Reviewerly/exHarmony huggingface dataset:
Recombination Samples Training for Robust Natural Language Visual Reasoning

Natural Language for Visual Reasoning (NLVR) is a challenging binary classification reasoning task, requiring the model to determine whether a textual description is true about a pair of images. However, in this task, if the part of input content (such as only color or quantity) is changed, the recent model is difficult to make the correct judgment. To this end, we propose a novel Recombination Samples Training (RST) scheme for increasing model robustness with stronger reasoning ability. Specifically, the RST synthesizes a lot of unlabeled new samples through the recombination and uses entropy minimization loss to train them. These samples through different combinations of the same content make the model focus on reasoning logic rather than some superficial correlations. Moreover, the RST calculates the consistency loss between the model trained with and without new samples to prevent overfitting. Experiments in the NLVR2 dataset demonstrate the effectiveness of the method.
ELAN: Enhancing Temporal Action Detection with Location Awareness

Current query-based temporal action detection methods lack multiple levels of location awareness, leading to performance degradation. In this paper, we present a novel query-based method called Enhanced Location-Aware Network (ELAN) for temporal action detection. ELAN adopts a lightweight convolution-based encoder, termed Temporal Location-Aware (TLA) encoder, to model temporal continuous location-aware context. Moreover, ELAN can re-aware the location-related context inside and between queries through our proposed Instance Location-Aware (ILA) decoder. As a result, ELAN can learn strong position discrimination of actions and effectively eliminates the ambiguity caused by sparse action decoding, yielding significant improvement in detection performance. ELAN achieves state-of-the-art performance on two temporal action detection benchmarks, including THUMOS-14 and ActivityNet-1.3.
Unleashing the Potential of Adjacent Snippets for Weakly-supervised Temporal Action Localization

Weakly-supervised temporal action localization (WTAL) intends to detect action instances with only weak supervision, e.g., video-level labels. The current de facto pipeline locates action instances by thresholding and grouping continuous high-score regions on temporal class activation sequences. In this route, the capacity of the model to recognize the relationships between adjacent snippets is of vital importance which determines the quality of the action boundaries. However, it is error-prone since the variations between adjacent snippets are typically subtle, and unfortunately this is overlooked in the literature. To tackle the issue, we propose a novel WTAL approach named Convex Combination Consistency between Neighbors (C <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> BN). C <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> BN consists of two key ingredients: a micro data augmentation strategy that increases the diversity in-between adjacent snippets by convex combination of adjacent snippets, and a macro-micro consistency regularization that enforces the model to be invariant to the transformations w.r.t. video semantics, snippet predictions, and snippet representations. Consequently, fine-grained patterns in-between adjacent snippets are enforced to be explored, thereby resulting in a more robust action boundary localization. Experimental results demonstrate the effectiveness of C <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> BN on top of various baselines for WTAL with video-level and point-level supervision. Code is at: https://github.com/canbaoburen/C3BN.
PACC: Perception Aware Congestion Control for Real-time Communication

Due to the network fluctuations, congestion control is indispensable to guarantee the quality of experience (QoE) for Real-Time Communication (RTC) users. This component adjusts the sending rate of media data, which determines the video encoding bitrate. However, existing control schemes either only focus on network numerical indicators or fail to adapt to various network environments. Logically, we propose PACC (Perception Aware Congestion Control) for RTC in this paper. Leveraging the convolutional neural network (CNN), we develop a quality sensor to infer the video quality increasing rate. Assisted with the variation trend analysis for user perception, PACC tunes the bitrate towards the direction of better QoE. Extensive tracedriven experiments demonstrate the effectiveness of PACC, which outperforms the existing landmark schemes by 8.2% to 32.4% and 6.8% to 18.0% in terms of transport and application layer QoE metrics, respectively.
Graph Information Interaction on Feature and Structure via Cross-modal Contrastive Learning

The abundant features and structure information on graphs provide a potential guarantee for learning high-quality representations without supervision. Feature attribute represents the inherent properties of nodes, while structure attribute describes their neighborhood relationship. These two types of attributes can be regarded as different modal forms of the same instance and should be consistent in identifying a member. We propose to directly regard feature and structure attributes as two separate views to embed this consistency into contrastive learning method, realizing graph information interaction on feature and structure in a cross-modal contrastive framework. Under this framework, node representations are learned in an unsupervised manner by maximizing the agreement between feature representation and structure representation. In terms of negative samples, instead of randomly sampling points from empirical distribution, a simple yet effective multi-sample mixing strategy is proposed to synthesize true negative samples with greater probability, alleviating the tricky false negative issue. Extensive experiments on multiple types of graphs demonstrate the effectiveness of the proposed method.
Model-Driven Compression for Digital Human Using Multi-Granularity Representations

