Reviewerly/exHarmony huggingface dataset:
Multimodal Sentiment Analysis With Image-Text Interaction Network

More and more users are getting used to posting images and text on social networks to share their emotions or opinions. Accordingly, multimodal sentiment analysis has become a research topic of increasing interest in recent years. Typically, there exist affective regions that evoke human sentiment in an image, which are usually manifested by corresponding words in people's comments. Similarly, people also tend to portray the affective regions of an image when composing image descriptions. As a result, the relationship between image affective regions and the associated text is of great significance for multimodal sentiment analysis. However, most of the existing multimodal sentiment analysis approaches simply concatenate features from image and text, which could not fully explore the interaction between them, leading to suboptimal results. Motivated by this observation, we propose a new image-text interaction network (ITIN) to investigate the relationship between affective image regions and text for multimodal sentiment analysis. Specifically, we introduce a cross-modal alignment module to capture region-word correspondence, based on which multimodal features are fused through an adaptive cross-modal gating module. Moreover, considering the complementary role of context information on sentiment analysis, we integrate the individual-modal contextual feature representations for achieving more reliable prediction. Extensive experimental results and comparisons on public datasets demonstrate that the proposed model is superior to the state-of-the-art methods.
Effects of Virtual Hand Representation on Interaction and Embodiment in HMD-based Virtual Environments Using Controllers

Many studies have been conducted in the past few years that focus on interaction and embodiment in the field of virtual reality. However, despite the recent widespread use and continuing rise of controller-based head-mounted display (HMD) hardware for VR, there is little research on the use of handheld controllers in this context. We explore the effects of different virtual hand representations on interaction and the user’s sense of embodiment, extending the work of Argelaguet et al. in 2016, in this case using controllers. We designed an experiment where users perform the task of selecting and moving a cube from and to specific positions on a table inside an immersive virtual environment, interacting with three representations: the abstract shape of a Sphere, the 3D model of the Controller, and a realistic human-looking Hand. For each representation, users were asked to perform the same task with and without obstacles (Brick Wall, Barbed Wire, Electric Current). Statistical analysis of the results show that although no significant differences were identified in the sense of agency, the users’ performance with the Sphere was significantly worse compared to the other two, and in the case of the positioning task the Controller outperformed the others. Additionally, the Hand generated the strongest sense of ownership, and it was the favorite representation.
Exploring How Human Activities Disturb the Balance of Biogeochemical Cycles: Evidence from the Carbon, Nitrogen and Hydrologic Cycles

All around us today there is a clear evidence proof suggesting an observable influence by human on biogeochemical cycles which are likely to cause serious consequences for humanity. Activities of human affects the biogeochemical cycles, both directly as well as indirectly. If scholars were able to make clear the degree of how specific human actions is affecting the biogeochemical cycles, they (researchers) would be able to come up with ideas on how to better position toward suggesting strategies aimed at mitigating against disturbances that are worst. This paper aims at exploring how human activities affect the balance of biogeochemical cycles in relation to the Carbon, Nitrogen and Hydrologic Cycles. A literature analysis attempts to use a diversity of search engines such as Scopus, Researchgate, PubMed, Google Scholar, Summon, Hinari, CAB Abstract, OARE Abstract, Academia, Dimension, Mendeley, SSRN search strategy toward retrieving research publications, “grey literature” as well as expert reports from working group. This paper review some of the work done on this topic as well as locate the converging results of interest in all the studies as well as proffered solutions toward this menace with the purpose of forestalling future occurrence. Due to the actions of numerous human activities, the characteristics/features of several components of biogeochemical cycles are experiencing changes which are most likely led to long-term changes. It is essential to forecast the magnitude as well as degree of these changes so that the needed ameliorative actions could be originated well in time.
Detection of Scaled Hand Interactions in Virtual Reality: The Effects of Motion Direction and Task Complexity

In virtual reality (VR), natural physical hand interaction allows users to interact with virtual content using physical gestures. While the most straightforward use of tracked hand motion maintains a one-to-one mapping between the physical and virtual world, some cases might benefit from changing this mapping through scaled or redirected interactions that modify the mapping between user’s physical movements and the magnitude of corresponding virtual movements. However, large deviations in interaction fidelity may potentially provide distractions or a loss of perceived realism. Therefore, it is important to know the extent to which remapping techniques can be applied to scaled interactions in VR without users detecting the difference. In this paper, we extend prior research on redirected hand techniques by investigating user perception of scaled hand movements and estimating detection thresholds for different types of hand motion in VR. We conducted two experiments with a two-alternative forced-choice (2AFC) design to estimate the detection thresholds of remapped interaction. The first experiment tested the perception of motion scaling for simple hand movements, and the second experiment involved more complex reaching motions in a cognitively demanding game scenario. We present estimated detection thresholds for scale values that can be applied to virtual hand movements without users noticing the difference. Our findings show that detection thresholds differ significantly based on the type of hand movement (horizontal, vertical, and depth).
Heterogeneous Ensemble Deep Learning Model for Enhanced Arabic Sentiment Analysis

