of very low-cost processing made by accelerators such as GPUs, it is essential to run them at sustained high utilization. Doing so requires cluster-scale resource management that performs detailed scheduling of GPUs, reasoning about groups of DNN invocations that need to be co-scheduled, and moving from the conventional whole-DNN execution model to executing fragments of DNNs. Nexus is a fully implemented system that includes these innovations. In large-scale case studies on 16 GPUs, when required to stay within latency constraints at least 99% of the time, Nexus can process requests at rates 1.8-12.7X higher than state of the art systems can. A long-running multi-application deployment stays within 84% of optimal utilization and, on a 100-GPU cluster, violates latency SLOs on 0.27% of requests.
Outlier detection using isolation forest and local outlier factor

Outlier detection, also named as anomaly detection, is one of the hot issues in the field of data mining. As well-known outlier detection algorithms, Isolation Forest(iForest) and Local Outlier Factor(LOF) have been widely used. However, iForest is only sensitive to global outliers, and is weak in dealing with local outliers. Although LOF performs well in local outlier detection, it has high time complexity. To overcome the weaknesses of iForest and LOF, a two-layer progressive ensemble method for outlier detection is proposed. It can accurately detect outliers in complex datasets with low time complexity. This method first utilizes iForest with low complexity to quickly scan the dataset, prunes the apparently normal data, and generates an outlier candidate set. In order to further improve the pruning accuracy, the outlier coefficient is introduced to design a pruning threshold setting method, which is based on outlier degree of data. Then LOF is applied to further distinguish the outlier candidate set and get more accurate outliers. The proposed ensemble method takes advantage of the two algorithms and concentrates valuable computing resources on the key stage. Finally, a large number of experiments are carried out to verify the ensemble method. The results show that compared with the existing methods, the ensemble method can significantly improve the outlier detection rate and greatly reduce the time complexity.
Machine learning models to quantify and map daily global solar radiation and photovoltaic power

Global solar radiation (Rs) reaching Earth's surface is the primary information for the design and application of solar energy-related systems. High-resolution Rs measurements are limited owing to the high costs of measuring devices, and their stringent operational maintenance procedures. This study evaluated a newly developed machine learning model, namely the hybrid particle swarm optimization and extreme learning machine (PSO-ELM), to accurately predict daily Rs. The newly proposed model was compared with five other machine learning models, namely the original ELM, support vector machine, generalized regression neural networks, M5 model tree, and autoencoder, under two training scenarios using long-term Rs and other climatic data taken during 1961–2016 from seven stations located on the Loess Plateau of China. Overall, the PSO-ELM with full climatic data as inputs provided more accurate Rs estimations. We also calculated the daily Rs at fifty other stations without Rs measurements on the Loess Plateau using the PSO-ELM model, as well as the potential photovoltaic (PV) power using an empirical PV power model, and then generated high-resolution (0.25°) Rs and PV power data to investigate the patterns of Rs and PV power. Significant reductions in Rs (−6.49 MJ m−2 per year, p < 0.05) and PV power (−0.46 kWh m−2 per year, p < 0.05) were observed. The northwestern parts of the study area exhibited more Rs and PV power and are therefore considered more favorable for solar energy-related applications. Our study confirms the effectiveness of the PSO-ELM for solar energy modeling, particularly in areas where in-situ measurements are unavailable.
Temporal Recurrent Networks for Online Action Detection

Most work on temporal action detection is formulated as an offline problem, in which the start and end times of actions are determined after the entire video is fully observed. However, important real-time applications including surveillance and driver assistance systems require identifying actions as soon as each video frame arrives, based only on current and historical observations. In this paper, we propose a novel framework, the Temporal Recurrent Network (TRN), to model greater temporal context of each frame by simultaneously performing online action detection and anticipation of the immediate future. At each moment in time, our approach makes use of both accumulated historical evidence and predicted future information to better recognize the action that is currently occurring, and integrates both of these into a unified end-to-end architecture. We evaluate our approach on two popular online action detection datasets, HDD and TVSeries, as well as another widely used dataset, THUMOS'14. The results show that TRN significantly outperforms the state-of-the-art.
What’s next for Ed-Tech? Critical hopes and concerns for the 2020s

