Reviewerly/exHarmony huggingface dataset:
Addressing cyber security skills: the spectrum, not the silo

With cyber security gaining ever-greater recognition as a key concern in today's organisations, there is an accompanying appreciation that specialist skills are required to support it. However, this has created challenges for employers in recruiting the associated talent, not least because skilled staff are in short supply. With cyber security becoming a key concern in today's organisations, there is an accompanying appreciation that specialist skills are required to support it. As Steven Furnell of the University of Plymouth and Matt Bishop of the University of California at Davis explain, it is important to recognise the breadth of skills needed. The natural way to do this is to recruit talent with suitable qualifications, but for this to work we need to understand what they cover and whether they match what organisations need.
UAV track planning based on evolution algorithm in embedded system

The embedded controller is small in size and powerful in computing power, and can quickly complete the related processing and calculation of flight attitude and track planning data. Wireless data transmission can realize a long-distance data transmission between the aircraft command center and the airborne control platform. Based on the data fusion algorithm of the Kalmánz filter, the data collected by multiple sensors can be integrated, which can effectively reduce the measurement noise amplitude. At the same time, the cumulative error of a single sensor is reduced. In order to solve the problems of ant colony algorithm in case it is easy to fall into the local extremum and the convergence speed is slow, an improved ant colony algorithm for 3D navigation of unmanned aerial vehicles is proposed for trace planning. This study divides the three-dimensional track planning into two parts based on the improved ant colony algorithm for two-dimensional plane planning and height planning. Geometric optimization methods to enhance the guidance of ant search are used. According to the distance and height constraints between the track point and the threat source, the altitude of the track points to plan the 3D track of the drone is calculated and adjusted. At the same time, the adaptive parameter adjustment method is used to improve the ant colony search ability and the interaction ability between individuals, and effectively get rid of the situation to avoid to falling into a local optimum. In addition, the index function is established and the path is smoothed. Simulation results show that the proposed improved algorithm cannot only safely avoid threats in the three-dimensional environment, but also has the ability to find the optimal solution and the convergence speed is better than the original algorithm.
The SERUMS tool-chain: Ensuring Security and Privacy of Medical Data in Smart Patient-Centric Healthcare Systems

Future-generation healthcare systems will be highly distributed, combining centralised hospital systems with decentralised home-, work-and environment-based monitoring and diagnostics systems. These will reduce costs and injury-related risks whilst both improving quality of service, and reducing the response time for diagnostics and treatments made available to patients. To make this vision possible, medical data must be accessed and shared over a variety of mediums including untrusted networks. In this paper, we present the design and initial implementation of the SERUMS tool-chain for accessing, storing, communicating and analysing highly confidential medical data in a safe, secure and privacy-preserving way. In addition, we describe a data fabrication framework for generating large volumes of synthetic but realistic data, that is used in the design and evaluation of the tool-chain. We demonstrate the present version of our technique on a use case derived from the Edinburgh Cancer Centre, NHS Lothian, where information about the effects of chemotherapy treatments on cancer patients is collected from different distributed databases, analysed and adapted to improve ongoing treatments.
Nonparametric Topic Modeling with Neural Inference

This work focuses on combining nonparametric topic models with Auto-Encoding Variational Bayes (AEVB). Specifically, we first propose iTM-VAE, where the topics are treated as trainable parameters and the document-specific topic proportions are obtained by a stick-breaking construction. The inference of iTM-VAE is modeled by neural networks such that it can be computed in a simple feed-forward manner. We also describe how to introduce a hyper-prior into iTM-VAE so as to model the uncertainty of the prior parameter. Actually, the hyper-prior technique is quite general and we show that it can be applied to other AEVB based models to alleviate the collapse-to-prior problem elegantly. Moreover, we also propose HiTM-VAE, where the document-specific topic distributions are generated in a hierarchical manner. HiTM-VAE is even more flexible and can generate topic representations with better variability and sparsity. Experimental results on 20News and Reuters RCV1-V2 datasets show that the proposed models outperform the state-of-the-art baselines significantly. The advantages of the hyper-prior technique and the hierarchical model construction are also confirmed by experiments.
Curbing profanity online: A network-based diffusion analysis of profane speech on Chinese social media

