Reviewerly/exHarmony huggingface dataset:
On Performance and Calibration of Natural Gradient Langevin Dynamics

Producing deep neural network (DNN) models with calibrated confidence is essential for applications in many fields, such as medical image analysis, natural language processing, and robotics. Modern neural networks have been reported to be poorly calibrated compared with those from a decade ago. The stochastic gradient Langevin dynamics (SGLD) algorithm offers a tractable approximate Bayesian inference applicable to DNN, providing a principled method for learning the uncertainty. A recent benchmark study showed that SGLD could produce a more robust model to covariate shifts than other competing methods. However, vanilla SGLD is also known to be slow, and preconditioning can improve SGLD efficacy. This paper proposes eigenvalue-corrected Kronecker factorization (EKFAC) preconditioned SGLD (EKSGLD), in which a novel second-order gradient approximation is employed as a preconditioner for the SGLD algorithm. This approach is expected to bring together the advantages of both second-order optimization and the approximate Bayesian method. Experiments were conducted to compare the performance of EKSGLD with existing preconditioning methods and showed that it could achieve higher predictive accuracy and better calibration on the validation set. EKSGLD improved the best accuracy by 3.06% on CIFAR-10 and 4.15% on MNIST, improved the best negative log-likelihood by 16.2% on CIFAR-10 and 11.4% on MNIST, and improved the best thresholded adaptive calibration error by 4.05% on CIFAR-10.
Rotational Invariance Using Gabor Convolution Neural Network and Color Space for Image Processing

Convolutional neural networks (CNNs) are deep learning methods that are utilized in image processing such as image classification and recognition. It has achieved excellent results in various sectors; however, it still lacks rotation invariant and spatial information. To establish whether two images are rotational versions of one other, one can rotate them exhaustively to see if they compare favorably at some angle. Due to the failure of current algorithms to rotate images and provide spatial information, the study proposes to transform color spaces and use the Gabor filter to address the issue. To gather spatial information, the HSV and CieLab color spaces are used, and Gabor is used to orient images at various orientation. The experiments show that HSV and CieLab color spaces and Gabor convolutional neural network (GCNN) improves image retrieval with an accuracy of 98.72% and 98.67% on the CIFAR-10 dataset.
A Bibliometrics and Visual Analysis of Global Publications for Cognitive Map (1970-2022)

Cognitive map is a comprehensive research field of many disciplines, such as psychology, geography, computer science, and sociology. This multidisciplinary synthesis reflects the complexity of the cognitive map itself, which until now has lacked a unified conceptual definition because of its complexity. The literature on cognitive maps is increasing yearly, but few bibliometric studies on cognitive maps exist. To fill this gap, this paper uses literature relations and visualization tools Biblioshiny, Origin, and VOSviewer software to review systematically, cluster, country, author cooperation, and keyword analysis of cognitive map documentation data in different fields to understand the study hotspots and development situation of cognitive maps in different disciplines. This research found that the number of documents published in the past five years (2018-2022) increased the most, with nearly a third of the total number of studies published. Cognitive maps mainly focused on four research areas: computer science, engineering electrical electronic, geography, neuroscience, and psychology. This study draws its scientific overview through bibliometrics, explores the global profile, theme change, and frontier trend of cognitive map research results, and provides the direction and way for future research.
Implementing Intelligent Virtual Assistant

Our daily lives have been immensely enhanced by the rise of intelligent virtual assistants, which has revolutionised the way we engage with technology. These computer programmes replicate human interaction and carry out tasks for users by utilising artificial intelligence and natural language processing. They can be used on a variety of devices and are useful for scheduling, taking notes, doing research, and even writing prescriptions. These virtual assistants respond to consumer inquiries quickly and thoughtfully by using voice input and tools like OpenAI'sChatgpt. They can do searches on a number of different platforms, including Wikipedia, YouTube, Google, and even forecast the weather. These virtual assistants can converse with people in a more human-like manner thanks to the inclusion of natural language processing, which makes the interaction more natural and user-friendly. As more and more smart home and automobile products incorporate these virtual assistants, people will find it simpler to operate their homes and vehicles with just their voice. Additionally, they are used in customer service to offer immediate assistance to customers as well as in healthcare to assist doctors with patient care and diagnosis.Intelligent virtual assistants have countless potential uses, and as technology advances, we may anticipate even more fascinating and creative ways they will enhance our daily lives. Intelligent virtual assistants have developed into a necessary tool for both consumers and enterprises due to their capacity to offer individualised support and streamline corporate procedures
Fine-Grained Relation Extraction for Drug Instructions Using Contrastive Entity Enhancement

