Reviewerly/exHarmony huggingface dataset:
Multimodal Bias: Assessing Gender Bias in Computer Vision Models with NLP Techniques

Large multimodal deep learning models such as Contrastive Language Image Pretraining (CLIP) have become increasingly powerful with applications across several domains in recent years. CLIP works on visual and language modalities and forms a part of several popular models, such as DALL-E and Stable Diffusion. It is trained on a large dataset of millions of image-text pairs crawled from the internet. Such large datasets are often used for training purposes without filtering, leading to models inheriting social biases from internet data. Given that models such as CLIP are being applied in such a wide variety of applications ranging from social media to education, it is vital that harmful biases are detected. However, due to the unbounded nature of the possible inputs and outputs, traditional bias metrics such as accuracy cannot detect the range and complexity of biases present in the model. In this paper, we present an audit of CLIP using an established technique from natural language processing called Word Embeddings Association Test (WEAT) to detect and quantify gender bias in CLIP and demonstrate that it can provide a quantifiable measure of such stereotypical associations. We detected, measured, and visualised various types of stereotypical gender associations with respect to character descriptions and occupations and found that CLIP shows evidence of stereotypical gender bias.
Classification of Human Faces with or without Mask Based on the CNN Model in the Context of the COVID-19 Pandemic

Even though vision for humans is intuitive, computer vision has been a major problem for several decades. Fortunately, the technological developments of recent years are contributing to the rise of Artificial Intelligence and in particular Deep Learning, which has risen since the 2010s. Human’s face analysis includes a variety of specific problems such as face detection, facial recognition, gender recognition, age estimation to name just the most common. Significant research efforts have been devoted to deep learning problems. The explosion of the Deep Learning paradigm, which determines a dramatic increase in performance, is now well known to the public; therefore, the number of approaches based on deep learning increases impressively. In the current context of the COVID-19 pandemic where wearing a mask is recommended in public places especially, this paper aims to implement a convolutional neural network model capable of classifying faces with or without masks.
Swarm-based search algorithms: A comparative study on sizing optimization of truss structures

Metaheuristic algorithms belong to the category of non-deterministic optimization methods. Extensive research conducted in this domain has elucidated that each of these methods possesses distinctive merits and demerits. To illustrate, one algorithm may exhibit a notable penchant for exploration, whereas another algorithm may demonstrate exceptional prowess in exploitation. The judicious selection of a suitable and efficient algorithm for a given problem can profoundly influence both the rate of convergence and the level of accuracy. Over the past few decades, various swarm-based metaheuristic algorithms have been introduced in technical literature. Consequently, undertaking a comprehensive comparative evaluation of a subset of these methodologies can furnish researchers with an essential framework to discern the most appropriate algorithm for their objectives. The current investigation focuses on evaluating and comparing four swarm-based metaheuristic algorithms especially on solving size optimization of truss structures for this purpose. To cover the research conducted in the past two decades as comprehensively as possible, a hierarchical selection process was employed for choosing the methods. As a result, the following algorithms were chosen Firefly Algorithm (FA), Drosophila Food-search Algorithm (DFO), Harris Hawk Optimization (HHO), and Butterfly Optimization Algorithm (BOA). Various characteristics of the selected algorithms, such as convergence rate, diversity variation, complexity, and accuracy of the final solutions, were compared. The findings indicate Harris Hawk Optimization (HHO) could nearly outperform the other selected methods on solving structural size optimization problems.
MetaRegNet: Metamorphic Image Registration Using Flow-Driven Residual Networks

Deep learning based methods provide efficient solutions to medical image registration, including the challenging problem of diffeomorphic image registration. However, most methods register normal image pairs, facing difficulty handling those with missing correspondences, e.g., in the presence of pathology like tumors. We desire an efficient solution to jointly account for spatial deformations and appearance changes in the pathological regions where the correspondences are missing, i.e., finding a solution to metamorphic image registration. Some approaches are proposed to tackle this problem but cannot properly handle large pathological regions and deformations around pathologies. In this paper, we propose a deep metamorphic image registration network (MetaRegNet), which adopts time-varying flows to drive spatial diffeomorphic deformations and generate intensity variations. We evaluate MetaRegNet on two datasets, i.e., BraTS 2021 with brain tumors and 3D-IRCADb-01 with liver tumors, showing promising results in registering a healthy and tumor image pair. The source code is available at https://github.com/ankitajoshi15/MetaRegNet .
Real-Time Pedestrian Tracking System Based on Depth Camera for Quadruped Robot

