Reviewerly/exHarmony huggingface dataset:
SE-PSO: Resource Scheduling Strategy for Multimedia Cloud Platform Based on Security Enhanced Virtual Migration

In the multimedia cloud platform, the resource scheduling performance directly affects the energy consumption, resource utilization of the active physical machine (PM) and virtual machine (VM) security. Besides, service level agreement (SLA) violation rate also fluctuates with the strategy. Many optimization methods have been launched to cope with this scheduling task, while none of them accommodate all the above aspects in a uniform manner to our best knowledge. In this paper, aiming at optimizing the four sides performance, we propose a new resource scheduling strategy called Security Enhanced Particle Swarm Optimization (SE-PSO) based on VM migration which uses Particle Swarm Optimization (PSO) as a kernel part. Firstly, the inertia factor and the learning factor are dynamically adapted to improve the search performance of SE-PSO. Then, by periodically predicting physical hotspots with the exponential smoothing model, we reduce unnecessary migrations and thus minimize the VM migration security risk. Finally, roulette wheel idea is applied to achieve long-term optimization of the platform resources. The experiments conducted in CloudSim with real-world dataset also show that SE-PSO has a good overall performance in energy consumption, resource utilization, SLA violation rate and migration security compared with the mainstream PSO algorithm.
MaxGain: Regularisation of Neural Networks by Constraining Activation Magnitudes

Effective regularisation of neural networks is essential to combat overfitting due to the large number of parameters involved. We present an empirical analogue to the Lipschitz constant of a feed-forward neural network, which we refer to as the maximum gain. We hypothesise that constraining the gain of a network will have a regularising effect, similar to how constraining the Lipschitz constant of a network has been shown to improve generalisation. A simple algorithm is provided that involves rescaling the weight matrix of each layer after each parameter update. We conduct a series of studies on common benchmark datasets, and also a novel dataset that we introduce to enable easier significance testing for experiments using convolutional networks. Performance on these datasets compares favourably with other common regularisation techniques. Data related to this paper is available at: https://www.cs.waikato.ac.nz/~ml/sins10/ .
Prototypical Metric Transfer Learning for Continuous Speech Keyword Spotting with Limited Training Data

Continuous Speech Keyword Spotting (CSKS) is the problem of spotting keywords in recorded conversations, when a small number of instances of keywords are available in training data. Unlike the more common Keyword Spotting, where an algorithm needs to detect lone keywords or short phrases like “Alexa”, “Cortana”, “Hi Alexa!”, “Whatsup Octavia?” etc. in speech, CSKS needs to filter out embedded words from a continuous flow of speech, ie. spot “Anna” and “github” in “I know a developer named Anna who can look into this github issue.” Apart from the issue of limited training data availability, CSKS is an extremely imbalanced classification problem. We address the limitations of simple keyword spotting baselines for both aforementioned challenges by using a novel combination of loss functions (Prototypical networks’ loss and metric loss) and transfer learning. Our method improves F1 score by over 10%.
Automated Bundle Pagination Using Machine Learning

Coherent division of legal document bundles, whether this is done in the context of court bundles, briefs or some other application, is a time consuming and challenging task. We propose an approach whereby this process can be automated. Two variations are considered. The first addresses the scenario where the topic labelling is pre-defined and adopts a supervised learning approach. The second addresses the scenario where the topic labelling, for whatever reason, is not specified in advance and adopts an unsupervised learning approach. This paper reports on an investigation of both mechanisms using accident claims bundles. The evaluation results indicate that the proposed approaches can be successfully applied to divide legal document bundles.
A Mixture of Views Network With Applications to the Classification of Breast Microcalcifications

In this paper we examine data fusion methods for multi-view data classification. We present a decision concept which explicitly takes into account the input multi-view structure, where for each case there is a different subset of relevant views. The proposed method, which we dub Mixture of Views, is implemented by a special purpose neural network architecture. It is demonstrated on the task of classifying breast microcalcifications as benign or malignant based on several mammography views. The single view decisions are combined by a data-driven decision, according to the relevance of each view in a given case, into a global decision. The method is evaluated on a large multi-view dataset extracted from the standardized digital database for screening mammography (DDSM). The experimental results show that our method outperforms previously suggested fusion methods.
Hierarchical feature representation for unconstrained video analysis

