Reviewerly/exHarmony huggingface dataset:
Simulation tools for a smart grid and energy management for microgrid with wind power using multi-agent system

The smart grid concept is predicated upon the pervasive use of advanced digital communication, information techniques, and artificial intelligence for the current power system to be more characteristics on the real-time monitoring and controlling of the supply/demand. Therefore, in recent years, researchers increasingly couple distinct simulators to form novel “co-simulations.” In this article, we will present a survey of different electrical power and communication simulators, a literature survey of 20 smart grid co-simulations frameworks, and the characteristics of each platform applicable in the intelligent electrical network. Finally, we proposed multi-agent systems for controlling the microgrid that consists of wind power and storage system using MACSimJX co-simulation that combines Simulink simulator and JADE (Java Agent Development Environment).
Enhancing Gaussian Estimation of Distribution Algorithm by Exploiting Evolution Direction With Archive

As a typical model-based evolutionary algorithm, estimation of distribution algorithm (EDA) possesses unique characteristics and has been widely applied in global optimization. However, the commonly used Gaussian EDA (GEDA) usually suffers from premature convergence, which severely limits its search efficiency. This paper first systematically analyzes the reasons for the deficiency of traditional GEDA, then tries to enhance its performance by exploiting the evolution direction, and finally develops a new GEDA variant named EDA2. Instead of only utilizing some good solutions produced in the current generation to estimate the Gaussian model, EDA2 preserves a certain number of high-quality solutions generated in the previous generations into an archive and employs these historical solutions to assist estimating the covariance matrix of Gaussian model. By this means, the evolution direction information hidden in the archive is naturally integrated into the estimated model, which in turn can guide EDA2 toward more promising solution regions. Moreover, the new estimation method significantly reduces the population size of EDA2 since it needs fewer individuals in the current population for model estimation. As a result, a fast convergence can be achieved. To verify the efficiency of EDA2, we tested it on a variety of benchmark functions and compared it with several state-of-the-art EAs. The experimental results demonstrate that EDA2 is efficient and competitive.
Quantum state certification

We consider the problem of quantum state certification, where one is given n copies of an unknown d-dimensional quantum mixed state ρ, and one wants to test whether ρ is equal to some known mixed state σ or else is є-far from σ. The goal is to use notably fewer copies than the Ω(d2) needed for full tomography on ρ (i.e., density estimation). We give two robust state certification algorithms: one with respect to fidelity using n = O(d/є) copies, and one with respect to trace distance using n = O(d/є2) copies. The latter algorithm also applies when σ is unknown as well. These copy complexities are optimal up to constant factors.
The MalSource Dataset: Quantifying Complexity and Code Reuse in Malware Development

During the last decades, the problem of malicious and unwanted software (malware) has surged in numbers and sophistication. Malware plays a key role in most of today's cyberattacks and has consolidated as a commodity in the underground economy. In this paper, we analyze the evolution of malware from 1975 to date from a software engineering perspective. We analyze the source code of 456 samples from 428 unique families and obtain measures of their size, code quality, and estimates of the development costs (effort, time, and number of people). Our results suggest an exponential increment of nearly one order of magnitude per decade in aspects such as size and estimated effort, with code quality metrics similar to those of benign software. We also study the extent to which code reuse is present in our dataset. We detect a significant number of code clones across malware families and report which features and functionalities are more commonly shared. Overall, our results support claims about the increasing complexity of malware and its production progressively becoming an industry.
Artificial Intelligence in Intelligent Tutoring Robots: A Systematic Review and Design Guidelines

This study provides a systematic review of the recent advances in designing the intelligent tutoring robot (ITR) and summarizes the status quo of applying artificial intelligence (AI) techniques. We first analyze the environment of the ITR and propose a relationship model for describing interactions of ITR with the students, the social milieu, and the curriculum. Then, we transform the relationship model into the perception-planning-action model to explore what AI techniques are suitable to be applied in the ITR. This article provides insights on promoting a human-robot teaching-learning process and AI-assisted educational techniques, which illustrates the design guidelines and future research perspectives in intelligent tutoring robots.
Fast Color Blending for Seamless Image Stitching

