Reviewerly/exHarmony huggingface dataset:
Performance Analysis and Modeling of Video Transcoding Using Heterogeneous Cloud Services

High-quality video streaming, either in form of Video-On-Demand (VOD) or live streaming, usually requires converting (i.e., transcoding) video streams to match the characteristics of viewers' devices (e.g., in terms of spatial resolution or supported formats). Considering the computational cost of the transcoding operation and the surge in video streaming demands, Streaming Service Providers (SSPs) are becoming reliant on cloud services to guarantee Quality of Service (QoS) of streaming for their viewers. Cloud providers offer heterogeneous computational services in form of different types of Virtual Machines (VMs) with diverse prices. Effective utilization of cloud services for video transcoding requires detailed performance analysis of different video transcoding operations on the heterogeneous cloud VMs. In this research, for the first time, we provide a thorough analysis of the performance of the video stream transcoding on heterogeneous cloud VMs. Providing such analysis is crucial for efficient prediction of transcoding time on heterogeneous VMs and for the functionality of any scheduling methods tailored for video transcoding. Based upon the findings of this analysis and by considering the cost difference of heterogeneous cloud VMs, in this research, we also provide a model to quantify the degree of suitability of each cloud VM type for various transcoding tasks. The provided model can supply resource (VM) provisioning methods with accurate performance and cost trade-offs to efficiently utilize cloud services for video streaming.
On learning guarantees to unsupervised concept drift detection on data streams

Motivated by the Statistical Learning Theory (SLT), which provides a theoretical framework to ensure when supervised learning algorithms generalize input data, this manuscript relies on the Algorithmic Stability framework to prove learning bounds for the unsupervised concept drift detection on data streams. Based on such proof, we also designed the Plover algorithm to detect drifts using different measure functions, such as Statistical Moments and the Power Spectrum. In this way, the criterion for issuing data changes can also be adapted to better address the target task. From synthetic and real-world scenarios, we observed that each data stream may require a different measure function to identify concept drifts, according to the underlying characteristics of the corresponding application domain. In addition, we discussed about the differences of our approach against others from literature, and showed illustrative results confirming the usefulness of our proposal.
Specifics of medical data mining for diagnosis aid: A survey

Data mining continues to play an important role in medicine; specifically, for the development of diagnosis aid models used in expert and intelligent systems. Although we can find abundant research on this topic, clinicians remain reluctant to use decision support tools. Social pressure explains partly this lukewarm position, but concerns about reliability and credibility are also put forward. To address this reticence, we emphasize the importance of the collaboration between both data miners and clinicians. This survey lays the foundation for such an interaction, by focusing on the specifics of diagnosis aid, and the related data modeling goals. On this regard, we propose an overview on the requirements expected by the clinicians, who are both the experts and the final users. Indeed, we believe that the interaction with clinicians should take place from the very first steps of the process and throughout the development of the predictive models, thus not only at the final validation stage. Actually, against a current research approach quite blindly driven by data, we advocate the need for a new expert-aware approach. This survey paper provides guidelines to contribute to the design of daily helpful diagnosis aid systems.
SUAA: A Secure User Authentication Scheme with Anonymity for the Single &amp; Multi-server Environments

The rapid increase in user base and technological penetration has enabled the use of a wide range of devices and applications. The services are rendered to these devices from single-server or highly distributed server environments, irrespective of their location. As the information exchanged between servers and clients is private, numerous forms of attacks can be launched to compromise it. To ensure the security, privacy, and availability of the services, different authentication schemes have been proposed for both single-server and multi-server environments. The primary performance objective of such schemes is to prevent most (if not all) attacks, with minimal computational costs at the server and user ends. To address this challenge, this paper presents a secure user authentication scheme with anonymity (SUAA) for single-server and multi-server environments. It works on 3-factor authentication, involving passwords, smart cards, and biometric data. We use symmetric and asymmetric encryption for single-server and multi-server architectures respectively, to reduce the computational costs. Through a comprehensive security analysis, we show that the proposed scheme is reliable through mutual authentication, and is resilient to attacks addressed by state of the art solutions. Time cost analysis also shows less time required to complete the authentication process.
Repository and Mutation based Particle Swarm Optimization (RMPSO): A new PSO variant applied to reconstruction of Gene Regulatory Network

