Reviewerly/exHarmony huggingface dataset:
Pedestrian Attribute Recognition in Video Surveillance Scenarios Based on View-attribute Attention Localization

Pedestrian attribute recognition in surveillance scenarios is still a challenging task due to the inaccurate localization of specific attributes. In this paper, we propose a novel view-attribute localization method based on attention (VALA), which utilizes view information to guide the recognition process to focus on specific attributes and attention mechanism to localize specific attribute-corresponding areas. Concretely, view information is leveraged by the view prediction branch to generate four view weights that represent the confidences for attributes from different views. View weights are then delivered back to compose specific view-attributes, which will participate and supervise deep feature extraction. In order to explore the spatial location of a view-attribute, regional attention is introduced to aggregate spatial information and encode inter-channel dependencies of the view feature. Subsequently, a fine attentive attribute-specific region is localized, and regional weights for the view-attribute from different spatial locations are gained by the regional attention. The final view-attribute recognition outcome is obtained by combining the view weights with the regional weights. Experiments on three wide datasets (richly annotated pedestrian (RAP), annotated pedestrian v2 (RAPv2), and PA-100K) demonstrate the effectiveness of our approach compared with state-of-the-art methods.
An Overview of Blockchain-Based Smart Contract

This work is motivated by the recent flare-up in the rapid technological developments of the evolving blockchain technology. Blockchain technology provides a mechanism to do transactional communication in a trustless manner, without the need of a trusted third party. An appealing feature or application of blockchain technology is a smart contract. The smart contract is a piece of code which executes on the blockchain technology to enforce the negotiation between two parties in the absence of a trusted third party. This paper aims to provide an ample overview of blockchain-based smart contract, starting with the introduction, architectural overview, working mechanism, application areas and research gaps which can be addressed in future research.
Application of DASH client optimization and artificial intelligence in the management and operation of big data tourism hotels

The hotel uses modern information technology to establish a customer data group, which contains information about the customer's information on space configuration, color design, etc., and uses scientific operation methods to accelerate the rapid, transformation and upgrading of the hotel. The rapid development of information technology represented by the big intelligent mobile cloud has a lot of functions, can meet the needs of different customers and diversified characteristics at the same time, and provides a complete, stable and accurate information integration platform for hotel system management. The main research directions of DASH include client adaptive algorithm design, multi-client fairness and resource utilization issues, client improvement under multiple servers, and server load balancing. The development and application of scientific and technological information has broken through the development path of the hotel management system, providing new development space and opportunities for the improvement of the hotel management system, but at the same time it will also face more new challenges. Therefore, this article focuses on the characteristics of the tourism accommodation management system under the big data environment, and analyzes the development of the hotel management system under the network environment. The purpose is to provide some reference and reference significance.
Keyword extraction using Renyi entropy: a statistical and domain independent method

Enormous data is currently available in textual form and is increasing at a very fast pace through various social, academic and economic activities. The need of the hour is to assimilate, analyse, interpret this unstructured data and put the inferences for better use wherever and whenever required. This is where keyword extraction plays an important role by helping in determining the relevant documents from a large pool of available data. In this article we propose and analyse a new domain independent statistical method for keyword extraction using Rényi entropy. Both actual and relative performance of proposed word ranking metric has been discussed. Results of experimental evaluation indicate that Rényi entropy-based word ranking metric has reliable performance and is coherent with previously defined entropy-based methods. Being a statistical method, it is computationally less intensive and domain independent method, and could be of great utility in organizing dynamic text collection and other applications.
Ayuuk-Spanish Neural Machine Translator

This paper presents the first neural machine translator system for the Ayuuk language. In our experiments we translate from Ayuuk to Spanish, and fromSpanish to Ayuuk. Ayuuk is a language spoken in the Oaxaca state of Mexico by the Ayuukjä’äy people (in Spanish commonly known as Mixes. We use different sources to create a low-resource parallel corpus, more than 6,000 phrases. For some of these resources we rely on automatic alignment. The proposed system is based on the Transformer neural architecture and it uses sub-word level tokenization as the input. We show the current performance given the resources we have collected for the San Juan Güichicovi variant, they are promising, up to 5 BLEU. We based our development on the Masakhane project for African languages.
A Comprehensive Analysis on Video Forgery Detection Techniques

