Reviewerly/exHarmony huggingface dataset:
Optimizing functionals using Differential Evolution

Metaheuristic algorithms are typically used for optimizing a function f:A→R, where A is a subset of RN. Nevertheless, many real-life problems require A to be a set of functions which makes f a functional. In this paper, we present a methodology to address the optimization of functionals by using the evolutionary algorithm known as Differential Evolution. Unlike traditional techniques where continuity and differentiability assumptions are required to solve some associated differential equations—like calculus of variations, Pontryagin’s principle or dynamic programming, the optimization is carried out directly on the functional without the need of any of the assumptions mentioned before. Lagrangians involving derivatives are considered, these derivatives are computed implementing Automatic Differentiation with dual numbers. To the best of our knowledge, this is the first time that a metaheuristic optimization approach has been applied to directly optimize a broad variety of functionals. The effectiveness of our methodology is validated by solving two problems. The first problem is related to the implementation of quarantine and isolation in SARS epidemics and the second validation problem deals with the well-known brachistochrone curve problem. The results of both validation problems are in outstanding agreement with those obtained with the application of traditional techniques, specifically with the Forward–Backward-Sweep method in the first problem, and with the calculus of variations for the latter problem. We also found that interpolation may be employed to solve the large scale global optimization problems arisen in the optimization of functionals.
Grounding-Tracking-Integration

In this paper, we study tracking by language that localizes the target box sequence in a video based on a language query. We propose a framework called GTI that decomposes the problem into three sub-tasks: Grounding, Tracking, and Integration. The three sub-task modules operate simultaneously and predict the box sequence frame-by-frame. “Grounding” predicts the referred region directly from the language query. “Tracking” localizes the target based on the history of the grounded regions in previous frames. “Integration” generates final predictions by synergistically combining grounding and tracking. With the “integration” task as the key, we explore how to indicate the quality of the grounded regions in each frame and achieve the desired mutually beneficial combination. To this end, we propose an “RT-integration” method that defines and predicts two scores to guide the integration: 1) R-score represents the Region correctness whether the grounding prediction accurately covers the target, and 2) T-score represents the Template quality whether the region provides informative visual cues to improve tracking in future frames. We present our real-time GTI implementation with the proposed RT-integration, and benchmark the framework on LaSOT and Lingual OTB99 with highly promising results. Moreover, we produce a disambiguated version of LaSOT queries to facilitate future tracking by language studies.
Bilingual and Multilingual Mental Lexicon: A Modeling Study With Linear Discriminative Learning

Abstract This study addresses whether there is anything special about learning a third language, as compared to learning a second language, that results solely from the order of acquisition. We use a computational model based on the mathematical framework of Linear Discriminative Learning to explore this question for the acquisition of a small trilingual vocabulary, with English as L1, German or Mandarin as L2, and Mandarin or Dutch as L3. Our simulations reveal that when qualitative differences emerge between the learning of a first, second, and third language, these differences emerge from distributional properties of the particular languages involved rather than the order of acquisition per se, or any difference in learning mechanism. One such property is the number of homophones in each language, since within‐language homophones give rise to errors in production. Our simulations also show the importance of suprasegmental information in determining the kinds of production errors made.
A feature disentangling approach for person re-identification via self-supervised data augmentation

To address the problem of insufficient training data in person ReID, this paper proposes a data augmentation method based on image channels shuffling, by which a large volume of diversified training samples sharing similar edges can be produced. In the meantime, a soft label assignment strategy is designed to characterize the correlations between the original image and the generated counterparts. Furthermore, we design an encoder–decoder based learning structure for the person ReID task, where the encoder module tackles feature disentangling according to the introduced correlations, and the decoder module handles reconstruction using the combinations of decoupled features. Extensive experiments on four benchmark datasets demonstrate the effectiveness and robustness of the proposed method by attaining significant improvement over some state-of-the-art approaches. Source code is released at: https://github.com/flychen321/feature_disentangle_reid.
Splicing learning: A novel few-shot learning approach

In recent years, among most approaches for few-shot learning, there exists a default premise that a big homogeneous-annotated dataset is applied to pre-train the few-shot learning model. However, since few-shot learning approaches are always used in the domain where annotated samples are rare, it would be difficult to collect another big annotated dataset in the same domain. Therefore, we propose Splicing Learning to complete the few-shot learning task without the help of a big homogeneous-annotated dataset. Splicing Learning can increase the sample size of the few-shot set by splicing multiple original images to a spliced-image. Unlike data augmentation technologies, there is no false information on the spliced-image. Through experiments, we find that the configuration "All-splice + WSG" can achieve the best test accuracy of 90.81%, 9.19% better than the baseline. The performance improvement of the model can be attributed to Splicing Learning mostly and has little to do with the complexity of the CNN framework. Compared with metric learning, meta-learning, and GAN models, both of Splicing Learning and data augmentation have achieved more outstanding performance. At the same time, the combination of Splicing Learning and data augmentation can further improve the test accuracy of the model to 96.33%. The full implementation is available at https://github.com/xiangxiangzhuyi/Splicing-learning.
Novel View Synthesis on Unpaired Data by Conditional Deformable Variational Auto-Encoder

