Reviewerly/exHarmony huggingface dataset:
Dynamic Weighted Adversarial Learning for Semi-Supervised Classification under Intersectional Class Mismatch

Nowadays, class-mismatch problem has drawn intensive attention in Semi-Supervised Learning (SSL), where the classes of labeled data are assumed to be only a subset of the classes of unlabeled data. However, in a more realistic scenario, the labeled data and unlabeled data often share some common classes while they also have their individual classes, which leads to an “intersectional class-mismatch’’ problem. As a result, existing SSL methods are often confused by these individual classes and suffer from performance degradation. To address this problem, we propose a novel Dynamic Weighted Adversarial Learning (DWAL) framework to properly utilize unlabeled data for boosting the SSL performance. Specifically, to handle the influence of the individual classes in unlabeled data ( i.e. , Out-Of-Distribution classes), we propose an enhanced adversarial domain adaptation to dynamically assign weight for each unlabeled example from the perspectives of domain adaptation and a class-wise weighting mechanism, which consists of transferability score and prediction confidence value. Besides, to handle the influence of the individual classes in labeled data ( i.e. , private classes), we propose a dissimilarity maximization strategy to suppress the inaccurate correlations caused by the examples of individual classes within labeled data. Therefore, our DWAL can properly make use of unlabeled data to acquire an accurate SSL classifier under intersectional class-mismatch setting, and extensive experimental results on five public datasets demonstrate the effectiveness of the proposed model over other state-of-the-art SSL methods.
Deformation Gated Recurrent Network for Lane-Level Abnormal Driving Behavior Recognition

As a significant part of traffic accident prevention, abnormal driving behavior recognition has been receiving extensive attention. However, the granularity of existing abnormal driving behavior recognition is mostly at road-level, and these methods’ high complexity leads to high overhead on training and recognition. In this article, we propose a deformation gated recurrent network for lane-level abnormal driving behavior recognition. Firstly, we use conditional random field model to calculate the lane change necessity of the vehicle, which helps us to distinguish whether the lane-changing behavior is reasonable. Secondly, we propose deformation gated recurrent network (DF-GRN) and trajectory entropy to capture the implicit relationship between trajectories and shorten recognition time. Finally, we get classified results including aggressive, distracted and normal driving behavior from the network. Distracted and aggressive behavior will be marked as anomaly. The effectiveness and real-time nature of the network are verified by experiments on Hangzhou and Chengdu location datasets.
I Know This Looks Bad, But I Can Explain: Understanding When AI Should Explain Actions In Human-AI Teams

Explanation of artificial intelligence (AI) decision-making has become an important research area in human-computer interaction (HCI) and computer-supported teamwork research. While plenty of research has investigated AI explanations with an intent to improve AI transparency and human trust in AI, how AI explanations function in teaming environments remains unclear. Given that a major benefit of AI giving explanations is to increase human trust understanding how AI explanations impact human trust is crucial to effective human-AI teamwork. An online experiment was conducted with 156 participants to explore this question by examining how a teammate’s explanations impact the perceived trust of the teammate and the effectiveness of the team and how these impacts vary based on whether the teammate is a human or an AI. This study shows that explanations facilitate trust in AI teammates when explaining why AI disobeyed humans’ orders but hindered trust when explaining why an AI lied to humans. In addition, participants’ personal characteristics (e.g., their gender and the individual’s ethical framework) impacted their perceptions of AI teammates both directly and indirectly in different scenarios. Our study contributes to interactive intelligent systems and HCI by shedding light on how an AI teammate’s actions and corresponding explanations are perceived by humans while identifying factors that impact trust and perceived effectiveness. This work provides an initial understanding of AI explanations in human-AI teams, which can be used for future research to build upon in exploring AI explanation implementation in collaborative environments.
Mixed-Trust Computing: Safe and Secure Real-Time Systems

Verifying complex Cyber-Physical Systems (CPS) is increasingly important given the push to deploy safety-critical autonomous features. Unfortunately, traditional verification methods do not scale to the complexity of these systems and do not provide systematic methods to protect verified properties when not all the components can be verified. To address these challenges, this article proposes a real-time mixed-trust computing framework that combines verification and protection. The framework introduces a new task model, where an application task can have both an untrusted and a trusted part. The untrusted part allows complex computations supported by a full OS with a real-time scheduler running in a VM hosted by a trusted hypervisor. The trusted part is executed by another scheduler within the hypervisor and is thus protected from the untrusted part. If the untrusted part fails to finish by a specific time, the trusted part is activated to preserve safety (e.g., prevent a crash) including its timing guarantees. This framework is the first allowing the use of untrusted components for CPS critical functions while preserving logical and timing guarantees, even in the presence of malicious attackers. We present the framework its schedulability analysis and the coordination protocol between the trusted and untrusted parts. Our implementation on a Raspberry Pi 3 is also discussed along with experiments showing the behavior of the system under failures of untrusted components, and a drone application to demonstrate its practicality.
Towards Effective and Efficient Sparse Neural Information Retrieval