Profanity, also known as swearing, refers to the use of foul language that is often linked to incivility. In Chinese digital space, the state government actively censors profanity under the rationale of protecting online civility. This study examines the diffusion of profanity in Sina Weibo, one of the largest Chinese social media platforms. The study applied computational methods to reconstruct the cascade networks of swearing and non-swearing posts and analyzed the network diffusion processes based on a set of structural metrics including reposting depth, width, and interlayer width ratios. Findings suggest profanity may influence the process of message diffusion, but this effect was ephemeral. Based on the understanding of diffusion processes of profanity online, this study contends the viral potential of profanity may not be as severe as the regulators claim. The discussion analyzes the extent to which content moderation efforts are necessary for the nurturing of civility online.
Efficient Parallel and Serial Systolic Structures for Multiplication and Squaring Over GF(${2^{m}}$ )

In this paper, we present efficient parallel and serial systolic structures for combined multiplication and squaring over GF(2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sup> ). The proposed structures have the advantage of computing both modular multiplication and squaring simultaneously for fast execution of modular exponentiation. They share the same hardware components for both operations and this gives them the advantage of saving more space than the case of using a separate structure for each operation. In addition, they have a regular structure and local communication between the processing elements and this makes them more efficient for VLSI implementation. Complexity analysis of the proposed designs and the existing most recent and efficient designs indicates that the proposed serial design has 31.7% lower area-time (AT) complexity than the previously reported most recent serial one. In addition, the proposed parallel design has a significant lower AT complexity over the recent efficient parallel designs by at least 96.9%.
Weakly supervised easy-to-hard learning for object detection in image sequences

Object detection is an important research problem in computer vision. Convolutional Neural Networks (CNN) based deep learning models could be used for this problem, but it would require a large number of manual annotated objects for training or fine-tuning. Unfortunately, fine-grained manually annotated objects are not available in many cases. Usually, it is possible to obtain imperfect initialized detections by some weak object detectors using some weak supervisions like the prior knowledge of shape, size or motion. In some real-world applications, objects have little inter-occlusions and split/merge difficulties, so the spatio-temporal consistency in object tracking are well preserved in the image sequences/videos. Starting from the imperfect initialization, this paper proposes a new easy-to-hard learning method to incrementally improve the object detection in image sequences/videos by an unsupervised spatio-temporal analysis which involves more complex examples that are hard for object detection for next-iteration training. The proposed method does not require manual annotations, but uses weak supervisions and spatio-temporal consistency in tracking to simulate the supervisions in the CNN training. Experimental results on three different tasks show significant improvements over the initialized detections by the weak object detectors.
Efficient Semi-Supervised Learning for Natural Language Understanding by Optimizing Diversity

Expanding new functionalities efficiently is an ongoing challenge for single-turn task-oriented dialogue systems. In this work, we explore functionality-specific semi-supervised learning via self-training. We consider methods that augment training data automatically from unlabeled data sets in a functionality-targeted manner. In addition, we examine multiple techniques for efficient selection of augmented utterances to reduce training time and increase diversity. First, we consider paraphrase detection methods that attempt to find utterance variants of labeled training data with good coverage. Second, we explore sub-modular optimization based on n-grams features for utterance selection. Experiments show that functionality-specific self-training is very effective for improving system performance. In addition, methods optimizing diversity can reduce training data in many cases to 50% with little impact on performance.
Hybrid Dimensionality Reduction Forest With Pruning for High-Dimensional Data Classification

The classification of high-dimensional data is a challenge in machine learning. Traditional classifier ensemble methods improve the diversity of classifiers through either dimensionality reduction or sample selection for high-dimensional data classification. However, these methods have several limitations: 1) dimensionality reduction methods easily cause information loss, which leads to a decrease in accuracy; 2) sample selection methods are susceptible to noise and redundant features. To address the above limitations, we propose a novel hybrid dimensionality reduction forest (HDRF) to increase the diversity of an integrated system from feature space and sample space. First, a tree-based feature selection algorithm is employed to partition effective features. Then the Bagging method is applied to obtain diverse training subsets. To fully retain and mine the important information of the unselected samples, a sample-feature based transformation process (SFTP) is proposed to generate the extended features. Since PCA can effectively reduce dimension and remove noise features, it is applied to compress the unselected features and the extended features into the new features which are compact and compensatory. Further, a novel classifier ensemble pruning framework (HDRFPF) based on HDRF is designed to remove redundant and invalid classifiers. Experimental results on 23 high-dimensional data sets verify that our method outperforms mainstream classifier ensemble methods, and the better results are obtained on 19 out of 23 datasets.
MultiLyra: Scalable Distributed Evaluation of Batches of Iterative Graph Queries

