Reviewerly/exHarmony huggingface dataset:
Computational UAV Cinematography for Intelligent Shooting Based on Semantic Visual Analysis

Audiovisual coverage of sports events using Unmanned Aerial Vehicles (UAVs) is becoming increasingly popular. Intelligent audiovisual (A/V) shooting tools, accurately identifying the 2D region of cinematographic attention (RoCA) depicting rapidly moving target ensembles and automatically controlling the UAVs/cameras through visual content analysis, are thus needed. A novel algorithmic pipeline is proposed, implementing computational UAV cinematography for assisting sports coverage, based on semantic, human-centered visual analysis. Athlete and ball detection / tracking results as well as their spatial distribution on the image plane are the semantic features extracted from UAV video feed and exploited for RoCA extraction, based solely on present and past target detections. A PID controller visually controlling a real or virtual camera to track the RoCA and produce aesthetically pleasing shots, without exploiting 3D location-related information, is employed. The proposed method is evaluated on actual UAV footage from soccer matches and promising results are obtained.
Machine Learning and Nature Inspired Based Phishing Detection: A Literature Survey

Phishing detection remains one of the most challenging problems currently faced by cyber users as sophisticated phishing schemes evolves rapidly. This paper presents a comprehensive survey of different phishing detection techniques with a focus of identifying gaps and proffering solutions to the current phishing detection problems. As shown in the survey, numerous techniques have been proposed to tackle phishing, including Machine Learning (ML) based, Nature Inspired (NI) based, heuristic-based, blacklist-based and whitelist-based techniques. However, ML-based techniques outperform other techniques in terms of classification accuracy due to their ability to analyze data contents, extract unknown or concealed patterns from the data and consequently turn the patterns into meaningful information for decision making. Unfortunately, ML algorithms cannot effectively handle big datasets, hence they can be combined with NI algorithms to build fast and improved models for phishing detection. Although, some surveys on phishing detection techniques exist, very few focused on ML-based and NI-based techniques. Therefore, this study presents a survey on ML-based and NI-based phishing detection techniques. The survey reveals the various shortcomings of phishing detection techniques, including limited dataset, use of third-party services (age of domain, search engine query, etc.), use of small feature set, use of classification rules, use of blacklist and whitelist, etc. There is an obvious need for efficient and reliable solutions for phishing detection. The recent success and prevalence of deep learning provides a rare opportunity for researchers seeking to design improved and effective phishing detection techniques. This study aims to empower the research community with suitable solutions and motivating insights that can be used to effectively tackle the phishing detection problem. It provides a good picture of the current state-of-the-art on phishing detection and serves as a reliable springboard for further studies.
People Re-Identification by Multi-Branch CNN with Multi-Scale Features

People re-identification is a retrieval problem to find a person of interest among a gallery of person images from different cameras in various poses or view angles. How to get a strong feature representation for a person image plays an important role in performing people re-identification. In this paper, we present a novel end-to-end framework that extracts both global and local features with multiple scales to generate more discriminative representations. The model we design is a multi-branch network consisting of one global branch to obtain features of the whole input image from different convolutional layers and several local branches to obtain features from horizontal partitions in different granularities. Our method achieves state-of-the-art results on three challenging datasets (Market-1501, CUHK03 and DukeMTMC-reid).
The Model Selection Curse

A statistician takes an action on behalf of an agent, based on the agent’s self-reported personal data and a sample involving other people. The action that he takes is an estimated function of the agent’s report. The estimation procedure involves model selection. We ask the following question: Is truth-telling optimal for the agent given the statistician’s procedure? We analyze this question in the context of a simple example that highlights the role of model selection. We suggest that our simple exercise may have implications for the broader issue of human interaction with machine learning algorithms. (JEL C52)
Age estimation via attribute-region association

Human age has been treated as an important biometric trait in many practical applications. In this paper, we propose an Attribute-Region Association Network (ARAN) to tackle the challenging age estimation problem. Instead of performing prediction from a global perspective, we delve into the relationship between face attributes and regions. First, the proposed network is guided by the auxiliary demographic information, as different demographic information (e.g., gender and ethnicity) intrinsically correlates to human age. Second, different face components are separately handled and then involved in the proposed ensemble network, as these components vary differently along with human age. To explore both global and local information, the proposed network consists of several sub-network, each of them takes the global face and a face sub-region as input. Each sub-network leverages the intrinsic correlation between different face attributes (i.e., age, gender, and ethnicity), and it is trained in a multi-task manner. These attribute-region sub-networks are associated to yield the final predictions. To properly train and coordinate such a complex network, a new hierarchical-scheduling training method is proposed to balance the learning complexity in the multi-task learning. In this way, the performance of the most difficult task (i.e., age estimation) can be significantly improved. Extensive experiments on the MORPH Album II and FG-NET show that the proposed method outperforms the state-of-the-art age estimation methods by a significant margin. In particular, for the challenging age estimation, the Mean Absolute Errors (MAE) are decreased to 2.51 years compared to the state-of-the-arts on the MORPH Album II dataset.
Accurate Eye Pupil Localization Using Heterogeneous CNN Models