The extraction of relations between drug-related entities from drug instructions is essential for clinical diagnostic decision-making and drug use regulations, which is a critical task. However, due to the complexity of the textual descriptions in drug instructions, it is challenging to extract fine-grained relations, even with a considerable amount of training data. Moreover, since manually-labeled, high-quality datasets in the pharmaceutical domain are typically expensive, obtaining an extensive and accurate training dataset could be challenging. To overcome the above challenges, this paper proposes a drug relation extraction framework that combines entity information enhancement and contrastive feature learning, which can better extract fine-grained relations with limited data. More specifically, a sample generator creates a group of different samples with role semantic information from the training set, an entity encoder embeds the entity role information and context information to enhance the semantic representation, and a contrastive learning module employs a hybrid loss function to learn inter-sample and intra-sample differences. Empirical study indicates that the contrastive entity enhancement approach can achieve higher extraction accuracy and has better generalization capability. More specifically, the experimental results show that the F1 value of the model can reach 0.8892, which provides a 7.13% improvement compared to the baseline pre-training method.
FedDAD: Federated Domain Adaptation for Object Detection

Training an object detection model often requires numerous annotated images on a centralized host, which may violate user privacy and data confidentiality.Federated learning (FL) resolves this issue by allowing multiple clients, e.g., cameras, to collaboratively train a model while protecting user privacy.However, models trained with FL may fail to be generalized for new target domain due to domain shift when the data between source and target domains are statistically different.In this work, we formulate a real-world object detection problem as a source-free multi-domain adaptation problem in FL architecture.Moreover, we propose an adaptive FL algorithm, called FedDAD (Federated Domain Adaptive Detector), which aggregates models with dynamic attention targeting the unsupervised domain on server, and utilize instance-level alignment to alleviate the effects of scene variation on clients.Experimental results show that FedDAD improves the average precision (AP) by up to 10.05% and 19.15% compared to the popular FedAvg for specific object classes in the KAIST and MI3 datasets, respectively.
Dynamic adversarial jamming-based reinforcement learning for designing constellations

To resist various types of jamming in wireless channels, appropriate constellation modulation is used in wireless communication to ensure a low bit error rate. Due to the complexity and variability of the channel environment, a simple preset constellation is difficult to adapt to all scenarios, so the online constellation optimization method based on Reinforcement Learning (RL) shows its potential. However, the existing RL technology is difficult to ensure the optimal convergence efficiency. Therefore, in this paper, Dynamic Adversarial Interference (DAJ) waveforms are introduced and the DAJ-RL method is proposed by referring to adversarial training in Deep Learning (DL). The algorithm can converge to the optimal state quickly by self-adaptive power and probability direction of dynamic strong adversary of DAJ. In this paper, a rigorous theoretical proof of the symbol error rate is given and it is shown that the method approaches the mathematical limit. Also, numerical and hardware experiments show that the constellations generated by DAJ-RL have the best error rate at all noise levels. In the end, the proposed DAJ-RL method effectively improves the RL-based anti-jamming modulation for cognitive electronic warfare.
Constraint‐based multi‐agent reinforcement learning for collaborative tasks

Abstract In order to be successfully executed, collaborative tasks performed by two agents often require a cooperative strategy to be learned. In this work, we propose a constraint‐based multi‐agent reinforcement learning approach called constrained multi‐agent soft actor critic (C‐MSAC) to train control policies for simulated agents performing collaborative multi‐phase tasks. Given a task with phases, the first phases are treated as constraints for the final task phase objective, which is addressed with a centralized training and decentralized execution approach. We highlight our framework on a tray balancing task including two phases: tray lifting and cooperative tray control for target following. We evaluate our proposed approach and compare it against its unconstrained variant (MSAC). The performed comparisons show that C‐MSAC leads to higher success rates, more robust control policies, and better generalization performance.
Discriminative analysis dictionary learning with adaptively ordinal locality preserving

