Reviewerly/exHarmony huggingface dataset:
Attribute and State Guided Structural Embedding Network for Vehicle Re-Identification

Vehicle re-identification (Re-ID) is a crucial task in smart city and intelligent transportation, aiming to match vehicle images across non-overlapping surveillance camera scenarios. However, the images of different vehicles may have small visual discrepancies when they have the same/similar attributes, e.g., the same/similar color, type, and manufacturer. Meanwhile, the images from a vehicle may have large visual discrepancies with different states, e.g., different camera views, vehicle viewpoints, and capture time. In this paper, we propose an attribute and state guided structural embedding network (ASSEN) to achieve discriminative feature learning by attribute-based enhancement and state-based weakening for vehicle Re-ID. First, we propose an attribute-based enhancement and expanding module to enhance the discrimination of vehicle features through identity-related attribute information, and we design an attribute-based expanding loss to increase the feature gap between different vehicles. Second, we design a state-based weakening and shrinking module, which not only weakens the state information that interferes with identification but also reduces the intra-class feature gap by a state-based shrinking loss. Third, we propose a global structural embedding module that exploits the attribute information and state information to explore hierarchical relationships between vehicle features, then we use these relationships for feature embedding to learn more robust vehicle features. Extensive experiments on benchmark datasets VeRi-776, VehicleID, and VERI-Wild demonstrate the superior performance and generalization of the proposed method against state-of-the-art vehicle Re-ID methods. The code is available at https://github.com/ttaalle/fast_assen.
Video Question Answering With Prior Knowledge and Object-Sensitive Learning

Video Question Answering (VideoQA), which explores spatial-temporal visual information of videos given a linguistic query, has received unprecedented attention over recent years. One of the main challenges lies in locating relevant visual and linguistic information, and therefore various attention-based approaches are proposed. Despite the impressive progress, two aspects are not fully explored by current methods to get proper attention. Firstly, prior knowledge, which in the human cognitive process plays an important role in assisting the reasoning process of VideoQA, is not fully utilized. Secondly, structured visual information (e.g., object) instead of the raw video is underestimated. To address the above two issues, we propose a Prior Knowledge and Object-sensitive Learning (PKOL) by exploring the effect of prior knowledge and learning object-sensitive representations to boost the VideoQA task. Specifically, we first propose a Prior Knowledge Exploring (PKE) module that aims to acquire and integrate prior knowledge into a question feature for feature enriching, where an information retriever is constructed to retrieve related sentences as prior knowledge from the massive corpus. In addition, we propose an Object-sensitive Representation Learning (ORL) module to generate object-sensitive features by interacting object-level features with frame and clip-level features. Our proposed PKOL achieves consistent improvements on three competitive benchmarks (i.e., MSVD-QA, MSRVTT-QA, and TGIF-QA) and gains state-of-the-art performance. The source code is available at https://github.com/zchoi/PKOL.
Artificial Intelligence Biosensing System on Hand Gesture Recognition for the Hearing Impaired

AI technologies have the potential to help deaf individuals communicate. Due to the complexity of sign fragmentation and the inadequacy of capturing hand gestures, the authors present a sign language recognition (SLR) system and wearable surface electromyography (sEMG) biosensing device based on a Deep SLR that converts sign language into printed message or speech, allowing people to better understand sign language and hand motions. On the forearms, two armbands containing a biosensor and multi-channel sEMG sensors are mounted to capture quite well arm and finger actions. Deep SLR was tested on an Android and iOS smartphone, and its usefulness was determined by comprehensive testing. Sign Speaker has a considerable limitation in terms of recognising two-handed signs with smartphone and smartwatch. To solve these issues, this research proposes a new real-time end-to-end SLR method. The average word error rate of continuous sentence recognition is 9.6%, and detecting signals and recognising a sentence with six sign words takes less than 0.9 s, demonstrating Deep SLR's recognition.
Should the chatbot “save itself” or “be helped by others”? The influence of service recovery types on consumer perceptions of recovery satisfaction

