Reviewerly/exHarmony huggingface dataset:
ISRchain: Achieving efficient interdomain secure routing with blockchain

Extensive efforts have been made for securing interdomain routing during the last two decades, but the highly centralized nature and complex management of existing security solutions impede their deployment progress. This paper presents ISRchain, a blockchain-based interdomain secure routing framework. We utilize blockchain as a distributed, tamper-proof, and traceable ledger to store the distributions of Internet number resources consistently and design a smart contract-based mechanism for route origin verification, AS adjacency attestation, and route policy compliance check. ISRchain decouples the data required for route validation from routing protocols to work independently without requiring any changes in the current interdomain routing system. We conduct extensive experiments to evaluate ISRchain’s performance by simulating Internet resource allocation in the real world and show its effectiveness and efficiency by performing two case studies with BHARTI Airtel prefix hijack in 2015 and Google route leak in 2017. Experimental results demonstrate the feasibility of ISRchain for securing interdomain routing.
Steganalysis of meshes based on 3D wavelet multiresolution analysis

3D steganalysis aims to find the information hidden in 3D models and graphical objects. It is assumed that the information was hidden by 3D steganography or watermarking algorithms. A new set of 3D steganalysis features, derived by using multiresolution 3D wavelet analysis, is proposed in this research study. 3D wavelets relate a given mesh representation with its lower and higher graph resolutions by means of a set of Wavelet Coefficient Vectors (WCVs). The 3D steganalysis features are derived from transformations between a given mesh and its corresponding higher and lower resolutions. They correspond to geometric measures such as ratios and angles between various geometric measures. These features are shown to significantly increase the steganalysis accuracy when detecting watermarks which have been embedded by 3D wavelet-based watermarking algorithms. The proposed features, when used in combination with a previously proposed feature set, is shown to provide the best results in detecting the hidden information embedded by other information hiding algorithms.
Self-Aware Machine Learning for Multimodal Workload Monitoring during Manual Labor on Edge Wearable Sensors

The design of reliable wearable technologies for real-time and long-term monitoring presents a major challenge.Self-awareness is a promising solution that enables the system to monitor itself in interaction with the environment and to manage its resources more efficiently.In this work, we aim to utilize the notion of self-awareness to improve the battery life of edge wearable sensors for multimodal health and workload monitoring.Specifically, we consider cognitive workload detection during manual labor as a case study to illustrate the impact of our proposed technique in wearable technologies.Our multimodal machine-learning algorithm is able to detect cognitive workload during manual labor with a performance of 81.75%.By adopting the notion of self-awareness, we achieve an improvement of 27.6% in energy consumption, with less than 6% of performance loss.
On a granular functional link network for classification

In this paper, we present a new granular classifier in two versions (iterative and non–iterative), by adopting some ideas originating from a kind of Functional Link Artificial Neural Network and the Functional Network schemes. These two architectures are substantially the same: they both use a function basis instead of the usual activation function, but they are different for the learning algorithm. We augment them from the perspective of Granular Computing and information granules, designing a new kind of classifier and two learning algorithms, by taking into account granularity of information. The proposed classifier exhibits the advantages of the granular architectures, that is higher accuracy and transparency. We formally discuss the convergence of the iterative learning scheme. We carry out some numerical experiments using publicly available data, by comparing the results against those results produced by the state-of-the-art methods. In particular, we achieved sound results by invoking the iterative learning scheme.
Beyond Happy‐or‐Not: Using Emoji to Capture Visitors’ Emotional Experience

Abstract Museums are emotionally driven sites. People visit museums to feel and their emotions influence how the museum and its artefacts are perceived. Thus, evaluating emotional states are increasingly important for museums. However, evaluating visitors’ experiences is increasingly challenging, especially with the introduction of new and emerging technology. Moreover, people’s behaviour is not strictly objective and rational. While emotional states are subjective and hard to verbalize or observe, emoji are often used to express emotions on mobile and smartphone messaging applications. In this paper we investigate whether emoji can capture emotional states elicited by museum experiences, supporting traditional methods such as interviews. While other non‐verbal self‐report methods have been used to evaluate emotions, this is the first tool of this kind designed specifically to measure emotions elicited by museum experiences. We designed a set of 9 emoji illustrating a variety of emotional states beyond happy‐or‐not. Then, we confirmed that participants understood our emoji’s intended concept using a word association task. Finally, we used our 9 emoji to evaluate an interactive museum experience. We also run interviews and we investigated the correspondence between participants’ comments and the emoji they chose. Through this study we gained a better understanding of how the emoji can be deployed to capture a range of visitors’ emotional experiences. Our findings suggest that emoji can capture which emotional states participants felt beyond the happy‐or‐not dichotomy, but that they should be complemented with traditional methods such as interviews to understand why specific emotions were felt.
Image Contrast Enhancement by Homomorphic Filtering based Parametric Fuzzy Transform

