Reviewerly/exHarmony huggingface dataset:
MSAANet: Multi-scale Axial Attention Network for medical image segmentation

U-Net and its variants have achieved impressive results in medical image segmentation. However, the downsampling operation of such U-shaped networks causes the feature maps to lose a certain degree of spatial information, and most existing methods use convolution and transformer sequentially, it is hard to extract more comprehensive feature representation of the image. In this paper, we propose a novel U-shaped segmentation network named Multi-scale Axial Attention Network (MSAANet) to solve the above problems. Specifically, we propose a cross-scale interactive attention: multi-scale axial attention (MSAA), which achieves direction-perception attention of different scales interaction. So that the downsampling deep features and the shallow features can maintain context spatial consistency. Besides, we propose a Convolution-Transformer (CT) block, which makes transformer and convolution complement each other to enhance comprehensive feature representation. We evaluate the proposed method on the public datasets Synapse and ACDC. Experimental results demonstrate that MSAANet effectively improves segmentation accuracy.
Assessing the Attacks Against the Online Authentication Methods Using a Comparison Matrix: A Case of Online Banking

This paper aims to examine commonly existing online authentication methods, review the current authentication threats and attacks against these methods, and discuss and analyze the methods used in banks operating in several countries. The paper established a new comparison matrix with 11 characteristics of authentication methods. The discussion and analysis section are based on the data collected by sending a questionnaire to more than 25 people in several countries. The data is captured through observation while navigating the targeted banks' internet banking services from the user's perspective who has the privilege (credentials) to access banks' online services. A total of 16 international banks were analyzed in this research. This research shows that authentication methods such as security questions, smart cards, PINs, and virtual keyboards will provide strong authentication to protect customers' data confidentiality and integrity. The results contribute to the practice by confirming the importance of having strong authentication methods to protect the confidentiality and integrity of the customer's data. The particularity of our study helps to identify the common authentication methods used in banks operating in different countries.
Temporal-enhanced Cross-modality Fusion Network for Video Sentence Grounding

Video sentence grounding aims to localize a segment semantically aligning to the given language query from a video. Most existing works simply interact video and query only once at a single early stage. Not only multi-level dependencies within videos are not explored since interactions act fixedly on a specific level, but also the guiding role of the query is neglected. To tackle these issues, we propose an efficient network namely Temporal-enhanced Cross-modality Fusion Network (TCFN). By directly modulating the temporal receptive field, TCFN captures multilevel temporal enhanced visual features effectively. Furthermore, TCFN explicitly exploits the query to interact with the temporal-enhanced features in multiple stages for better alignment. Benefiting from its succinct architecture, TCFN achieves competitive performance compared to state-of-the-art with a much lower computational cost. Experiments on three benchmark datasets verify the effectiveness of the proposed TCFN.
Software-Friendly Subjective Bayesian Networks: Reasoning within a Software-Centric Mission Impact Assessment Framework

Subjective Bayesian networks (SBN) combine Bayesian Networks (BN) with Subjective Logic in order to express the second-order uncertainty (i.e., the uncertainty about a probability distribution of an event – as opposed to the uncertainty about the event itself). While SBNs provide a strong formalism for treating the uncertainty in a higher level, the literature lacks support for extensive software implementations focused on compatibility with current software solutions or standards. Our work explores the structural congruence between BN and SBN (in terms of software data structure) and a semantic bijection between Subjective Logic opinions to Dirichlet distributions to introduce a SBN reasoning framework that targets on effectively reusing the existing BN solutions. Particularly, we developed two inference algorithms that apply a Monte Carlo method to existing BN inference algorithms (we chose the Junction Tree algorithm, for test), respectively for batch and interactive SBN reasoning. A method for translating an evidence in SBN to uncertain (virtual) evidence in BN is also presented. The main contribution of this paper is the introduction of a simple, yet flexible empirical estimation method and a software architecture that virtually adapts any BN inference algorithm to an approximate computational inference framework for SBN. We also developed a Java component to demonstrate the reusability, and we developed a case study of Mission Impact Assessment of Unmanned Aerial Vehicles transporting critical items between hospitals in order to illustrate the applicability in a knowledge engineering process.
Robust Person Re-Identification with Wireless Signals