• This study divides the service recovery types into chatbot self-recovery and human-involved recovery. • Based on social response theory, this study expands the exploration of the causes of consumer service recovery satisfaction. • This study introduced the level of chatbot intelligence as a moderating variable into the research of chatbot service failure. The application of artificial intelligence is considered essential to adapt to a new cycle of industrial transformation and technological advancements in many fields and industries. The extensive use of artificial intelligence technology is expected to improve the level and quality of services provided by companies adopting these methods. In this study, we propose a novel approach to self-recovery by chatbot systems after service failures based on social response theory. Moreover, we explore differences in consumer perceptions of different service recovery types and their impact on recovery satisfaction, and discusses whether the intelligence of the computational agent also has an effect. We present the results of three scenario-based experiments, which demonstrate the positive effect of chatbot self-recovery on consumer satisfaction, and show the mediating paths of service recovery types in terms of perceived functional value and privacy risks as well as the boundary condition of the level of robot intelligence. This work expands the range of applications of chatbots in the service industry and provides a new framework for the governance of artificial intelligence.
A decision-support framework for data anonymization with application to machine learning processes

The application of machine learning techniques to large and distributed data archives might result in the disclosure of sensitive information about the data subjects.Data often contain sensitive identifiable information, and even if these are protected, the excessive processing capabilities of current machine learning techniques might facilitate the identification of individuals, raising privacy concerns.To this end, we propose a decision-support framework for data anonymization, which relies on a novel approach that exploits data correlations, expressed in terms of relaxed functional dependencies (RFDs) to identify data anonymization strategies providing suitable trade-offs between privacy and data utility.Moreover, we investigate how to generate anonymization strategies that leverage multiple data correlations simultaneously to increase the utility of anonymized datasets.In addition, our framework provides support in the selection of the anonymization strategy to apply by enabling an understanding of the trade-offs between privacy and data utility offered by the obtained strategies.Experiments on real-life datasets show that our approach achieves promising results in terms of data utility while guaranteeing the desired privacy level, and it allows data owners to select anonymization strategies balancing their privacy and data utility requirements.
Time Optimal Trajectory Planning Algorithm for Robotic Manipulator Based on Locally Chaotic Particle Swarm Optimization

Optimal trajectory planning is a fundamental problem in the area of robotic research. On the time-optimal trajectory planning problem during the motion of a robotic arm, the method based on segmented polynomial interpolation function with a locally chaotic particle swarm optimization (LCPSO) algorithm is proposed in this paper. While completing the convergence in the early or middle part of the search, the algorithm steps forward on the problem of local convergence of traditional particle swarm optimization (PSO) and improved learning factor PSO (IFPSO) algorithms. Finally, simulation experiments are executed in joint space to obtain the optimal time and smooth motion trajectory of each joint, which shows that the method can effectively shorten the running time of the robotic manipulator and ensure the stability of the motion as well.
Exploratory Adversarial Attacks on Graph Neural Networks for Semi-Supervised Node Classification

Graph neural networks (GNNs) have been successfully used to analyze non-Euclidean network data. Recently, there emerge a number of works to investigate the robustness of GNNs by adding adversarial noises into the graph topology, where the gradient-based attacks are widely studied due to their inherent efficiency and high effectiveness. However, the gradient-based attacks often lead to sub-optimal results due to the discrete structure of graph data. To address this issue, we propose a novel exploratory adversarial attack (termed as EpoAtk) to boost the gradient-based perturbations on graphs. The exploratory strategy in EpoAtk includes three phases, generation, evaluation and recombination, with the goal of sidestepping the possible misinformation that the maximal gradient provides. In particular, our evaluation phase introduces a self-training objective containing three effective evaluation functions to fully exploit the useful information of unlabeled nodes. EpoAtk is evaluated on multiple benchmark datasets for the task of semi-supervised node classification in different attack settings. Extensive experimental results demonstrate that the proposed method achieves consistent and significant improvements over the state-of-the-art adversarial attacks with the same attack budgets.
Neural Rendering for Stereo 3D Reconstruction of Deformable Tissues in Robotic Surgery

Reconstruction of the soft tissues in robotic surgery from endoscopic stereo videos is important for many applications such as intra-operative navigation and image-guided robotic surgery automation. Previous works on this task mainly rely on SLAM-based approaches, which struggle to handle complex surgical scenes. Inspired by recent progress in neural rendering, we present a novel framework for deformable tissue reconstruction from binocular captures in robotic surgery under the single-viewpoint setting. Our framework adopts dynamic neural radiance fields to represent deformable surgical scenes in MLPs and optimize shapes and deformations in a learning-based manner. In addition to non-rigid deformations, tool occlusion and poor 3D clues from a single viewpoint are also particular challenges in soft tissue reconstruction. To overcome these difficulties, we present a series of strategies of tool mask-guided ray casting, stereo depth-cueing ray marching and stereo depth-supervised optimization. With experiments on DaVinci robotic surgery videos, our method significantly outperforms the current state-of-the-art reconstruction method for handling various complex non-rigid deformations. To our best knowledge, this is the first work leveraging neural rendering for surgical scene 3D reconstruction with remarkable potential demonstrated. Code is available at: https://github.com/med-air/EndoNeRF .
Tripartite sub-image histogram equalization for slightly low contrast gray-tone image enhancement