In this paper, a new image contrast enhancement technique by taking the advantages of Homomorphic decomposition and fuzzy transform is been employed. For increasing the clarity of the image generally, histogram or Retinex theory based algorithms were used. These procedures worked on enhancing the reflectance layer by ignoring illumination, which is not a better strategy and leads to poor results. Fuzzy based image enhancement approach makes use of illumination by omitting reflectance. In our proposed algorithm, Homomorphic decomposition is used for getting the exact illumination image from the value layer of HSV (Hue, Saturation, Value) image. Next, the parametric fuzzy transform is employed to enhance the image by updating its membership functions and thereby smoothing the luminance layer. Finally, a weighted image is generated by pixel neighborhood property for preserving the image details. The results show the profoundness of the algorithm in terms of its clarity and complexity even for nonuniform illumination images.
Domain Expansion in DNN-Based Acoustic Models for Robust Speech Recognition

Training acoustic models with sequentially incoming data - while both leveraging new data and avoiding the forgetting effect - is an essential obstacle to achieving human intelligence level in speech recognition. An obvious approach to leverage data from a new domain (e.g., new accented speech) is to first generate a comprehensive dataset of all domains, by combining all available data, and then use this dataset to retrain the acoustic models. However, as the amount of training data grows, storing and retraining on such a large-scale dataset becomes practically impossible. To deal with this problem, in this study, we study several domain expansion techniques which exploit only the data of the new domain to build a stronger model for all domains. These techniques are aimed at learning the new domain with a minimal forgetting effect (i.e., they maintain original model performance). These techniques modify the adaptation procedure by imposing new constraints including (1) weight constraint adaptation (WCA): keeping the model parameters close to the original model parameters; (2) elastic weight consolidation (EWC): slowing down training for parameters that are important for previously established domains; (3) soft KL-divergence (SKLD): restricting the KL-divergence between the original and the adapted model output distributions; and (4) hybrid SKLD-EWC: incorporating both SKLD and EWC constraints. We evaluate these techniques in an accent adaptation task in which we adapt a deep neural network (DNN) acoustic model trained with native English to three different English accents: Australian, Hispanic, and Indian. The experimental results show that SKLD significantly outperforms EWC, and EWC works better than WCA. The hybrid SKLD-EWC technique results in the best overall performance.
Learning local representations for scalable RGB-D face recognition

In this article we present a novel RGB-D learned local representations for face recognition based on facial patch description and matching. The major contribution of the proposed approach is an efficient learning and combination of data-driven descriptors to characterize local patches extracted around image reference points. We explored the complementarity between both of deep learning and statistical image features as data-driven descriptors. In addition, we proposed an efficient high-level fusion scheme based on a sparse representation algorithm to leverage the complementarity between image and depth modalities and also the used data-driven features. Our approach was extensively evaluated on four well-known benchmarks to prove its robustness against known challenges in the case of face recognition. The obtained experimental results are competitive with the state-of-the-art methods while providing a scalable and adaptive RGB-D face recognition method.
Evaluating Sentiment C1assifiers for Bitcoin Tweets in Price Prediction Task

Bitcoin alongside other cryptocurrencies became one of the largest trends recently, due to its redefinition of the concept of money, and its price fluctuation. Especially on the social media, people keep discussing Bitcoin topics, consulting, and advising about cryptocurrency trading. This paper explores the relationship between Twitter feed on Bitcoin and sentiment analysis of it, comparing and evaluating different data mining classifiers and deep learning methods that might help in better sentiment classification of Bitcoin tweets, the study uses different language modeling approaches, such as tweet embedding and N-Gram modeling. We also evaluate the quality of automated sentiment classification in comparison to manually assigned sentiment labeling. The results show that the manual approach gives significantly better results in some datasets, and superior performance of MLP, WiSARD and decision tree methods. On the other hand, R-Auto Tweets Sentiment (RATS) gives more stable performance overall datasets. using time-series, we found partial correlation between Bitcoin price fluctuation and sentiment class accuracy fluctuations using different machine learning algorithms.
Sparse Feature Learning of Hyperspectral Imagery via Multiobjective-Based Extreme Learning Machine