In the recent year video forgery detection is a major problem in video forensics. Unauthorised changes in video frames causing degradation of authenticity and integrity of originality. With the advancement in technology video processing tools and techniques are available for altering the videos for forgery. The modification or changes in current video is vital to detect, since this Video can be used in the authentication process. Video credibility hence required to be verified. There are different ways by which Video can be tempered. For example: frame insertion, deletion, duplication, copy and move, splicing etc. This paper presents analysis of forgery detection techniques like inter frame forgery detection & intra frame forgery detection that can be used for video tampering detection. In this paper present comparative analysis of newly developed techniques of forgery detection and helpful for finding the difficulties and brings out opportunities in the field of forgery detection.
An exploratory study of interpretability for face presentation attack detection

Biometric recognition and presentation attack detection (PAD) methods strongly rely on deep learning algorithms. Though often more accurate, these models operate as complex black boxes. Interpretability tools are now being used to delve deeper into the operation of these methods, which is why this work advocates their integration in the PAD scenario. Building upon previous work, a face PAD model based on convolutional neural networks was implemented and evaluated both through traditional PAD metrics and with interpretability tools. An evaluation on the stability of the explanations obtained from testing models with attacks known and unknown in the learning step is made. To overcome the limitations of direct comparison, a suitable representation of the explanations is constructed to quantify how much two explanations differ from each other. From the point of view of interpretability, the results obtained in intra and inter class comparisons led to the conclusion that the presence of more attacks during training has a positive effect in the generalisation and robustness of the models. This is an exploratory study that confirms the urge to establish new approaches in biometrics that incorporate interpretability tools. Moreover, there is a need for methodologies to assess and compare the quality of explanations.
Federated Composite Optimization

Federated Learning (FL) is a distributed learning paradigm that scales on-device learning collaboratively and privately. Standard FL algorithms such as FedAvg are primarily geared towards smooth unconstrained settings. In this paper, we study the Federated Composite Optimization (FCO) problem, in which the loss function contains a non-smooth regularizer. Such problems arise naturally in FL applications that involve sparsity, low-rank, monotonicity, or more general constraints. We first show that straightforward extensions of primal algorithms such as FedAvg are not well-suited for FCO since they suffer from the of primal averaging, resulting in poor convergence. As a solution, we propose a new primal-dual algorithm, Federated Dual Averaging (FedDualAvg), which by employing a novel server dual averaging procedure circumvents the curse of primal averaging. Our theoretical analysis and empirical experiments demonstrate that FedDualAvg outperforms the other baselines.
Exploring the role and mechanism of COVID-19 apps in fighting the current pandemic

As COVID-19 pandemic spread worldwide, policies have been developed to contain the disease and prevent viral transmission. One of the key strategies has been the principle of “‘test, track, and trace” to minimize spread of the virus. Numerous COVID-19 contact tracing applications have been rolled around the world to monitor and control the spread of the disease. We explore the characteristics of various COVID-19 applications and especially the Aarogya Setu COVID-19 app from India in its role in fighting the current pandemic. We assessed the current literature available to us using conventional search engines, including but not limited to PubMed, Google Scholar, and Research Gate in May 2020 till the time of submission of this article. The search criteria used MeSH keywords such as “COVID-19,” “pandemics,” “contact tracing,” and “mobile applications.” A variable uptake of different COVID-19 applications has been noted with increasing enrolment around the world. Security concerns about data privacy remain. The various COVID-19 applications will complement manual contact tracing system to assess and prevent viral transmission. Test, track, trace, and support policy will play a key role in avoidance of a “second wave” of the novel coronavirus severe acute respiratory syndrome coronavirus 2 outbreak.
OLANET: Self-Supervised 360° Depth Estimation with Effective Distortion-Aware View Synthesis and L1 Smooth Regularization

The distortion introduced by different projection (e.g. ERP, CUBE, etc) pose a great challenge to the depth estimation task of 360° images. We propose a novel approach, named OlaNet, to solve the self-supervised 360° depth estimation problem. Our method is motivated by two aspects: 1) the content of 360° imagery can be better learned by effective fields-of-views technology, i.e., atrous spatial pyramid pooling combined with projection coordinate prior; 2) L1-norm can learn more robust and sparse representation than L2-norm in smooth regularization of depth estimation. By considering these two evidence, we develop an end-to-end network that adopts the distortion-aware view synthesis, atrous spatial pyramid pooling and L1-norm regularized smooth term, to achieve the 360° depth estimation effectively and robustly. Extensive experiments on the 3D60 dataset demonstrate the superior performance of our OlaNet approach in comparison with the SOTA methods.
Learning from evolving data streams through ensembles of random patches