In this paper, a neoteric tripartite sub-image histogram equalization method is proposed to enhance slightly low contrast gray-tone images, which is a less explored area in the literature. An image is decomposed into three sub-images to preserve its mean brightness, and the histograms of the sub-images are calculated. Then, the snipping procedure is applied to each histogram to constrain the pace of contrast enhancement. Subsequently, the equalization of the three histograms is performed independently, and finally, the three equalized sub-images are composed into a single image. The proposed method offers better outcomes as compared to several common and state-of-the-art histogram equalization-based methods regarding contrast improvement, blind/reference-less image spatial quality evaluator, mean brightness preservation, peak signal-to-noise ratio, mean structural similarity, gradient magnitude similarity deviation, feature similarity, bit-plane to bit-plane similarity, and visual image quality.
FedBCD: A Communication-Efficient Collaborative Learning Framework for Distributed Features

We introduce a novel federated learning framework allowing multiple parties having different sets of attributes about the same user to jointly build models without exposing their raw data or model parameters. Conventional federated learning approaches are inefficient for cross-silo problems because they require the exchange of messages for gradient updates at every iteration, and raise security concerns over sharing such messages during learning. We propose a <i>Federated Stochastic Block Coordinate Descent (FedBCD)</i> algorithm, allowing each party to conduct multiple local updates before each communication to effectively reduce communication overhead. Under a practical security model, we show that parties cannot infer others&#x0027; exact raw data (<i>&#x201C;deep leakage&#x201D;</i>) from collections of messages exchanged in our framework, regardless of the number of communication to be performed. Further, we provide convergence guarantees and empirical evaluations on a variety of tasks and datasets, demonstrating significant improvement inefficiency.
Symmetric uncertainty-incorporated probabilistic sequence-based ant colony optimization for feature selection in classification

Feature selection (FS), which aims to select informative feature subsets and improve classification performance, is a crucial data-mining technique. Recently, swarm intelligence has attracted considerable attention and has been successfully applied to FS. Ant colony optimization (ACO), a swarm intelligence algorithm, has shown great potential in FS owing to its graphical representation and search ability. However, designing an effective ACO-based approach for FS is challenging because of issues originating from feature interactions and premature convergence problems. In this study, a novel ACO is proposed that incorporates symmetric uncertainty (SU). By constructing a probabilistic sequence-based graphical representation, the proposed algorithm significantly outperformed six other algorithms on 16 problems in terms of the classification error rate. This study also considers an extensive investigation of the contribution of the two components, namely, probabilistic sequence and SU. The experimental results indicated that these components significantly improved the performance of the ACO-based approach.
On the Robustness of Aspect-based Sentiment Analysis: Rethinking Model, Data, and Training

Aspect-based sentiment analysis (ABSA) aims at automatically inferring the specific sentiment polarities toward certain aspects of products or services behind the social media texts or reviews, which has been a fundamental application to the real-world society. Since the early 2010s, ABSA has achieved extraordinarily high accuracy with various deep neural models. However, existing ABSA models with strong in-house performances may fail to generalize to some challenging cases where the contexts are variable, i.e., low robustness to real-world environments. In this study, we propose to enhance the ABSA robustness by systematically rethinking the bottlenecks from all possible angles, including model, data, and training. First, we strengthen the current best-robust syntax-aware models by further incorporating the rich external syntactic dependencies and the labels with aspect simultaneously with a universal-syntax graph convolutional network. In the corpus perspective, we propose to automatically induce high-quality synthetic training data with various types, allowing models to learn sufficient inductive bias for better robustness. Last, we based on the rich pseudo data perform adversarial training to enhance the resistance to the context perturbation and meanwhile employ contrastive learning to reinforce the representations of instances with contrastive sentiments. Extensive robustness evaluations are conducted. The results demonstrate that our enhanced syntax-aware model achieves better robustness performances than all the state-of-the-art baselines. By additionally incorporating our synthetic corpus, the robust testing results are pushed with around 10% accuracy, which are then further improved by installing the advanced training strategies. In-depth analyses are presented for revealing the factors influencing the ABSA robustness.
Path analysis of the roles of age, self-efficacy, and TAM constructs in the acceptance of performing upper limb exercises through immersive virtual reality games

