Reviewerly/exHarmony huggingface dataset:
Model Predictive Control for Energy-Efficient, Quality-Aware, and Secure Virtual Machine Placement

Modern datacenters rely on virtualization to deliver complex and scalable cloud services. To avoid inflating costs or reducing the perceived service level, suitable resource optimization techniques are needed. Placement can be used to prevent inefficient maps between virtual and physical machines. In this perspective, we propose a holistic placement framework considering conflicting performance metrics, such as the service level delivered by the cloud, the energetic footprint, hardware or software outages, and security policies. Unfortunately, computing the best placement strategies is nontrivial, as it requires the ability to trade among several goals, possibly in a real-time manner. Therefore, we approach the problem via model predictive control to devise optimal maps between virtual and physical machines. Results show the effectiveness of our technique in comparison with classical heuristics.
Color-Sensitivity-Based Combined PSNR for Objective Video Quality Assessment

The peak signal-to-noise ratio (PSNR) has been widely employed as an objective video quality assessment (VQA) metric. Usually, videos are represented in the YCbCr color space, which results in three PSNR values for each video frame. Several VQA metrics have been proposed to measure the video quality with a single combined PSNR. However, these metrics are derived heuristically without theoretical justification. In this paper, based on our extensive subjective tests on the sensitivity of the human visual system to different color components, we derive the optimal weighting coefficients of a color-sensitivity-based combined PSNR (CSPSNR). Moreover, to verify the performance of the combined PSNR, test sequences with different levels of combined PSNRs are used to evaluate the quality of the videos. However, no such database is currently available for measuring the effectiveness of different methods regarding combined PSNRs. In this paper, we design a novel coding scheme to produce sequences whose PSNRs are the combinations of different levels of PSNRs of YCbCr, with which the correlation between the subjective score and the combined PSNR is analyzed. Experiment results and statistical analysis demonstrate that the proposed CSPSNR correlates better with the mean opinion score than the existing methods.
Fast cascade face detection with pyramid network

Numerous recent face detectors based on convolutional neural networks (CNNs) have significantly improved the detection performance. However, CNNs usually have a huge number of parameters which lead to very low detection speeds. To address this issue, this paper proposes a fast CNNs cascade face detector with multi-task learning and network acceleration techniques. In particular, the first stage of the detector is an elaborately designed fully convolutional network with a novel pyramid architecture, which can generate multi-scale face proposals efficiently with no more than twice image resizing operations. Several network compression and acceleration techniques including multi-layer merging and knowledge distilling are adopted to further improve inference speed. In addition, online and offline hard sample mining are jointly utilized to further strengthen the power of networks. The experimental results on challenging FDDB show that the proposed face detector is comparable in performance with the-state-of-arts while the speed reaches astonishing 165 fps on Titan GPU.
Density-Preserving Hierarchical EM Algorithm: Simplifying Gaussian Mixture Models for Approximate Inference

We propose an algorithm for simplifying a finite mixture model into a reduced mixture model with fewer mixture components. The reduced model is obtained by maximizing a variational lower bound of the expected log-likelihood of a set of virtual samples. We develop three applications for our mixture simplification algorithm: recursive Bayesian filtering using Gaussian mixture model posteriors, KDE mixture reduction, and belief propagation without sampling. For recursive Bayesian filtering, we propose an efficient algorithm for approximating an arbitrary likelihood function as a sum of scaled Gaussian. Experiments on synthetic data, human location modeling, visual tracking, and vehicle self-localization show that our algorithm can be widely used for probabilistic data analysis, and is more accurate than other mixture simplification methods.
Nonparametric Event Detection in Multiple Time Series for Power Distribution Networks

With the unprecedented advancement of sensing technology, smart city applications are now enriched with massive measurement data related to system states, patterns, and the behavior of its users. However, classic data analysis or machine learning tools ignore some unique characteristics of the multistream measurement data, in particular, the coexistence of strong temporal correlation and interstream relatedness. To this end, in this paper we discuss the problem of novelty detection with multiple coevolving time series data. To capture both the temporal dependence and the interseries relatedness, a multitask nonparametric model is proposed, which can be extended to family of data distributions by adopting the notion of Bregman divergence. Albeit convex, the learning problem can be hard as the time series accumulate. In this regard, an efficient randomized block coordinate descent algorithm is proposed. The model and the algorithm is tested with a real-world application, involving novelty detection and event analysis in smart city power distribution networks with high-resolution multistream measurements. It is shown that the incorporation of interseries relatedness enables the detection of system-level events, which would otherwise be unobservable with traditional methods. The experimental results not only justify the benefits of incorporating information from different sources, but also demonstrate the potential of the proposed multistream analysis tool as one of the core computational components to improve smart city observability, security, and reliability.
Markov Chains With Maximum Entropy for Robotic Surveillance