Person re-identification (re-ID) has already attracted considerable attention. Most studies focus only on visual appearance, but the visual clue is susceptible to clothing changes, blur, occlusion, etc. The previous promising solution is to introduce data from different modalities to attenuate the impact of defects in the visual data. Some works have introduced wireless positioning to help person re-identification but have yet to explore the relationship between wireless data and visual data. To this end, we build a large synthetic wireless positioning person re-ID dataset (SWPR). With 2,000 labelled IDs captured by 24 cameras, we also collect over 32 million records of 2000 mobile trajectories in this dataset. In addition, we advocate a novel unsupervised multimodal framework (UMF). Our framework includes a multimodal information fusion strategy (MIFS) and a multimodal transformer network (MTN). Numerous experiments conducted on SWPR and WP-ReID datasets have shown the effectiveness of our approach, outperforming previous state-of-the-art methods.
Understanding Decision-Making in Terms of its Semantics

This article evaluates the state of strategic decision-making at the moment. It requires investigation and comprehension of the variables that affect judgment. We conducted qualitative research for our empirical study and used TROPES software for semantic analysis. With an emphasis on the influence of the recommendation on strategic decisionmaking, the findings describe the numerous aspects that affect the decision-making process. Strategic choices are founded on data gathering and consultation, as we've already indicated. The recommendation is important in making this
Multiple degraded image restoration via degradation history estimation

Image restoration is a fundamental task in low-level computer vision. Most existing algorithms assume that the input image has a single known degradation type. In reality, images usually contain multiple degradations, making the restoration challenging. Though recent works restore the multiple degraded images, they assume that the degradation history is known. Obviously, such an ideal assumption often does not hold in real applications. This work proposes a novel restoration framework for multiple degraded images via degradation history estimation. Specifically, we first develop a sequential model to estimate the degradation history, including both the degradation operation chain and the corresponding parameters. By resorting to designed self-attention and cross-attention mechanisms, our method can effectively model the correlation of the input image, degradation operation chain, and parameters. Then, we apply our estimation framework for the multiple degraded image restoration, without requiring the degradation history. Experiment results demonstrate much better performance than existing approaches.
Domain-Invariant Feature Learning for General Face Forgery Detection

Though existing methods for face forgery detection achieve fairly good performance under the intra-dataset scenario, few of them gain satisfying results in the case of cross-dataset testing with more practical value. To tackle this issue, in this paper, we propose a novel domain-invariant feature learning framework - DIFL for face forgery detection. In the framework, an adversarial domain generalization is introduced to learn the domain-invariant features from the forged samples synthesized by various algorithms. Then a center loss in fractional form (CL) is utilized to learn more discriminative features by aggregating the real faces while separating the fake faces from the real ones in the embedding space. In addition, a global and local random crop augmentation strategy is utilized to generate more data views of forged facial images at various scales. Extensive experimental results demonstrate the effectiveness and generalization of the proposed method compared with other state-of-the-art methods.
MSAbox: A spatially stable face detector

We observe an elusive defect in face detectors which is overlooked in existing works. Specifically, we find that face detectors output unstable confidence scores when faces are slightly shifted in position. The confidence scores of the shifted faces can be lower than the detection threshold and result in false negatives. We define this phenomenon as face detection spatial instability. In essence, detectors face the spatial instability problem because they perform badly for challenging object positions. An object position is challenging when it is not sufficiently modeled by any existing anchors. To deal with this problem, we propose Matching Similarity Auxiliary (MSA) box, which consists of three parts: MSA assign, Image Digitization Compensator (IDC), and Soft Smooth L1 Loss (SSL-Loss). Specifically, MSA assign and IDC are designed to dig more challenging samples, which guide the face detectors to enhance detection performance in these extreme cases. SSL-Loss balances the training phase based on the regression efforts of each target. It improves the localization performance even with more challenging positive samples involved. Experiments demonstrate that our MSAbox achieves spatially stable face detection.
Unsupervised anomaly detection under a multiple modeling strategy via model set optimization through transfer learning

Unsupervised anomaly detection approaches have been widely accepted in applications for industrial systems. Industrial systems often operate with multiple modes since they work for multiple purposes or under different conditions. In order to deal with the difficulty of anomaly detection due to multiple operating modes, multiple modeling strategies are employed. However, estimating the optimal set of models is a challenging problem due to the lack of supervision and computational burden. In this paper, we propose DeconAnomaly, a deep learning framework to estimate the optimal set of models using transfer learning for unsupervised anomaly detection under a multiple modeling strategy. It reduces computational burden with transfer learning and optimizes the number of models based on a surrogate metric of detection performance. The experimental results show clear advantages of DeconAnomaly.
Automated Software Vulnerability Detection via Curriculum Learning

