Reviewerly/exHarmony huggingface dataset:
Design and Implementation of Secure Transmission Architecture for IIOT Based on Blockchain

With the wave of digital economy sweeping the world, 5G, artificial intelligence, big data technology, cloud computing and other emerging technologies continue to integrate with industry to accelerate the development of industrial Internet. As an important strategy of traditional industrial digital transformation, industrial Internet of Things plays an important role in today's social development. However, the data transmission security of industrial equipment in the Internet of Things has been facing severe challenges. At present, most of the industrial Internet of Things adopt centralized architecture for equipment access authentication and industrial data transmission. Coupled with the poor industrial field application environment, equipment is difficult to resist malicious attacks. Centralized authentication institutions and data transmission may cause the abuse of power of trusted institutions and the acquisition and tampering of data by third parties, resulting in data transmission security problems. In this paper, combined with blockchain technology and MQTT protocol based on TLS, a data security transmission architecture for industrial IoT is proposed to ensure the safety of equipment access and data transmission in industrial IoT.
Facial Kinship Verification using Computational Approaches

The objective of Facial Kinship Verification (FKV) is to automatically ascertain from two different person-provided facial photos or videos the extent to which they are related. It is emerging due to its potential applicability in real-world situations, regardless of its difficulty. As deep learning models are doing an amazing job in this era so we used pretrained Convolutional Neural Networks, and Ensembled Models in order to solve the issue of kinship verification. Also, we proposed a new model to achieve kinship verification. Finally, this paper compared their results. For better understand, we used the KinFaceW dataset and our own dataset.
Video-based road accident detection on highways: A less complex YOLOv5 approach

Advancement of Artificial Intelligence (AI) technologies and the availability of high-end computing devices create scope for the implementation of intelligent transport infrastructure for road safety. This paper proposes an intelligent model for accident detection on highways using more robust, less complex, and more accurate YOLOv5 and StrongSort to locate and track vehicles. It provides a reliable approach for detecting accidents based on vehicle speed, acceleration, trajectory anomalies, and area anomalies. The methodology follows three major steps. The first stage performs vehicle detection, the next stage performs vehicle tracking and feature extraction, and the last stage does crash detection. In this study, only vehicle detection and tracking are addressed using a deep learning-based methodology. However, the accident prediction is done by an algorithm using threshold levels of various parameters such as the speed of vehicles, acceleration anomalies, etc. The model shows good performance when evaluated using the test crash videos under different ambient conditions such as daylight and night.
Experimental Study using Unsupervised Anomaly Detection on Server Resources Monitoring

Monitoring resources in a server environment is an essential and indispensable process that ensures a decent resource allocation to show better performance. Existing resource monitoring solutions or tools can be used but require additional staff to monitor resource usage. It is a high workload task when there are thousands of servers and each server with thousands of application instances. When the consumption surpasses an unusual limit, it is considered an anomaly record. Unsupervised machine learning algorithms can detect anomalies through clustering. This study developed an experimental analysis using three clustering techniques to assess the performance of clustering for discovering the anomalies pattern in a multivariate time series dataset with 25,918 data points. The procedure to get the optimal numbers of clusters is performed before the experiment, which can be considered between 3 and 4 numbers of clusters. Cluster pattern analysis has been done for selecting the best optimal number of clusters among both selections. The experiment has included two dimensionality reduction techniques for dataset compression to investigate their influences towards clustering performance. Finally, three performance metrics are used for evaluating the cluster quality. This study concluded that the combination of K-Means with Principal Component Analysis produced the best cluster quality among 9 combinations of clustering techniques. Finally, the anomalies behaviors are identified in the distinguishable cluster from all the monitoring items. Nonetheless, it is worth noting that the output of the anomaly detection is different when clustering is performed using different numbers of clusters.
Gaze-probe joint guidance with multi-task learning in obstetric ultrasound scanning

In this work, we exploit multi-task learning to jointly predict the two decision-making processes of gaze movement and probe manipulation that an experienced sonographer would perform in routine obstetric scanning. A multimodal guidance framework, Multimodal-GuideNet, is proposed to detect the causal relationship between a real-world ultrasound video signal, synchronized gaze, and probe motion. The association between the multi-modality inputs is learned and shared through a modality-aware spatial graph that leverages useful cross-modal dependencies. By estimating the probability distribution of probe and gaze movements in real scans, the predicted guidance signals also allow inter- and intra-sonographer variations and avoid a fixed scanning path. We validate the new multi-modality approach on three types of obstetric scanning examinations, and the result consistently outperforms single-task learning under various guidance policies. To simulate sonographer’s attention on multi-structure images, we also explore multi-step estimation in gaze guidance, and its visual results show that the prediction allows multiple gaze centers that are substantially aligned with underlying anatomical structures.
Achieving Students' Effectiveness Through Entrepreneurial Spirit and Musical Theatres Focusing Info- Internet Connectivity: Unveiling the Nexus Among Students' Effectiveness, Socio-economic Environment and Sustainable Performance