This paper provides a comprehensive analysis of the following optimization problem: Maximize the entropy rate generated by a Markov chain over a connected graph of order n and subject to a prescribed stationary distribution. First, we show that this problem is strictly convex with global optimum lying in the interior of the feasible space. Second, using Lagrange multipliers, we provide a closed-form expression for the maxentropic Markov chain as a function of an n-dimensional vector, referred to as the maxentropic vector; we provide a provably converging iteration to compute this vector. Third, we show that the maxentropic Markov chain is reversible, compute its entropy rate and describe special cases, among other results. Fourth, through analysis and simulations, we show that our proposed procedure is more computationally efficient than semidefinite programming methods. Finally, we apply these results to robotic surveillance problems. We show realizations of the maxentropic Markov chains over prototypical robotic roadmaps and find that maxentropic Markov chains outperform minimum mean hitting time Markov chains for the so-called “intelligent intruders” with short attack durations. A comprehensive analysis of the following optimization problem: maximize the entropy rate generated by a Markov chain over a connected graph of order n and subject to a prescribed stationary distribution.
Ensemble Pruning Based on Objection Maximization With a General Distributed Framework

Ensemble pruning, selecting a subset of individual learners from an original ensemble, alleviates the deficiencies of ensemble learning on the cost of time and space. Accuracy and diversity serve as two crucial factors while they usually conflict with each other. To balance both of them, we formalize the ensemble pruning problem as an objection maximization problem based on information entropy. Then we propose an ensemble pruning method including a centralized version and a distributed version, in which the latter is to speed up the former. At last, we extract a general distributed framework for ensemble pruning, which can be widely suitable for most of the existing ensemble pruning methods and achieve less time consuming without much accuracy degradation. Experimental results validate the efficiency of our framework and methods, particularly concerning a remarkable improvement of the execution speed, accompanied by gratifying accuracy performance.
Expert ranking techniques for online rated forums

Web 2.0 or social web applications such as online discussion forums, blogs and Wikipedia have improved knowledge sharing by providing an environment in which users can generate and find their favorite content in, a flexible way. With the passage of time, online discussion forums accumulate a huge amount of content and this can introduce issues of content quality and user credibility. A poor-quality answer in a discussion forum indicates the presence of unprofessional or unqualified users; therefore, a priority is to find experts or reputable users. Most of the existing expert-ranking approaches consider basic features, such as the total number of answers provided by a user, but ignore the quality and consistency of the user's answer. In this paper, expert-ranking techniques using g-index are proposed, and are applied to a StackOverflow forum dataset. Three techniques are proposed including Exp-PC, Rep-FS and Weighted Exp-PC. Exp-PC is an adaptation of g-index for ranking experts in StackOverflow forum. In Rep-FS, several features like voters reputation, vote ratio are proposed to measure users' expertise while Weighted Exp-PC computes user expertise by combining their Exp-PC and Rep-FS scores. We measure users' reputation and expertise according to both the quality of their answer and their consistency in providing quality answers. The experimental results of the proposed expert-ranking techniques, Exp-PC and Weighted Exp-PC in particular, validate that these methods identify genuine experts in a more effective way.
Nonlinear Critical-Current Thermal Response of an Asymmetric Josephson Tunnel Junction

We theoretically investigate the critical current of a thermally-biased SIS Josephson junction formed by electrodes made by different BCS superconductors. The response of the device is analyzed as a function of the asymmetry parameter, $r=T_{c_1} /T_{c_2}$. We highlight the appearance of jumps in the critical current of an asymmetric junction, namely, when $r\neq1$. In fact, in such case at temperatures at which the BCS superconducting gaps coincide, the critical current suddenly increases or decreases. In particular, we thoroughly discuss the counterintuitively behaviour of the critical current, which increases by enhancing the temperature of one lead, instead of monotonically reducing. In this case, we found that the largest jump of the critical current is obtained for moderate asymmetries, $r\simeq3$. In view of these results, the discussed behavior can be speculatively proposed as a temperature-based threshold single-photon detector with photon-counting capabilities, which operates non-linearly in the non-dissipative channel.
A Novel Feature Incremental Learning Method for Sensor-Based Activity Recognition