With the development of deep learning, software vulnerability detection methods based on deep learning have achieved great success, which outperform traditional methods in efficiency and precision. At the training stage, all training samples are treated equally and presented in random order. However, in software vulnerability detection tasks, the detection difficulties of different samples vary greatly. Similar to the human learning mechanism following an easy-to-difficult curriculum learning procedure, vulnerability detection models can also benefit from the easy-to-hard curriculums. Motivated by this observation, we introduce curriculum learning for automated software vulnerability detection, which is capable of arranging easy-to-difficult training samples to learn better detection models without any human intervention. Experimental results show that our method achieves obvious performance improvements compared to baseline models.
Coarse Helps Fine: A Multi-Granularity Discriminative Adversarial Network for Fine-Grained Open-Set Domain Adaptation

Open-set domain adaptation (OSDA) aims to align shared classes between the source and the target domain and recognize the private classes of the target domain as unknown. Although unknown classes represent semantic novelty, current OSDA benchmarks lack clear definitions of semantic categories. We propose to use fine-grained visual categorization (FGVC) datasets for the issue because of their specific descriptions of semantic classes. This introduces the new setting named fine-grained OSDA. The entanglement among FGVC, unknown class recognition, and domain adaptation makes fine-grained OSDA a challenging problem. In this paper, we propose a multi-granularity discriminative adversarial network. It utilizes multi-grained labels of the source domain and curriculum learning to improve FGVC performance, exploits discriminative information to recognize unknown classes, and adapts domains through a conditional domain discriminator. Extensive experiments demonstrate our approach outperforms the state-of-the-art methods.
Learning IMM Filter Parameters from Measurements using Gradient Descent

The performance of data fusion and tracking algorithms often depends on parameters that not only describe the sensor system, but can also be task-specific. While for the sensor system tuning these variables is time-consuming and mostly requires expert knowledge, intrinsic parameters of targets under track can even be completely unobservable until the system is deployed. With state-of-the-art sensor systems growing more and more complex, the number of parameters naturally increases, necessitating the automatic optimization of the model variables. In this paper, the parameters of an interacting multiple model (IMM) filter are optimized solely using measurements, thus without necessity for any ground-truth data. The resulting method is evaluated through an ablation study on simulated data, where the trained model manages to match the performance of a filter parametrized with ground-truth values.
P<sup>3</sup>O: Transferring Visual Representations for Reinforcement Learning via Prompting

It is important for deep reinforcement learning (DRL) algorithms to transfer their learned policies to new environments that have different visual inputs. In this paper, we introduce Prompt based Proximal Policy Optimization (P <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> O), a three-stage DRL algorithm that transfers visual representations from a target to a source environment by applying prompting. The process of P <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> O consists of three stages: pre-training, prompting, and predicting. In particular, we specify a prompt-transformer for representation conversion and propose a two-step training process to train the prompt-transformer for the target environment, while the rest of the DRL pipeline remains unchanged. We implement P <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> O and evaluate it on the OpenAI CarRacing video game. The experimental results show that P <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> O outperforms the state-of-the-art visual transferring schemes. In particular, P <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> O allows the learned policies to perform well in environments with different visual inputs, which is much more effective than retraining the policies in these environments.
NLCUnet: Single-Image Super-Resolution Network with Hairline Details

Pursuing the precise details of super-resolution images is challenging for single-image super-resolution tasks. This paper presents a single-image super-resolution network with hairline details (termed NLCUnet), including three core designs. Specifically, a non-local attention mechanism is first introduced to restore local pieces by learning from the whole image region. Then, we find that the blur kernel trained by the existing work is unnecessary. Based on this finding, we create a new network architecture by integrating depth-wise convolution with channel attention without the blur kernel estimation, resulting in a performance improvement instead. Finally, to make the cropped region contain as much semantic information as possible, we propose a random 64×64 crop inside the central 512×512 crop instead of a direct random crop inside the whole image of 2K size. Numerous experiments conducted on the benchmark DF2K dataset demonstrate that our NLCUnet performs better than the state-of-the-art in terms of the PSNR and SSIM metrics and yields visually favorable hairline details.
Attribute-assisted Multimodal Network for Image Aesthetics Assessment