With the popularity of the "metaverse", the applications of virtual digital humans are emerging in entertainment and communication fields, etc, where effective digital human compression schemes are urgently needed to process huge amounts of data. Most of the existing coding methods focus on reducing spatial redundancy in terms of the signal level but ignore the consideration of visual perception. In this paper, we propose a novel digital human model-driven compression framework by using multi-granularity representations. A coarse-grained layer based on the Skinned Multi-Person Linear model (SMPL) is introduced to extract general structures, while the normal images are used to represent fine-grained details via pose consistency-based predictions. Then, the SMPL parameters and normal images are encoded to achieve high-quality compression at extremely low bitrates. Experimental results demonstrate that the proposed method provides better coding performance with superior perceptual quality compared to the state-of-the-art 3D model compression methods.
Information Selection-based Domain Adaptation from Black-box Predictors

Unsupervised domain adaptation aims to address the problem of under labeling by performing knowledge transfer between labeled source domains and unlabeled target domains. Despite impressive progress, learning methods that rely on raw data and raw source model parameters all have the potential for privacy leakage in real life. In recent studies, the source model is set up as a black-box model with only inputs and outputs available, and knowledge distillation is introduced to fit the target model. However, the results of knowledge distillation are affected by confusion-prone instances and incorrect predictions of teacher networks, so we propose an Information Selection-based Knowledge Distillation (ISKD) strategy to perform more efficient distillation. We first perform semantic-level optimization of the source model output information through the association of categories and then filter the instance-level information with constructed confidence scores. In addition to this, the introduction of the self-distillation mechanism further improves the model performance. We conduct experiments on three benchmark datasets and obtain state-of-the-art performance.
Reinforcement Learning Agent Design and Optimization with Bandwidth Allocation Model

Reinforcement learning (RL) is currently used in various real-life applications. RL-based solutions have the potential to generically address problems, including the ones that are difficult to solve with heuristics and meta-heuristics and, in addition, the set of problems and issues where some intelligent or cognitive approach is required. However, reinforcement learning agents require a not straightforward design and have important design issues. RL agent design issues include the target problem modeling, state-space explosion, the training process, and agent efficiency. Research currently addresses these issues aiming to foster RL dissemination. A BAM model, in summary, allocates and shares resources with users. There are three basic BAM models and several hybrids that differ in how they allocate and share resources among users. This paper addresses the issue of an RL agent design and efficiency. The RL agent's objective is to allocate and share resources among users. The paper investigates how a BAM model can contribute to the RL agent design and efficiency. The AllocTC-Sharing (ATCS) model is analytically described and simulated to evaluate how it mimics the RL agent operation and how the ATCS can offload computational tasks from the RL agent. The essential argument researched is whether algorithms integrated with the RL agent design and operation have the potential to facilitate agent design and optimize its execution. The ATCS analytical model and simulation presented demonstrate that a BAM model offloads agent tasks and assists the agent's design and optimization.
Track-To-Track Association for Fusion of Dimension-Reduced Estimates

Network-centric multitarget tracking under communication constraints is considered, where dimension-reduced track estimates are exchanged. Previous work on target tracking in this subfield has focused on fusion aspects only and derived optimal ways of reducing dimensionality based on fusion performance. In this work we propose a novel problem formalization where estimates are reduced based on association performance. The problem is analyzed theoretically and problem properties are derived. The theoretical analysis leads to an optimization strategy that can be used to partly preserve association quality when reducing the dimensionality of communicated estimates. The applicability of the suggested optimization strategy is demonstrated numerically in a multitarget scenario.
Iterated Filters for Nonlinear Transition Models

A new class of iterated linearization-based nonlinear filters, dubbed dynamically iterated filters, is presented. Contrary to regular iterated filters such as the iterated extended Kalman filter (IEKF), iterated unscented Kalman filter (IUKF) and iterated posterior linearization filter (IPLF), dynamically iterated filters also take nonlinearities in the transition model into account. The general filtering algorithm is shown to essentially be a (locally over one time step) iterated Rauch-Tung-Striebel smoother. Three distinct versions of the dynamically iterated filters are especially investigated: analogues to the IEKF, IUKF and IPLF. The developed algorithms are evaluated on 25 different noise configurations of a tracking problem with a nonlinear transition model and linear measurement model, a scenario where conventional iterated filters are not useful. Even in this “simple” scenario, the dynamically iterated filters are shown to have superior root mean-squared error performance as compared with their respective baselines, the EKF and UKF. Particularly, even though the EKF diverges in 22 out of 25 configurations, the dynamically iterated EKF remains stable in 20 out of 25 scenarios, only diverging under high noise.
Analyzing Attacks on Client-Side Honeypots from Representative Malicious Web Sites

