Reviewerly/exHarmony huggingface dataset:
Modeling and solving cloud service purchasing in multi-cloud environments

Nowadays, the range of cloud services offered by cloud providers varies sharply and poses a challenge for cloud consumers aiming for the most cost-effective and compliant solutions, especially when operating highly scalable microservice architectures. A remedy can be provided by a cloud brokerage intelligent mechanism selecting cloud services across multiple clouds on behalf of consumers by considering individual goals and requirements. In this paper, we present the Cloud Service Purchasing Problem (CSPP) which aims to minimize costs while incorporating specific consumer and application task requirements. To solve this problem, we propose a mixed-integer programming model and two large neighborhood search approaches. Using well-defined problem instances incorporating data from real cloud providers, we conduct several computational experiments to evaluate the performance of the proposed algorithms. They exhibit a competitive performance providing solutions for all scenarios within short computational times. Finally, the significance of this problem and decision support approaches is analyzed by comparing usage patterns with respect to different nowadays cloud providers and offered virtual machine types for various scenarios.
Corpus Wide Argument Mining—A Working Solution

One of the main tasks in argument mining is the retrieval of argumentative content pertaining to a given topic. Most previous work addressed this task by retrieving a relatively small number of relevant documents as the initial source for such content. This line of research yielded moderate success, which is of limited use in a real-world system. Furthermore, for such a system to yield a comprehensive set of relevant arguments, over a wide range of topics, it requires leveraging a large and diverse corpus in an appropriate manner. Here we present a first end-to-end high-precision, corpus-wide argument mining system. This is made possible by combining sentence-level queries over an appropriate indexing of a very large corpus of newspaper articles, with an iterative annotation scheme. This scheme addresses the inherent label bias in the data and pinpoints the regions of the sample space whose manual labeling is required to obtain high-precision among top-ranked candidates.
Constrained path planning of autonomous underwater vehicle using selectively-hybridized particle swarm optimization algorithms

This paper presents an autonomous underwater vehicle (AUV) path planning scenario as an optimization problem constrained by the combination of hard constraints and soft constraints. The path planner aims to generate the optimum path that safely guides an AUV through an ocean environment with priori known obstacles and non-uniform currents in both 2D and 3D. The path planner uses 2 variants of particle swarm optimization (PSO) algorithms, which are the selectively Differential Evolution (DE)-hybridized Quantum PSO (SDEQPSO) and Adaptive PSO (SDEAPSO). The performances of the path planners using different constraints are analyzed in a series of extensive Monte Carlo simulations and ANOVA (analysis of variance) procedures based on their respective solution qualities, stabilities and computational efficiencies. Based on the simulation results, the SDEQPSO path planner with the setting of hard constraint for boundary condition and soft constraint for obstacle avoidance was found to be able to generate smooth and feasible AUV path with higher efficiency than other algorithms, as indicated by its relatively low computational requirement and excellent solution quality.
Verifying and Quantifying Side-channel Resistance of Masked Software Implementations

Power side-channel attacks, capable of deducing secret data using statistical analysis, have become a serious threat. Random masking is a widely used countermeasure for removing the statistical dependence between secret data and side-channel information. Although there are techniques for verifying whether a piece of software code is perfectly masked, they are limited in accuracy and scalability. To bridge this gap, we propose a refinement-based method for verifying masking countermeasures. Our method is more accurate than prior type-inference-based approaches and more scalable than prior model-counting-based approaches using SAT or SMT solvers. Indeed, our method can be viewed as a gradual refinement of a set of type-inference rules for reasoning about distribution types. These rules are kept abstract initially to allow fast deduction and then made concrete when the abstract version is not able to resolve the verification problem. We also propose algorithms for quantifying the amount of side-channel information leakage from a software implementation using the notion of quantitative masking strength. We have implemented our method in a software tool and evaluated it on cryptographic benchmarks including AES and MAC-Keccak. The experimental results show that our method significantly outperforms state-of-the-art techniques in terms of accuracy and scalability.
Machine-tool condition monitoring with Gaussian mixture models-based dynamic probabilistic clustering