We aimed to assess whether individuals accept performing upper limb exercises using immersive VR games, indicated by behavioral intention, and the roles of age, self-efficacy, and technology acceptance model (TAM) constructs in the acceptance. Sixty healthy participants aged 20–79 years were examined for their perceptions and acceptance of VR game-based exercises using a questionnaire. Participants were instructed to perform upper limb exercises by playing five immersive VR games in a laboratory. A theoretical model delineated the roles of the focal constructs in the acceptance was formulated and tested using path analysis. At least 76.6% of participants (n = 46) expressed their intention to perform upper limb exercises through immersive VR games in the future. Overall, the model explained 83% variance in behavioral intention. Significant associations were observed, as follows. Age was positively associated with perceived usefulness (β = 0.27, p = 0.01), which positively influenced attitude (β = 0.64, p < 0.001); in turn, attitude positively affected behavioral intention (β = 0.84, p < 0.001). Moreover, self-efficacy positively affected perceived ease of use (β = 0.84, p < 0.001). Factors such as age, perceived usefulness, attitude, and self-efficacy were found to play significant roles in the acceptance of immersive VR games for upper limb exercises. In the future, strategies should be devised to meet the requirements of end users of different ages and promote usefulness, attitude, and self-efficacy in performing upper limb exercises through immersive VR games.
Machine Learning-Powered Encrypted Network Traffic Analysis: A Comprehensive Survey

Traffic analysis is the process of monitoring network activities, discovering specific patterns, and gleaning valuable information from network traffic. It can be applied in various fields such as network assert probing and anomaly detection. With the advent of network traffic encryption, however, traffic analysis becomes an arduous task. Due to the invisibility of packet payload, traditional traffic analysis methods relying on capturing valuable information from plaintext payload are likely to lose efficacy. Machine learning has been emerging as a powerful tool to extract informative features without getting access to payload, and thus is widely employed in encrypted traffic analysis. In this paper, we present a comprehensive survey on recent achievements in machine learning-powered encrypted traffic analysis. To begin with, we review the literature in this area and summarize the analysis goals that serve as the basis for literature classification. Then, we abstract the workflow of encrypted traffic analysis with machine learning tools, including traffic collection, traffic representation, traffic analysis method, and performance evaluation. For the surveyed studies, the requirements of classification granularity and information timeliness may vary a lot for different analysis goals. Hence, in terms of the goal of traffic analysis, we present a comprehensive review on existing studies according to four categories: network asset identification, network characterization, privacy leakage detection, and anomaly detection. Finally, we discuss the challenges and directions for future research on encrypted traffic analysis.
Effective and Efficient Training for Sequential Recommendation using Recency Sampling

Many modern sequential recommender systems use deep neural networks, which can effectively estimate the relevance of items but require a lot of time to train. Slow training increases expenses, hinders product development timescales and prevents the model from being regularly updated to adapt to changing user preferences. Training such sequential models involves appropriately sampling past user interactions to create a realistic training objective. The existing training objectives have limitations. For instance, next item prediction never uses the beginning of the sequence as a learning target, thereby potentially discarding valuable data. On the other hand, the item masking used by BERT4Rec is only weakly related to the goal of the sequential recommendation; therefore, it requires much more time to obtain an effective model. Hence, we propose a novel Recency-based Sampling of Sequences training objective that addresses both limitations. We apply our method to various recent and state-of-the-art model architectures – such as GRU4Rec, Caser, and SASRec. We show that the models enhanced with our method can achieve performances exceeding or very close to state-of-the-art BERT4Rec, but with much less training time.
Does utilizing smart contracts induce a financial connectedness between Ethereum and non-fungible tokens?

The majority of NFTs utilize the Ethereum blockchain platform to facilitate smart contracts. In this paper, we execute various econometric analyses to determine if this technical dependence induces a financial linkage to the risk, return, and prices of assets. For robustness, we also test the same relation between Bitcoin and NFTs. Empirical analyses are conducted through SADF bubbles test, DCC-GARCH time-varying correlation analysis, Bootstrap causality tests and spillover analysis. According to the results of various price, return, and volatility analyses, we find that NFTs do not demonstrate idiosyncratic features in their price developments and thus they cannot be considered as a separate asset class. Additionally, NFTs do not possess a specific financial linkage with Ethereum from using its infrastructure. Finally, we suggest NFT investors use alternative financial instruments, rather than Ether and Bitcoin in portfolio diversification, due to the presence of significant time-varying relationships and interactions.