In many ways, the passing of another decade is nothing remarkable. The world does not transform periodically every ten years. Nevertheless, the fact that the 2020s are now upon us provides good rea...
Preventing undesirable behavior of intelligent machines

Intelligent machines using machine learning algorithms are ubiquitous, ranging from simple data analysis and pattern recognition tools to complex systems that achieve superhuman performance on various tasks. Ensuring that they do not exhibit undesirable behavior-that they do not, for example, cause harm to humans-is therefore a pressing problem. We propose a general and flexible framework for designing machine learning algorithms. This framework simplifies the problem of specifying and regulating undesirable behavior. To show the viability of this framework, we used it to create machine learning algorithms that precluded the dangerous behavior caused by standard machine learning algorithms in our experiments. Our framework for designing machine learning algorithms simplifies the safe and responsible application of machine learning.
Mask OBB: A Semantic Attention-Based Mask Oriented Bounding Box Representation for Multi-Category Object Detection in Aerial Images

Object detection in aerial images is a fundamental yet challenging task in remote sensing field. As most objects in aerial images are in arbitrary orientations, oriented bounding boxes (OBBs) have a great superiority compared with traditional horizontal bounding boxes (HBBs). However, the regression-based OBB detection methods always suffer from ambiguity in the definition of learning targets, which will decrease the detection accuracy. In this paper, we provide a comprehensive analysis of OBB representations and cast the OBB regression as a pixel-level classification problem, which can largely eliminate the ambiguity. The predicted masks are subsequently used to generate OBBs. To handle huge scale changes of objects in aerial images, an Inception Lateral Connection Network (ILCN) is utilized to enhance the Feature Pyramid Network (FPN). Furthermore, a Semantic Attention Network (SAN) is adopted to provide the semantic feature, which can help distinguish the object of interest from the cluttered background effectively. Empirical studies show that the entire method is simple yet efficient. Experimental results on two widely used datasets, i.e., DOTA and HRSC2016, demonstrate that the proposed method outperforms state-of-the-art methods.
Global and Local-Contrast Guides Content-Aware Fusion for RGB-D Saliency Prediction

Many RGB-D visual attention models have been proposed with diverse fusion models; thus, the main challenge lies in the differences in the results between the different models. To address this challenge, we propose a local-global fusion model for fixation prediction on an RGB-D image; this method combines global and local information through a content-aware fusion module (CAFM) structure. First, it comprises a channel-based upsampling block for exploiting global contextual information and scaling up this information to the same resolution as the input. Second, our Deconv block contains a contrast feature module to utilize multilevel local features stage-by-stage for superior local feature representation. The experimental results demonstrate that the proposed model exhibits competitive performance on two databases.
Blockchain-Based Remote Patient Monitoring in Healthcare 4.0

In Healthcare 4.0, Remote patient monitoring (RPM) becomes a more powerful and flexible patient observation through wearable sensors at any time and anywhere. The most focused application area of RPM which allows doctors to get real-time information of their patient remotely with the help of wireless communication system. Thus, RPM reduces the time and cost of the patient. It also provides the quality care to the patient. To enhance the security and privacy of the patient data, in this paper, we have presented a Permissioned blockchain-based healthcare architecture. We have also discussed the challenges and their solutions. We have described the applications of blockchain. We also have given the usage of Machine learning with blockchain technology which can impact the healthcare industry.
Multi-Scale Context Aggregation for Semantic Segmentation of Remote Sensing Images