The combination of artificial intelligence with data, computing power, and new algorithms can provide important tools for solving engineering problems, such as machine-tool condition monitoring. However, many of these problems require algorithms that can perform in highly dynamic scenarios where the data streams have extremely high sampling rates from different types of variables. The unsupervised learning algorithm based on Gaussian mixture models called Gaussian-based dynamic probabilistic clustering (GDPC) is one of these tools. However, this algorithm may have major limitations if a large amount of concept drifts associated with transients occurs within the data stream. GDPC becomes unstable under these conditions, so we propose a new algorithm called GDPC+ to increase its robustness. GDPC+ represents an important improvement because we introduce: (a) automatic selection of the number of mixture components based on the Bayesian information criterion (BIC), and (b) concept drift transition stabilization based on Cauchy–Schwarz divergence integrated with the Dickey–Fuller test. Thus, GDPC+ can perform better in highly dynamic scenarios than GDPC in terms of the number of false positives. The behavior of GDPC+ was investigated using random synthetic data streams and in a real data stream-based condition monitoring obtained from a machine-tool that produces engine crankshafts at high speed. We found that the initial temporal window size can be used to adapt the algorithm to different analytical requirements. The clustering results were also investigated by induction of the rules generated by the repeated incremental pruning to produce error reduction (RIPPER) algorithm in order to provide insights from the underlying monitored process and its associated concept drifts.
Adherent Raindrop Removal with Self-Supervised Attention Maps and Spatio-Temporal Generative Adversarial Networks

With the rapid increase of outdoor computer vision applications requiring robustness to adverse weather conditions such as automotive and robotics, the loss in image quality that is due to raindrops adherent to the camera lenses is becoming a major concern. In this paper we propose to remove raindrops and improve image quality in the spatio-temporal domain by leveraging the inherent robustness of adopting motion cues and the restorative capabilities of conditional generative adversarial networks. We first propose a competitive single-image baseline capable of estimating the raindrop locations in a self-supervised manner, and then use it to bootstrap our novel spatio-temporal architecture. This shows encouraging performance when compared to both state of the art single-image de-raining methods, and recent video-to-video translation approaches.
Deep Reference Generation With Multi-Domain Hierarchical Constraints for Inter Prediction

Inter prediction is an important module in video coding for temporal redundancy removal, where similar reference blocks are searched from previously coded frames and employed to predict the block to be coded. Although existing video codecs can estimate and compensate for block-level motions, their inter prediction performance is still heavily affected by the remaining inconsistent pixel-wise displacement caused by irregular rotation and deformation. In this paper, we address the problem by proposing a deep frame interpolation network to generate additional reference frames in coding scenarios. First, we summarize the previous adaptive convolutions used for frame interpolation and propose a factorized kernel convolutional network to improve the modeling capacity and simultaneously keep its compact form. Second, to better train this network, multi-domain hierarchical constraints are introduced to regularize the training of our factorized kernel convolutional network. For spatial domain, we use a gradually down-sampled and up-sampled auto-encoder to generate the factorized kernels for frame interpolation at different scales. For quality domain, considering the inconsistent quality of the input frames, the factorized kernel convolution is modulated with quality-related features to learn to exploit more information from high quality frames. For frequency domain, a sum of absolute transformed difference loss that performs frequency transformation is utilized to facilitate network optimization from the view of coding performance. With the well-designed frame interpolation network regularized by multi-domain hierarchical constraints, our method surpasses HEVC on average 3.8% BD-rate saving for the luma component under the random access configuration and also obtains on average 0.83% BD-rate saving over the upcoming VVC.
Financial Development, Institutional Quality and Inclusive Growth in Africa

We investigate the relationship between Financial Development (FD) and Inclusive Growth (IG) unlike extant literature whose concentration has been on economic growth, which we refer to as wholesale growth. While we examine the effect of FD on IG, we investigate the moderating role played by institutions and the regulatory ambience in conveying the strides of FD towards the poor. We measure IG through a social mobility function approach and also construct an IG Index using Asian Development Bank’s framework of IG for robustness. We employ a 27-year panel data collected from across 48 African countries in our dynamic estimations of the FD-IG nexus. We find a non-linear relationship between finance and IG. Our results show that for FD to lead to IG, there is the need to have an effective institutional setup that regulates financial market participants to be inclusive in their operations. With weak institutions, FD’s effect on IG is negative. Our study does not just investigate how FD explains how much money there is overall, but how the money is shared across societies as well. This study is the first to examine the finance-growth nexus from the perspective of inclusivity. Practical policy implications are also discussed.
Enhancing Re-finding Behavior with External Memories for Personalized Search