In this letter, we propose a fast and robust method for stitching overlapped images captured by the unmanned aerial vehicle. First, we apply the shape-preserving half-projective method to precisely and stably align a pair of partially overlapped input images. Then, an optimal stitching line is searched to remove ghosts caused by the moving objects in the overlapped area. We subsequently propose a color blending method to eliminate all the color inconsistencies in the prealigned image. In accordance with the color differences of the pixels on the optimal stitching seam, we utilize weighted value coordinate interpolation algorithms to compute accurate color changes for all the pixels in the target image. The calculated color changes are then added to the target image to remove the color inconsistency. Furthermore, we introduce the superpixel segmentation to divide the target image into a reduced number of superpixels, and we assign each superpixel the same color change value. Such a superpixel level operation can greatly reduce the computational complexity. Experiments show that our method is promising to achieve effective and efficient stitching results.
A Novel Fuzzy Clustering-Based Histogram Model for Image Contrast Enhancement

Histogram equalization is a famous method for enhancing the contrast and image features. However, in few cases, it causes the overenhancement, and hence demolishes the natural display of the image. Therefore, in this article, a new fuzzy clustering based subhistogram scheme using discrete cosine transform (DCT) for contrast enhancement has been proposed. For preserving the distinctive appearance of the image, histogram division and separate histogram equalization is done on each subhistogram. The way of dividing histogram and calculating the numbers of parts for histogram division are the major problems which directly affects the quality of the output image. The proposed fuzzy-DCT scheme includes automatic calculation of a number of parts in which histogram is divided. Histogram division has done on the basis of density function and histogram separation is computed in such a way that each main peak can be divided in a different segment. The proposed scheme consists of four stages. The first stage includes the automatic calculation of number of clusters for image brightness levels. The second stage includes clustering of brightness levels by the fuzzy c-means clustering method and utilizing the given transfer function of histogram equalization. In the third stage, contrast enhancement is computed on each individual cluster separately. In the final stage, DCT is employed on the resulting image of the third step for better contrast and brightness preservation. The simulation results of the proposed scheme reveal not only clearer features along with a contrast enhancement, but also remarkably more natural look in the images.
Automatic Summarization of Legal Decisions using Iterative Masking of Predictive Sentences

We report on a pilot experiment in automatic, extractive summarization of legal cases concerning Post-traumatic Stress Disorder from the US Board of Veterans' Appeals. We hypothesize that length-constrained extractive summaries benefit from choosing among sentences that are predictive for the case outcome. We develop a novel train-attribute-mask pipeline using a CNN classifier to iteratively select predictive sentences from the case, which measurably improves prediction accuracy on partially masked decisions. We then select a subset for the summary through type classification, maximum marginal relevance, and a summarization template. We use ROUGE metrics and a qualitative survey to evaluate generated summaries along with expert-extracted and expert-drafted summaries. We show that sentence predictiveness does not reliably cover all decision-relevant aspects of a case, illustrate that lexical overlap metrics are not well suited for evaluating legal summaries, and suggest that future work should focus on case-aspect coverage.
Security and Privacy Implications on Database Systems in Big Data Era: A Survey

For over many decades, relational database model has been considered as the leading model for data storage and management. However, as the Big Data explosion has generated a large volume of data, alternative models like NoSQL and NewSQL have emerged. With the advancement of communication technology, these database systems have given the potential to change the existing architecture from centralized mechanism to distributed in nature, to deploy as cloud-based solutions. Though all of these evolving technologies mostly focus on performance guarantees, it is still being a major concern how these systems can ensure the security and privacy of the information they handle. Different datastores support different types of integrated security mechanisms, however, most of the non-relational database systems have overlooked the security requirements of modern Big Data applications. This paper reviews security implementations in today's leading database models giving more emphasis on security and privacy attributes. A set of standard security mechanisms have been identified and evaluated based on different security classifications. Further, it provides a thorough review and a comprehensive analysis on maturity of security and privacy implementations in these database models along with future directions/enhancements so that data owners can decide on most appropriate datastore for their data-driven Big Data applications.
Fine-Grained Land Use Classification at the City Scale Using Ground-Level Images