Ensemble methods represent an effective way to solve supervised learning problems. Such methods are prevalent for learning from evolving data streams. One of the main reasons for such popularity is the possibility of incorporating concept drift detection and recovery strategies in conjunction with the ensemble algorithm. On top of that, successful ensemble strategies, such as bagging and random forest, can be easily adapted to a streaming setting. In this work, we analyse a novel ensemble method designed specially to cope with evolving data streams, namely the streaming random patches (SRP) algorithm. SRP combines random subspaces and online bagging to achieve competitive predictive performance in comparison with other methods. We significantly extend previous theoretical insights and empirical results illustrating different aspects of SRP. In particular, we explain how the widely adopted incremental Hoeffding trees are not, in fact, unstable learners, unlike their batch counterparts, and how this fact significantly influences ensemble methods design and performance. We compare SRP against state-of-the-art ensemble variants for streaming data in a multitude of datasets. The results show how SRP produces a high predictive performance for both real and synthetic datasets. We also show how ensembles of random subspaces can be an efficient and accurate option to SRP and leveraging bagging as we increase the number of base learners. Besides, we analyse the diversity over time and the average tree depth, which provides insights on the differences between local subspace randomization (as in random forest) and global subspace randomization (as in random subspaces). Finally, we analyse the behaviour of SRP when using Naive Bayes as its base learner instead of Hoeffding trees.
Hear Me out: Fusional Approaches for Audio Augmented Temporal Action Localization

State of the art architectures for untrimmed video Temporal Action Localization (TAL) have only considered RGB and Flow modalities, leaving the information-rich audio modality totally unexploited. Audio fusion has been explored for the related but arguably easier problem of trimmed (clip-level) action recognition. However, TAL poses a unique set of challenges. In this paper, we propose simple but effective fusion-based approaches for TAL. To the best of our knowledge, our work is the first to jointly consider audio and video modalities for supervised TAL. We experimentally show that our schemes consistently improve performance for state of the art video-only TAL approaches. Specifically, they help achieve new state of the art performance on large-scale benchmark datasets - ActivityNet-1.3 (54.34 mAP@0.5) and THUMOS14 (57.18 mAP@0.5). Our experiments include ablations involving multiple fusion schemes, modality combinations and TAL architectures. Our code, models and associated data will be made available.
Relative Variational Intrinsic Control

In the absence of external rewards, agents can still learn useful behaviors by identifying and mastering a set of diverse skills within their environment. Existing skill learning methods use mutual information objectives to incentivize each skill to be diverse and distinguishable from the rest. However, if care is not taken to constrain the ways in which the skills are diverse, trivially diverse skill sets can arise. To ensure useful skill diversity, we propose a novel skill learning objective, Relative Variational Intrinsic Control (RVIC), which incentivizes learning skills that are distinguishable in how they change the agent's relationship to its environment. The resulting set of skills tiles the space of affordances available to the agent. We qualitatively analyze skill behaviors on multiple environments and show how RVIC skills are more useful than skills discovered by existing methods in hierarchical reinforcement learning.
High speed data encryption technique with optimized memory based RSA algorithm for communications

Purpose The privacy of the information is a major challenge in the communication process. In the present modern generation, the cryptography plays a vital role in providing security for data, such as text, images and video while transmitting from source to destination through internet or intranet. The Rivest-Shamir-Adleman (RSA) is an asymmetric key cryptographic system, where the security of the method works on the strength of the key. Design/methodology/approach In an asymmetric key crypto system, a pair of keys is generated one public key for encryption and one private key for decryption. The major challenge of implementing the RSA is the power function which becomes tedious and time consuming as the exponential value increases. The Chinese remainder theorem proves to be the best for data encryption when it comes to execution time of the algorithm. The proposed novel RSA algorithm with lookup table (LUT) is an extension to the Chinese remainder algorithm, which works better for image and video in terms of time complexity. Findings This paper presents a LUT approach for implementing the RSA with a minimal processing time. The proposed algorithm was compared with the standard algorithms like, Chinese remainder theorem, binary approach and squared multiplication approach. As the size of the exponent value increases, the proposed method shows better performance compared to other standard methods. Originality/value This paper presents a LUT approach for implementing the RSA with a minimal processing time. The proposed algorithm was compared with the standard algorithms like, Chinese remainder theorem, binary approach and squared multiplication approach. As the size of the exponent value increases, the proposed method shows better performance compared to other standard methods.
Learning Transition Times in Event Sequences: The Temporal Event-Based Model of Disease Progression