The goal of personalized search is to tailor the document ranking list to meet user's individual needs. Previous studies showed users usually look for the information that has been searched before. This is called re-finding behavior which is widely explored in existing personalized search approaches. However, most existing methods for identifying re-finding behavior focus on simple lexical similarities between queries. In this paper, we propose to construct memory networks (MN) to support the identification of more complex re-finding behavior. Specifically, incorporating semantic information, we devise two external memories to make an expansion of re-finding based on the query and the document respectively. We further design an intent memory to recognize session-based re-finding behavior. Endowed with these memory networks, we can build a fine-grained user model dynamically based on the current query and documents, and use the model to re-rank the results. Experimental results show the significant improvement of our model compared with traditional methods.
Practical Reinforcement Learning -Experiences in Lot Scheduling Application

With recent advances in deep reinforcement learning, it is time to take another look at reinforcement learning as an approach for discrete production control. We applied proximal policy optimization (PPO), a recently developed algorithm for deep reinforcement learning, to the stochastic economic lot scheduling problem. The problem involves scheduling manufacturing decisions on a single machine under stochastic demand, and despite its simplicity remains computationally challenging. We implemented two parameterized models for the control policy and value approximation, a linear model and a neural network, and used a modified PPO algorithm to seek the optimal parameter values. Benchmarking against the best known control policy for the test case, in which Paternina-Arboleda and Das (2005) combined a base-stock policy and an older reinforcement learning algorithm, we improved the average cost rate by 2 %. Our approach is more general, as we do not require a priori policy parameters such as base-stock levels, and the entire policy is learned.
Solving Nonlinear Equation Systems by a Two-Phase Evolutionary Algorithm

A two-phase evolutionary algorithm is developed to find multiple solutions of a nonlinear equations system. It transforms a nonlinear equations system into a multimodal optimization problem. In phase one of the proposed algorithm, a strategy combines a multiobjective optimization technique and a niching technique to maintain the population diversity. Phase two consists of a detection method and a local search method for encouraging the convergence. The detection method finds several promising subregions and the local search method locates the corresponding optimal solutions in each promising subregion. The experiments on a set of 30 nonlinear equation systems demonstrate that the proposed algorithm is better than other state-of-the-art algorithms.
Novel quantum inspired approaches for automatic clustering of gray level images using Particle Swarm Optimization, Spider Monkey Optimization and Ageist Spider Monkey Optimization algorithms

This paper is intended to identify the optimal number of clusters automatically from an image dataset using some quantum behaved nature inspired meta-heuristic algorithms. Due to the lack of sufficient information, it is difficult to identify the appropriate number of clusters from a dataset, which has enthused the researchers to solve the problem of automatic clustering and to open up a new era of cluster analysis with the help of several natures inspired meta-heuristic algorithms. In this paper, three quantum inspired meta-heuristic techniques, viz., Quantum Inspired Particle Swarm Optimization (QIPSO), Quantum Inspired Spider Monkey Optimization (QISMO) and Quantum Inspired Ageist Spider Monkey Optimization (QIASMO), have been proposed. A comparison has been outlined between the quantum inspired algorithms with their corresponding classical counterparts. The efficiency of the quantum inspired algorithms has been established over their corresponding classical counterparts with regards to fitness, mean, standard deviation, standard errors of fitness, convergence curves (for benchmarked mathematical functions) and computational time. Finally, the results of two statistical superiority tests, viz., t- test and Friedman test have been provided to prove the superiority of the proposed methods. The superiority of the proposed methods has been established on five publicly available real life image datasets, five Berkeley image datasets of different dimensions and four benchmark mathematical functions both visually and quantitatively.
Automatic Speaker Recognition with Limited Data

Automatic speaker recognition (ASR) is a stepping-stone technology towards semantic multimedia understanding and benefits versatile downstream applications. In recent years, neural network-based ASR methods have demonstrated remarkable power to achieve excellent recognition performance with sufficient training data. However, it is impractical to collect sufficient training data for every user, especially for fresh users. Therefore, a large portion of users usually has a very limited number of training instances. As a consequence, the lack of training data prevents ASR systems from accurately learning users acoustic biometrics, jeopardizes the downstream applications, and eventually impairs user experience.
A neurodynamic approach to nonsmooth constrained pseudoconvex optimization problem