Eye pupil localization is one of the indispensable technologies in various computer vision applications such as virtual reality and augmented reality. In general, the algorithm consists of finding the approximate eye region and finding the pupil position by extracting the semantic feature from each eye region. However, the performance is affected not only by illumination and image resolution but also by glasses wear. Therefore, this paper proposes an eye pupil localization algorithm which is robust against the above disturbance conditions and also has high accuracy using heterogeneous CNN models. First, faces in the image and landmarks in the face(s) are detected sequentially, and the eye region is determined based on the landmarks. Especially, if glasses are present, the glasses are removed by GAN to find the correct eye region. Next, the pupil region is segmented using fully convolutional networks. Finally, the position of the segmented pupil is calculated. Experimental results show that the proposed algorithm outperforms the state-of-the-art algorithms for public databases such as BioID and GI4E.
PV power forecasting improvement by means of a selective ensemble approach

Research dealing with renewable energy sources is focusing on the possibility to forecast the daily output power profile of a given power system. In particular, photovoltaic has gained more interest because it is relatively easy to install but the power output shows high level of variability, strongly depending on meteorological conditions. The present work is devoted to improve the accuracy of the photovoltaic power prediction. For this reason an Hybrid Artificial Neural Network based system is used to the scope and in order to reduce the difference between the forecast and the daily measured power profile, a novel criterion based on night hours forecast is proposed. The implemented methodologies have been applied and validated throughout the work, emphasizing their partial contribution. The simulations and results are obtained on the basis of a photovoltaic module recordings of the year 2017 in Solar Tech Lab in Milano, Italy. Weather forecasts and real hourly photovoltaic power data are made available to the readers.
Towards Integrated Quantitative Security and Safety Risk Assessment

Although multiple approaches for the combination of safety and security analysis exist, there are still some major gaps to overcome before they can be used for combined risk management. This paper presents the existing gaps, based on an overview of available methods, which is followed by the proposal towards a solution to achieve coordinated risk management by applying a quantitative security risk assessment methodology. This methodology extends established safety and security risk analysis methods with an integrated model, denoting the relationship between adversary and victim, including the used capabilities and infrastructure. This model is used to estimate the resistance strength and threat capabilities, to determine attack probabilities and security risks.
Violent Vocabulary Extraction Methodology: Application to the Radicalism Detection on Social Media

Nowadays, social networks have become powerful mediums of communication providing information, learning and entertainment. Unfortunately, these platforms can be sorely manipulated by vicious users sharing malicious contents. Therefore, the process of mining and analyzing such published suspicious content is a considerably challenging task that serves to fight against the online radicalization. For this purpose, we propose, in this paper, a new methodology for extracting and analyzing violent vocabulary shared on social networks with the exploration a set of natural language processing and data mining techniques. Our method relies mainly on extracting a set of profiles judged by a domain expert as extremist and non-extremist' users. Then, we focus on their shared textual content in order to detect malicious vocabulary published within the radical context as well as their violence' degrees. Finally, in order to evaluate the performance of our method, we resort to an expert who verifies the final list of the extracted vocabulary annotated by our method. Thus, the given results show its effectiveness as well as its efficiency.
Sentiment Analysis Based on Multiple Reviews by using Machine learning approaches

Sentiment is an attitude, thought, or judgment prompted by feeling. Sentiment Analysis can be defined as the process of analyzing online pieces of writing to determine the emotional tone they carry. With the vast growth of the social media content on the Internet in the past few years, people now express their opinion on almost anything in discussion. With respect to this, Bag-of-Words (BoW) is the most popular way to model text in statistical machine learning (ML) approaches. However, the performance of BoW sometimes remains unlimited due to some fundamental deficiencies in handling the polarity shift problem and other few challenges like quality of the opinions, hidden state representations, polarity categorization etc. To come across these challenges our focus will be on Dual Sentiment Analysis which processes the Sentiment with all the perspectives (positive, negative or neutral). This may lead towards the accurate prediction for final decision making based on the reviews given by the customers. The proposed work is being experimented on the Amazon Product reviews specifically the Mobile device reviews. This work aims at overcoming the limitation of existing system and improving the accuracy.
Perceptual Video Coding with Block-Level Staircase Just Noticeable Distortion

