Reviewerly/exHarmony huggingface dataset:
Efficient single and dual axis solar tracking system controllers based on adaptive neural fuzzy inference system

Artificial Intelligence is widely used in solar applications. Adaptive Neural Fuzzy Inference System (ANFIS) principle is one of the intelligent techniques that is sufficient to be used in control systems. This paper proposes two new efficient intelligent solar tracking control systems based on ANFIS principle. The aim of this paper is to design and implement efficient single and dual-axis solar tracking control systems that can increase the performance of solar trackers, predict the trajectory of the sun across the sky accurately, and minimize the error, therefore, maximize the energy output of solar tracking systems. Experimental data are used to train and test the proposed solar tracking controllers by using month, day and time as input variables to predict the optimum positions for solar tracking systems (tilt/orientation angles). The proposed ANFIS models have been evaluated to find its capability and robustness in tracking the optimum angles that gain the maximum solar radiation. It is found that the proposed controllers are optimum to control solar tracking systems with high prediction rate and the low error rate. Besides, the selected variables along with the selected architecture could successfully predict the optimum tilt and orientation angles. The proposed models provide superior results with five membership functions, and it could obtain high performance for both single-axis and dual-axis solar tracking systems.
Lower bounds on the non-Clifford resources for quantum computations

Treating stabilizer operations as free, we establish lower bounds on the number of resource states, also known as magic states, needed to perform various quantum computing tasks.Our bounds apply to adaptive computations using measurements with an arbitrary number of stabilizer ancillas.We consider (1) resource state conversion, (2) single-qubit unitary synthesis, and (3) computational subroutines including the quantum adder and the multiply-controlled Z gate.To prove our resource conversion bounds we introduce two new monotones, the stabilizer nullity and the dyadic monotone, and make use of the already-known stabilizer extent.We consider conversions that borrow resource states, known as catalyst states, and return them at the end of the algorithm.We show that catalysis is necessary for many conversions and introduce new catalytic conversions, some of which are optimal.By finding a canonical form for post-selected stabilizer computations, we show that approximating a single-qubit unitary to within diamond-norm precision ε requires at least 1/7 • log 2 (1/ε) -4/3 T -states on average.This is the first lower bound that applies to synthesis protocols using fall-back, mixing techniques, and where the number of ancillas used can depend on ε.Up to multiplicative factors, we optimally lower bound the number of T or CCZ states needed to implement the ubiquitous modular adder and multiplycontrolled-Z operations.When the probability of Pauli measurement outcomes is 1/2, some of our bounds become tight to within a small additive constant.
3D dense connectivity network with atrous convolutional feature pyramid for brain tumor segmentation in magnetic resonance imaging of human heads

The existing deep convolutional neural networks (DCNNs) based methods have achieved significant progress regarding automatic glioma segmentation in magnetic resonance imaging (MRI) data. However, there are two main problems affecting the performance of traditional DCNNs constructed by simply stacking convolutional layers, namely, exploding/vanishing gradients and limitations to the feature computations. To address these challenges, we propose a novel framework to automatically segment brain tumors. First, a three-dimensional (3D) dense connectivity architecture is used to build the backbone for feature reuse. Second, we design a new feature pyramid module using 3D atrous convolutional layers and add this module to the end of the backbone to fuse multiscale contexts. Finally, a 3D deep supervision mechanism is equipped with the network to promote training. On the multimodal brain tumor image segmentation benchmark (BRATS) datasets, our method achieves Dice similarity coefficient values of 0.87, 0.72, and 0.70 on the BRATS 2013 Challenge, 0.84, 0.70, and 0.61 on the BRATS 2013 LeaderBoard, 0.83, 0.70, and 0.62 on the BRATS 2015 Testing, 0.8642, 0.7738, and 0.7525 on the BRATS 2018 Validation in terms of whole tumors, tumor cores, and enhancing cores, respectively. Compared to the published state-of-the-art methods, the proposed method achieves promising accuracy and fast processing, demonstrating good potential for clinical medicine.
Classification of Poetry Text Into the Emotional States Using Deep Learning Technique

The classification of emotional states from poetry or formal text has received less attention by the experts of computational intelligence in recent times as compared to informal textual content like SMS, email, chat, and online user reviews. In this study, an emotional state classification system for poetry text is proposed using the latest and cutting edge technology of Artificial Intelligence, called Deep Learning. For this purpose, an attention-based C-BiLSTM model is implemented on the poetry corpus. The proposed approach classifies the text of poetry into different emotional states, like love, joy, hope, sadness, anger, etc. Different experiments are conducted to evaluate the efficiency of the proposed system as compared to other state-of-art methods as well as machine learning and deep learning methods. Experimental results depict that the proposed model outperformed the baselines studies with 88% accuracy. Furthermore, the analysis of the statistical experiment also validates the performance of the proposed approach.
Named Entity Recognition using Conditional Random Fields