This paper presents a new neurodynamic approach for solving the constrained pseudoconvex optimization problem based on more general assumptions. The proposed neural network is equipped with a hard comparator function and a piecewise linear function, which make the state solution not only stay in the feasible region, but also converge to an optimal solution of the constrained pseudoconvex optimization problem. Compared with other related existing conclusions, the neurodynamic approach here enjoys global convergence and lower dimension of the solution space. Moreover, the neurodynamic approach does not depend on some additional assumptions, such as the feasible region is bounded, the objective function is lower bounded over the feasible region or the objective function is coercive. Finally, both numerical illustrations and simulation results in support vector regression problem show the well performance and the viability of the proposed neurodynamic approach.
A Joint Sentence Scoring and Selection Framework for Neural Extractive Document Summarization

Extractive document summarization methods aim to extract important sentences to form a summary. Previous works perform this task by first scoring all sentences in the document then selecting most informative ones; while we propose to jointly learn the two steps with a novel end-to-end neural network framework. Specifically, the sentences in the input document are represented as real-valued vectors through a neural document encoder. Then the method builds the output summary by extracting important sentences one by one. Different from previous works, the proposed joint sentence scoring and selection framework directly predicts the relative sentence importance score according to both sentence content and previously selected sentences. We evaluate the proposed framework with two realizations: a hierarchical recurrent neural network based model; and a pre-training based model that uses BERT as the document encoder. Experiments on two datasets show that the proposed joint framework outperforms the state-of-the-art extractive summarization models which treat sentence scoring and selection as two subtasks.
Cyber security fear appeals

Cyber security researchers are starting to experiment with fear appeals, with a wide variety of designs and reported efficaciousness. This makes it hard to derive recommendations for designing and deploying these interventions. We thus reviewed the wider fear appeal literature to arrive at a set of guidelines to assist cyber security researchers. Our review revealed a degree of dissent about whether or not fear appeals are indeed helpful and advisable. Our review also revealed a wide range of fear appeal experimental designs, in both cyber and other domains, which confirms the need for some standardized guidelines to inform practice in this respect. We propose a protocol for carrying out fear appeal experiments, and we review a sample of cyber security fear appeal studies, via this lens, to provide a snapshot of the current state of play. We hope the proposed experimental protocol will prove helpful to those who wish to engage in future cyber security fear appeal research.
A Coarse-to-Fine Network for Ship Detection in Optical Remote Sensing Images

With the increasing resolution of optical remote sensing images, ship detection in optical remote sensing images has attracted a lot of research interests. The current ship detection methods usually adopt the coarse-to-fine detection strategy, which firstly extracts low-level and manual features, and then performs multi-step training. Inadequacies of this strategy are that it would produce complex calculation, false detection on land and difficulty in detecting the small size ship. Aiming at these problems, a sea-land separation algorithm that combines gradient information and gray information is applied to avoid false alarms on land, the feature pyramid network (FPN) is used to achieve small ship detection, and a multi-scale detection strategy is proposed to achieve ship detection with different degrees of refinement. Then the feature extraction structure is adopted to fuse different hierarchical features to improve the representation ability of features. Finally, we propose a new coarse-to-fine ship detection network (CF-SDN) that directly achieves an end-to-end mapping from image pixels to bounding boxes with confidences. A coarse-to-fine detection strategy is applied to improve the classification ability of the network. Experimental results on optical remote sensing image set indicate that the proposed method outperforms the other excellent detection algorithms and achieves good detection performance on images including some small-sized ships and dense ships near the port.
AVT-VQDB-UHD-1: A Large Scale Video Quality Database for UHD-1

4K television screens or even with higher resolutions are currently available in the market. Moreover video streaming providers are able to stream videos in 4K resolution and beyond. Therefore, it becomes increasingly important to have a proper understanding of video quality especially in case of 4K videos. To this effect, in this paper, we present a study of subjective and objective quality assessment of 4K ultra-high-definition videos of short duration, similar to DASH segment lengths. As a first step, we conducted four subjective quality evaluation tests for compressed versions of the 4K videos. The videos were encoded using three different video codecs, namely H.264, HEVC, and VP9. The resolutions of the compressed videos ranged from 360p to 2160p with framerates varying from 15fps to 60fps. All the source 4K contents used were of 60fps. We included low quality conditions in terms of bitrate, resolution and framerate to ensure that the tests cover a wide range of conditions, and that e.g. possible models trained on this data are more general and applicable to a wider range of real world applications. The results of the subjective quality evaluation are analyzed to assess the impact of different factors such as bitrate, resolution, framerate, and content. In the second step, different state-of-the-art objective quality models were applied to all videos and their performance was analyzed in comparison with the subjective ratings, e.g. using Netflix's VMAF. The videos, subjective scores, both MOS and confidence interval per sequence and objective scores are made public for use by the community for further research.