Perceptual video coding (PVC) is able to improve video compression efficiency by employing just noticeable distortion (JND) models. However, there are limitations of conventional JND models on simulating complex human visual system. To address this issue, a novel PVC framework is proposed in this work, in which a JND model based on staircase perceptual characteristics is designed to calculate block-level JND (BLJND) levels and a convolutional neural network based predictive model is developed to predict BLJND levels for video coding. Experimental results demonstrate that the proposed PVC framework is effective and robust in terms of video compression efficiency and subjective video quality.
Cost-Efficient Scheduling of Bulk Transfers in Inter-Datacenter WANs

With the quick growth of traffic between data centers, inefficient transfer scheduling in inter-datacenter networks can lead to a huge waste of bandwidth thus significant bandwidth cost. Previous work have explored different ways, such as software-defined WANs and dynamic pricing mechanisms, to overcome the inefficiency of inter-datacenter networks. However, there is a big challenge in addressing the fundamental conflicts between the deadline-aware transfer scheduling and minimizing the bandwidth cost. Unlike existing efforts that schedule inter-datacenter transfers under fixed link capacities, wherein some deadlines are violated and the service quality is degraded, we aim to finish all the transfers on time with as little bandwidth as possible to minimize the bandwidth cost. We take into account the variation of bandwidth price and the deadline requirements of services, and formulate the problem of cost-efficient scheduling of bulk transfers with deadline guarantee, which is shown to be NP-hard. Benefitting from the relax-and-round method, we propose a progressively-descending algorithm (PDA) to schedule bulk transfers and meet the above goals with a guaranteed approximation ratio. We apply our algorithm in a bulk transfer scheduler, Butler, and build a small-scale testbed to evaluate its efficiency. Both large-scale simulation and testbed experiment results validate the ability of our scheme on cutting down the bandwidth cost. Compared with existing approaches, it reduces up to 60% bandwidth cost and increases the network utilization by up to 140%.
Distributed Kerr Non-linearity in a Coherent All-Optical Fiber-Ring Reservoir Computer

We investigate, both numerically and experimentally, the usefulness of a distributed nonlinearity in a passive coherent photonic reservoir computer. This computing system is based on a passive coherent optical fiber-ring cavity in which part of the nonlinearities are realized by the Kerr nonlinearity. Linear coherent reservoirs can solve difficult tasks but are aided by nonlinear components in their input and/or output layer. Here, we compare the impact of nonlinear transformations of information in the reservoir's input layer, its bulk - the fiber-ring cavity - and its readout layer. For the injection of data into the reservoir, we compare a linear input mapping to the nonlinear transfer function of a Mach Zehnder modulator. For the reservoir bulk, we quantify the impact of the optical Kerr effect. For the readout layer we compare a linear output to a quadratic output implemented by a photodiode. We find that optical nonlinearities in the reservoir itself, such as the optical Kerr nonlinearity studied in the present work, enhance the task solving capability of the reservoir. This suggests that such nonlinearities will play a key role in future coherent all-optical reservoir computers.
Representation-based classification methods with enhanced linear reconstruction measures for face recognition

Representation-based classification (RBC) methods have recently been the promising pattern recognition technologies for object recognition. The representation coefficients of RBC as the linear reconstruction measure (LRM) can be well used for classifying objects. In this article, we propose two enhanced linear reconstruction measure-based classification methods based on the sparsity-augmented collaborative representation-based classification method (SA-CRC). One is the weighted enhancement linear reconstruction measure-based classification method (WELRMC) that introduces data localities into SA-CRC. Another is the two-phase weighted enhancement linear reconstruction measure-based classification method (TPWELRMC) that integrates both the coarse and fine representations into SA-CRC. To demonstrate the effectiveness of the proposed methods, experiments are conducted on several public face databases in comparison with the state-of-the-art representation-based classification methods. The experimental results show that the proposed methods significantly outperform the competing RBC methods.
Fast Uniform Generation of Random Graphs with Given Degree Sequences