Sparse representation learning based on Pre-trained Language Models has seen a growing interest in Information Retrieval. Such approaches can take advantage of the proven efficiency of inverted indexes, and inherit desirable IR priors such as explicit lexical matching or some degree of interpretability. In this work, we thoroughly develop the framework of sparse representation learning in IR, which unifies term weighting and expansion in a supervised setting. We then build on SPLADE – a sparse expansion-based retriever – and show to which extent it is able to benefit from the same training improvements as dense bi-encoders, by studying the effect of distillation, hard negative mining as well as the Pre-trained Language Model’s initialization on its effectiveness – leading to state-of-the-art results in both in- and out-of-domain evaluation settings (SPLADE++). We furthermore propose efficiency improvements, allowing us to reach latency requirements on par with traditional keyword-based approaches (Efficient-SPLADE).
Formation of Value Orientations of Learners in Professional Education

The article reveals the process of formation of value orientations of education seekers in vocational education institutions. The peculiarities of the formation of value orientations are also highlighted. It has been proven that education seekers need the formation of value orientations, as this is the key to the formation of emotional and value attitudes towards the surrounding world. Research by scientists characterized the formation of value orientations as a generalization of the experience of other theories and concepts of the educational process, the integration of everything innovative in pedagogical theory and practice.&#x0D; The most rational ways of the process of formation of value orientations of education seekers in vocational education institutions are characterized, as well as methodological principles. The importance of the development of the process of formation of value orientations of education seekers in vocational education institutions in Ukraine based on the development of their components is emphasized. It has been proven that the formation of value orientations of education seekers in vocational education institutions should be based on the formation of their system.&#x0D; It is noted that the process of forming the value orientations of education seekers in vocational education institutions should be carried out according to the principle of collective learning, which will allow the realization of the requirement of compliance of the mental and personal development of the education seeker with the parameters of the norm, on the one hand, and recognition of the indisputable fact of the uniqueness and inimitability of the path of development of each personality - on the other.&#x0D; It is noted that in Ukraine it is necessary to create such a process of formation of value orientations of education seekers in vocational education institutions, which would ensure the task of forming in teenagers unconscious driving forces of prosocial behavior and internal «brakes» restraining negative behavior.&#x0D; The concept of an educational program for the formation of value orientations of learners in vocational (vocational-technical) education has been presented. It is expected that the process of shaping the value orientations of education recipients in vocational education institutions through the implementation of a new program in this field will contribute to the healthy development of individuals, enhance their success not only in the learning process but also in any important activities for them, and serve as a preventive measure against antisocial behavior
Federated Learning and Blockchain-Enabled Privacy-Preserving Healthcare 5.0 System: A Comprehensive Approach to Fraud Prevention and Security in IoMT

The proliferation of Internet and Communication Technologies (ICTs) has ushered in a period often referred to as Industry 5.0. The subsequent development is accompanied by the healthcare industry coining Healthcare 5.0. Healthcare 5.0 incorporates the Internet of Things (IoT), enabling medical imaging technologies to facilitate early diagnosis of diseases and enhance the quality of healthcare facilities' service. Nevertheless, the healthcare sector is currently experiencing a delay in adopting Artificial Intelligence (AI) and big data technologies compared to other sectors under the umbrella of Industry 5.0. This delay may be attributed to the prevailing concerns about data privacy within the healthcare domain. In recent times, there has been a noticeable increase in the use of Machine Learning (ML) enabled adaptive Internet of Medical Things (IoMT) systems with different technologies for medical applications. ML is an essential component of the IoMT system, as it optimizes the trade-off between delay and energy consumption. The issue of data fraud in classical learning models inside the distributed IoMT system for medical applications remains a significant research challenge in practical settings. This paper proposes Federated Learning and Blockchain-Enabled Privacy-Preserving (FL-BEPP) for Fraud Prevention and Security (FPS) in the IoMT framework. The system incorporates numerous dynamic strategies. This research examines the medical applications that exhibit hard constraints, such as deadlines, and soft constraints, such as resource consumption, when executed on distributed fog and cloud nodes. The primary objective of FL-BEPP is to effectively detect and safeguard the confidentiality and integrity of data across many tiers, including local fog nodes and faraway clouds. This is achieved by minimizing power use and delay while simultaneously meeting the time constraints associated with healthcare workloads.
Face Detection Method with Mask by Improved YOLOv5