Particle Swarm Optimization (PSO) is a meta-heuristic approach based on swarm intelligence, which is inspired by the social behaviour of bird flocking or fish schooling. The main disadvantage of the basic PSO is that it suffers from premature convergence. To prevent the process of search from premature convergence as well as to improve the exploration and exploitation capability as a whole, here, in this paper, a modified variant, named Repository and Mutation based PSO (RMPSO) is proposed. In RMPSO variant, apart from applying five-staged successive mutation strategies for improving the swarm best as referred in Enhanced Leader PSO (ELPSO), two extra repositories have been introduced and maintained to store personal best and global best solutions having same fitness values. In each step, the personal and global best solutions are chosen randomly from their respective repositories which enhance exploration capability further, retaining the exploitation capability. The computational experiment on benchmark problem instances shows that in most of the cases, RMPSO performs better than other algorithms in terms of the statistical metrics taken into account. Moreover, the performance of the proposed algorithm remains consistent in most of the cases when the dimension of the problem is scaled up. RMPSO is further applied to a practical scenario: the reconstruction of Gene Regulatory Networks (GRN) based on Recurrent Neural Network (RNN) model. The experimental results ensure that the RMPSO performs better than the state-of-the-art methods in the synthetic gene data set (gold standard) as well as real gene data set.
Multi-Scale Attention Deep Neural Network for Fast Accurate Object Detection

Object detection remains a challenging task in computer vision due to the tremendous extent of changes in the appearances of objects caused by clustered backgrounds, occlusion, truncation, and scale change. Current deep neural network (DNN)-based object detection methods cannot simultaneously achieve a high accuracy and a high efficiency. To overcome this limitation, in this paper, we propose a novel multi-scale attention (MSA) DNN for accurate object detection with high efficiency. The proposed MSA-DNN method utilizes a novel multi-scale feature fusion module (MSFFM) to construct high-level semantic features. Subsequently, a novel MSA module (MSAM) based on the fused layers of the MSFFM is introduced to exploit the global semantic information of image-level labels to guide detection. On the one hand, MSAM can capture global semantic information to further enhance the semantic feature representation of the fused layers constructed by the MSFFM, thereby improving the detection accuracy. On the other hand, the MSA maps generated by MSAM can be employed to rapidly and coarsely locate objects at different scales. In addition, an attention-based hard negative mining strategy is introduced to filter out negative samples to reduce the search space, dramatically alleviating the severe class imbalance problem. Extensive experimental results on the challenging PASCAL VOC 2007, PASCAL VOC 2012, and MS COCO datasets demonstrate that MSA-DNN achieves a state-of-the-art detection accuracy while maintaining a high efficiency. Furthermore, MSA-DNN significantly improves the small-object detection accuracy.
Evolutionary aspects of reservoir computing

Reservoir computing (RC) is a powerful computational paradigm that allows high versatility with cheap learning. While other artificial intelligence approaches need exhaustive resources to specify their inner workings, RC is based on a reservoir with highly nonlinear dynamics that does not require a fine tuning of its parts. These dynamics project input signals into high-dimensional spaces, where training linear readouts to extract input features is vastly simplified. Thus, inexpensive learning provides very powerful tools for decision-making, controlling dynamical systems, classification, etc. RC also facilitates solving multiple tasks in parallel, resulting in a high throughput. Existing literature focuses on applications in artificial intelligence and neuroscience. We review this literature from an evolutionary perspective. RC's versatility makes it a great candidate to solve outstanding problems in biology, which raises relevant questions. Is RC as abundant in nature as its advantages should imply? Has it evolved? Once evolved, can it be easily sustained? Under what circumstances? (In other words, is RC an evolutionarily stable computing paradigm?) To tackle these issues, we introduce a conceptual morphospace that would map computational selective pressures that could select for or against RC and other computing paradigms. This guides a speculative discussion about the questions above and allows us to propose a solid research line that brings together computation and evolution with RC as test model of the proposed hypotheses. This article is part of the theme issue 'Liquid brains, solid brains: How distributed cognitive architectures process information'.
On PIR and Symmetric PIR From Colluding Databases With Adversaries and Eavesdroppers

We consider the problem of private information retrieval (FIR) and symmetric private information retrieval (SFIR) from replicated databases with colluding servers, in the presence of Byzantine adversaries and eavesdroppers. Specifically, there are K messages replicatively stored at N databases. A user wants to retrieve one message by communicating with the databases, without revealing the identity of the message retrieved. For T-colluding databases, any T out of N databases may communicate their interactions with the user to guess the identity of the requested message. We consider the situation where the communication system can be vulnerable to attachers, namely, there is an adversary in the system that can tap in on or even try to corrupt the communication. The capacity is defined as the maximum number of information bits of the desired message retrieved per downloaded bit. For SPIR, it is further required that the user learns nothing about the other K - 1 messages in the database. Three types of adversaries are considered: a Byzantine adversary who can overwrite the transmission of any B servers to the user; a passive eavesdropper who can tap in on the incoming and outgoing transmissions of any E servers; and a combination of both -an adversary who can tap in on a set of any E nodes, and overwrite the transmission of a set of any B nodes. The problems of SPIR with colluding servers and the three types of adversaries are named T-BSPIR, T-ESPIR and T-BESPIR, respectively. We derive the capacities of the three secure SPIR problems. The results resemble those of secure network coding problems with adversaries and eavesdroppers. The capacity of T-colluding PIR with Byzantine adversaries is characterized in [1]. In this work, we consider T-colluding PIR with an eavedropper (named T-EPIR). We derive the T-EPIR capacity when E ≥ T; for the case where E ≤ T, we find an outer bound (converse bound) and an inner bound (achievability) on the optimal achievable rate.
Public key authenticated encryption with keyword search: revisited