Recognizing activities of daily living is an important research topic for health monitoring and elderly care. However, most existing activity recognition models only work with static and pre-defined sensor configurations. Enabling an existing activity recognition model to adapt to the emergence of new sensors in a dynamic environment is a significant challenge. In this paper, we propose a novel feature incremental learning method, namely the Feature Incremental Random Forest (FIRF), to improve the performance of an existing model with a small amount of data on newly appeared features. It consists of two important components - 1) a mutual information based diversity generation strategy (MIDGS) and 2) a feature incremental tree growing mechanism (FITGM). MIDGS enhances the internal diversity of random forests, while FITGM improves the accuracy of individual decision trees. To evaluate the performance of FIRF, we conduct extensive experiments on three well-known public datasets for activity recognition. Experimental results demonstrate that FIRF is significantly more accurate and efficient compared with other state-of-the-art methods. It has the potential to allow the dynamic exploitation of new sensors in changing environments.
Pioneer Networks: Progressively Growing Generative Autoencoder

We introduce a novel generative autoencoder network model that learns to encode and reconstruct images with high quality and resolution, and supports smooth random sampling from the latent space of the encoder. Generative adversarial networks (GANs) are known for their ability to simulate random high-quality images, but they cannot reconstruct existing images. Previous works have attempted to extend GANs to support such inference but, so far, have not delivered satisfactory high-quality results. Instead, we propose the Progressively Growing Generative Autoencoder (Pioneer) network which achieves high-quality reconstruction with $$128\times 128$$ images without requiring a GAN discriminator. We merge recent techniques for progressively building up the parts of the network with the recently introduced adversarial encoder–generator network. The ability to reconstruct input images is crucial in many real-world applications, and allows for precise intelligent manipulation of existing images. We show promising results in image synthesis and inference, with state-of-the-art results in CelebA inference tasks.
Understanding User Behaviour through Action Sequences: From the Usual to the Unusual

Action sequences, where atomic user actions are represented in a labelled, timestamped form, are becoming a fundamental data asset in the inspection and monitoring of user behaviour in digital systems. Although the analysis of such sequences is highly critical to the investigation of activities in cyber security applications, existing solutions fail to provide a comprehensive understanding due to the complex semantic and temporal characteristics of these data. This paper presents a visual analytics approach that aims to facilitate a user-involved, multi-faceted decision making process during the identification and the investigation of "unusual" action sequences. We first report the results of the task analysis and domain characterisation process. Then we describe the components of our multi-level analysis approach that comprises of constraint-based sequential pattern mining and semantic distance based clustering, and multi-scalar visualisations of users and their sequences. Finally, we demonstrate the applicability of our approach through a case study that involves tasks requiring effective decision-making by a group of domain experts. Although our solution here is tightly informed by a user-centred, domain-focused design process, we present findings and techniques that are transferable to other applications where the analysis of such sequences is of interest.
A high capacity algorithm for information hiding in Arabic text

Recently, the need for digital communication has increased greatly. As a result, the Internet has become the most economical and speedy medium of communication today. Nevertheless, such accessible communication channels have a great chance of being exposed to security threats causing illegal information access. Steganography provides a type of data hiding method that disguises the presence of the secret messages in the media. In this paper, a steganography algorithm for information hiding in Arabic text is proposed. The new algorithm improves the length of the secret message that can be embedded in an Arabic text document without affecting its quality as much as possible. The proposed algorithm utilizes different characteristics and properties of Arabic language. It utilizes both the Arabic extension character (Kashida) and small space characters. Each existing Kashida can hide one bit and each existing space can hide three bits. The proposed algorithm was tested for different length stego-text messages. It provides superiority in achieving high capacity hiding ratio in comparison with the most related Kashida-based techniques and spaces-based techniques.
LUT-Based Hierarchical Reversible Logic Synthesis