Facial recognition has become a major challenge today as more and more individuals wear masks to avoid contracting the COVID-19 virus. The rapid spread of the COVID-19 pandemic has made it necessary for people to use a face mask, especially in public places, to prevent the spread of this disease. Therefore, recognizing faces and distinguishing a person's identity has become a problem that cannot be easily recognized, as many researches have proposed finding solutions to detect faces. But faces wearing a mask were not accurately detected, so in this research it was proposed to use a deep learning algorithm, which is the improved YOLOv5, which is a YOLO model that is characterized by accuracy and speed compared to YOLO models a deep learning algorithm. The YOLOv5 algorithm is proposed here from YOLO Network to detect and recognize faces with and without wearing a mask. It is an advanced and fast system for detecting faces in real time. As we reviewed most of the experiences with previous versions of YOLO, we noticed that YOLOv5 is a better model than previous YOLO models at detecting faces while wearing a mask, but needs to improve accuracy. As face detection is of great importance in various fields in terms of security in all public places and requires accuracy in detection. It is known that there is very little data available on images of wearing masks. So the training and evaluation was performed on the dataset available on Google Colab to the improved YOLOv5 algorithm in this paper.
Resources building for sentiment analysis of content disseminated by Tunisian medias in social networks

Nowadays, social networks play a fundamental role in promoting and diffusing television and radio programs to different categories of audiences. So, political parties, influential groups and political activists have rapidly seized these new communication media to spread their ideas and give their sentiments concerning critical issues. In this context, Twitter, Facebook and YouTube have become very popular tools for sharing videos and communicating with users who interact with each other to discuss some problems, propose solutions and give viewpoints. This interaction on the social media sites yields to a huge amount of unstructured and noisy texts; hence the need for automated analysis techniques to classify sentiments conveyed in the users' comments. In this work, we focus on opinions written in a less resourced Arabic language: Tunisian dialect (TD). In this work, we present a process for building a sentiment analyses model for comments written on Tunisian television broadcasts published in social media. These comments are written in a particular way with different spellings due to the fact that the Tunisian Dialect (TD) does not have an orthographic standard. For this we design crucial resources, namely sentiment lexicon and annotated corpus that we have used to investigate machine-learning and deep-learning models in order to identify the best sentiment analysis model for Tunisian Dialect.
Performance Index Model of Main Structure (CASE STUDY IN IPDMIP)

Purpose: This research intends to build a performance index model of main stucture by case study in IPDMIP. Theoretical reference: It is important to evaluate the percentage weights of 6 (six) variables in the Irrigation System Performance Index (ISPI) assessment so that irrigation managers get assessment results close to actual conditions in the field, improving the quality of irrigation system management. Method: The methodology or the main structure model in this study was analyzed by using SEM-PLS software and GRG analysis. The selection of a research location in the Central Authority Irrigation Area was by considering the same data availability as previous research. In this research, the author used the Smart-PLS (Partial Least Square) application to analyze and reduce variables, which were then analyzed again using the GRG (Generalized Reduced Gradient) method to calculate non-linear equations. Result and Conclusion: The result shows that in this equation, there is only 1 (one) variable, 3 (three) dimensions, and 13 (thirteen) indicators analyzed. After analyzing with SEM PLS, followed by GRG (Generalized Reduced Gradient) analysis with the solver in Microsoft Excel, changes in variable weights occur, so the new model equation for the main structure performance index (Y) is 0.348, with structure body dimensions of + 0.492 and door &amp; gear to operate of +0.160. Mud Bags and drain doors apply to the Central Authority. Implication of research: Based on the results of previous research, where the assessment weight of the variable of the main irrigation network (with a weight of 0.744) is greater than the tertiary irrigation network (with a weight of 0.256). Originality/ value: the role of the main irrigation network is more dominant in channeling water from the intake to the tertiary irrigation network. However, previous research has not been completed since the main structure still has a level below it and has not been included in the scope of previous research.
Task-Driven Compression for Collision Encoding Based on Depth Images