Novel view synthesis often needs the paired data from both the source and target views. This paper proposes a view translation model under cVAE-GAN framework without requiring the paired data. We design a conditional deformable module (CDM) which uses the view condition vectors as the filters to convolve the feature maps of the main branch in VAE. It generates several pairs of displacement maps to deform the features, like the 2D optical flows. The results are fed into the deformed feature based normalization module (DFNM), which scales and offsets the main branch feature, given its deformed one as the input from the side branch. Taking the advantage of the CDM and DFNM, the encoder outputs a view-irrelevant posterior, while the decoder takes the code drawn from it to synthesize the reconstructed and the view-translated images. To further ensure the disentanglement between the views and other factors, we add adversarial training on the code. The results and ablation studies on MultiPIE and 3D chair datasets validate the effectiveness of the framework in cVAE and the designed module.
Webly Supervised Image Classification with Self-contained Confidence

This paper focuses on webly supervised learning (WSL), where datasets are built by crawling samples from the Internet and directly using search queries as web labels. Although WSL benefits from fast and low-cost data collection, noises in web labels hinder better performance of the image classification model. To alleviate this problem, in recent works, self-label supervised loss $$\mathcal {L}_s$$ is utilized together with webly supervised loss $$\mathcal {L}_w$$ . $$\mathcal {L}_s$$ relies on pseudo labels predicted by the model itself. Since the correctness of the web label or pseudo label is usually on a case-by-case basis for each web sample, it is desirable to adjust the balance between $$\mathcal {L}_s$$ and $$\mathcal {L}_w$$ on sample level. Inspired by the ability of Deep Neural Networks (DNNs) in confidence prediction, we introduce Self-Contained Confidence (SCC) by adapting model uncertainty for WSL setting, and use it to sample-wisely balance $$\mathcal {L}_s$$ and $$\mathcal {L}_w$$ . Therefore, a simple yet effective WSL framework is proposed. A series of SCC-friendly regularization approaches are investigated, among which the proposed graph-enhanced mixup is the most effective method to provide high-quality confidence to enhance our framework. The proposed WSL framework has achieved the state-of-the-art results on two large-scale WSL datasets, WebVision-1000 and Food101-N. Code is available at https://github.com/bigvideoresearch/SCC .
A secure framework for remote diagnosis in health care: A high capacity reversible data hiding technique for medical images

The health care industry involves the processing of large-scale images for various applications. Remote diagnosis is one such significant area where medical images are sent across vulnerable communication media. Hence, a secure and robust framework is necessary to hide and retrieve patient information in medical images. Here, we propose a high capacity reversible data hiding technique that can be used to embed patient data using a new weighted interpolation technique. In this approach, to improve the payload capacity, interpolated pixels are effectively utilized for the data embedding process using modular arithmetic. The original cover pixels are also employed to embed data using the difference expansion method. The framework developed is tested with standard benchmark images and medical images. The experimental results prove that the proposed method proffers a better output in comparison with the other contemporary methods in this domain.
An Ensemble Approach for Compressive Sensing with Quantum Annealers

We leverage the idea of a statistical ensemble to improve the quality of quantum annealing based binary compressive sensing. Since executing quantum machine instructions on a quantum annealer can result in an excited state, rather than the ground state of the given Hamiltonian, we use different penalty parameters to generate multiple distinct quadratic unconstrained binary optimization (QUBO) functions whose ground state(s) represent a potential solution of the original problem. We then employ the attained samples from minimizing all corresponding (different) QUBOs to estimate the solution of the problem of binary compressive sensing. Our experiments, on a D-Wave 2000Q quantum processor, demonstrated that the proposed ensemble scheme is notably less sensitive to the calibration of the penalty parameter that controls the trade-off between the feasibility and sparsity of recoveries.
PLCDefender: Improving Remote Attestation Techniques for PLCs Using Physical Model