Client-side cyberattacks are becoming more common relative to server-side cyberattacks. This work tested the ability of the honeyclient software Thug to detect malicious or compromised servers that secretly download malicious files to clients, and tested its ability to classify these exploits. We tested Thug's analysis of delivered exploits in different configurations. Results on randomly generated Internet addresses found a low rate of maliciousness of 5.6%, and results on a blacklist of 83,667 suspicious Web sites found 163 unique malware files. Thug demonstrates the usefulness of client-side honeypots in analyzing harmful data presented by malicious Web sites.
2S-DFN: Dual-semantic Decoding Fusion Networks for Fine-grained Image Recognition

In previous fine-grained image recognition (FGIR) methods, the single global or local semantic fusion view may not be comprehensive to reveal the semantic associations between image and text. Besides, the encoding fusion strategy cannot fuse the semantics finely because the low-order text semantic dependence and the irrelevant semantic concepts are fused. To address these issues, a novel Dual-Semantic Decoding Fusion Networks (2S-DFN) is proposed for FGIR. Specifically, a multilayer text semantic encoder is first constructed to extract the higher-order semantics dependence among text. To obtain sufficient semantic association, two decoding semantic fusion streams are symmetrically designed from the global and local perspectives. Moreover, by decoding way to implant text features to semantic fusion layer as well as cascading it deeply, two streams fuse the semantics of text and image finely. Extensive experiments demonstrate that the effectiveness of the proposed method and 2S-DFN attains the state-of-the-art results on two benchmark datasets.
Unbiased Electro-optical/Infrared Camera Angular Measurements and their Cross-Correlated Errors

Electro-optical/Infrared (EO/IR) camera systems are commonly used in target detection and tracking applications. Such camera systems typically comprise a suite of sensors such as narrow/wide Field of View (FOV) cameras that provide target-originated angular measurements. To estimate the target position in Cartesian space, existing techniques in literature employ the non-linear measurement mapping from the Focal Plane Array (FPA) to azimuth and elevation space. A common assumption made in using this conversion is that azimuth and elevation measurement errors have the same standard deviation, are uncorrelated and are uniform across the camera’s FOV. This paper presents an approach to derive the azimuth and elevation statistics including the cross-correlation of their errors. This approach converts the raw target measurements and their covariance in the image space (FPA) to the angular space for subsequent use in Cartesian state filtering. This conversion has been validated to be unbiased and consistent, and results show that the Line of Sight (LOS) angle error variances and their correlations are in fact variable, with magnitudes dependent on the target’s location in the FPA. The correct LOS angle covariance matrices should be used in Cartesian state estimation and fusion rather than the assumed constant angle variances and uncorrelated errors between the azimuth and elevation.
Bounding Box Dynamics in Visual Tracking: Modeling and Noise Covariance Estimation

Common visual tracking algorithms make use of bounding box (BB) motion models. These models are parameterized by quantities such as noise covariance parameters corresponding to the evolution of the position, velocity, aspect ratio, width, or height of the bounding box, or their respective velocities. The noise covariance parameters are often hand-selected, using no principled knowledge regarding various aspects such as camera pose or frame rate. This paper aims to analyze how these aspects influence parameter estimates obtained from annotated datasets that are well-known to the visual tracking community. To obtain the estimates, the recently developed measurement difference method (MDM) is modified and used.
ANYRES: Generating High-Resolution visible-face images from Low-Resolution thermal-face images

Fig. 1.Qualitative results of HiFaceGAN, SRGAN, Pix2Pix, AxialGAN and the proposed ANYRES on the ARL-VTF dataset.We decrease resolution in each row (re-scaled to 128×128).While previous methods are impaired to super resolve facial images for a given resolution by using one specific network for each resolution, our proposed ANYRES achieves a balance between realism and fidelity across resolutions with solely one unified network.
A Token-wise Graph-based Framework for Multimodal Named Entity Recognition

Multimodal Named Entity Recognition (MNER) on social media posts is a leading but challenging task. However, most existing MNER methods fail to effectively exploit the visual information from the image. Besides, the multimodal interaction and alignment remains unsettled. In this paper, we propose a novel token-wise graph-based framework to deal with the MNER task. Specifically, a token-wise image processing manner is established. A muti-modal graph is constructed based on the textual token derived from BERT and the visual token derived from SwinT. Then, the muti-modal graph is fed into a multi-layer Transformer-based module for intra- and inter-modal information fusion. In addition, multiple contrastive learning is devised to perform the global and local alignment between textual and visual nodes. Experimental results on two benchmark multimodal datasets indicate that our model achieves state-of-the-art performance in MNER tasks.
The Impact of Human Resource Management Strategies on the Strategic Success of Cellular Communications Companies in Jordan