The semantic segmentation of remote sensing images (RSIs) is important in a variety of applications. Conventional encoder-decoder-based convolutional neural networks (CNNs) use cascade pooling operations to aggregate the semantic information, which results in a loss of localization accuracy and in the preservation of spatial details. To overcome these limitations, we introduce the use of the high-resolution network (HRNet) to produce high-resolution features without the decoding stage. Moreover, we enhance the low-to-high features extracted from different branches separately to strengthen the embedding of scale-related contextual information. The low-resolution features contain more semantic information and have a small spatial size; thus, they are utilized to model the long-term spatial correlations. The high-resolution branches are enhanced by introducing an adaptive spatial pooling (ASP) module to aggregate more local contexts. By combining these context aggregation designs across different levels, the resulting architecture is capable of exploiting spatial context at both global and local levels. The experimental results obtained on two RSI datasets show that our approach significantly improves the accuracy with respect to the commonly used CNNs and achieves state-of-the-art performance.
A Multilingual Evaluation for Online Hate Speech Detection

The increasing popularity of social media platforms such as Twitter and Facebook has led to a rise in the presence of hate and aggressive speech on these platforms. Despite the number of approaches recently proposed in the Natural Language Processing research area for detecting these forms of abusive language, the issue of identifying hate speech at scale is still an unsolved problem. In this article, we propose a robust neural architecture that is shown to perform in a satisfactory way across different languages; namely, English, Italian, and German. We address an extensive analysis of the obtained experimental results over the three languages to gain a better understanding of the contribution of the different components employed in the system, both from the architecture point of view (i.e., Long Short Term Memory, Gated Recurrent Unit, and bidirectional Long Short Term Memory) and from the feature selection point of view (i.e., ngrams, social network–specific features, emotion lexica, emojis, word embeddings). To address such in-depth analysis, we use three freely available datasets for hate speech detection on social media in English, Italian, and German.
Generative Pre-Training for Speech with Autoregressive Predictive Coding

Learning meaningful and general representations from unannotated speech that are applicable to a wide range of tasks remains challenging. In this paper we propose to use autoregressive predictive coding (APC), a recently proposed self-supervised objective, as a generative pre-training approach for learning meaningful, non-specific, and transferable speech representations. We pre-train APC on large-scale unlabeled data and conduct transfer learning experiments on three speech applications that require different information about speech characteristics to perform well: speech recognition, speech translation, and speaker identification. Extensive experiments show that APC not only outperforms surface features (e.g., log Mel spectrograms) and other popular representation learning methods on all three tasks, but is also effective at reducing downstream labeled data size and model parameters. We also investigate the use of Transformers for modeling APC and find it superior to RNNs.
Blockchain-based decentralized storage networks: A survey

Blockchain is a new approach to create a distributed network which was first introduced in 2008. By the help of this disruptive technology a peer-to-peer network can be formed where nodes have to reach a consensus and form a chain from accepted blocks, while no central party or trusted controller is required. Among all the existing uses of this technology, decentralized storage systems are one of its prominent applications. Decentralized storage networks, are consisted of a group of people willing to rent out their unused hardware storage space. By implementing end-to-end encryption clients can securely transmit their files through a fully decentralized network and eliminate the risk of data failures that arise from centralized controls. In this network, storage providers are required to prove that they store unaltered files during the time. Also a smart contract between two parties is set out in which rental duration and cost that the customer needs to pay for using that storage space is clarified. This paper presents a comprehensive survey on the blockchain-based storage systems and how they work and then compares them with cloud-based storage networks. Also different techniques of consensus protocols in each group are explored. Next, we have an overview on the advantages and also weak points of blockchain-based storage systems. Finally, we discuss about future research directions.
Segmentation and Feature Extraction in Medical Imaging: A Systematic Review

Image processing techniques being crucial towards analyzing and resolving issues in medical imaging since last two decades. Medical imaging is a process or technique to find the inner or outer construction of mortal body. The process observes medicinal diagnosis, analyze illnesses and develop data-sets of normal and abnormal imageries. Medical imaging is divided in two folds such as invisible-light medical imaging and visible-light medical imaging. The second type of medical imaging were can be understood by a common person whereas the first type can be interpreted by a radiologist. Analysis of all these require segmentation and feature extraction. In fact a lot of medical imaging techniques are available but authors restrict survey to tumor detection through mammograms or magnetic resonance imaging. In this paper, authors survey on various segmentation and feature extraction methods in medicinal images used for preprocessing.