Reviewerly/exHarmony huggingface dataset:
A hybrid swarm intelligence based approach for abnormal event detection in crowded environments

In this paper, we propose a hybrid swarm intelligence based approach to tackle the problem of abnormal event detection in crowded environments in surveillance videos. In the proposed approach, a video frame is subjected to a series of operations to extract most salient information from it. Initially, a novel discriminative 2D variance plane corresponding to (and equal in dimensions to) each video frame is constructed in which the value at each pixel location represents the variance of optical flow field magnitude in the local spatio-temporal neighborhood of that pixel. Consequently, a modified ant colony optimization (ACO) clustering algorithm is employed to partition the 2D variance plane into salient and non-salient clusters. The cluster with salient pixels represents the regions of a video frame where optical flow variations inside the local spatio-temporal neighborhood of a pixel are high and is selected for further computation. Finally, a novel predator-prey algorithm is implemented and predators are advected over the prey values in the selected cluster to compute histogram of swarms (HOS) for a particular frame. The proposed approach outperforms state of the art on two commonly used datasets in our experiments, i.e., UMN crowd anomaly dataset and UCF web dataset.
Changing the Image Memorability: From Basic Photo Editing to GANs

Memorability is considered to be an important characteristic of visual content, whereas for advertisement and educational purposes it is often crucial. Despite numerous studies on understanding and predicting image memorability, there are almost no achievements in memorability modification. In this work, we study two approaches to image editing - GAN and classical image processing - and show their impact on memorability. The visual features which influence memorability directly stay unknown till now, hence it is impossible to control it manually. As a solution, we let GAN learn it deeply using labeled data, and then use it for conditional generation of new images. By analogy with algorithms which edit facial attributes, we consider memorability as yet another attribute and operate with it in the same way. Obtained data is also interesting for analysis, simply because there are no real-world examples of successful change of image memorability while preserving its other attributes. We believe this may give many new answers to the question "what makes an image memorable?" Apart from that we also study the influence of conventional photo-editing tools (Photoshop, Instagram, etc.) used daily by a wide audience on memorability. In this case, we start from real practical methods and study it using statistics and recent advances in memorability prediction. Photographers, designers, and advertisers will benefit from the results of this study directly.
Non-Intrusive Deployment of Blockchain in Establishing Cyber-Infrastructure for Smart City

Internet-of-Things has emerged to develop smart communities so that real time sensing and decision can improve operational efficiency and quality of lives. However, establishing such infrastructure can be exceptionally intricate due to the vast variety of devices and the implemented technologies. Also, it poses several unique challenges, such as heterogeneity of the infrastructure, type, and scale of deployment, security, privacy, and inter-operability. One primary concern in a smart city environment is the capabilities of typically end-IoT devices which are vulnerable to security threats and prone to confidentiality and integrity breach of data. Blockchain can potentially address these security challenges due to the distributed ledger's inherent properties. In this paper, we propose a decentralized architecture using the Blockchain to provide a secure and resilient smart city infrastructure that can run the ledger service over a distributed network. We consider a permissioned Blockchain, Hyperledger Sawtooth, and to automate and to overcome the infrastructural challenges concerning the smart city deployment, and we provide a systematic methodology that automates the deployment process and saves a significant amount of time. We simulate and deploy a Blockchain-integrated smart city environment using the proposed seamless deployment strategy using our automation module. With the proposed deployment scheme, we improve the Blockchain-based infrastructure development time by 82% compared to the traditional deployment approach.
Using Information Flow to Design an ISA that Controls Timing Channels

Information-flow control (IFC) enforcing languages can provide high assurance that software does not leak information or allow an attacker to influence critical systems.IFC hardware description languages have also been used to design secure circuits that eliminate timing channels.However, there remains a gap between IFC hardware and software; these two components are built independently with no abstraction for how to compose their security guarantees.This paper presents a proposal for an instruction set architecture (ISA) that can provide the appropriate abstraction for joining hardware and software IFC mechanisms.Our ISA describes a RISC-V processor that tracks information-flow labels at run time and uses these labels to eliminate or mitigate timing channels.To make the ISA more practical, it allows constrained downgrading of information; it permits trading off security for performance; and still offers control primitives such as system calls.We prove timing-sensitive noninterference modulo downgrading and nonmalleability for programs executing our ISA.This involves novel restrictions on the mutability of labels beyond previous dynamic IFC systems.Furthermore, we define specific security conditions which correct hardware can implement to provide software-level security and sketch how such hardware may be designed and verified.
Powering Robust Fashion Retrieval With Information Rich Feature Embeddings