In 2017, the notion of public key authenticated encryption with keyword search (PAEKS) and its security model was defined by Huang and Li. Their main motivation was providing security against inside keyword guessing attacks (KGA). They also proposed a concrete PAEKS scheme secure in their proposed model. In this study, the authors first show that their security model has an important drawback and therefore, cannot handle multi-user settings. As such settings are a necessity in the public-key environment, it is vital to improving the model to capture multiple users. This is what they do in the first part of this study. Then, they consider Huang and Li's PAEKS scheme and prove that it is not secure against inside (and even outside) KGA. Finally, they propose a modified scheme to fix the problem without any additional communication or computation costs. They further prove that the new scheme is secure in the improved model.
A Study of High Frame Rate Video Formats

High frame rates are acknowledged to increase the perceived quality of certain video content. However, the lack of high frame rate test content has previously restricted the scope of research in this area-especially in the context of immersive video formats. This problem has been addressed through the publication of a high frame rate video database BVI-HFR, which was captured natively at 120 fps. BVI-HFR spans a variety of scenes, motions, and colors, and is shown to be representative of BBC broadcast content. In this paper, temporal down-sampling is utilized to enable both subjective and objective comparisons across a range frame rates. A large-scale subjective experiment has demonstrated that high frame rates lead to increases in perceived quality, and that a degree of content dependence exists-notably related to camera motion. Various image and video quality metrics have been benchmarked on these subjective evaluations, and analysis shows that those which explicitly account for temporal distortions (e.g., FRQM) provide improved correlation with subjective opinions compared to generic quality metrics such as PSNR.
Remote sensing data in lithium (Li) exploration: A new approach for the detection of Li-bearing pegmatites

Remote sensing has proved to be a powerful resource in geology capable of delineating target exploration areas for several deposit types. Only recently, these methodologies have been used for the detection of lithium (Li)-bearing pegmatites. This happened because of the growing importance and demand of Li for the construction of Li-ion batteries for electric cars. The objective of this study was to develop innovative and effective remote sensing methodologies capable of identifying Li-pegmatites through alteration mapping and through the direct identification of Li-bearing minerals. For that, cloud free Landsat-5, Landsat-8, Sentinel-2 and ASTER images with low vegetation coverage were used. The image processing methods included: RGB (red, green, blue) combinations, band ratios and selective principal component analysis (PCA). The study area of this work is the Fregeneda (Salamanca, Spain)-Almendra (Vila Nova de Foz Côa, Portugal) region, where different known types of Li-pegmatites have been mapped. This study proposes new RGB combinations, band ratios and subsets for selective PCA capable of differentiating the spectral signatures of the Li-bearing pegmatites from the spectral signatures of the host rocks. The potential and limitations of the methodologies proposed are discussed, but overall there is a great potential for the identification of Li-bearing pegmatites using remote sensing. The results obtained could be improved using sensors with a better spatial and spectral resolution.
Laplacian pyramid adversarial network for face completion

Recently, generative adversarial networks (GANs) have demonstrated high-quality reconstruction in face completion. There is still much room for improvement over the conventional GAN models that do not explicitly address the texture details problem. In this paper, we propose a Laplacian-pyramid-based generative framework for face completion. This framework can produce more realistic results (1) by deriving precise content information of missing face regions in a coarse-to-fine fashion and (2) by propagating the high-frequency details from the surrounding area via a modified residual learning model. Specifically, for the missing regions, we design a Laplacian-pyramid-based convolutional network framework that can predict missing regions under different resolutions; this framework takes advantage of multiscale features shared from low levels and extracted from middle layers for the next finer level. For high-frequency details, we construct a new residual learning network to eliminate color discrepancies between the missing and surrounding regions progressively. Furthermore, a multiloss function is proposed to supervise the generative process. To optimize the model, we train the entire generative model with deep supervision using a joint reconstruction loss, which ensures that the generated image is as realistic as the original. Extensive experiments on benchmark datasets show that the proposed framework exhibits superior performance over state-of-the-art methods in terms of predictive accuracy, both quantitatively and qualitatively.
Unsupervised Feature Learning of Human Actions As Trajectories in Pose Embedding Manifold