Complex video analysis is a challenging problem due to the long and sophisticated temporal structure of unconstrained videos. This paper introduces pooled-feature representation (PFR) which is derived from a double layer encoding framework (DLE) to address this problem. Considering that a complex video is composed of a sequence of simple frames, the first layer generates temporal sub-volumes from the video and represents them individually. The second layer constructs the pool of features by fusing the represented vectors from the first layer. The pool is compressed and then encoded to provide video-parts vector (VPV). This framework allows distilling the representation and extracting new information in a hierarchical way. Compared with recent video encoding approaches, VPV can preserve the higher-level information through typical encoding in the higher layer. Furthermore, the encoded vectors from both layers of DLE are fused along with a compression stage to develop PFR. The early and late fusion stages are adopted based on the priority of compression stage over concatenation of represented vectors. To validate the proposed framework, we conduct extensive experiments on four complex action datasets: UCF50, HMDB51, URADL, and Olympic. Experimental results demonstrate that PFR with early fusion achieves the state-of-the-art performance by capturing the most prominent features with minimum dimension.
High-Speed Video from Asynchronous Camera Array

This paper presents a method for capturing high-speed video using an asynchronous camera array. Our method sequentially fires each sensor in a camera array with a small time offset and assembles captured frames into a high-speed video according to the time stamps. The resulting video, however, suffers from parallax jittering caused by the viewpoint difference among sensors in the camera array. To address this problem, we develop a dedicated novel view synthesis algorithm that transforms the video frames as if they were captured by a single reference sensor. Specifically, for any frame from a non-reference sensor, we find the two temporally neighboring frames captured by the reference sensor. Using these three frames, we render a new frame with the same time stamp as the non-reference frame but from the viewpoint of the reference sensor. Specifically, we segment these frames into super-pixels and then apply local content-preserving warping to warp them to form the new frame. We employ a multi-label Markov Random Field method to blend these warped frames. Our experiments show that our method can produce high-quality and high-speed video of a wide variety of scenes with large parallax, scene dynamics, and camera motion and outperforms several baseline and state-of-the-art approaches.
Data analytics and processing platforms in CPS

The speed of new developments in the IoT and CPS poses new challenges for data scientists and business owners to leverage smarter insights, demanding a real-time dashboard of information extracted from data in movement. Business is developed on top of Big Data Analytics and revenue is returned with high percentages from events prediction. Since many business areas can benefit from it, Big Data Analytics as a research topic faces multiple challenges. These ones are: fundamental understanding of models, architectures, security, privacy, but also data science and mentality to accommodate big data-driven decisions. This is argued by a series of facts such as: inference from multiple data sources, observation measuring, missing events and surrogate variables, incomplete information. Thus, this chapter aims to present a broad overview of the most common methods and techniques, including processing data platforms, used for analytics applied to large volume of unstructured, semistructured, and structured data coming with high velocity. Moreover, the tutorial character of this material helps to develop some capabilities in analyzing small data, to be followed up with massive amounts of data.
Nonlinear, flexible, semisupervised learning scheme for face beauty scoring

Automatic facial beauty scoring in images is an emerging research topic in face-based biometrics. All existing methods adopt fully supervised schemes. We introduce the use of semisupervised learning schemes for solving the problem of face beauty scoring. The paper has two main contributions. First, instead of using fully supervised techniques, we show that graph-based score propagation methods can enrich model learning without the need of additional labeled face images. Second, we propose a nonlinear flexible manifold embedding for solving the score propagation. This model can be used for transductive and inductive settings. The proposed semisupervised schemes were tested on three recent public datasets for face beauty analysis: SCUT-FBP, M<sup>2</sup>B, and SCUT-FBP5500. These experiments, as well as many comparisons with supervised schemes, show that the nonlinear semisupervised scheme compares favorably with many supervised schemes. They also show that its performances in terms of error prediction and Pearson correlation are better than those reported for the used datasets.
Value assessment and revision in legal interpretation

The research aims at a formal definition of constructive interpretation in law as the dynamic of revision of theories about the normative system, embedding a model of balancing values [13] into an architecture of i/o logics representing conceptual, deontological and axiological rules [11]. We also introduce new revision operators which are relevant in the context of value assessments.
On the combinatorics of the 2-class classification problem

A set of points X = X B ∪ X R ⊆ R d is linearly separable if the convex hulls of X B and X R are disjoint, hence there exists a hyperplane separating X B from X R . Such a hyperplane provides a method for classifying new points, according to which side of the hyperplane the new points lie. When such a linear separation is not possible, it may still be possible to partition X B and X R into prespecified numbers of groups , in such a way that every group from X B is linearly separable from every group from X R . We may also discard some points as outliers , and seek to minimize the number of outliers necessary to find such a partition. Based on these ideas, Bertsimas and Shioda proposed the classification and regression by integer optimization (CRIO) method in 2007. In this work we explore the integer programming aspects of the classification part of CRIO, in particular theoretical properties of the associated formulation. We are able to find facet-inducing inequalities coming from the stable set polytope, hence showing that this classification problem has exploitable combinatorial properties.
Convex class model on symmetric positive definite manifolds