Visual content based product retrieval has become increasingly important for e-commerce. Fashion retrieval, in particular, is a challenging problem owing to a wide range of deformations of clothing items along with visual distortions in their product images. In this paper, we propose a Grid Search Network (GSN) for learning feature embeddings for fashion retrieval. The proposed approach posits the training procedure as a search problem, focused on locating matches for a reference query image in a grid containing both positive and negative images w.r.t the query. The proposed framework significantly outperforms existing state-of-art methods on benchmark fashion datasets. We also utilize a reinforcement learning based strategy to learn a specialized transformation function which further improves retrieval performance when applied over the feature embeddings. We also extend the reinforcement learning based strategy to learn custom kernel functions for SVM based classification over FashionMNIST and MNIST datasets, showing improved performance. We highlight the generalization capabilities of this search strategy by showing performance improvement in search and attribution tasks in domains beyond fashion.
A Personalized E-Learning Services Recommendation Algorithm Based on User Learning Ability

The E-learning services recommendation is essential in enabling precision instruction and personalized learning. In this paper, a new personalized E-learning services recommendation algorithm is proposed to solve the problem of low accuracy, recall and effectiveness. The algorithm builds user similarity matrix based on both user information data and user behavior data. In order to achieve the goal of bettering things, this paper creates an asymmetric similarity matrix based on the user learning ability and designs an E-learning services ranking strategy to make personalized E-learning service recommendation better. The application of the recommendation algorithm in the personalized E-learning platform of a software college shows that the new algorithm can improve the accuracy, recall and effectiveness compared with the traditional recommendation algorithm.
A design framework for playful wearables

Deployment of wearables for games has attracted the interest of designers and researchers both in academia and industry. However, few of these projects treat wearables as an integral part of the gameplay, often considering them as an extension of the central on-screen experience. While preliminary forays into wearable play show promise, we see a need for a rigorous design framework to illuminate the possibilities for the future of wearables for playful interaction design. In this paper we propose a Design Framework for Playful Wearables stemming from our extensive research and hands-on experience in leading four long-term game research projects incorporating wearables. We divide our framework into three high-level categories: the performative, the social and the interactive. We contend that this design space can be both a design tool for creators of wearable playful activities, as well as an analytical lens for evaluating existing wearable systems.
Analysis of Blockchain Selfish Mining Attacks

The paper presents vector of attack on the mechanism of achieving a proof of work consensus, such as the application of selfish mining strategy. The aim of the work was to analyze to what extent these risks may affect the fairness of the cryptocurrency extraction process. This goal was achieved by performing simulations using appropriate models. The aim of the experiment was to check how the choice of parameters affects the distribution of block extraction between network users. We use an algorithm of persistent mining in the pursuit of a longer chain with a modified selfish extraction algorithm.
Improving anti-spoofing with octave spectrum and short-term spectral statistics information

The long-term window based features have been found to be effective for spoofing attack detection. One such important countermeasure is constant-Q cepstral coefficients (CQCC) that is derived from constant-Q transform. During its extraction, the octave power spectrum is converted to the linear power spectrum by performing uniform resampling. However, the information from the octave power spectrum is different from that carried by the linear power spectrum. We believe that the octave power spectrum can offer complementary information to the linear power spectrum for spoofing attack detection. In this regard, we propose to combine the coefficients generated using both linear and octave power spectrum. The combined feature is referred to as extended CQCC (eCQCC), which is hypothesized to have better discriminative information for detection of spoofing attacks. In addition, we use the short-term spectral statistics information (STSSI) along with eCQCC feature to form another novel feature representation referred to as eCQCC-STSSI to have improved anti-spoofing countermeasure. We perform the studies with the proposed features for both synthetic and playback attacks using ASVspoof 2015 and ASVspoof 2017 version 2.0 corpus, respectively. The studies reveal that eCQCC outperforms the conventional CQCC feature as well most of the known systems showing importance of octave spectrum information. Further, the hybrid feature eCQCC-STSSI improve the performance of eCQCC feature due to the STSSI information combined with it.
Agile Transformation: A Summary and Research Agenda from the First International Workshop

Organisations are up-scaling their use of agile. Agile ways of working are used in larger projects and also in organisational units outside IT. This paper reports on the results of the first international workshop on agile transformation, which aimed to focus research on practice in a field which currently receives great attention. We report on participants' definitions of agile transformation, summaries of experiences from such transformations, and the challenges that require research attention.
Secure Image Transmission in Wireless OFDM Systems Using Secure Block Compression-Encryption and Symbol Scrambling