Sentiment analysis was nominated as a hot research topic a decade ago for its increasing importance in analyzing the people's opinions extracted from social media platforms. Although the Arabic language has a significant share of the content shared across social media platforms, its content's sentiment analysis is still limited due to its complex morphological structures and the varieties of dialects. Traditional machine learning and deep neural algorithms have been used in a variety of studies to predict sentiment analysis. Therefore, a need of changing current mechanisms is required to increase the accuracy of sentiment analysis prediction. This paper proposed an optimized heterogeneous stacking ensemble model for enhancing the performance of Arabic sentiment analysis. The proposed model combines three different of pre-trained Deep Learning (DL) models: Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU) in conjunction with three meta-learners Logistic Regression (LR), Random Forest (RF), and Support Vector Machine (SVM) in order to enhance model's performance for predicting Arabic sentiment analysis. The performance of the proposed model with RNN, LSTM, GRU, and the five regular ML techniques: Decision Tree (DT), LR, K-Nearest Neighbor (KNN), RF, and Naive Bayes (NB) are compared using three benchmarks Arabic dataset. Parameters of Machine Learning (ML) and DL are optimized using Grid search and KerasTuner, respectively. Accuracy, precision, recall, and f1-score were applied to evaluate the performance of the models and validate the results. The results show that the proposed ensemble model has achieved the best performance for each dataset compared with other models.
Multi-objective particle swarm optimization with multi-mode collaboration based on reinforcement learning for path planning of unmanned air vehicles

In order to solve the multiple unmanned aerial vehicles (UAVs) collaborative path planning problem under complex environments with multiple constraints, the multi-objective particle swarm optimization algorithm with multi-mode collaboration based on reinforcement learning (MCMOPSO-RL) is proposed in this paper to find optimal paths and handle constraints simultaneously. Reinforcement learning (RL) is applied to enable the proposed algorithm to choose the suitable position updated mode to achieve the high performance. Multi-mode collaboration strategy is developed to update the particle positions, where three modes are designed to balance the population diversity and the convergence speed, including the exploration, exploitation modes, and the hybrid update mode. Experimental results show that MCMOPSO-RL can solve the path planning problem for multiple UAVs more efficiently and robustly than other algorithms.
An Implementation of Modified Blowfish Technique with Honey Bee Behavior Optimization for Load Balancing in Cloud System Environment

Cloud computing is a system that allows data to be saved in the cloud on a virtual worker. Outsiders and virtual machines in the cloud worker supplier played a critical part in efficiently storing and accessing information. Security, access control, and load balancing are critical challenges in cloud engineering. In the past, various solutions for adjusting cloud load have been proposed. Operator-based burden adjustment calculation surpassed all other offered CPU use, cost, and idle time strategies. The productivity of the specialist-based load adjustment computation decreased when any of the client hubs changed regions. Experimental outcomes show that Modified-HBB-LB performs better than the existing load balancing strategies such as HBB-LB, DLB, FCFS, WRR, HDLB, and FIFO by achieving the load balance of the complete system. The Modified-HBB-LB technique reduces the number of migrations tasks (30%, 25% and 20%) as compared to HDLB, DLB, and HBB-LB. The proposed Modified-HBB-LB technique maintains the 3-5% higher performance levels on makespan, completion, and response time as compared to existing comparative techniques.
Chinese mineral named entity recognition based on BERT model

Mineral named entity recognition (MNER) is the extraction for the specific types of entities from unstructured Chinese mineral text, which is a prerequisite for building a mineral knowledge graph. MNER can also provide important data support for the work related to mineral resources. Chinese mineral text has many types of entities, complex semantics, and a large number of rare characters. To extract entities from Chinese mineral literature, this paper proposes an MNER model based on deep learning. To create word embeddings for mineral text, Bidirectional Encoder Representations from Transformers (BERT) is used. Moreover, the transfer matrix of the Conditional Random Field (CRF) algorithm is combined to improve the accuracy of sequence labeling. Finally, some experiments are conducted on the constructed dataset. The results show that the model can effectively recognize seven mineral entities with an average F1-score of 0.842.
EAMM: One-Shot Emotional Talking Face via Audio-Based Emotion-Aware Motion Model