In this paper we provide an algorithm that generates a graph with given degree sequence uniformly at random. Provided that Δ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sub> =O(m), where Δ is the maximal degree and m is the number of edges, the algorithm runs in expected time O(m). Our algorithm significantly improves the previously most efficient uniform sampler, which runs in expected time O(m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> Δ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) for the same family of degree sequences. Our method uses a novel ingredient which progressively relaxes restrictions on an object being generated uniformly at random, and we use this to give fast algorithms for uniform sampling of graphs with other degree sequences as well. Using the same method, we also obtain algorithms with expected run time which is (i) linear for power-law degree sequences in cases where the previous best was O(n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4.081</sup> ), and (ii) O(nd+d <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sup> ) for d-regular graphs when d=o(√ n), where the previous best was O(nd <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> ).
A Framework for the Development of a Dynamic Adaptive Intelligent User Interface to Enhance the User Experience

The aim of this paper is to present PhD research that aims to enhance the User Experience by proposing a framework that combines the three core components of: dynamic interfaces; adaptive interfaces; and intelligent interfaces. Initial research into the field has identified a gap at the intersection of these types of interaction. A dynamic interaction understands the user, their device and their physical environment to provide a basic User Experience. An adaptive interaction understands the user's capabilities further to implement an enhanced experience via usability and accessibility whilst recognising the flow of the user and their pipeline. The intelligent interaction builds further upon this through the incorporation of Machine Learning algorithms that assist in making the interface intelligent and provide a personalised experience for each user based upon their end goal. This in turn will reduce a user's cognitive load and enhance their interactive experience with an interface.
Improved QUBO Formulation of the Graph Isomorphism Problem

In this paper, we provide a practically efficient QUBO formulation for the Graph Isomorphism Problem that is suitable for quantum annealers such as those produced by D-Wave. After proving the correctness of our new method, based on exploiting vertex degree classes, we did some experimental work on a D-Wave 2X computer. We observe that for all “hard” graphs of 6 vertices, we save around 50–95% of the number of required physical qubits over the standard QUBO formulation that was given earlier by Calude et al. (Theor Comput Sci 701:54–69, 2017). We also provide some theoretical analysis showing that, for two random graphs with the same degree sequence, our new method substantially improves in qubit savings as the number of vertices increases beyond 6.
Web Crawling-based Search Engine using Python

A data mining powered search engine for the help of education sector. Getting information on schools and colleges from the internet are a big task also many institutes can be missed as they don't have good SEO. Internet based services for admission in school and colleges with site crawler and many features to give statistics. The decision making is made easier with easy access to detailed information of educational institutes nearby using location-based search, important dates, documents, online forms, contact numbers and the procedure. There are websites like www.shiksha.com and www.pathshala.com tells about the college admissions and certified courses, they are crawling over 14,000 institutions, 40,000 plus courses and have a registered database of more than 3.5 million students. So, taking inspiration from them we are trying to create our own website for school admission in Delhi NCR.
AI based Reading System for Blind using OCR

The new generation of mobile phones has great hardware ability and faster processing which is powerful enough to develop applications which help the user to connect and interact with the world at their own comfort zone. This system is an OCR reading system which uses camera application present in your smart phones combined with OCR (Optical Character Recognition). OCR is a mechanism which coverts images of typed, handwritten, or printed text into machine encoded text. This system will help you to take a picture or scan the document present with user using the phone's camera, the image will be scanned and the application will read the text written in English language and convert the output in speech format. The speech output is generated using Text To Speech Module. The purpose of delivering the output in form of voice/speech is to serve the information that is present on the document to the visually impaired.
CoEvil: A Coevolutionary Model for Crime Inference Based on Fuzzy Rough Feature Selection

Millions of crimes arise each year, which threatens public safety and harms the victims. Precise crime inference is of great significance in preventing crimes. The sharply increasing large-scale heterogeneous data provide a chance to reveal the patterns and trends in crimes. Several approaches employing feature-based regression or spatiotemporal distribution fitting are proposed but lack of some considerations: 1) ignore the dynamic mutual influences among crimes and locations; and 2) overlook the large scale, incompleteness, uncertainty, and vagueness in the heterogeneous data. This article comprehensively investigates the reliability and applicability of proposing a coevolutionary model to formulate the interaction pattern among the crimes and locations and develops a fuzzy-rough-set-based feature selection method to discover the distinctiveness and permanence properties of the crimes and locations with different latent features. Extensive experiments show that our algorithm achieves the mean absolute error of 1.529 (hour) in the crime time inference and the accuracy of 0.653 and 0.633 in the crime type and location inference, which surpass the state of the arts more than 6.5, 1.9, and 1.8 times, respectively. Additional experiments on different parameter settings of our model are provided to further explore its effectiveness and scalability.