In recent days, the role of Internet connectivity has been highlighted as a means of creating socio-economic space by providing access to diverse knowledge resources and virtual learning opportunities. Understanding dynamic interactions between these variables can be aware of management strategies for creating a comprehensive and empowered educational environment that promotes students’ flexibility and sustainable performance. Furthermore, the important role of Internet connectivity in modern education has become undeniable. This research seeks to link these factors and open up their collective impact on student's academic success and overall development. A review of the literature shows that business education promotes important skills such as critical thinking, problem-solving, and adaptation. In addition, musical theatre not only promotes creativity but also enhances communication and teamwork skills. Meanwhile, Internet connectivity operates as a gateway to a wide range of knowledge resources and innovative learning opportunities, crossing geographical barriers. By reviewing comprehensive literature and using quantitative research methods, this study analyzes data collected from 300 college students. Using structural equation modeling (SEM), data is tested. The basis of this study is the method of sampling the facility, taking a non-probability sampling. As a result, academic achievements and overall effectiveness increase. In light of these insights, the discussion will focus on the implications for academics, policymakers, and stakeholders in the field of education. Using the potential of the entrepreneurial spirit, musical theatres, and Internet connectivity, academics and policymakers can develop a generation of flexible students. Be equipped to meet future challenges with a commitment to creativity and sustainable performance. Finally, this study contributes to the growing body of knowledge that seeks to transform education for students into a comprehensive and empowering experience in a rapidly changing world.
Use of the k-nearest neighbour and its analysis for fall detection on Systems on a Chip for multiple datasets

Fall of an elderly person often leads to serious injuries and death. Many falls occur in the home environment, and hence a reliable fall detection system that can raise alarms with minimum latency is a necessity. Wrist-worn accelerometer-based fall detection systems and multiple datasets are available, but no attempt has been made to analyze the accuracy and precision. Wherever the comparison does exist, it has been run on a cloud. No analysis of the models, convergence, and dataset analysis on Systems on a Chip (SoCs) has ever been attempted. In this paper, we attempt to present why Machine Learning (ML) algorithms in their current state cannot be run on existing SoCs. We have used Snapdragon 410c SoC to do our analytics. In this paper, we have used the kth-nearest neighbour to prove that ML cannot be directly run on SoCs. We have looked at the effect of distance metrics and neighbors as well as the effect of feature extraction on the accuracies and the latencies. In this paper, we establish the need for model compression and data pruning for fall detection using ML/Deep Learning algorithms on SoCs. We have done this by analyzing various datasets on varying architectural parameters.
Multi-Aspect Enhanced Convolutional Neural Networks for Knowledge Graph Completion

Knowledge graph completion (KGC, also referred to as link prediction) aims at predicting missing entities and relations in knowledge graphs (KGs). Knowledge graph embedding (KGE) techniques have been proven to be effective for link prediction. Currently, a series of convolutional neural networks (CNNs) based models (e.g., ConvE and its extended models) have attained excellent results for link prediction. However, several aspects that are important for link prediction using CNNs have not been considered and enhanced simultaneously, which significantly limit the performance of these models. In this paper we explore an effective KGE model based on CNNs. We investigate and discover four extremely important aspects that have a strong influence on ConvE: entity and relation embeddings, entity-to-relation interaction approaches, CNN structure, and loss function. Based on the optimization of the above four aspects, we propose a novel KGE method called ConvEICF. Through extensive experiments, we find that ConvEICF outperforms the previous state-of-the-art link prediction baselines on FB15k-237 and WN18RR datasets. In particular, ConvEICF achieves a Hits@10 score that is 11.2% and 6.5% better than ConvE on FB15k-237 and WN18RR datasets respectively. Additionally, through in-depth experiments we observe an interesting phenomenon and important finding that the very common 1-N scoring technique in KGE can be considerably improved by just adding a dropout operation. Our code is available at https://github.com/NEU-IDKE/ConvEICF.
Local context is not enough! Towards Query Semantic and Knowledge Guided Multi-Span Medical Question Answering