An unsupervised human action modeling framework can provide useful pose-sequence representation, which can be utilized in a variety of pose analysis applications. In this work we propose a novel temporal pose-sequence modeling framework, which can embed the dynamics of 3D human-skeleton joints to a latent space in an efficient manner. In contrast to an end-to-end framework explored by previous works, we disentangle the task of individual pose representation learning from the task of learning actions as a sequence of pose embeddings. In order to realize a continuous pose embedding manifold along with better reconstructions, we propose an unsupervised, manifold learning procedure named Encoder GAN, (or EnGAN). Further we use the pose embeddings generated by EnGAN to model human actions using an RNN auto-encoder architecture, PoseRNN. We introduce first-order gradient loss to explicitly enforce temporal regularity in the predicted motion sequence. A hierarchical feature fusion technique is also investigated for simultaneous modeling of local skeleton joints along with global pose variations. We demonstrate state-of-the-art transfer-ability of the learned representation against other supervisedly and unsupervisedly learned motion embeddings for the task of fine-grained action recognition on SBU interaction dataset. Further, we show the qualitative strengths of the proposed framework by visualizing skeleton pose reconstructions and interpolations in pose-embedding space, and low dimensional principal component projections of the reconstructed pose trajectories.
Extrinsic 6DoF calibration of a radar–LiDAR–camera system enhanced by radar cross section estimates evaluation

Autonomous navigation of mobile robots is often based on information from a variety of heterogeneous sensors; hence, extrinsic sensor calibration is a fundamental step in the fusion of such information. In this paper, we address the problem of extrinsic calibration of a radar–LiDAR–camera sensor system. This problem is primarily challenging due to sparse informativeness of radar measurements. Namely, radars cannot extract rich structural information about the environment, while their lack of elevation resolution, that is nevertheless accompanied by substantial elevation field of view, introduces uncertainty in the origin of the measurements. We propose a novel calibration method which involves a special target design and two-step optimization procedure to solve the aforementioned challenges. First step of the optimization is minimization of a reprojection error based on an introduced point–circle geometric constraint. Since the first step is not able to provide reliable estimates of all the six extrinsic parameters, we introduce a second step to refine the subset of parameters with high uncertainty. We exploit a pattern discovered in the radar cross section estimation that is correlated to the missing elevation angle. Additionally, we carry out identifiability analysis based on the Fisher Information Matrix to show minimal requirements on the dataset and to verify the method through simulations. We test the calibration method on a variety of sensor configurations and address the problem of radar vertical misalignment. In the end, we show via extensive experiment analysis that the proposed method is able to reliably estimate all the six parameters of the extrinsic calibration.
Gated Attentive-Autoencoder for Content-Aware Recommendation

The rapid growth of Internet services and mobile devices provides an excellent opportunity to satisfy the strong demand for the personalized item or product recommendation. However, with the tremendous increase of users and items, personalized recommender systems still face several challenging problems: (1) the hardness of exploiting sparse implicit feedback; (2) the difficulty of combining heterogeneous data. To cope with these challenges, we propose a gated attentive-autoencoder (GATE) model, which is capable of learning fused hidden representations of items' contents and binary ratings, through a neural gating structure. Based on the fused representations, our model exploits neighboring relations between items to help infer users' preferences. In particular, a word-level and a neighbor-level attention module are integrated with the autoencoder. The word-level attention learns the item hidden representations from items' word sequences, while favoring informative words by assigning larger attention weights. The neighbor-level attention learns the hidden representation of an item's neighborhood by considering its neighbors in a weighted manner. We extensively evaluate our model with several state-of-the-art methods and different validation metrics on four real-world datasets. The experimental results not only demonstrate the effectiveness of our model on top-N recommendation but also provide interpretable results attributed to the attention modules.
Learning Bilevel Layer Priors for Single Image Rain Streaks Removal

Rain streaks removal is an important issue of the outdoor vision system and recently has been investigated extensively. In the past decades, maximum a posterior and network-based architecture have been attracting considerable attention for this problem. However, it is challenging to establish effective regularization priors and the cost function with complex prior is hard to optimize. On the other hand, it is still hard to incorporate data-dependent information into conventional numerical iterations. To partially address the above limits and inspired by the leader-follower gaming perspective, we introduce an unrolling strategy to incorporate data-dependent network architectures into the established iterations, i.e., a learning bilevel layer priors method to jointly investigate the learnable feasibility and optimality of rain streaks removal problem. Both visual and quantitative comparison results demonstrate that our method outperforms the state of the art.