In this paper, a joint compression-encryption and symbol scrambling scheme is proposed for orthogonal frequency-division multiplexing (OFDM) systems for image date.First, in the compressionencryption phase, the original image data is fist divided into sub-blocks and then an secure image block compressive sensing (BCS) is applied to resulting image data, in which a chaotic sparse basis matrix and a chaotic measurement matrix based on the discrete cosine transform (DCT) are used.Secondly, the resulting secure CS data is transform into binary phase shift keying (BPSK) symbols and then a chaotic scrambling operation is applied to the obtained BPSK symbols.The simulation results show that the proposed secure BCS scheme can achieve better peak signal-to-nose ratio (PSNR) performance than that of the conventional chaotic measurement method.The simulation results also show that a good trade-off performance of signals between PSNR and computational complexity can be achieved when the proper sub block size of image is adopted.In addition, the performances of the BPSK OFDM signal, which is generated from the secure BCS of the image, are evaluated over AWGN and multipath fading channels.The simulation results show that the proposed scheme not only achieve effective secure block compressive sensing and achieve secure transmission in physical layer.The security of the transmitted image data is enhanced by the joint compression-encryption and symbol scrambling.
Machine learning study of the relationship between the geometric and entropy discord

As an important resource to realize quantum information, quantum correlation displays different behaviors, freezing phenomenon and non-localization, which are dissimilar to the entanglement and classical correlation, respectively. In our setup, the ordering of quantum correlation is represented for different quantization methods by considering an open quantum system scenario. The machine learning method (neural network method) is then adopted to train for the construction of a bridge between the R\`{e}nyi discord ($\alpha=2$) and the geometric discord (Bures distance) for $X$ form states. Our results clearly demonstrate that the machine learning method is useful for studying the differences and commonalities of different quantizing methods of quantum correlation.
Towards an Understanding of Cryptocurrency: A Comparative Analysis of Cryptocurrency, Foreign Exchange, and Stock

Cryptocurrency is a cutting-edge Fintech innovation and currently a worldwide hotspot. However, the high-speed evolution of it has already caused a series of public security related events all around the world. Cryptocurrency was built initially as a possible implementation of digital currency, then various derivatives were created in a variety of fields such as financial transactions, capital management, and even nonmonetary applications. This paper aims to offer analytical insights to help understand cryptocurrency by treating it as a financial asset. We position cryptocurrency by comparing its dynamic characteristics with two traditional and massively adopted financial assets: foreign exchange and stock. Based on the daily close prices about four years, we first construct the correlation matrices and asset trees of all three markets, then conduct comparisons on five properties: volatility, centrality, clustering structure, robustness, and risk. Our investigation suggests that the dynamics of cryptocurrency are more similar to stock. As to the robustness and clustering structure, our analysis shows cryptocurrency market is more fragile than stock market, thus it is currently a high-risk financial market. Our work is the first to study cryptocurrency with the help of well-understood financial assets and may shed some light on investment decisions, regulation, and legislation.
Echo State Networks for Named Entity Recognition

This paper explores a simple method for obtaining contextual word representations. Recently, it was shown that random sentence representations obtained from echo state networks (ESNs) were able to achieve near state-of-the-art results in several sequence classification tasks. We explore a similar direction while considering a sequence labeling task specifically named entity recognition (NER). The idea is to simply use reservoir states of an ESN as contextual word embeddings by passing pre-trained word-embeddings as its input. Experimental results show that our approach achieves competitive results in terms of accuracy and faster training times when compared to state-of-the-art methods. In addition, we provide an empirical evaluation of hyper-parameters that influence this performance.
A modified deep learning architecture for vehicle detection in traffic monitoring system

Deployment of adequate surveillance and security measures is crucial in today's scenario. The proposed system is designed to handle the surveillance that detects the vehicles present in the traffic. With the evolution of Deep convolutional neural network, the vision-based vehicle detection has reached a higher level in performance. The proposed method deals with the detection of vehicles and capable of handling the distant small-scale vehicles without bothering the image scalability. The method uses Deep Learning along with ROI pooling for handling image scalability. The method consists of a noise-reducing component and the convolutional layers. The method calculates varying scales of the input and does the filtering according to each scale without generalizing. Thereby it handles the image scalability issue. The filter size is small in the proposed method, hence there is a reduction in system complexity and an increase in performance rate. The global average pooling is introduced to the final layers so that the overfitting is avoided. The performance of the system is assessed by the comparison with already established methods for vehicle detection like MS-CNN and YOLO v2 and provides much better experimental results.
Privacy-Enhanced Machine Learning with Functional Encryption