Medical Question Answering (MedQA) is one of the most popular and significant tasks in developing healthcare assistants. When humans extract an answer to a question from a document, they first (a) understand the question itself in detail and (b) utilize relevant knowledge/experiences to determine the answer segments. In multi-span question answering, it becomes increasingly important to comprehend the query accurately and possess relevant knowledge, as the interrelationship among different answer segments is essential for achieving completeness. Motivated by this, we first propose a transformer-based query semantic and knowledge (QueSemKnow) guided multi-span question-answering model. The proposed QueSemKnow works in a two-phased manner; in the first stage, a multi-task model is proposed to extract query semantics: (i) intent identification and (ii) question type prediction. In the second stage, QueSemKnow selects a relevant subset of the knowledge graph as the underlying context/document and extracts answers depending on the semantic information extracted from the first stage and context. We build a multi-task query semantic extraction model for query intent and query type identification to investigate the co-relation among these tasks. Furthermore, we created a semantically aware medical question-answering corpus named QueSeMSpan MedQA wherein each question is annotated with its corresponding semantic information. The proposed model outperforms several baselines and existing state-of-the-art models by a large margin on multiple datasets, which firmly demonstrates the effectiveness of the human-inspired multi-span question-answering methodology.
Reinforcement Learning in RDPs by Combining Deep RL with Automata Learning

Regular Decision Processes (RDPs) are a recently introduced model for decision-making in non-Markovian domains in which states are not postulated a-priori, and the next observation depends in a regular manner on past history. As such, they provide a more succinct and understandable model of the dynamics and reward function. Existing algorithms for learning RDPs attempt to learn an automaton that reflects the regularity of the underlying domain. However, their scalability is limited due to the practical difficulty of learning automata. In this paper we propose to leverage the power of Deep reinforcement learning in partially observable domain to learn RDPs: First, we learn an RNN-based policy. Then, we generate an automaton that reflects the policy’s structure and use our old data to transform it into an MDP, which we solve. This results in a finite, explainable policy structure, and, as our empirical evaluation on old and new RDP benchmarks shows, much better sample complexity.
A Dynamic Selective Parameter Sharing Mechanism Embedded with Multi-Level Reasoning Abstractions

Cooperative multi-agent reinforcement learning (Co-MARL) commonly employs different parameter sharing mechanisms, such as full and partial sharing. However, imprudent application of these mechanisms can potentially constrain policy diversity and limit cooperation flexibility. Recent methods that group agents into distinct sharing categories often exhibit poor performance due to challenges in precisely differentiating agents and neglecting the issue of promoting cooperation among these categories. To address these issues, we introduce a dynamic selective parameter sharing mechanism embedded with multi-level reasoning abstractions (DSPS-MA). Our approach uses self-comparison sequences to infer agents’ abstract concepts, defining the differences between agents and allowing them to dynamically select partners to share parameters based on these abstract concepts. We also design an intrinsic reward to offer comprehensive collaboration guidance for agents, and introduce a policy cosine similarity regularization term to ensure sufficient policy diversity. Empirical evaluations demonstrate that our approach yields higher returns and faster convergence than state-of-the-art methods.
Visualization Enhancement of Saliency Methods Based on the Sliding Window Mechanism

Deep neural networks are widely used in image classification tasks, but their internal decision-making mechanisms are often difficult to explain. While various algorithms have been developed to visualize these mechanisms, many of them produce coarse, noisy results that are not always convincing. To address this issue, we propose a method for enhancing saliency maps produced by saliency methods. Our method uses a fixed-size sliding window to upsample local regions of the input image and feed them into the selected visualization algorithm to generate class-specific saliency maps and probability scores. We then downsample the resulting saliency maps and multiply them by the probability scores to obtain maps with greater detail. We evaluate our method using different saliency methods and network architectures, and demonstrate its effectiveness through both quantitative metrics and intuitive evaluation. Our results show that our method significantly improves the performance of these saliency methods, providing a more valid and reliable means of visualizing the decision mechanisms of deep neural networks. Code is available at https://github.com/LuoLogic/Enhuncement-saliency.
Enhancing Text Generation with Cooperative Training

Recently, there has been a surge in the use of generated data to enhance the performance of downstream models, largely due to the advancements in pre-trained language models. However, most prevailing methods trained generative and discriminative models in isolation, which left them unable to adapt to changes in each other. These approaches lead to generative models that are prone to deviating from the true data distribution and providing limited benefits to discriminative models. While some works have proposed jointly training generative and discriminative language models, their methods remain challenging due to the non-differentiable nature of discrete data. To overcome these issues, we introduce a self-consistent learning framework in the text field that involves training a discriminator and generator cooperatively in a closed-loop manner until a scoring consensus is reached. By learning directly from selected samples, our framework are able to mitigate training instabilities such as mode collapse and non-convergence. Extensive experiments on four downstream benchmarks, including AFQMC, CHIP-STS, QQP, and MRPC, demonstrate the efficacy of the proposed framework.
Deep Co-Training for Cross-Modality Medical Image Segmentation