Although significant progress has been made to audio-driven talking face generation, existing methods either neglect facial emotion or cannot be applied to arbitrary subjects. In this paper, we propose the Emotion-Aware Motion Model (EAMM) to generate one-shot emotional talking faces by involving an emotion source video. Specifically, we first propose an Audio2Facial-Dynamics module, which renders talking faces from audio-driven unsupervised zero- and first-order key-points motion. Then through exploring the motion model’s properties, we further propose an Implicit Emotion Displacement Learner to represent emotion-related facial dynamics as linearly additive displacements to the previously acquired motion representations. Comprehensive experiments demonstrate that by incorporating the results from both modules, our method can generate satisfactory talking face results on arbitrary subjects with realistic emotion patterns.
Smart Charging Station for Electric Vehicle with Different Topologies

With the rising popularity of electric and hybrid vehicles, the need for consumer-friendly and advanced charging infrastructure solutions is increasing significantly. Additionally, future self-driving and parking automobiles will need whole new battery charging procedures. Nowadays, electric vehicles must be recharged manually. In other words. This article discusses computerized charging systems for electric vehicles. To detect tethered charging, a fully automated system aids in the process. The first section of the painting discusses the advantages of computerized conductive charging structures in relation to various computerized principles and the necessity for such structures. The second component provides information on the country of origin of the artwork. Thus, its miles attested to the structures that had previously developed and been published. Following that, difficult scenarios and difficulties associated with computerized conductive structures are shown. Thus, the character flaws are deconstructed and presented. Finally, a basic concept for a computerized charging station is shown and discussed as a possible solution for charging a variety of different types of automobiles at public parking facilities.
A Gaussian-guided adversarial adaptation transfer network for rolling bearing fault diagnosis

Most current unsupervised domain networks try to alleviate domain shifts by only considering the difference between source domain and target domain caused by the classifier, without considering task-specific decision boundaries between categories. In addition, these networks aim to completely align data distributions, which is difficult because each domain has its characteristics. In light of these issues, we develop a Gaussian-guided adversarial adaptation transfer network (GAATN) for bearing fault diagnosis. Specifically, GAATN introduces a Gaussian-guided distribution alignment strategy to make the data distribution of two domains close to the Gaussian distribution to reduce data distribution discrepancies. Furthermore, GAATN adopts a novel adversarial training mechanism for domain adaptation, which designs two task-specific classifiers to identify target data to consider the relationship between target data and category boundaries. Massive experimental results prove that the superiority and robustness of the proposed method outperform existing popular methods.
Advancement of Hybrid Energy Storage System with PWM Technique for Electric Vehicles

Massive use of IC engine vehicles has made an adverse effect on environment and also depleted fuel reservoirs hence rising the fuel prices over these years. Due to the inefficiency of traditional drivetrains, the car industry has shifted its focus to vehicle electrification. In electric cars, propulsion is supplied entirely or in part by electric motors that are powered by an on-board energy storage device. This article analyses a hybrid energy storage system that includes ultra-capacitor units and a battery to overcome the current limits of energy storage devices.
Stand-Alone Inter-Frame Attention in Video Models

Motion, as the uniqueness of a video, has been critical to the development of video understanding models. Modern deep learning models leverage motion by either executing spatio-temporal 3D convolutions, factorizing 3D convolutions into spatial and temporal convolutions separately, or computing self-attention along temporal dimension. The implicit assumption behind such successes is that the feature maps across consecutive frames can be nicely aggregated. Nevertheless, the assumption may not always hold especially for the regions with large deformation. In this paper, we present a new recipe of inter-frame attention block, namely Stand-alone Inter-Frame Attention (SIFA), that novelly delves into the deformation across frames to estimate local self-attention on each spatial location. Technically, SIFA remoulds the deformable design via re-scaling the offset predictions by the difference between two frames. Taking each spatial location in the current frame as the query, the locally deformable neighbors in the next frame are regarded as the keys/values. Then, SIFA measures the similarity between query and keys as stand-alone attention to weighted average the values for temporal aggregation. We further plug SIFA block into ConvNets and Vision Transformer, respectively, to devise SIFA-Net and SIFA-Transformer. Extensive experiments conducted on four video datasets demonstrate the superiority of SIFA-Net and SIFA-Transformer as stronger backbones. More remarkably, SIFA-Transformer achieves an accuracy of 83.1% on Kinetics-400 dataset. Source code is available at https://github.com/FuchenUSTC/SIFA.
SHIFT: A Synthetic Driving Dataset for Continuous Multi-Task Domain Adaptation