Multimedia researchers have exploited large collections of community-contributed geo-referenced images to better understand a particular image, such as its subject matter or where it was taken, as well as to better understand a geographic location, such as the most visited tourist spots in a city or what the local cuisine is like. The goal of this paper is to better understand location. In particular, we use geo-referenced image collections to better understand what occurs in different parts of a city at fine spatial and activity class scales. This problem is known as land use mapping in the geographical sciences. We propose a novel framework to perform fine-grained land use mapping at the city scale using ground-level images. Mapping land use is considerably more difficult than mapping land cover and is generally not possible using overhead imagery as it requires close-up views and seeing inside buildings. We postulate that the growing collections of geo-referenced, ground-level images suggest an alternate approach to this geographic knowledge discovery problem. We develop a general framework that uses Flickr images to map 45 different land-use classes for the city of San Francisco, CA, USA. Individual images are classified using a novel convolutional neural network containing two streams: one for recognizing objects and another for recognizing scenes. This network is trained in an end-to-end manner directly on the labeled training images. We propose several novel strategies to overcome the noisiness of our user-generated data including search-based training set augmentation and online adaptive training. We derive a ground truth map of San Francisco in order to evaluate our method. We demonstrate the effectiveness of our approach through geovisualization and quantitative analysis. Our framework achieves over 29% recall at the individual land parcel level that represents a strong baseline for the challenging 45-way land use classification problem, especially given the noisiness of the image data.
Enhancing LMS Experience through AIML Base and Retrieval Base Chatbot using R Language

According to UNICEF, globally youth literacy rate has increased from 83% to 91%. Despite of continuous growth of youth literacy providing education and educational tools in many undeveloped countries remains a challenge. Online education a medium of E-Learning can be one of the most effective platforms to bridge this gap. As today approximately 51% percent of world's population is using Internet so using the platform of internet for the education can be very useful. System advocates about closed environment while interaction, as when a student start interaction, all details of student is already captured by chatbot (like, name, Programme, courses, which are registered with institution) Learning management system (LMS) is one of the categories of E-Learning. In this paper we have discussed a concept of chatbot, which is easy to implement with the help of R programming language. Major keywords which will be classified with R programming Language can be further framed as a query with the help of Artificial Intelligent Markup language (AIML) script. Along with AIML script of the query is unsuccessful then this will be framed as query with help of SQL lite. If both of the queries are unsuccessful then same will be transferred to human interface. AIML is a free scripting language and easy to implement as well. This entire concept will add value to any LMS system, and at the very same time, it will be helpful for student to get answer for ad-hoc quires and institution will also be benefited, as this institution has all information about each student, which will help institution to offer services in more personalized way.
Shallow and deep neural network training by water wave optimization

It is well known that the performance of artificial neural networks (ANNs) is significantly affected by their structure design and parameter selection, for which traditional training methods have drawbacks such as long training times, over-fitting, and premature convergence. Evolutionary algorithms (EAs) have provided an effective tool for ANN parameter optimization. However, simultaneously optimizing ANN structures and parameters remains a difficult problem. In this study, we adapt water wave optimization (WWO), a relatively new EA, for optimizing both the parameters and structures of ANNs, including classical shallow ANNs and deep neural networks (DNNs). We use a variable-dimensional solution encoding to represent both the structure and parameters of an ANN, and adapt WWO propagation, refraction, and breaking operators to efficiently evolve variable-dimensional solutions to solve the complex network optimization problems. Computational experiments on a variety of benchmark datasets show that the WWO algorithm achieves a very competitive performance compared to other popular gradient-based algorithms and EAs.
Knowledge Graph Embedding Based on Multi-View Clustering Framework

Knowledge representation is one of the critical problems in knowledge engineering and artificial intelligence, while knowledge embedding as a knowledge representation methodology indicates entities and relations in knowledge graph as low-dimensional, continuous vectors. In this way, knowledge graph is compatible with numerical machine learning models. Major knowledge embedding methods employ geometric translation to design score function, which is weak-semantic for natural language processing. To overcome this disadvantage, in this paper, we propose our model based on multi-view clustering framework, which could generate semantic representations of knowledge elements (i.e., entities/relations). With our semantic model, we also present an empowered solution to entity retrieval with entity description. Extensive experiments show that our model achieves substantial improvements against baselines on the task of knowledge graph completion, triple classification, entity classification, and entity retrieval.
LSCD: Low-rank and sparse cross-domain recommendation