Quadruped robots have a wide range of applications now because of their strong adaptability to unstructured terrain. In the field of service robots, quadruped robots can track pedestrian users to provide more personalized services. This paper proposes a real-time depth camera-based pedestrian detection and tracking system for a quadruped robot. Firstly, YOLOv5 method is used for target detection, and then the DeepSORT method is used to collect real-time pedestrian features to complete pedestrian tracking. In order to reduce the adverse effects of target occlusion and fast movement on real-time tracking, this paper improves the performance of pedestrian feature extraction network by adding the CBAM attention module and verifies its superiority on the Market-1501 dataset. Compared with the original network, the Rank-1 accuracy increases by 6.3%, and the mean Average Precision (mAP) increases by 11.9%. Finally, a controller is designed based on kinematic method, and its stability is guaranteed by Lyapunov method.
Correlation Filter Feature Selection Strategy Based on Inland Ship Tracking

The early correlation filters algorithm has been widely used in many scenes because of its good real-time performance. With the pursuit of high-precision tracking effect and the introduction of various image features, such as CN features and depth features, the calculation speed of correlation filtering algorithm has been decreasing. In recent years, the operation speed of correlation filtering algorithms applied to inland waterway scenes is generally lower than 3 FPS, which cannot meet the demand of real-time inland waterway ship tracking. In order to ensure the tracking accuracy while improving the computing speed, this paper selects a suitable feature selection strategy based on different feature application characteristics CFFS(Correlation filter feature selection strategy). First, we obtain the HOG features, depth features and corresponding labels of the current frame from the basic correlation filter framework. Second, we select the HOG features and depth features using the swarm intelligence optimization algorithm and principal component analysis, respectively. Finally, the selected features are put into the correlation filtering framework to obtain the tracking results. The performance of the proposed algorithm is evaluated on the inland waterway ship dataset of our group. The results show that compared with the performance of the basic correlation filtering algorithm, CFFS has improved the accuracy by 1.3%, the success rate by 2.8%, and the speed by 12.946 FPS.
Stereovision-Based Target Trajectory Tracking Control for Robotic Manipulator

The stereovision-based visual servo control is designed to enable robotic manipulator to track the target object. A method of target position estimation with existence of measurement noise is achieved through Kalman filter. The adaptive proportional-derivative control is designed to track the desired position and velocity of each joint angle in the joint space. Stability analysis based on the Lyapunov theory is used to prove the rationality and strictness of the designed controllers. Numerical simulations on 2 degree-of-freedom manipulator demonstrate the effectiveness of estimation and control.
Circumnavigation Control of Fixed-Wing UAVs Using Distance Measurements

This paper concerns the target circumnavigation of fixed-wing unmanned aerial vehicles (UAVs) in a three-dimensional space. A sliding-mode control law for the flight path angle and a dynamic feedback control law for the roll angle is derived. The proposed control scheme enables the UAV, under any initial condition, to reach a desired height in a finite time and maintain a prescribed horizontal standoff distance from the target. A modified controller is then developed to overcome the discontinuity of control inputs. The stability of the closed-loop system is rigorously justified. The control algorithms have low computational complexity and are easy to implement, as they use only the distance from the UAV to the target and from the UAV to the ground. Numerical simulations verify the effectiveness of the control method.
Multi-class Cancer Classification of Whole Slide Images Through Transformer and Multiple Instance Learning

Whole slide images (WSIs) are high-resolution and lack localized annotations, whose classification can be treated as a multiple instance learning (MIL) problem while slide-level labels are available. We introduce a approach for WSI classification that leverages the MIL and Transformer, effectively eliminating the requirement for localized annotations. Our method consists of three key components. Firstly, we use ResNet50, which has been pre-trained on ImageNet, as an instance feature extractor. Secondly, we present a Transformer-based MIL aggregator that adeptly captures contextual information within individual regions and correlation information among diverse regions within the WSI. Thirdly, we introduce the global average pooling (GAP) layer to increase the mapping relationship between WSI features and category features. To evaluate our model, we conducted experiments on the The Cancer Imaging Archive (TCIA) Clinical Proteomic Tumor Analysis Consortium (CPTAC) dataset. Our proposed method achieves a top-1 accuracy of 94.8% and an area under the curve (AUC) exceeding 0.996, establishing state-of-the-art performance in WSI classification without reliance on localized annotations. The results demonstrate the superiority of our approach compared to previous MIL-based methods.
Radiology Report Generation via Visual Recalibration and Context Gating-Aware