The effectiveness of Symmetric Positive Definite (SPD) manifold features has been proven in various computer vision tasks. However, due to the non-Euclidean geometry of these features, existing Euclidean machineries cannot be directly used. In this paper, we tackle the classification tasks with limited training data on SPD manifolds. Our proposed framework, named Manifold Convex Class Model, represents each class on SPD manifolds using a convex model, and classification can be performed by computing distances to the convex models. We provide three methods based on different metrics to address the optimization problem of the smallest distance of a point to the convex model on SPD manifold. The efficacy of our proposed framework is demonstrated both on synthetic data and several computer vision tasks including object recognition, texture classification, person re-identification and traffic scene classification.
The Nigeria Police and Internal Security Management in Nigeria

Over the years, the relative peace that placed Nigeria among the most secure nations in the West African sub-region has been eroded and the country has suddenly metamorphosed into an abode of serial bombings, hostage-taking, kidnapping, armed robbery, cold-blooded murders and ethno-religious conflicts. The increasing nature of the indices of insecurity in the country is not only disturbing but also questions the effectiveness of the Nigerian security architecture, especially that of the Nigerian Police which is primarily saddled with the responsibility of providing security to the people. This has led to a series of debates and arguments on how best to reform the Nigerian Police in order to reposition it to meet the new wave of security challenges confronting the country. It is against this backdrop that this chapter attempts a discussion of the performance level of the Nigeria Police in the management of internal security in the wake of the challenge for the struggle for sustainable peace and the safety of lives and properties in the country. The chapter argues that the performance of the police, especially with regard to civil relations, is significantly influenced by the colonial legacy of its establishment. The security agency is highly centralized making it difficult for swift and dynamic decision-making needed to confront the trajectories of conflict and security challenges. The chapter identifies a number of challenges hindering the effectiveness of the Nigerian Police which include manpower shortage and elite policing, poor public perception, the issue of politicization, and overly centralized and non-meritocratic leadership. The chapter concludes that there is high prospect for the Nigerian Police to be effective if the challenges facing the institution are holistically addressed.
Ranked Enumeration of Minimal Triangulations

A tree decomposition of a graph facilitates computations by grouping vertices into bags that are interconnected in an acyclic structure; hence their importance in a plethora of problems such as query evaluation over databases and inference over probabilistic graphical models. The relative benefit from different tree decompositions is measured by diverse (sometime complex) cost functions that vary from one application to another. For generic cost functions like width and fill-in, an optimal tree decomposition can be efficiently computed in some cases, notably when the number of minimal separators is bounded by a polynomial (due to Bouchitte and Todinca); we refer to this assumption as "poly-MS.'' To cover the variety of cost functions in need, it has recently been proposed to devise algorithms for enumerating many decomposition candidates for applications to choose from using specialized, or even machine-learned, cost functions. We explore the ability to produce a large collection of "high quality'' tree decompositions. We present the first algorithm for ranked enumeration of the proper (non-redundant) tree decompositions, or equivalently minimal triangulations, under a wide class of cost functions that substantially generalizes the generic ones above. On the theoretical side, we establish the guarantee of polynomial delay if poly-MS is assumed, or if we are interested in tree decompositions of a width bounded by a constant. We describe an experimental evaluation on graphs of various domains (including join queries, Bayesian networks, treewidth benchmarks and random), and explore both the applicability of the poly-MS assumption and the performance of our algorithm relative to the state of the art.
Implementing YewPar: A Framework for Parallel Tree Search

Combinatorial search is central to many applications yet hard to parallelise. We argue for improving the reuse of parallel searches, and present the design and implementation of a new parallel search framework. YewPar generalises search by abstracting search tree generation, and by providing algorithmic skeletons that support three search types, together with a set of search coordination strategies. The evaluation shows that the cost of YewPar generality is low (6.1%); global knowledge is inexpensively shared between workers; irregular tasks are effectively distributed; and YewPar delivers good runtimes, speedups and efficiency with up to 255 workers on 17 localities.
Online Robust Fuzzy Clustering of Data with Omissions Using Similarity Measure of Special Type