Graph analytics is being increasingly used for analyzing large scale networks representing entities and relationships in many domains. Various distributed graph processing frameworks have been developed to deliver scalable performance for evaluation of individual iterative graph queries. In practice though, we may need to evaluate many queries. In this paper we develop MultiLyra, a distributed framework that efficiently evaluates a batch of graph queries. To deliver high performance, this system is designed to amortize the communication and synchronization costs of distributed query evaluation across multiple queries. Our experiments with MultiLyra for four iterative algorithms on a cluster of four 32-core machines show the following. Basic batching technique for amortizing communication and synchronization costs yield maximum speedups ranging from 3.08× to 5.55× across different batch sizes, algorithms and input graphs. After employing optimizations that improve scalability of expensive phases and perform reuse across the distributed computation, the improved maximum speedups range from 7.35× to 11.86×. MultiLyra also delivers superior scalabilty than the Quegel batch processing system.
ICT for agriculture extension: actor network theory for understanding the establishment of agricultural knowledge centers in South Wollo, Ethiopia

This study aims at understanding how various actors interacted in establishing and managing an Information Communication Technology (ICT)- based initiative called Agricultural Knowledge Centers (AKCs) in Ethiopia. Additionally, it explores the diverging and shared interests of the actors in the benefits of the AKCs. We gathered and analyzed data from in-depth interviews in five extension offices in the South Wollo zone, Ethiopia, and supplemented it with project documents and observations. We used the Actor Network Theory (ANT), particularly the four moments of translation, to analyze the results. The findings show how people and technology came together to establish the AKCs and to provide extension experts access to digital knowledge. Factors that contributed to creating and stabilizing the AKC actor network included the presence of an actor to facilitate the process, alignment of interests among actors in the network, building the capacities and motivation of the various actors to execute their roles, and availability of computers with strong internet connections. These findings contribute to practical and policy debates on harnessing ICT’s potential for facilitating socioeconomic development in the Global South; and to the theoretical discussions on the merits of the ANT perspective in analyzing the adoption of technological innovations.
Explainable AI Approach Towards Toxic Comment Classification

In this paper, we have documented results and discussed our approach towards solving a serious problem that people face on the Internet, which is encountering "toxic", "abusive", "inappropriate" and "offensive" textual comments on social media. The purpose of taking up this project is to stop "cyber bullying" and to have a safe online environment. This study highlights the best and most trustworthy model for toxic comment classification out of the models which were compared. For comparison and selection, standard evaluation techniques (like accuracy, f1-score) along with LIME (model interpretability technique) were used to judge the "reliability" of a model. The combination that gave us the best result in the study is gated recurrent unit (GRU) + pre-trained word embeddings with high accuracy scores and intuitive LIME explanations.
Short-Term QoS Forecasting at the Edge for Reliable Service Applications

Accurate short-term forecasts allow dynamic systems to adapt their behaviour when degradation is forecast e.g., transportation forecasting allows for alternative routing of traffic before gridlock. This rationale can be applied to service-oriented computing when creating and managing service applications. Recent approaches to improve reliability in service applications have focused on reducing the time to recovery of application using collaborative filtering-based approaches to make QoS predictions for similar users. In this article, we focus on reducing the time to detection of a failure by forecasting when a service is about to degrade in quality. Previous approaches that have focused on QoS forecasting have used traditional time-series methods that are not designed for sudden peaks caused by network congestion or battery-powered IoT devices that can reduce processing capabilities to extend battery life. More modern recurrent neural network-based approaches such as GRUs and LSTMs have long training times, which are unsuitable for dynamic environments. We propose a noisy echo state network-based approach that has been designed to reduce training time allowing the model to incorporate recent QoS values on devices at the edge. Our results show increased response time forecasting accuracy compared to state of the art approaches when tested on IoT and web services datasets.
An improved GrabCut on multiscale features

the GrabCut can effectively extract the foreground according to features in a cartoon image; however, the performance is not so effective for a real image, because the feature extraction is independent of segmentation. To improve segmentation performance, this paper proposes an improved GrabCut which combines the segmentation and multiscale feature extraction into a unified model. In this model, the segmentation relies on multiscale features, and the multiscale features depend on multiscale decomposition. A novel total variation regularization is proposed in multiscale decomposition to preserve edges and remove the region inhomogeneity, by which the generalization of features for segmentation is improved. The features obtained by the multiscale decomposition are integrated into the segmentation process, and the foreground can be easily extracted from a proper scale. Experimental results indicate that, compared to the existing GrabCut and improved techniques, this method provides competitive performance in terms of the segmentation accuracy, while being insensitive to inhomogeneity.
Internal search of the evolution matrix in QUasi-Affine TRansformation Evolution (QUATRE) algorithm