The task of radiology report generation aims to analyze medical images, extract key information, and then assist medical personnel in generating detailed and accurate reports. Therefore, automatic radiology report generation plays an important role in medical diagnosis and healthcare. However, radiology medical data face the problems of visual and text data bias: medical images are similar to each other, and the normal feature distribution is larger than the abnormal feature distribution; second, the accurate location of the lesion and the generation of accurate and coherent long text reports are important challenges. In this paper, we propose Visual Recalibration and Context Gating-aware model (VRCG) to alleviate visual and textual data bias for enhancing report generation. We employ a medical visual recalibration module to enhance the key lesion feature extraction. We use the context gating-aware module to combine lesion location and report context information to solve the problem of long-distance dependence in diagnostic reports. Meanwhile, the context gating-aware module can identify text fragments related to lesion descriptions, improve the model’s perception of lesion text information, and then generate coherent, consistent medical reporting. Extensive experiments demonstrate that our proposed model outperforms existing baseline models on a publicly available IU X-Ray dataset. The source code is available at: https://github.com/Eleanorhxd/VRCG .
SQL injection attacks

SQL Injection (SQLi) attacks continue to pose significant threats to modern web applications, compromising data integrity and confidentiality. This research delves into the development and evaluation of methodologies designed to detect and mitigate these malicious attacks. Employing a diverse set of web applications, the study unfolds in a controlled environment, simulating real-world conditions to assess the effectiveness of current defense mechanisms against SQLi. Building upon this baseline, the research introduces a two-pronged defense mechanism: a Static Analysis Tool to pre-emptively identify vulnerabilities in application code and a Runtime Query Sanitizer that employs rule-based patterns and machine learning models to scrutinize and sanitize SQL queries in real-time. Performance evaluation metrics, encompassing detection rate, false positives, response time, and machine learning efficiency, are meticulously documented. Further robustness of these mechanisms is ascertained through real-world simulations involving unsuspecting users and ethical hackers. Initial results indicate promising potential for the introduced methodologies in safeguarding web applications against SQLi attacks. The study's findings serve as a critical step towards fortifying web applications, emphasizing the amalgamation of static analysis and real-time query sanitization as an effective countermeasure against SQLi threats.
Event Contrastive Representation Learning Enhanced with Image Situational Information

Event representation learning is a crucial prerequisite for many domains in event understanding, which aims to project events into dense vectors, where similar events are embedded close to each other while distinct events are separated. Most existing methods enhanced representation with external knowledge, which only based on unimodal and margin loss, ignoring the diversity of data and the impact of multiple samples. Therefore, we propose a novel Multimodal Event Contrastive Representation Learning (ECRL) framework. ECRL enhances event representations by making better use of image situational information. Specifically, we introduce multimodal contrastive learing and prototype-based clustering methods that allows us to avoid semantic-related samples in same batch being represent as negative, while considering multiple negatives. At the same time, we proposed action-guided-attention mechanism to capture core event participants in image. Extensive experiments on three benchmark datasets of event similarity task show that the ECRL outperforms other baseline.
A visual transformer-based smart textual extraction method for financial invoices

In era of big data, the computer vision-assisted textual extraction techniques for financial invoices have been a major concern. Currently, such tasks are mainly implemented via traditional image processing techniques. However, they highly rely on manual feature extraction and are mainly developed for specific financial invoice scenes. The general applicability and robustness are the major challenges faced by them. As consequence, deep learning can adaptively learn feature representation for different scenes and be utilized to deal with the above issue. As a consequence, this work introduces a classic pre-training model named visual transformer to construct a lightweight recognition model for this purpose. First, we use image processing technology to preprocess the bill image. Then, we use a sequence transduction model to extract information. The sequence transduction model uses a visual transformer structure. In the stage target location, the horizontal-vertical projection method is used to segment the individual characters, and the template matching is used to normalize the characters. In the stage of feature extraction, the transformer structure is adopted to capture relationship among fine-grained features through multi-head attention mechanism. On this basis, a text classification procedure is designed to output detection results. Finally, experiments on a real-world dataset are carried out to evaluate performance of the proposal and the obtained results well show the superiority of it. Experimental results show that this method has high accuracy and robustness in extracting financial bill information.
Recurrent Transformers for Long Document Understanding