Hyperspectral image (HSI) consists of hundreds of narrow spectral band components with rich spectral and spatial information. Extreme Learning Machine (ELM) has been widely used for HSI analysis. However, the classical ELM is difficult to use for sparse feature leaning due to its randomly generated hidden layer. In this paper, we propose a novel unsupervised sparse feature learning approach, called Evolutionary Multiobjective-based ELM (EMO-ELM), and apply it to HSI feature extraction. Specifically, we represent the task of constructing the ELM Autoencoder (ELM-AE) as a multiobjective optimization problem that takes the sparsity of hidden layer outputs and the reconstruction error as two conflicting objectives. Then, we adopt an Evolutionary Multiobjective Optimization (EMO) method to solve the two objectives, simultaneously. To find the best solution from the Pareto solution set and construct the best trade-off feature extractor, a curvature-based method is proposed to focus on the knee area of the Pareto solutions. Benefited from the EMO, the proposed EMO-ELM is less prone to fall into a local minimum and has fewer trainable parameters than gradient-based AEs. Experiments on two real HSIs demonstrate that the features learned by EMO-ELM not only preserve better sparsity but also achieve superior separability than many existing feature learning methods.
Extrapolation-Based Video Retargeting With Backward Warping Using an Image-to-Warping Vector Generation Network

Video retargeting is a technique used to transform a given video to a target aspect ratio. Current methods often cause severe visual distortion due to frequent temporal incoherence during the retargeting. In this study, we propose a new extrapolation-based video retargeting method using an image-to-warping vector generation network to maintain temporal coherence and prevent deformation of an input frame by extending the side area of an input frame. Backward warping-based extrapolation is performed using a displacement vector (DV) that is generated by a proposed convolutional neural network (CNN). The DV is defined as the displacement between the current hole to be filled in the extended area and a pixel in the input frame used to fill the hole. We also propose a technique to efficiently train the CNN including a method for ground-truth DV generation. After the extrapolation, we propose a technique for the maintenance of temporal coherence of the extended region and a distortion suppression scheme (DSC) for minimizing visual artifacts. The simulation results demonstrated that the proposed method improved bidirectional similarity (BDS) up to 3.69, which is a measure of the quality of video retargeting, compared with existing video retargeting methods.
Single Image Snow Removal Using Sparse Representation and Particle Swarm Optimizer

Images are often corrupted by natural obscuration (e.g., snow, rain, and haze) during acquisition in bad weather conditions. The removal of snowflakes from only a single image is a challenging task due to situational variety and has been investigated only rarely. In this article, we propose a novel snow removal framework for a single image, which can be separated into a sparse image approximation module and an adaptive tolerance optimization module. The first proposed module takes the advantage of sparsity-based regularization to reconstruct a potential snow-free image. An auto-tuning mechanism for this framework is then proposed to seek a better reconstruction of a snow-free image via the time-varying inertia weight particle swarm optimizers in the second proposed module. Through collaboration of these two modules iteratively, the number of snowflakes in the reconstructed image is reduced as generations progress. By the experimental results, the proposed method achieves a better efficacy of snow removal than do other state-of-the-art techniques via both objective and subjective evaluations. As a result, the proposed method is able to remove snowflakes successfully from only a single image while preserving most original object structure information.
Casting Geometric Constraints in Semantic Segmentation as Semi-Supervised Learning

We propose a simple yet effective method to learn to segment new indoor scenes from video frames: State-of- the-art methods trained on one dataset, even as large as the SUNRGB-D dataset, can perform poorly when applied to images that are not part of the dataset, because of the dataset bias, a common phenomenon in computer vision. To make semantic segmentation more useful in practice, one can exploit geometric constraints. Our main contribution is to show that these constraints can be cast conveniently as semi-supervised terms, which enforce the fact that the same class should be predicted for the projections of the same 3D location in different images. This is interesting as we can exploit general existing techniques de- veloped for semi-supervised learning to efficiently incorporate the constraints. We show that this approach can efficiently and accurately learn to segment target sequences of ScanNet and our own target sequences using only annotations from SUNRGB-D, and geometric relations between the video frames of target sequences.
Participant Recruitment for Coverage-Aware Mobile Crowdsensing with Location Differential Privacy

Mobile crowdsensing is recognized to be a promising paradigm wherein location-based sensing tasks are outsourced to participants carrying mobile devices. A prominent issue of crowdsensing is to guarantee the sensing coverage by appropriately recruiting participating devices, which requires the disclosure of participants' locations and leads to potential location privacy threats. In this paper, we aim to develop a privacy-preserving participant recruiting scheme for mobile crowdsensing, which guarantees the crowdsensing coverage while preserving participants' location differential privacy against a semi-honest crowdsensing aggregator. Briefly, based on the differential private geo-indistinguishability method, we enable candidate participants to locally perturb their location data. With the obfuscated location information, we formulate the crowdsensing coverage optimization as an Integer Program (IP), and develop a 1-(1 - 1/f) <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">f</sup> -approximation algorithm, which yields a near-optimal participant recruiting solution. Through extensive simulations, we demonstrate the tradeoff between privacy preservation and crowdsensing utility, and show that satisfactory crowdsensing coverage can be achieved while preserving the participants' differential location privacy.
From Artificial to Extended Intelligence in Music Composition