Identifying named entities (NEs) present in electronic newspapers in regional languages is an important step in machine translation and summarization systems. In this paper, we propose a statistical named entity recognition system based on machine learning for the identification and classification of named entities present in Marathi language text. In our system, named entities are identified and classified using conditional random fields (CRFs). As being a morphologically rich language, statistical algorithms achieves good NE identification and classification accuracy but needs extra knowledge to improve accuracy. Experiments conducted on the FIRE-2010 corpus show that our system submitted for the challenge achieves the precision, recall and F1-measure of 82.33%, 70.68% and 75.51% under the CRF algorithm.
Automated Fine-Grained CPU Cap Control in Serverless Computing Platform

Serverless computing has emerged as a new cloud computing execution model that liberates users and application developers from explicitly managing `physical' resources, leaving such a resource management burden to service providers. In this article, we study the problem of resource allocation for multi-tenant serverless computing platforms explicitly taking into account workload fluctuations including sudden surges. In particular, we investigate different root causes of performance degradation in these platforms where tenants (their applications) have different workload characteristics. To this end, we develop a fine-grained CPU cap control solution as a resource manager that dynamically adjusts CPU usage limit (or CPU cap) concerning applications with same/similar performance requirements, i.e., application groups. The adjustment of CPU caps applies primarily to co-located worker processes of serverless computing platforms to minimize resource contention, which is the major source of performance degradation. The actual adjustment decisions are made based on performance metrics (e.g., throttled time and queue length) using a group-aware scheduling algorithm. The extensive experimental results performed in our local cluster confirm that the proposed resource manager can effectively eliminate the burden of explicit reservation of computing capacity, even when fluctuations and sudden surges in the incoming workload exist. We measure the robustness of the proposed resource manager by comparing it with several heuristics which extensively used in practice, including the enhanced version of round robin and the least length queue scheduling policies, under various workload intensities driven by real-world scenarios. Notably, our resource manager outperforms other heuristics by decreasing skewness and average response time up to 44 and 94 percent, respectively, while it does not over-use the CPU resources.
Intermittent Contextual Learning for Keyfilter-Aware UAV Object Tracking Using Deep Convolutional Feature

Visual tracking, one of the most favorable multimedia applications, has been widely used in unmanned aerial vehicle (UAV) for civil infrastructure monitoring, aerial cinematography, autonomous navigation, etc. Most existing trackers utilize deep convolutional feature to enhance tracking robustness in scenarios of various appearance variation. However, they commonly neglect speed which is crucial for UAV with restricted calculation resources. In this work, a novel correlation filter-based keyfilter-aware tracker with a new intermittent context learning strategy is proposed to efficiently and effectively alleviate the problems of background clutter, deficient description, occlusion, illumination change, etc. Specifically, context information is utilized to empower the filter higher discriminating ability through response repression of the omnidirectional context patches. Furthermore, keyfilter is produced from the periodically selected keyframe. The latest produced keyfilter is used to restrain the current filter's corrupted changes. Most importantly, context learning of correlation filter is implemented intermittently to fully increase the tracking efficiency. This intermittent learning strategy can ensure every filter maintain context awareness owing to the restriction of keyfilter, periodically enhancing the context awareness. Substantial experiments on three challenging UAV benchmarks totally with 213 image sequences have shown that our tracker surpasses the state-of-the-art results, and exhibits a remarkable generality in short-term and long-term UAV tracking tasks as well as a variety of challenging attributes.
Dilated Convolution and Feature Fusion SSD Network for Small Object Detection in Remote Sensing Images

Noting the shortcomings of current methods in detecting small objects in image-based remote sensing applications, in this paper, we propose a novel implementation of single shot multibox detector (SSD) networks based on dilated convolution and feature fusion. We call this algorithm dilated convolution and feature fusion single shot multibox detector (DFSSD). This algorithm removes the random clipping steps of data preprocessing layers in conventional SSD networks and utilizes the structure of feature pyramid network (FPN) network to fuse the low-level feature map with high resolution and the high-level feature map with rich semantic information. It also enhances the receptive field of the third-level feature map of the DFSSD network by using dilated convolution. In the data processing step of the model, we use the image segmentation of the feature point region proposals to improve the training sample size. The mean average precision (mAP) value of the proposed DFSSD network, when tested on remote sensing datasets, achieves 76.51%, which is significantly higher than that of the SSD model (69.81%).
Big data approach for sentiment analysis of twitter data using Hadoop framework and deep learning