Progressive diseases worsen over time and can be characterised by sequences of events that correspond to changes in observable features of disease progression. Here we connect ideas from two formerly separate methodologies – event-based and hidden Markov modelling – to derive a new generative model of disease progression: the Temporal Event-Based Model (TEBM). TEBM can uniquely infer the most likely group-level sequence and timing of events (natural history) from mixed data types. Moreover, it can infer and predict individual-level trajectories (prognosis) even when data are missing, giving it high clinical utility. Here we derive TEBM and provide an inference scheme based on the expectation maximisation algorithm. We use imaging, clinical and biofluid data from the Alzheimer’s Disease Neuroimaging Initiative to demonstrate the validity and utility of our model. First, we train TEBM to uncover a new sequence and timing of events in Alzheimer’s disease, which are inferred to occur over a period of $${\sim }17.6$$ years. Next, we demonstrate the utility of TEBM in predicting clinical progression, and that TEBM provides improved utility over a comparative disease progression model. Finally, we demonstrate that TEBM maintains predictive accuracy with up to $$50\%$$ missing data. These results support the clinical validity of TEBM and its broader utility in real-world medical applications.
A Review on the Integration of Deep Learning and Service-Oriented Architecture

In recent years, machine learning has been used for data processing and analysis, providing insights to businesses and policymakers. Deep learning technology is promising to further revolutionize this processing leading to better and more accurate results. Current trends in information and communication technology are accelerating widespread use of web services in supporting a service-oriented architecture (SOA) consisting of services, their compositions, interactions, and management. Deep learning approaches can be applied to support the development of SOA-based solutions, leveraging the vast amount of data on web services currently available. On the other hand, SOA has mechanisms that can support the development of distributed, flexible, and reusable infrastructures for the use of deep learning. This paper presents a literature survey and discusses how SOA can be enabled by as well as facilitate the use of deep learning approaches in different types of environments for different levels of users.
Controller optimization using data-driven constrained bat algorithm with gradient-based depth-first search strategy

The meta-heuristic algorithms have aroused great attention for controller optimization. However, most of them are inseparable from the explicit system models when addressing a constrained optimization problem (COP). In this paper, we propose a data-driven constrained bat algorithm via a gradient-based depth-first search (GDFS) strategy. In the proposed scheme, the GDFS strategy can predetermine a search space that satisfies some strict constraints (e.g., stability requirements) of the optimized system. Meanwhile, an improved boundary constraint handling method is proposed to limit the exploration process to the predetermined space. In this way, the proposed algorithm can solve the COP by utilizing experimental data from real scenes, thereby relieving the dependence on precisely modeling the complex system. Together with an ɛ-constraint-handling method, the bat algorithm is employed to seek the global optimum of the COP. The search performance is enhanced by the designed linear-varying elite layer-based local search and a social learning-based walk mechanism to dynamically balance exploration and exploitation. The convergence is ensured based on the criteria of the stochastic optimization algorithm. Experimental results on a servo drive system and benchmark test functions verify the effectiveness of the proposed algorithm.
Sample Selection for Universal Domain Adaptation

This paper studies the problem of unsupervised domain adaption in the universal scenario, in which only some of the classes are shared between the source and target domains. We present a scoring scheme that is effective in identifying the samples of the shared classes. The score is used to select samples in the target domain for which to apply specific losses during training; pseudo-labels for high scoring samples and confidence regularization for low scoring samples. Taken together, our method is shown to outperform, by a sizeable margin, the current state of the art on the literature benchmarks.
A Calibration and 3-D Measurement Method for an Active Vision System With Symmetric Yawing Cameras

In this study, we propose a 3-D measurement method for an active stereo vision system with two symmetric yawing cameras. First, we establish a measurement model with five constant systematic parameters and five variables. Second, we present a design for a relatively simple and convenient calibration method to determine the constant systematic parameters for the active vision system. In our proposed method, the initial yaw angles of two cameras are calibrated with a minimum of three equal interval points in a common field of view (FOV). The initial pitch angles are calibrated with at least one point in this common FOV. The initial yaw angles are used in the following calibration processes of the baseline length and the optical offset. The baseline length is calibrated with respect to two such points with known horizontal distance. An offset used to compensate for the difference between the actual optical centers of the two cameras along their optical axes is also computed with at least one feature point with the cameras yawed at different angles. Third, an associated measurement method for objects in the common FOV is presented. The proposed method computes 3-D positions of feature points in the common FOV using these five constant systematic parameters and five variables. This active stereo vision system design does not require repeated calibration for varying yaw of the two cameras to conduct measurements once the five constant systematic parameters have been calibrated offline. We conduct comparative experiments, and the results verify the effectiveness of the proposed method.