Due to the expensive segmentation annotation cost, cross-modality medical image segmentation aims to leverage annotations from a source modality (e.g. MRI) to learn a model for target modality (e.g. CT). In this paper, we present a novel method to tackle cross-modality medical image segmentation as semi-supervised multi-modal learning with image translation, which learns better feature representations and is more robust to source annotation scarcity. For semi-supervised multi-modal learning, we develop a deep co-training framework. We address the challenges of co-training on divergent labeled and unlabeled data distributions with a theoretical analysis on multi-view adaptation and propose decomposed multi-view adaptation, which shows better performance than a naive adaptation method on concatenated multi-view features. We further formulate inter-view regularization to alleviate overfitting in deep networks, which regularizes deep co-training networks to be compatible with the underlying data distribution. We perform extensive experiments to evaluate our framework. Our framework significantly outperforms state-of-the-art domain adaptation methods on three segmentation datasets, including two public datasets on cross-modality cardiac substructure segmentation and abdominal multi-organ segmentation and one large scale private dataset on cross-modality brain tissue segmentation. Our code is publicly available at https://github.com/zlheui/DCT.
Information Bound and Its Applications in Bayesian Neural Networks

Bayesian neural networks have drawn extensive interest because of their distinctive probabilistic representation framework. However, despite its recent success, little work focuses on the information-theoretic understanding of Bayesian neural networks. In this paper, we propose Information Bound as a metric of the amount of information in Bayesian neural networks. Different from mutual information on deterministic neural networks where modification of network structure or specific input data is usually necessary, Information Bound can be easily estimated on current Bayesian neural networks without any modification of network structures or training processes. By observing the trend of Information Bound during training, we demonstrate the existence of the “critical period” in Bayesian neural networks. Besides, we show that the Information Bound can be used to judge the confidence of the model prediction and to detect out-of-distribution datasets. Based on these observations of model interpretation, we propose Information Bound regularization and Information Bound variance regularization methods. The Information Bound regularization encourages models to learn the minimum necessary information and improves the model generality and robustness. The Information Bound variance regularization encourages models to learn more about complex samples with low Information Bound. Extensive experiments on KMNIST, Fashion-MNIST, CIFAR-10, and CIFAR-100 verify the effectiveness of the proposed regularization methods.
Detecting Out-of-Distribution Objects Using Neuron Activation Patterns

Object detection is essential to many perception algorithms used in modern robotics applications. Unfortunately, the existing models share a tendency to assign high confidence scores for out-of-distribution (OOD) samples. Although OOD detection has been extensively studied in recent years by the computer vision (CV) community, most proposed solutions apply only to the image recognition task. Real-world applications such as perception in autonomous vehicles struggle with far more complex challenges than classification. In our work, we focus on the prevalent field of object detection, introducing Neuron Activation PaTteRns for out-of-distribution samples detection in Object detectioN (NAPTRON). Performed experiments show that our approach outperforms state-of-the-art methods, without the need to affect in-distribution (ID) performance. By evaluating the methods in two distinct OOD scenarios and three types of object detectors we have created the largest open-source benchmark for OOD object detection.
Is Performance Fairness Achievable in Presence of Attackers Under Federated Learning?

In the last few years, Federated Learning (FL) has received extensive attention from the research community because of its capability for privacy-preserving, collaborative learning from heterogeneous data sources. Most FL studies focus on either average performance improvement or the robustness to attacks, while some attempt to solve both jointly. However, the performance disparities across clients in the presence of attackers have largely been unexplored. In this work, we propose a novel Fair Federated Learning scheme with Attacker Detection capability (abbreviated as FFL+AD) to minimize performance discrepancies across benign participants. FFL+AD enables the server to identify attackers and learn their malign intent (e.g., targeted label) by investigating suspected models via top performers. This two-step detection method helps reduce false positives. Later, we introduce fairness by regularizing the benign clients’ local objectives with a variable boosting parameter that gives more emphasis on low performers in optimization. Under standard assumptions, FFL+AD exhibits a convergence rate similar to FedAvg. Experimental results show that our scheme builds a more fair and more robust model, under label-flipping and backdoor attackers, compared to prior schemes. FFL+AD achieves competitive accuracy even when 40% of the clients are attackers.