This paper contributes a novel learning-based method for aggressive task-driven compression of depth images and their encoding as images tailored to collision prediction for robotic systems. A novel 3D image processing methodology is proposed that accounts for the robot’s size in order to appropriately “inflate” the obstacles represented in the depth image and thus obtain the distance that can be traversed by the robot in a collision-free manner along any given ray within the camera frustum. Such depth-and-collision image pairs are used to train a neural network that follows the architecture of Variational Autoencoders to compress-and-transform the information in the original depth image to derive a latent representation that encodes the collision information for the given depth image. We compare our proposed task-driven encoding method with classical task-agnostic methods and demonstrate superior performance for the task of collision image prediction from extremely low-dimensional latent spaces. A set of comparative studies show that the proposed approach is capable of encoding depth image-and-collision image tuples from complex scenes with thin obstacles at long distances better than the classical methods at compression ratios as high as 4050:1.
Inpainting of Depth Images Using Deep Neural Networks for Real-Time Applications

Depth sensors enjoy increased popularity throughout many application domains, such as robotics (SLAM) and telepresence. However, independent of technology, the depth images inevitably suffer from defects such as holes (invalid areas) and noise. In recent years, deep learning-based color image inpainting algorithms have become very powerful. Therefore, with this work, we propose to adopt existing deep learning models to reconstruct missing areas in depth images, with the possibility of real-time applications in mind. After empirical tests with various models, we chose two promising ones to build upon: a U-Net architecture with partial convolution layers that conditions the output solely on valid pixels, and a GAN architecture that takes advantage of a patch-based discriminator. For comparison, we took a standard U-Net and LaMa. All models were trained on the publically available NYUV2 dataset, which we augmented with synthetically generated noise/holes. Our quantitative and qualitative evaluations with two public and an own dataset show that LaMa most often produced the best results, however, is also significantly slower than the others and the only one not being real-time capable. The GAN and partial convolution-based models also produced reasonably good results. Which one was superior varied from case to case but, generally, the former performed better with small-sized holes and the latter with bigger ones. The standard U-Net model that we used as a baseline was the worst and most blurry.
RPU-PVB: robust object detection based on a unified metric perspective with bilinear interpolation

Abstract With the development of cloud computing and deep learning, an increasing number of artificial intelligence models have been applied to reality. Such as videos on cell phones can be uploaded to the cloud for storage, which is detected by cloud arithmetic. Nevertheless, achieving this goal requires frequent consideration of the security of the model, since videos or images that go to the cloud, it is very likely to receive an adversarial attack. Regarding object detection, there has however been slow advancement in robustness research in this area. This is because training a target detection model requires a lot of arithmetic and time. Moreover, the current research has only slightly reduced the gap between clean and adversarial samples. To alleviate this problem, we propose a uniform perspective object detection robustness model based on bilinear interpolation that can accurately identify clean and adversarial samples. We propose the robustness optimization based on uniform metric perspective (RPU) for feature learning of clean and adversarial samples, drawing on the fine-grained idea. Following this, we analyze the fragility of the adversarial samples and consequently use the proposed perturbation filtering verification (PVB) based on bilinear interpolation. With slightly degraded clean sample detection performance, it substantially improves the robustness of object detection. Extensive experiments on PASCAL VOC and MS COCO show that our model guarantees the detection performance of clean samples and increases the detection performance of adversarial samples. The work we did has been open-sourced on GitHub: https://github.com/KujouRiu/RPU-PVB .
Regularized Meta-Training with Embedding Mixup for Improved Few-Shot Learning

Few-shot learning has enabled techniques to grasp new, unseen tasks from a small set of labelled samples using previously taught knowledge. Although subfields in few-shot learning, such as metric learning, have demonstrated relative success, generalization towards unseen tasks continues to prove difficult, especially in an out-of-domain setting. To address this issue, we propose Embedding Mixup for Meta-Training (EMMeT), a novel regularization technique that creates new tasks through embedding shuffling and averaging for training metric-based backbones. In an experimental setting, our findings across in-domain and out-of-domain datasets indicate that application of EMMeT promotes generalization and increases few-shot accuracy across a range of backbone models.
L-TReiD: Logic Tensor Transformer for Re-identification

This article proposes a Neuro-Symbolic (NeSy) machine learning approach to Object Re-identification. NeSy is an emerging branch of artificial intelligence which combines symbolic reasoning and logic-based knowledge representation with the learning capabilities of neural networks. Since object re-identification involves assigning the identity of the same object across different images and different conditions, such a task could benefit greatly from leveraging the logic capabilities of a NeSy framework to inject prior knowledge about invariant properties of the objects. To test this assertion, we combined the Logic Tensor Networks (LTNs) NeSy framework with a state-of-the-art Transformer-based Re-Identification and Damage Detection Network (TransRe3ID). The LTN incorporates prior knowledge about the properties that two instances of the same object have in common. Experimental results on the Bent&Broken Bicycle re-identification dataset demonstrate the potential of LTNs to improve re-identification systems and provide novel opportunities to identify pitfalls during training.