Adapting to a continuously evolving environment is a safety-critical challenge inevitably faced by all autonomous-driving systems. Existing image- and video-based driving datasets, however, fall short of capturing the mutable nature of the real world. In this paper, we introduce the largest multi-task synthetic dataset for autonomous driving, SHIFT. It presents discrete and continuous shifts in cloudiness, rain and fog intensity, time of day, and vehicle and pedestrian density. Featuring a comprehensive sensor suite and annotations for several mainstream perception tasks, SHIFT allows to investigate how a perception systems' performance degrades at increasing levels of domain shift, fostering the development of continuous adaptation strategies to mitigate this problem and assessing the robustness and generality of a model. Our dataset and benchmark toolkit are publicly available at www.vis.xyz/shift.
Towards Secure and Intelligent Internet of Health Things: A Survey of Enabling Technologies and Applications

With the growth of computing and communication technologies, the information processing paradigm of the healthcare environment is evolving. The patient information is stored electronically, making it convenient to store and retrieve patient information remotely when needed. However, evolving the healthcare systems into smart healthcare environments comes with challenges and additional pressures. Internet of Things (IoT) connects things, such as computing devices, through wired or wireless mediums to form a network. There are numerous security vulnerabilities and risks in the existing IoT-based systems due to the lack of intrinsic security technologies. For example, patient medical data, data privacy, data sharing, and convenience are considered imperative for collecting and storing electronic health records (EHR). However, the traditional IoT-based EHR systems cannot deal with these paradigms because of inconsistent security policies and data access structures. Blockchain (BC) technology is a decentralized and distributed ledger that comes in handy in storing patient data and encountering data integrity and confidentiality challenges. Therefore, it is a viable solution for addressing existing IoT data security and privacy challenges. BC paves a tremendous path to revolutionize traditional IoT systems by enhancing data security, privacy, and transparency. The scientific community has shown a variety of healthcare applications based on artificial intelligence (AI) that improve health diagnosis and monitoring practices. Moreover, technology companies and startups are revolutionizing healthcare with AI and related technologies. This study illustrates the implication of integrated technologies based on BC, IoT, and AI to meet growing healthcare challenges. This research study examines the integration of BC technology with IoT and analyzes the advancements of these innovative paradigms in the healthcare sector. In addition, our research study presents a detailed survey on enabling technologies for the futuristic, intelligent, and secure internet of health things (IoHT). Furthermore, this study comprehensively studies the peculiarities of the IoHT environment and the security, performance, and progression of the enabling technologies. First, the research gaps are identified by mapping security and performance benefits inferred by the BC technologies. Secondly, practical issues related to the integration process of BC and IoT devices are discussed. Third, the healthcare applications integrating IoT, BC, and ML in healthcare environments are discussed. Finally, the research gaps, future directions, and limitations of the enabling technologies are discussed.
Phase extraction accuracy comparison based on multi-frequency phase-shifting method in fringe projection profilometry

For fringe projection profilometry (FPP), phase extraction accuracy determines three-dimensional (3D) measurement precision. The combination of phase-shifting (PS) and multi-frequency temporal phase unwrapping (MTPU) can reconstruct discontinuous or isolated surfaces with high accuracy and robustness. However, due to the introduction of inevitable random noise, the phase extraction is biased. To this end, this paper firstly compares the wrapped phase errors based on various PS methods in detail. Then, the phase errors and unwrapping reliability based on various dual-frequency temporal phase unwrapping methods are compared. Finally, the effects of three-frequency temporal phase unwrapping and reasonable selection of fringe frequency on phase extraction accuracy are further discussed. The accuracy of the wrapped and unwrapped phase extraction and the phase unwrapping reliability are compared experimentally. This paper can provide a reference for algorithm selection in 3D measurement applications.
Detection and tracking of moving cloud services from video using saliency map model

AbstractAbstractIn cloud computing, the services are observed in the video stream and clustering their pixels is the initial task in service detection. Tracking is the practice to observe or tracking the moments of a given item in each frame. Numerous false positives are included in the frame. Using the saliency map model and the Extended Kalman Filter, the proposed approach can recognize and track moving objects in video. The item is tracked using an Extended Kalman Filter. In the proposed research the evaluation is based on the delay and accuracy of the evaluation parameter. Finally, the suggested method is compared to existing object tracking methods, with an accuracy of greater than 90% attained.Subject Classification: Primary 93A30Secondary 49K15Keywords: Saliency mapExtended kalman filterObject detectionObject tracking
Adventures in the metaverse

Purpose This paper aims to highlight the reputation risks of doing business in the metaverse. Design/methodology/approach The viewpoint is a subjective review of the technical, economic and cultural underpinnings of the metaverse from a corporate perspective. Findings While still in its infancy, the metaverse poses some reputation risks inherited from the internet and social media as well as new risks of its own. Originality/value While there have been numerous examinations of the potential perils of the metaverse, to the best of the author’s knowledge, this viewpoint is one of the first to suggest an approach to dealing systematically with its reputation risks.