Image aesthetics assessment (IAA) is challenging due to its highly abstract nature. Nowadays, people tend to share images and comment them on social networks, which can provide rich information for judging image aesthetics. As a result, user comments of an image can be jointly utilized to learn better feature representations for IAA. Previous researches have shown that aesthetic attributes are crucial factors in determining image aesthetic quality and influencing people’s aesthetic perception. Accordingly, when commenting an image, people usually give descriptions from the perspective of aesthetic attributes. Inspired by this, this paper presents a new Attribute-Assisted Multimodal network (AAM-Net) for image aesthetics assessment. Specifically, we propose a cross-modal attribute interaction module to explore the related aesthetic attribute semantics shared by an image and the corresponding aesthetic comments. Then, a cross-modal gate unit is introduced to further refine significant attribute semantics interactively. Finally, informative aesthetic features can be obtained for predicting image aesthetic distributions. Experimental results on two public multimodal IAA databases demonstrate the superiority of the proposed model over the state-of-the-art methods.
Motion Planning for Mobile Robots with Temporal Logic Specifications

In this paper, we propose a motion planning method for mobile robots in order to satisfy task requirements specified in linear temporal logic (LTL). The proposed method follows the traditional hierarchical planning workflow, including path planning and trajectory planning. Firstly, we propose a path planning method based on rapidly exploring random trees (RRT*) and Dubins curve. The tree derived from RRT* is expanded with the help of Buchi automaton converted by an LTL formula. The proposed method can automatically adjust the iterations of sampling according to the scale of Buchi automata. Then, we formulate an optimization problem for the generation of trajectory. We use Bezier curve to express the trajectory and use differential flatness of mobile robots to simplify the optimization problem. Finally, we validate the practicality of our method through simulations and real-world experiments.
Optimizing DNN based quality assessment metric for image compression: A novel rate control method

In the existing coding standards, rate control (RC) plays a critical role in optimally allocating bit-rates to each coding unit, for improving rate-distortion performance under the limited bandwidth. However, the existing RC methods are mainly based on traditional distortion metrics, which fail to take the advantage of the emerging DNN based image quality assessment (IQA) metrics. In this paper, we set up the first attempt to achieve IQA score based RC for image compression. Specifically, a novel visualization based score-distortion (VSD) model and ρ-slope model are proposed to explicitly establish the relationship between IQA score and bit-rates. Then, by solving optimal rate-distortion optimization based on the IQA score, we propose a novel RC method for the HEVC standard. The experimental results show that, given the target bit-rates, the proposed RC method can accurately control the bit-rates and generate the compressed images with higher IQA score and better perceptual quality. More importantly, the proposed RC method is evaluated to be effective over two DNN based IQA metrics and four image datasets, exhibiting the potential in practical use. The code is available at https://github.com/Ffangqy/IQA-RC.
Unsupervised Fashion Style Learning by Solving Fashion Jigsaw Puzzles

Fashion style learning is the basis for many tasks in fashion AI, such as clothing recommendations, fashion trend analysis and popularity prediction. Most of the existing methods rely on the quality and quantity of the annotations. This paper proposes an efficient two-step unsupervised fashion style learning framework with "Fashion Jigsaw" task and centroid-based density clustering algorithm. First, we design the "Fashion Jigsaw" unsupervised learning task according to the distribution of fashion elements in full-body fashion images. By splitting and recovering fashion images, we pre-train a model that can extract both intra-image and inter-image information. Second, we propose a centroid-based density clustering algorithm and introduce the concept of "centroid" to cluster fashion image features and represent fashion styles. Meanwhile, we keep the noise features to discover the newly sprouted fashion styles. Experiment results demonstrate the effectiveness of our proposed method.
Progressive Event Alignment Network for Partial Relevant Video Retrieval

Currently, most existing text-based video retrieval methods are only adapted to trimmed videos. However, more complicated untrimmed videos are common in multimedia applications nowadays. In this paper, we focus on the Partially Relevant Video Retrieval (PRVR) task that retrieves untrimmed long videos with partial text descriptions. To tackle this challenging problem, we propose a novel method termed Progressive Event Alignment Network (PEAN) to align text queries with local video content progressively. Specifically, it consists of three key components: (1) A Multimodal Representation Module (MRM) that extracts text representations and hierarchical video representations. (2) An Event Searching Module (ESM) that localizes the described video content roughly. (3) An Event Aligning Module (EAM) that aligns text queries and local video content at a fine-grained level. Additionally, we also design a Gaussian-based pooling strategy in both the ESM and EAM, which thoroughly mines the semantic information in representative video frames. The extensive experiments on three PRVR benchmarks demonstrate our proposed PEAN method significantly outperforms current state-of-the-art methods.