Sentiment analysis acquired a great area of attention in the microblogging websites and analysis of sentiment is a practice of categorization and identification of opinions that are articulated as speech, text, database sources and tweets to detect if opinion is negative, positive or neutral. The challenge lies in determining sentiment from the tweets due to the unique characteristics of Twitter data. This paper presents an approach for sentiment analysis by adapting a Hadoop framework and deep learning classifier. The Hadoop cluster is used for the distribution of data for extracting the features. Then, the significant features are extracted using the twitter data. The deep learning classifier, namely deep recurrent neural network classifier is used assign a real-valued review to each input twitter data thus, classifying the input data into two classes, such as positive review and negative review. The analysis of the performance is done using metrics like, classification accuracy, sensitivity and specificity. In contrast to classical strategies, the proposed method offered improved classification accuracy of 0.9302, better sensitivity of 0.9404 and high specificity of 0.9157, respectively.
A novel population initialization strategy for accelerating Levy flights based multi-verse optimizer

In this paper, we have designed a new optimization technique, which is named as the Improved Multi-verse Algorithm with Levy Flights (ILFMVO) algorithm. The quality of the population is an important factor that can directly or indirectly affect the strength of an algorithm in searching for the give n search space for an optimal solution. Also, having an initialization of the initial population with randomly generated candidate solutions is not an effective idea in every case, especially when the search space is large. Hence, we have updated the Levy flights based Multi-verse Optimizer (LFMVO) by dividing initialization into two parts. To investigate the ability of ILFMVO, we have solved a constrained economic dispatch problem with a non-smooth, non-convex cost functions of three, six, and twenty thermal generator systems and two design engineering problems with nonlinear objectives and complex nonlinear constraints. We have compared our results with other standard algorithms. We have presented the sensitivity analysis to check the robustness and stability of our approach. The outcome demonstrated that ILFMVO has better accuracy, stability, and convergence.
Fusing Transformed Deep and Shallow features (FTDS) for image-based facial expression recognition

In this paper, we propose combining between the transformed hand-crafted and deep features using PCA to recognize the six-basic facial expressions from static images. To evaluate our approach, we use three popular databases (CK+, CASIA and MMI). We introduce the use of the Pyramid Multi Level (PML) face representation for facial expression recognition. The hand-crafted features are obtained with such representations. Initially, we determine the optimal level of the PML features of three hand-crafted descriptors (HOG, LPQ and BSIF) using CK+, CASIA and MMI databases. After the optimal level of the PML is found for each descriptor, we combine them together with the transformed final VGG-face layers (FC6 and FC7) in order to get a compact image descriptor. In within-database experiments, our approach achieved higher accuracy than the state-of-art methods on both the CK+ and CASIA databases, and competitive result on the MMI database. Likewise, our approach outperformed the static methods in all six experiments of cross-databases.
Robust Shadow Tracking for Video SAR

Video synthetic aperture radar (Video SAR) has sparked lots of research attention since it provides the ability of continuously observing and tracking the object of interest. Instead of tracking a target directly, in Video SAR, it is preferred to track its shadow due to the target's instable backscattering characteristic and defocused patterns. However, the current tracking algorithms do not work well since they always suffer from the interference caused by surrounding clutters and cannot locate the target precisely. To address these issues, we propose a robust shadow tracking algorithm for Video SAR in this letter. We equip our tracker with the spatial-temporal (ST) information and saliency-based detection mechanism (SD) against the distractions and background clutters. The proposed optimization formula could be solved efficiently using the alternating direction method of multipliers (ADMM) technique and fully carried in the Fourier domain at a low computational burden. Experiments have demonstrated that the proposed algorithm performs favorably against other state-of-the-art methods.
Designing with Intimate Materials and Movements

Menarche is the first occurrence of menstrual bleeding and it usually begins between the ages of 9-15. This makes menarche a crucial transition among other social, physiological and behavioural changes during puberty. In this soma-based research-through-design project we design an open-ended prototyping kit: Menarche Bits. The aim of Menarche Bits is to open a design space for young adolescents to create body-worn technologies that support them in making space for their experiences of menarche and trusting their menstruating bodies. Menarche Bits consists of heat elements and shape-changing actuators that can be worn directly on the body by adhering to the skin or being inserted into pockets in a stretchable fabric as part of a garment. We describe the soma design process behind Menarche Bits as an example of how body-worn technologies can intimately interact with the body and its movement, temporality and material changes.
Asymptotically Optimal Planning under Piecewise-Analytic Constraints