In order to guarantee the security of industrial control system (ICS) processes, the proper functioning of the programmable logic controllers (PLCs) must be ensured. In particular, cyberattacks can manipulate the PLC control logic program and cause terrible damage that jeopardize people's life when bringing the state of the critical system into an unreliable state. Unfortunately, no remote attestation technique has yet been proposed that can validate the PLC control logic program using a physics-based model that demonstrates device behavior. In this article, we propose PLCDefender, a mitigation method that combines hybrid remote attestation technique with a physics-based model to preserve the control behavior integrity of ICS. We implemented PLCDefender and evaluated its effectiveness against a wide range of attacks on a secure water treatment facility. As our evaluation shows, we can model PLC physical behavior with accuracy as high as 98%. The evaluation results show that by determining the different threshold values, PLCDefender can accurately detect a wide range of attack scenarios on PLCs.
Unsupervised learning trajectory anomaly detection algorithm based on deep representation

Without ground-truth data, trajectory anomaly detection is a hard work and the result lacks of interpretability. Moreover, in most current methods, trajectories are represented by geometric features or their low-dimensional linear combination, and some hidden features and high-dimensional combined features cannot be found efficiently. Meanwhile, traditional methods still cannot get rid of the limitation of space attributes. Therefore, a novel trajectory anomaly detection algorithm is present in this article. Unsupervised learning mechanism is used to overcome nonground-truth problem and deep representation method is used to represent trajectories in a comprehensive way. First, each trajectory is partitioned into segments according to its open angles, then the shallow features at each point of a segment are extracted and. In this way, each segment is represented as a feature sequence. Second, shallow features are integrated into auto-encoder-based deep feature fusion model, and the fusion feature sequences can be extracted. Third, these fused feature sequences are grouped into different clusters using a unsupervised clustering algorithm, and then segments which quite differ from others are detected as anomalies. Finally, comprehensive experiments are conducted on both synthetic and real data sets, which demonstrate the efficiency of our work.
Learn to Propagate Reliably on Noisy Affinity Graphs

Recent works have shown that exploiting unlabeled data through label propagation can substantially reduce the labeling cost, which has been a critical issue in developing visual recognition models. Yet, how to propagate labels reliably, especially on a dataset with unknown outliers, remains an open question. Conventional methods such as linear diffusion lack the capability of handling complex graph structures and may perform poorly when the seeds are sparse. Latest methods based on graph neural networks would face difficulties on performance drop as they scale out to noisy graphs. To overcome these difficulties, we propose a new framework that allows labels to be propagated reliably on large-scale real-world data. This framework incorporates (1) a local graph neural network to predict accurately on varying local structures while maintaining high scalability, and (2) a confidence-based path scheduler that identifies outliers and moves forward the propagation frontier in a prudent way. Both components are learnable and closely coupled. Experiments on both ImageNet and Ms-Celeb-1M show that our confidence guided framework can significantly improve the overall accuracies of the propagated labels, especially when the graph is very noisy.
Fighting for survival: persons with disabilities’ activism for the mediatisation of COVID-19 information

Crises times have an uncanny way of giving salience to struggles for democracy. The new coronavirus – also known as COVID-19 – became a global public health issue that stirred other democratic concerns from persons living with disabilities who wanted access to health information for their survival. People living with various types of disabilities have special communication and information needs, some of which require specific technologies, formats and language. The pandemic got people concerned about their safety and survival. This article contextualises and critiques US, Britain and Zimbabwean activists representing persons living with disabilities’ reactions to the manner their public authorities availed COVID-19 health messages to disabled constituencies via mainstream television. It compares how suitable was televised content from US, Britain, Zimbabwean and New Zealand stations for the Deaf and Hard of Hearing, before exploring complaints and lawsuits from the disability constituency pertaining to access to COVID-19 health information.
Toward security as a service: A trusted cloud service architecture with policy customization

With the rise of concerns over security and privacy in the cloud, the “security-on-demand” service mode dynamically provides cloud customers with trusted computing environments according to their specific security needs. Major challenges, however, remain to achieve this goal: (1) integrating an auditable, tamper-resistant trust-management mechanism into the cloud infrastructure and (2) building a protocol to guarantee the consistency of customers’ policies during virtual machine (VM) migrations. This study develops a new security-on-demand framework called a “policy-customized trusted cloud service” (PC-TCS) architecture that comprises two core components: an attribute-based signature (ABS)-based remote-attestation scheme to achieve trusted remote attestation with customized security policies and an ABS- and blockchain-based VM-migration protocol to support policy-customized trusted migration. To prove the availability of this architecture, we implemented a PC-TCS prototype based on Xen Hypervisor, the results of which indicate that (1) PC-TCS can be integrated into cloud infrastructure as part of a trusted computing base; (2) cloud users can customize the security policies of computing environments and validate their enforcement throughout the service life-cycle with the support of PC-TCS; and (3) PC-TCS can support policy-customized remote attestation and policy-customized migration with a minimal impact on performance.
Counting Vehicle with High-Precision in Brazilian Roads Using YOLOv3 and Deep SORT