Due to the ability of addressing the data sparsity and cold-start problems, Cross-Domain Collaborative Filtering (CDCF) has received a significant amount of attention. Despite significant success, most of the existing CDCF algorithms assume that all the domains are correlated, which is however not always guaranteed in practice. In this paper, we propose a novel CDCF algorithm termed Low-rank and Sparse Cross-Domain (LSCD) recommendation algorithm. Different from most of the CDCF algorithms, LSCD extracts a user and an item latent feature matrix for each domain respectively, rather than tri-factorizing the rating matrix of each domain into three low dimensional matrices. In order to simultaneously improve the performance of recommendations among correlated domains by transferring knowledge and among uncorrelated domains by differentiating features in different domains, the features of users are separated into shared and domain-specific parts adaptively. Specifically, a low-rank matrix is used to capture the shared features of each user across different domains and a sparse matrix is used to characterize the discriminative features in each specific domain. Extensive experiments on two real-world datasets have been conducted to confirm that the proposed algorithm transfers knowledge in a better way to improve the quality of recommendation and outperforms state-of-the-art recommendation algorithms.
Shot type constraints in UAV cinematography for autonomous target tracking

During the past years, camera-equipped Unmanned Aerial Vehicles (UAVs) have revolutionized aerial cinematography, allowing easy acquisition of impressive footage. In this context, autonomous functionalities based on machine learning and computer vision modules are gaining ground. During live coverage of outdoor events, an autonomous UAV may visually track and follow a specific target of interest, under a specific desired shot type, mainly adjusted by choosing appropriate focal length and UAV/camera trajectory relative to the target. However, the selected UAV/camera trajectory and the object tracker requirements (which impose limits on the maximum allowable focal length) affect the range of feasible shot types, thus constraining cinematography planning. Therefore, this paper explores the interplay between cinematography and computer vision in the area of autonomous UAV filming. UAV target-tracking trajectories are formalized and geometrically modeled, so as to analytically compute maximum allowable focal length per scenario, to avoid 2D visual tracker failure. Based on this constraint, formulas for estimating the appropriate focal length to achieve the desired shot type in each situation are extracted, so as to determine shot feasibility. Such rules can be embedded into practical UAV intelligent shooting systems, in order to enhance their robustness by facilitating on-the-fly adjustment of the cinematography plan.
Deep neural networks for rotation-invariance approximation and learning

Based on the tree architecture, the objective of this paper is to design deep neural networks with two or more hidden layers (called deep nets) for realization of radial functions so as to enable rotational invariance for near-optimal function approximation in an arbitrarily high-dimensional Euclidian space. It is shown that deep nets have much better performance than shallow nets (with only one hidden layer) in terms of approximation accuracy and learning capabilities. In particular, for learning radial functions, it is shown that near-optimal rate can be achieved by deep nets but not by shallow nets. Our results illustrate the necessity of depth in neural network design for realization of rotation-invariance target functions.
Enhanced Detection and Recognition of Road Markings Based on Adaptive Region of Interest and Deep Learning

The accurate detection and classification of road markings is required for autonomous vehicles. There have been several previous studies on the detection of road lane markings, but the detection and classification of arrows and bike markings has not received much attention. There exists previous research on the detection and classification of arrows and bike markings, but they comprise a performance limitation owing to the use of the entire input image. Therefore, our approach is focused on enhancing the performance of the detection and classification of arrows and bike markings based on the adaptive region of interest (ROI) and deep convolutional neural network (CNN). In the first stage, a vanishing point is detected in order to create the ROI image. The ROI image that covers the majority of the road region is then used as the input to train the CNN-based detector and classifier in the second stage. The proposed approach is evaluated using three open datasets, namely, the Cambridge dataset, Daimler dataset, and Malaga urban dataset on a desktop computer and NVIDIA Jetson TX2 embedded system. The experimental results show that the proposed method outperforms the state-of-the-art methods in recognition performance even with small road markings at a large distance.
Incremental Object Learning From Contiguous Views

In this work, we present CRIB (Continual Recognition Inspired by Babies), a synthetic incremental object learning environment that can produce data that models visual imagery produced by object exploration in early infancy. CRIB is coupled with a new 3D object dataset, Toys-200, that contains 200 unique toy-like object instances, and is also compatible with existing 3D datasets. Through extensive empirical evaluation of state-of-the-art incremental learning algorithms, we find the novel empirical result that repetition can significantly ameliorate the effects of catastrophic forgetting. Furthermore, we find that in certain cases repetition allows for performance approaching that of batch learning algorithms. Finally, we propose an unsupervised incremental learning task with intriguing baseline results.