Optimization demands exist everywhere in the real world especially in science studies and engineering practices, and it is important that the method to deal with intricacy optimization problems should itself be relative simple. Particle Swarm Optimization (PSO) and Differential Evolution (DE) both are simple evolutionary algorithms (EAs) which are proposed for single-objective optimization and both of them have been proved to be efficient methods for optimizing applications, however, there are still some weakness existing within them. A innovative evolutionary method named QUasi-Affine TRansformation Evolutionary (QUATRE) algorithm which is derived from, also tackles some weaknesses of PSO and DE algorithm, and obtains better performance on commonly used test suites. The key characteristic of QUATRE is that an automatically generated matrix named evolution matrix M is implemented in evolutionary process, which is taken as an alternative of employing the crossover rate CR. Here in this paper, we present a novel QUATRE variant, named IS-QUATRE, which can explore the search area in a better way comparison with the previous method, and relatively good optimization ability can be obtained by our proposed IS-QUATRE algorithm under CEC2013 test suit. And the conducted experimental results validate that our proposed IS-QUATRE is competitive with some other famous PSO and DE variants.
A Survey of Recent Advances in Crowd Density Estimation using Image Processing

Crowd analysis is an essential topic of research in artificial intelligence. There are several applications on crowd analysis, such as, environmental management, urban planning and public safety. This paper presents review of different methodologies those have been implemented for estimating crowd. This paper covers several methods of crowd density estimation, such as, image processing techniques, machine learning and deep learning based technique, and smartphone based technique.
Extensible image object co-segmentation with sparse cooperative relations

In the co-segmentation problems of sparse related image group, how to mine correlations between common objects has been one of the most significant steps. In this paper, we first prioritize the processing efficiency and construct sparse cooperative graph for related images in feature space, instead of examining the full correlations of common foregrounds which lead to high computation cost and great risk of false connection. And more importantly, we design a flexible and efficient extending strategy to reuse the cooperative graph for incremental image data. Then, with the sparse correlations of the image group, we propose a unilateral proposal co-selection method for pair-wise region level co-segmentation. Finally, we have achieved the segmentation results with further pixel level refinement. Our experimental results on publicly available datasets show that, compared with the approaches using dense cooperative constraints, the proposed method can achieve more competitive results with extremely sparse correlative constraints, which shows its bright application prospects for sparse correlated image groups with incremental data due to its high efficiency and flexible extensibility.
Director Tools for Autonomous Media Production with a Team of Drones

This paper proposes a set of director tools for autonomous media production with a team of drones. There is a clear trend toward using drones for media production, and the director is the person in charge of the whole system from a production perspective. Many applications, mainly outdoors, can benefit from the use of multiple drones to achieve multi-view or concurrent shots. However, there is a burden associated with managing all aspects in the system, such as ensuring safety, accounting for drone battery levels, navigating drones, etc. Even though there exist methods for autonomous mission planning with teams of drones, a media director is not necessarily familiar with them and their language. We contribute to close this gap between media crew and autonomous multi-drone systems, allowing the director to focus on the artistic part. In particular, we propose a novel language for cinematography mission description and a procedure to translate those missions into plans that can be executed by autonomous drones. We also present our director’s Dashboard, a graphical tool allowing the director to describe missions for media production easily. Our tools have been integrated into a real team of drones for media production and we show results of example missions.
Non-Rigid Registration for Infrared and Visible Images via Gaussian Weighted Shape Context and Enhanced Affine Transformation

Image registration is a prerequisite for image fusion from multiple modalities, such as infrared (IR) and visible (VIS) images. Although there have been many various methods of image registration, non-rigid registration for IR and VIS images is still challenging due to large differences between IR and VIS images. In this work, a point feature-based method is proposed to improve the performance on non-rigid IR and VIS image registration. Firstly, a feature descriptor - Gaussian weighted shape context (GWSC) - is improved from shape context (SC) to fast extract matching point pairs from edge maps in IR and VIS images. With the set of matching point pairs, a Gaussian-field-based objective function is established to measure the distance between IR and VIS images. Then, the enhanced affine transformation (EAT) model is proposed to generalize affine model from linear to non-linear case and describe the regularity of global deformation between IR and VIS images. At last, the derivative of the distance measure is expressed with respect to the EAT model and thus, the optimal parameters are estimated by using the quasi-Newton method. The qualitative and quantitative comparisons demonstrate that the proposed method (GWSC-EAT) can be successfully applied to non-rigid registration of IR and VIS images and moreover, it is superior to the state-of-the-art methods on the accuracy and speed of non-rigid registration.