The study aims to identify the impact of HRMS on the strategic success in the cellular communications companies in Jordan. To achieve the objective of the study, questionnaires were collected from 318 employees working at all administrative levels. The sample strategy was selected from the managers in the Jordanian telecommunications companies from the lower, middle, and senior management in all centers and branches in Jordan. The most important results of the study are the telecommunications companies in Jordan keep pace with the development in the fine specializations suitable for the services they provide to customers through attracting strategies that keep pace with technological developments in the communications environment. The recommendations are the need for an institutional governance of employment policies in telecommunications companies, which should be clear within the framework of instructions from within the company and from the government sector, and effective focus on applying the compensation strategy and its understanding by workers in the investigated companies.
Indoor dataset for Person Re-Identification: Exploring the impact of backpacks

Person Re-Identification (PR-Id) encounters misclassification issues when re-identifying persons with different backpacks. These bags manifest as large and distinct regions in the images surpassing other finer details of a person. As a result, a CNN model swiftly detects and prioritizes these image regions for re-identification. However, the bags are subject to alterations or may be similar among multiple persons, resulting in misclassification. To ensure that a CNN model does not consider bags as unique features of specific persons and prioritize them for re-identification, images of persons with diverse backpacks are crucial in the training dataset. Moreover, these images enhance the model’s focus on other unique regions of a person. Although the current datasets show potential, incorporating such images could enhance their effectiveness. Therefore, in this paper, we propose an indoor PR-Id dataset named “With Bag/Without Bag-ReID” (WB/WoB-ReID). The set “with_bag” in WB/WoB-ReID dataset includes identities with different backpacks for the first time. We also incorporate identities without bags and with varying numbers of image counts in three other sets, namely “without_bag”, “both_small,” and “both_large”. We assess WB/WoB-ReID and three other PR-Id datasets: Market1501, CUHK03, and DukeMTMC-reID on various existing approaches. The highest mAP achieved on the“with_bag” is 74%, “without_bag” is 96.7% and other datasets are 97.78%, 95.20% and 92.4%. The results show that incorporating identities with diverse bags reduces the mAP, highlighting the misclassifications that arise specifically in the presence of bags.
Navigating the Landscape of Distributed Computing Frameworks for Machine and Deep Learning

For a number of reasons, distributed computing is crucial to machine learning and deep learning models. In the beginning, it makes it possible to train big models that won't fit in a single machine's memory. Second, by distributing the burden over several machines, it expedites the training process. Thirdly, it enables the management of vast amounts of data that may be dispersed across multiple devices or kept remotely. The system can continue processing data even if one machine fails because of distributed computing, which further improves fault tolerance. This chapter summarizes major frameworks Tensorflow, Pytorch, Apache spark Hadoop, and Horovod that are enabling developers to design and implement distributed computing models using large datasets. Some of the challenges faced by the distributed computing models are communication overhead, fault tolerance, load balancing, scalability and security, and the solutions are proposed to overcome the abovementioned challenges.
Investigating the Implications of Virtual Reality and Augmented Reality in Tourism Marketing

This literature review aims to investigate the developments of virtual reality (VR) and augmented reality (AR) research in tourism marketing. This chapter also highlights fruitful directions for tourism marketing research regarding VR and AR applications. A total of 31 full-length articles published between 2010 to 2023 were retrieved from the Web of Science database and reviewed. The theoretical backgrounds of the articles were thoroughly examined, and a detailed report on the research progress of the theories and research methodologies are presented. Finally, future research directions for the improvement of the existing literature are explained.
Robotization and Smart Technologies in the Hospitality Industry

New-gen technologies have profoundly impacted all aspects of life and various economic sectors. The tourism industry, known for its inclination towards innovation, has been quick to embrace technological advancements. In response to the global pandemic, tourism businesses such as hotels, food services, and transportation have increasingly utilized robotic systems to ensure social distancing, hygiene, and sanitation measures. However, digitization presents significant challenges for the tourism industry, requiring companies to adapt their operations to stay competitive. Automation has emerged as a highly beneficial trend, simplifying tasks and introducing innovative processes to tourism business models. This enables companies to provide personalized services tailored to the preferences of “digital tourists.” Overall, new-gen technologies are reshaping the tourism industry and driving it toward enhanced efficiency and customer satisfaction.