The Brazilian National Department of Transport Infrastructure (DNIT) maintains the National Traffic Counting Plan (PNCT). The main goal of PNCT is to evaluate the current flow of traffic on federal highways aiming to define public policies. However, DNIT still performs the quantitative classificatory surveys not automated or with invasive equipment. It is crucial for conducting traffic studies to search for more modern solutions to accomplish a higher number of automated non-invasive, and low-cost classificatory surveys. This paper proposes a system that uses YOLOv3 for object detection and the Deep SORT for multiple objects tracking algorithms. From the results over real-world videos collected in Brazilian roads, we obtained a precision above 90 % in the global vehicle count. We also show that our proposal outperformed other previously proposed tools with 99.15% precision in public datasets. We believe this paper's proposal allows the development of a traffic analysis tool to be used for the automation of the volumetric traffic surveys, enabling to improve the DNIT agility and generating economy for the public coffers.
Dynamics of trace distance and Bures correlations in a three-qubit XY chain: Intrinsic noise model

In this paper, we analyze the robustness of the quantum correlations of the nearest neighbor and the next-to-neighbor qubits in the intrinsic noise model, that describes the dynamics of the decoherence for a system formed by 3-qubit Heisenberg XY chain. The effects of the uniformity and the inhomogeneity of the applied magnetic field are considered. The generated mixedness entropy, the robustness of the trace distance discord, measurement-induced non-locality and Bures distance correlations are examined against the spin interaction coupling, the uniformity and the inhomogeneity of the applied external magnetic field. In the absence of the intrinsic noise, the inhomogeneity of the applied magnetic field enhances the quantum correlations and the generated mixedness entropy. In the presence of the intrinsic noise for the nearest neighbor qubits, inhomogeneity effect of the magnetic field behaves as an additional decoherence resource.
Eye Thought You Were Sick! Exploring Eye Behaviors for Cybersickness Detection in VR

Cybersickness induced through Virtual Reality (VR) applications is still one of its main barriers as it can induce unwanted side-effects in players, significantly hindering the overall experience. Despite the wealth of research available on this topic, it is still an unsolved problem. Although previous studies have explored methods of cybersickness mitigation in addition to correlating physiological factors, there has been little research on the potential correlation of eye behavior and cybersickness. Thanks to advances in eye-tracking technology within HMDs, detecting eye behavior has become a much easier process. This paper explores the differences of pupil position and blink rate in relation to cybersickness intensity. The latter is measured through the standard and a customized version of the Simulator Sickness Questionnaire (SSQ). A total of 34 participant data was collected from two separate playing sessions of a VR maze game, where each session presented a unique control scheme.
USAD: An Intelligent System for Slang and Abusive Text Detection in PERSO-Arabic-Scripted Urdu

The use of slang, abusive, and offensive language has become common practice on social media. Even though social media companies have censorship polices for slang, abusive, vulgar, and offensive language, due to limited resources and research in the automatic detection of abusive language mechanisms other than English, this condemnable act is still practiced. This study proposes USAD (Urdu Slang and Abusive words Detection), a lexicon-based intelligent framework to detect abusive and slang words in Perso-Arabic-scripted Urdu Tweets. Furthermore, due to the nonavailability of the standard dataset, we also design and annotate a dataset of abusive, offensive, and slang word Perso-Arabic-scripted Urdu as our second significant contribution for future research. The results show that our proposed USAD model can identify 72.6% correctly as abusive or nonabusive Tweet. Additionally, we have also identified some key factors that can help the researchers improve their abusive language detection models.
Multilevel ensemble Kalman filtering for spatio-temporal processes

Abstract We design and analyse the performance of a multilevel ensemble Kalman filter method (MLEnKF) for filtering settings where the underlying state-space model is an infinite-dimensional spatio-temporal process. We consider underlying models that needs to be simulated by numerical methods, with discretization in both space and time. The multilevel Monte Carlo sampling strategy, achieving variance reduction through pairwise coupling of ensemble particles on neighboring resolutions, is used in the sample-moment step of MLEnKF to produce an efficent hierarchical filtering method for spatio-temporal models. Under sufficent regularity, MLEnKF is proven to be more efficient for weak approximations than EnKF, asymptotically in the large-ensemble and fine-numerical-resolution limit. Numerical examples support our theoretical findings.