Functional encryption is a generalization of public-key encryption in which possessing a secret functional key allows one to learn a function of what the ciphertext is encrypting. This paper introduces the first fully-fledged open source cryptographic libraries for functional encryption. It also presents how functional encryption can be used to build efficient privacy-enhanced machine learning models and it provides an implementation of three prediction services that can be applied on the encrypted data. Finally, the paper discusses the advantages and disadvantages of the alternative approach for building privacy-enhanced machine learning models by using homomorphic encryption.
Multi-pattern algorithm for first-break picking employing open-source machine learning libraries

Accurate first-break (FB) onsets are crucial in processing seismic data, e.g. for deriving statics solution or building velocity models via tomography. Ever-increasing volumes of seismic data require automatic algorithms for FB picking. Most of the existing techniques are based either on simple triggering algorithms that rely on basic properties of seismic traces or on neural network architectures that are too complex to be easily trained and re-used in another survey. Here we propose a solution that addresses the issue of multi-level analysis using a time-sequence pattern-recognition process implemented in the newest open-source machine learning library like Keras or Scikit-Learn. We use well-established methods such as STA/LTA, entropy, fractal dimension or higher-order statistics to provide patterns required for generative model training with artificial neural networks (ANN), Support Vector Regression (SVR) and an implementation of gradient boosted decision trees – XGBoost (Extreme Gradient Boosting). FB picking is cast as the binary classification that requires a model to differentiate FB sample from all other samples in a seismic trace. Our workflow (provided freely as a Jupyter Notebook) is robust, easily adaptable and flexible in a way of adding new pattern generators that might contribute to even better performance, while already-trained models can be saved and re-used for another dataset collected with similar acquisition parameters (e.g., in multi-line surveys). Application to real seismic data showed that the models trained on 1000 and 340,100 manually-picked FB onsets are able to predict FB on the rest of 470,000 of traces with the success rate of nearly 90% and 95%, respectively.
A spatially wind aware quadcopter (UAV) path planning approach

Wind aware path planning is essential towards the development of autonomous quadcopters (UAVs) which will be able to fly efficiently in outdoor environments. In this paper, the wind formation and quadcopter kinematics are presented so as to provide cost functions for A⁎ based on distance and wind information. Furthermore, a collision checking method is proposed, which is incorporated to a flight scenario in an outdoor terrain under various wind formations. A computational comparison of a uniform graph and a PRM based graph is finally evaluated. Experiments on artificial wind fields, through illustrative simulations, highlight the benefit of wind aware A⁎ searching algorithm.
Human action recognition based on action relevance weighted encoding

Human action recognition in videos is one of the most important and active topics in computer vision, and building more discriminative video representation is crucial for action classification. In this paper, we propose a new video representation which not only captures temporal dynamics in a video but also takes account of the importance of different trajectories and video frames in action recognition. More specifically, first, we propose a new trajectory descriptor based on traditionally used local descriptors including histograms of oriented gradients, histograms of optical flow and motion boundary histogram, and it takes the correlation between trajectories and target action into consideration. Secondly, a new method to compute saliency map based on optical flow is introduced to highlight regions of foreground motion. Thirdly, to identify discriminating trajectories and frames in a video, trajectory action relevance and frame action relevance are defined and used as weights during encoding. Finally, two video representations which describe a video at different levels of abstraction are proposed, they are complementary to each other and combined to generate more expressive and discriminative representation. Experimental results on KTH, JHMDB and HMDB51 demonstrate the effectiveness and excellent performance of the proposed approach.
A Survey of Three-Dimensional Flight Path Planning for Unmanned Aerial Vehicle

The unmanned aerial vehicles (UAVs) have been widely used in various fields in recent years, where the three-dimensional (3D) flight path planning is very important to realize the autonomous flight and complete the desired missions for the UAVs. Although the 3D flight path planning have become an active topic in the filed of the UAVs, there are still several critical problems to be further investigated to the authors' knowledge. Motivated by the aforementioned considerations, the purpose of this survey is to present several representative approaches already proposed in the literature and provide some problems to be further investigated for the 3D flight path planning of the UAVs.