Pre-trained models have been proved effective in natural language understanding. For long document understanding, the key challenges are long-range dependence and inference efficiency. Existing approaches, however, (i) usually cannot fully model the context structure and global semantics within a long document, (ii) and lack consistency assessment on common downstream tasks. To address these issues, we propose a novel Recurrent Transformers (RTrans) for long document understanding which can not only learn long contextual structure and relationships, but also be extended to diverse downstream tasks. Specifically, our model introduces recurrent transformer block to convey the token-level contextual information across segments and capture long-range dependence. The ranking strategy is utilized to aggregate the local and global information for final prediction. Experiments on diverse tasks that require understanding long document demonstrate superior and robust performance of RTrans and our approach achieves a better balance between effectiveness and efficiency.
The Application of Formative Assessment in High School English Reading Teaching

Teaching evaluation is a very important part of high school English teaching. Curriculum assessment has an important impact on students' learning effectiveness and achievement. The current research on English reading teaching dominated by summative assessment only focuses on the strategic research on how to promote the improvement of English reading ability in schools, which makes it difficult for students to form good reading habits. Compared with summative assessment, formative assessment can enable high school students to participate more fully in the classroom, and to a certain extent, effectively improve high school students' interest and mastery of English reading, so as to improve high school students' English reading ability. Using formative assessment in the English reading classroom can better help students learn English reading and achieve better classroom results.
Boosting-Based Construction of BDDs for Linear Threshold Functions and Its Application to Verification of Neural Networks

Understanding the characteristics of neural networks is important but difficult due to their complex structures and behaviors. Some previous work proposes to transform neural networks into equivalent Boolean expressions and apply verification techniques for characteristics of interest. This approach is promising since rich results of verification techniques for circuits and other Boolean expressions can be readily applied. The bottleneck is the time complexity of the transformation. More precisely, (i) each neuron of the network, i.e., a linear threshold function, is converted to a Binary Decision Diagram (BDD), and (ii) they are further combined into some final form, such as Boolean circuits. For a linear threshold function with n variables, an existing method takes $$O(n2^{\frac{n}{2}})$$ time to construct an ordered BDD of size $$O(2^{\frac{n}{2}})$$ consistent with some variable ordering. However, it is non-trivial to choose a variable ordering producing a small BDD among n! candidates. We propose a method to convert a linear threshold function to a specific form of a BDD based on the boosting approach in the machine learning literature. Our method takes $$O(2^n \text {poly}(1/\rho ))$$ time and outputs BDD of size $$O(\frac{n^2}{\rho ^4}\ln {\frac{1}{\rho }})$$ , where $$\rho $$ is the margin of some consistent linear threshold function. Our method does not need to search for good variable orderings and produces a smaller expression when the margin of the linear threshold function is large. More precisely, our method is based on our new boosting algorithm, which is of independent interest. We also propose a method to combine them into the final Boolean expression representing the neural network. In our experiments on verification tasks of neural networks, our methods produce smaller final Boolean expressions, on which the verification tasks are done more efficiently.
BrickStARt: Enabling In-situ Design and Tangible Exploration for Personal Fabrication using Mixed Reality

3D-printers enable end-users to design and fabricate unique physical artifacts but maintain an increased entry barrier and friction. End users must design tangible artifacts through intangible media away from the main problem space (ex-situ) and transfer spatial requirements to an abstract software environment. To allow users to evaluate dimensions, balance, or fit early and in-situ, we developed BrickStARt, a design tool using tangible construction blocks paired with a mixed-reality headset. Users assemble a physical block model at the envisioned location of the fabricated artifact. Designs can be tested tangibly, refined, and digitally post-processed, remaining continuously in-situ. We implemented BrickStARt using a Magic Leap headset and present walkthroughs, highlighting novel interactions for 3D-design. In a user study (n=16), first-time 3D-modelers succeeded more often using BrickStARt than Tinkercad. Our results suggest that BrickStARt provides an accessible and explorative process while facilitating quick, tangible design iterations that allow users to detect physics-related issues (e.g., clearance) early on.
Information processing from electronic word of mouth: an integrative framework

The authors propose a model of electronic word-of-mouth (eWOM) processing that integrates the conflict monitoring and the self-validation hypotheses. The model brings novel insights into the processing of eWOM by stressing cognitive conflict, a defining characteristic of eWOM. Extant models build on the notion that persuasion is inherent in eWOM and fail to account for what causes elaboration. This paper conceptualises eWOM-section as a gestalt of cues varying in their demand for cognitive resources, and the proposed model accounts for attitude appraisals relative to initial attitude. Also, eWOM literature has largely ignored time, an essential factor in consumer decision making, implicit in the proposed model. The model is contrasted with an earlier integrative model, research propositions are drawn, and conceptual contributions and practical implications are discussed.