We present the first asymptotically optimal algorithm for motion planning problems with piecewise-analytic differential constraints, like manipulation or rearrangement planning. This class of problems is characterized by the presence of differential constraints that are local in nature: a robot can only move an object once the object has been grasped. These constraints are not analytic and thus cannot be addressed by standard differentially constrained planning algorithms. We demonstrate that, given the ability to sample from the locally reachable subset of the configuration space with positive probability, we can construct random geometric graphs that contain optimal plans with probability one in the limit of infinite samples. This approach does not require a hand-coded symbolic abstraction. We demonstrate our approach in simulation on a simple manipulation planning problem, and show it generates lower-cost plans than a sequential task and motion planner.
Urban Anomaly Analytics: Description, Detection, and Prediction

Urban anomalies may result in loss of life or property if not handled properly. Automatically alerting anomalies in their early stage or even predicting anomalies before happening is of great value for populations. Recently, data-driven urban anomaly analysis frameworks have been forming, which utilize urban big data and machine learning algorithms to detect and predict urban anomalies automatically. In this survey, we make a comprehensive review of the state-of-the-art research on urban anomaly analytics. We first give an overview of four main types of urban anomalies, traffic anomaly, unexpected crowds, environment anomaly, and individual anomaly. Next, we summarize various types of urban datasets obtained from diverse devices, i.e., trajectory, trip records, CDRs, urban sensors, event records, environment data, social media and surveillance cameras. Subsequently, a comprehensive survey of issues on detecting and predicting techniques for urban anomalies is presented. Finally, research challenges and open problems as discussed.
Distributed service‐level agreement management with smart contracts and blockchain

Summary The current cloud market is dominated by a few providers, which offer cloud services in a take‐it‐or‐leave‐it manner. However, the dynamism and uncertainty of cloud environments may require the change over time of both application requirements and service capabilities. The current service‐level agreement (SLA) management solutions cannot easily guarantee a trustworthy, distributed SLA adaptation due to the centralized authority of the cloud provider who could also misbehave to pursue individual goals. To address the above issues, we propose a novel SLA management framework, which facilitates the specification and enforcement of dynamic SLAs that enable one to describe how, and under which conditions, the offered service level can change over time. The proposed framework relies on a two‐level blockchain architecture. At the first level, the smart SLA is transformed into a smart contract that dynamically guides service provisioning. At the second level, a permissioned blockchain is built through a federation of monitoring entities to generate objective measurements for the smart SLA/contract assessment. The scalability of this permissioned blockchain is also thoroughly evaluated. The proposed framework enables creating open distributed clouds, which offer manageable and dynamic services, and facilitates cost reduction for cloud consumers, while it increases flexibility in resource management and trust in the offered cloud services.
Forensics and Analysis of Deepfake Videos

The spread of smartphones with high quality digital cameras in combination with easy access to a myriad of software apps for recording, editing and sharing videos and digital images in combination with deep learning AI platforms has spawned a new phenomenon of faking videos known as Deepfake. We design and implement a deep-fake detection model with mouth features (DFT-MF), using deep learning approach to detect Deepfake videos by isolating, analyzing and verifying lip/mouth movement. Experiments conducted against datasets that contain both fake and real videos showed favorable classification performance for DFT-MF model especially when compared with other work in this area.
Multi-dimensional classification via kNN feature augmentation

In multi-dimensional classification (MDC), each training example is represented by a single instance (feature vector) while associated with multiple class variables, each of which specifies its class membership w.r.t. one specific class space. Most existing MDC approaches try to model dependencies among class variables in output space when inducing predictive functions, while the potential usefulness of manipulating feature space hasn't been investigated. As a first attempt towards feature manipulation in input space for MDC, a simple yet effective approach named Kram is proposed which enriches the original feature space with augmented features based on kNN techniques. Specifically, simple counting statistics on the class membership of neighboring MDC examples as well as distance information between MDC examples and their k nearest neighbors are used to generate augmented feature vector. In this way, discriminative information from class space is expected to be brought into the feature space which would be helpful to the following MDC predictive model induction. To validate the effectiveness of the proposed feature augmentation techniques, comprehensive comparative studies are conducted over fifteen benchmark data sets. Compared to the original feature space, it is clearly shown that the kNN-augmented features generated by the proposed Kram approach can significantly improve generalization abilities of existing MDC approaches.