This article explores the relationship and disparities between human and computational creativity by addressing the following questions: How well are computational creativity systems currently performing at creative tasks? Could computers outperform human composers? And, if not, is computational creativity a utopia? Automatic composition systems are examined with respect to Boden’s three criteria of creativity ( novelty, surprise and value ), as well as their assumptions about the nature of creativity. As an alternative to a competitive relationship between human and computational creativity, the article proposes the concept of a distributed human–computer co-creativity, in which computational creativity extends – rather than replaces – human creativity, by expanding the space of creative possibilities.
Domain-Specific Semantics Guided Approach to Video Captioning

In video captioning, the description of a video usually relies on the domain to which the video belongs. Typically, the videos belong to wide range domains such as sports, music, news, cooking, etc. In many cases, a video can be associated with more than one domain. In this paper, we propose an approach to video captioning that uses domain-specific decoders. We build a domain classifier to obtain the estimates of probabilities of a video belonging to different domains. For each video, we identify the top - k domains based on the estimated probabilities. Each video in the training data set is shared in training the domain-specific decoders of top-k labels obtained from the domain classifier. The domain-specific decoders use the domain-specific semantic tags for generating captions. The proposed approach uses the Temporal VLAD for preprocessing the features extracted from 2D-CNN and 3D-CNN features. The preprocessed features provide better feature representation of the videos. The effectiveness of the proposed approach is demonstrated through the results of experimental studies on Microsoft Video Description (MSVD) corpus and MSR-VTT dataset.
DQ-DASH

The significant popularity of HTTP adaptive video streaming (HAS), such as Dynamic Adaptive Streaming over HTTP (DASH), over the Internet has led to a stark increase in user expectations in terms of video quality and delivery robustness. This situation creates new challenges for content providers who must satisfy the Quality-of-Experience (QoE) requirements and demands of their customers over a best-effort network infrastructure. Unlike traditional single server DASH, we developed a D istributed Q ueuing theory bitrate adaptation algorithm for DASH (DQ-DASH) that leverages the availability of multiple servers by downloading segments in parallel. DQ-DASH uses a M x /D/1/K queuing theory based bitrate selection in conjunction with the request scheduler to download subsequent segments of the same quality through parallel requests to reduce quality fluctuations. DQ-DASH facilitates the aggregation of bandwidth from different servers and increases fault-tolerance and robustness through path diversity. The resulting resilience prevents clients from suffering QoE degradations when some of the servers become congested. DQ-DASH also helps to fully utilize the aggregate bandwidth from the servers and download the imminently required segment from the server with the highest throughput. We have also analyzed the effect of buffer capacity and segment duration for multi-source video streaming.
Interaction signatures and non-Gaussian photon states from a strongly driven atomic ensemble coupled to a nanophotonic waveguide

We study theoretically a laser-driven one-dimensional chain of atoms interfaced with the guided optical modes of a nanophotonic waveguide. The period of the chain and the orientation of the laser field can be chosen such that emission occurs predominantly into a single guided mode. We find that the fluorescence excitation line shape changes as the number of atoms is increased, eventually undergoing a splitting that provides evidence for the waveguide-mediated all-to-all interactions. Remarkably, in the regime of strong driving the light emitted into the waveguide is nonclassical with a significant negativity of the associated Wigner function. We show that both the emission properties and the non-Gaussian character of the light are robust against voids in the atom chain, enabling the experimental study of these effects with present-day technology. Our results offer a route towards novel types of fiber-coupled quantum light sources and an interesting perspective for probing the physics of interacting atomic ensembles through light.
Weighted log-rank tests for clustered censored data: Saddlepoint <i>p</i>-values and confidence intervals

Clustered data with censored failure times frequently arise in clinical trials and tumorigenicity studies. For such data, the common and extensively used class of two-sample tests is the weighted log-rank tests. In this article, a double saddlepoint approximation is used to calculate the p-values of the null permutation distribution of these tests. This technique is demonstrated using three real clustered data sets. Comprehensive simulation studies are conducted to appraise the efficiency of the saddlepoint approximation. This approximation is far superior to the asymptotic normal approximation. This precision allows us to determine almost exact confidence intervals for the treatment impact.