We present a synthesis framework to map logic networks into quantum circuits for quantum computing. The synthesis framework is based on lookup-table (LUT) networks, which play a key role in conventional logic synthesis. Establishing a connection between LUTs in an LUT network and reversible single-target gates in a reversible network allows us to bridge conventional logic synthesis with logic synthesis for quantum computing, despite several fundamental differences. We call our synthesis framework LUT-based hierarchical reversible logic synthesis (LHRS). Input to LHRS is a classical logic network representing an arbitrary Boolean combinational operation; output is a quantum network (realized in terms of Clifford+T gates). The framework allows one to account for qubit count requirements imposed by the overlying quantum algorithm or target quantum computing hardware. In a fast first step, an initial network is derived that only consists of single-target gates and already completely determines the number of qubits in the final quantum network. Different methods are then used to map each single-target gate into Clifford+T gates, while aiming at optimally using available resources. We demonstrate the versatility of our method by conducting a design space exploration using different parameters on a set of large combinational benchmarks. On the same benchmarks, we show that our approach can advance over the state-of-the-art hierarchical reversible logic synthesis algorithms.
Incremental personalized E-mail spam filter using novel TFDCR feature selection with dynamic feature update

Communication through e-mails remains to be highly formalized, conventional and indispensable method for the exchange of information over the Internet. An ever-increasing ratio and adversary nature of spam e-mails have posed a great many challenges such as uneven class distribution, unequal error cost, frequent change of content and personalized context-sensitive discrimination. In this research, we propose a novel and distinctive approach to develop an incremental personalized e-mail spam filter. The proposed work is described using three significant contributions. First, we applied a novel term frequency difference and category ratio based feature selection function TFDCR to select the most discriminating features irrespective of the number of samples in each class. Second, an incremental learning model is used which enables the classifier to update the discriminant function dynamically. Third, a heuristic function called selectionRankWeight is introduced to upgrade the existing feature set that determines new features carrying strong discriminating ability from an incoming set of e-mails. Three public e-mail datasets possessing different characteristics are used to evaluate the filter performance. Experiments are conducted to compare the feature selection efficiency of TFDCR and to observe the filter performance under both the batch and the incremental learning mode. The results demonstrate the superiority of TFDCR as the most effective feature selection function. The incremental learning model incorporating dynamic feature update function overcomes the problem of drifting concepts. The proposed filter validates its efficiency and feasibility by substantially improving the classification accuracy and reducing the false positive error of misclassifying legitimate e-mail as spam.
DeeSIL: Deep-Shallow Incremental Learning

Incremental Learning (IL) is an interesting AI problem when the algorithm is assumed to work on a budget. This is especially true when IL is modeled using a deep learning approach, where two complex challenges arise due to limited memory, which induces catastrophic forgetting and delays related to the retraining needed in order to incorporate new classes. Here we introduce DeeSIL, an adaptation of a known transfer learning scheme that combines a fixed deep representation used as feature extractor and learning independent shallow classifiers to increase recognition capacity. This scheme tackles the two aforementioned challenges since it works well with a limited memory budget and each new concept can be added within a minute. Moreover, since no deep retraining is needed when the model is incremented, DeeSIL can integrate larger amounts of initial data that provide more transferable features. Performance is evaluated on ImageNet LSVRC 2012 against three state of the art algorithms. Results show that, at scale, DeeSIL performance is 23 and 33 points higher than the best baseline when using the same and more initial data respectively.
Comparative analysis of advertising attention to Facebook social network: Evidence from eye-movement data

With the rapid development of social networking sites, Internet advertising has quickly become an important marketing tool. Previous studies either utilized the concept of click-through-rate (CTR) to estimate the number of people who responded to the presence of the advertisements, or employed a questionnaire to measure the advertisements' effectiveness. However, these results couldn't accurately reveal potential customers due to the lack of a common understanding on Internet advertising. This study used eye tracking technology to record the participants' eye gaze behaviors and analyze the differences in the distribution of visual attentions while browsing the researcher's Facebook page. The participants consisted of 65 students whose eye movements were tracked and recorded by the eye tracking system. These participants came from a northern university of science and technology in Taiwan, of which 35 were assigned to Facebook friend group and the other 30 were assigned to non-Facebook friend group according to their interpersonal relationships with the researcher. Eye tracking measurements, including total fixation duration (TFD) and number of fixations (NOF) on the defined regions of interest (ROIs) of the Facebook page were compared to indicate their visual attentions. The experimental results showed that 1) there was a significant difference on the right-hand side (RHS) ads between the two groups based on TFD and NOF; 2) there was a significant difference on the desktop news feed (DNF) ads between the two groups based on TFD and NOF; 3) there was the same viewing sequences between the two groups based on stimulus with RHS ads; and 4) there was a difference of viewing sequences between the two groups based on stimulus with DNF ads.