The task of clustering is important and the most difficult part of the overall problem of Data Mining because it is based on the self-learning paradigm, i.e. implies the absence of pre-tagged training sample. In real conditions, this task is complicated by the fact that in having data arrays some of the observations can be corrupted by anomalous outliers and some - contain missing data, that is, the “object-property” table has “empty” cells. In addition, data can be arrive in online mode on processing, especially for tasks related with Data Stream Mining and Big Data. In the paper the problem of fuzzy adaptive online clustering of data distorted by outliers that are sequentially fed to the processing when the original sample volume and the number of distorted observations are apriori unknown is considered. The probabilistic and possibilistic adaptive online clustering algorithms for such data, that are based on the similarity measure of a special type that weaken or overwhelming outliers are proposed. The computational experiment confirms the effectiveness of approach under consideration.
Weighted total generalized variation model for Poisson noise removal

We consider the image denoising problem under Poisson noise. The total generalized variation (TGV)-based method is high efficient in eliminating the staircase effect which often occurs in the total variation-based methods. However, the TGV-based method may produces over-smoothing edges. In order to avoid over-smoothing edges, a weighted total generalized variation (WTGV) model is proposed. Computationally, the WTGV minimization problem is transformed into a saddle-point problem by using the dual technique of optimization. Then, the Chambolle–Pock’s first-order primal–dual algorithm is used to solve the transformed saddle-point problem. At last, compared with several existing state-of-the-art methods, experimental results demonstrate the performance of our proposed method in edges preservation and staircase effect elimination.
Innovation Representation of Stochastic Processes With Application to Causal Inference

Typically, real-world stochastic processes are not easy to analyze. In this paper, we study the representation of different stochastic process as a memoryless innovation process triggering a dynamic system. We show that such a representation is always feasible for innovation processes taking values over a continuous set. However, the problem becomes more challenging when the alphabet size of the innovation is finite. In this case, we introduce both lossless and lossy frameworks, and provide closed-form solutions and practical algorithmic methods. In addition, we discuss the properties and uniqueness of our suggested approach. Finally, we show that the innovation representation problem has many applications. We focus our attention on entropic causal inference, which has recently demonstrated promising performance, compared to alternative methods.
Rank-Based Encoding Features for Stereo Matching

We propose a novel feature extraction method and a cost function for stereo matching, that is robust and stable in matching images from different photographing conditions. Based on the Spearman rank correlation, for the pixel in the window centered around a certain pixel, the code is proposed as a rank sequence obtained from an ordered set of the pixel values. We also apply the same approach to X-gradient image and Y-gradient image. Finally, three rank sequences are obtained from the matching windows of original image, X-gradient image and Y-gradient image. They are used as the matching features of the center pixel. Then, the matching cost of two pixels will be defined as the weighted combination of the difference between their features. Our algorithm has a better matching effect than existing algorithms, especially in low-texture areas and object boundaries. We conducted experiments on Middlebury dataset that has different illumination and exposure images and KITTI dataset whose images were taken outdoor under radiometric distortions. The experimental results indicate that our algorithm is superior to the recently developed algorithms under radiometric variations, such as fuzzy encoding pattern1 and robust soft rank transform,3 whereas the speed is still fast.
Polynomial functions as splines

Let V be a vector space over a finite field k. We give a condition on a subset $$A \subset V$$ that allows for a local criterion for checking when a function $$f:A \rightarrow k$$ is a restriction of a polynomial function of degree $$<m$$ on V. In particular, we show that high rank hypersurfaces of V of degree $$\ge m$$ satisfy this condition. In addition we show that the criterion is robust (namely locally testable in the theoretical computer science jargon).
A Platform Agnostic Solution for Inter-Communication between Virtual Reality Devices

Virtual Reality (VR) allows users to interact with intuitive environments to manipulate games, applications and even other Internet of Things (IoT) devices. VR devices have been brought to users by different vendors, supporting different development platforms and features. Many VR applications, however, do not support simultaneous VR devices from different vendors. The few applications that support this feature are not open to developers, with little information on their implementation and application performance. Using VRTK and Unity, this paper intends to present a VR application that supports multiple VR hardware platforms with the use of the VRTK SDK along with the appropriate VR hardware SDKs. This application supports multiple users in a VR environment where they can interact with each other and with objects in the VR space. Other features such as file transfer over the VR application are also supported. Such an application is important to allow developers to design future applications that support multiple devices regardless of the vendor, creating convenient multiuser VR applications with less limitations.