Dictionary learning has found broad applications in signal and image processing. By adding constraints to the traditional dictionary learning model, dictionaries with discriminative capability can be obtained which can deal with the task of image classification. The Discriminative Convolutional Analysis Dictionary Learning (DCADL) algorithm proposed recently has achieved promising results with low computational complexity. However, DCADL is still limited in classification performance because of the lack of constraints on dictionary structures. To solve this problem, this study introduces an adaptively ordinal locality preserving (AOLP) term to the original model of DCADL to further improve the classification performance. With the AOLP term, the distance ranking in the neighborhood of each atom can be preserved, which can improve the discrimination of coding coefficients. In addition, a linear classifier for the classification of coding coefficients is trained along with the dictionary. A new method is designed specifically to solve the optimization problem corresponding to the proposed model. Experiments are performed on several commonly used datasets to show the promising results of the proposed algorithm in classification performance and computational efficiency.
Innovative and Novel Research Datasets Related to Cybersecurity Risk Disclosures: A Research Note

ABSTRACT This research note describes an innovative dataset that was developed and used by Gao, Calderon, and Tang (2020) and Calderon and Gao (2021) to examine cybersecurity risk disclosures. Our innovative dataset and contemporary text analytics methodology allowed us to develop interesting insights and identify various patterns in the disclosure of cybersecurity risks that publicly traded companies face. We believe that researchers can learn from our data and methodology, and contribute further to the literature on corporate disclosure in general and cybersecurity risks in particular.
Decentraland: The Alleged Decentralization of Blockchain Applications

Breaking down the blockchain.
The influence of pupil ellipse noise on the convergence time of a glint-free 3D eye tracking algorithm

Eye-tracking is a key sensing technology for upcoming retinal projection augmented reality (AR) glasses. State-of-the-art eye-tracking sensor technologies rely on video oculography (VOG) and 3D model based gaze estimation algorithms, which infer gaze from observations of the projected pupil over time. The convergence time of these algorithms relies heavily on the pupil ellipse fitting accuracy. In this work, we investigate the effects of pupil ellipse contour noise and pupil center noise on the convergence time of a state-of-the-art eye-tracking approach and show that the convergence time relies heavily on a sub-pixel accurate pupil ellipse fitting and can reach tens of seconds for inaccurately fitted ellipses.
Model Similarity-Based Defense Scheme Against Backdoor Attacks on Federated Learning

Federated learning is a deep learning model that helps thousands of participants train data without compromising their local data. For example, multiple smartphones can jointly learn and train the predicted value of a word under the keyboard without revealing what the user typed. However, federated learning is still fragile, and due to a large number of participating clients, it is impossible to guarantee that participants will engage in malicious behavior in the training process. Malicious actors will embed backdoors in their data, malicious clients will converge and show good accuracy in their primary task, and in a particular task, the attacker will embed backdoor input in a specific way that the attacker wants. Normally, the parameters sent by the abnormal client are completely different from those sent by the normal client. This paper proposes a new method to filter dishonest participants. The scheme mainly looks for the similarity between each client, identifies benign clients and abnormal clients by looking for the largest cluster, and then ignores the abnormal local model when the global model is updated.
Program Source Code Vulnerability Mining Scheme Based on Abstract Syntax Tree

With the continuous growth of the number and scale of computing software, the occurrence rate of vulnerabilities in program source code has also greatly increased, which is not a good phenomenon for the entire computer open source environment, and also makes vulnerability mining an important research direction. Traditional vulnerability mining relies on security researchers to manually analyze programs or find vulnerabilities based on predefined rules, which can't effectively cope with the rapidly increasing amount of software. At present, there are some methods that combine machine learning or deep learning technology for automatic vulnerability mining. However, these methods only detect the program source code at function level or code block level to predict whether a given program has vulnerabilities, which makes the results of vulnerability mining difficult to be explained and utilized, and also leads to low accuracy and high false alarm rate. On the other hand, the current program processing method does not have a particularly perfect processing solution for the program source code, which leads to a large amount of data redundancy and additional computational overhead. For example, treat it as a textual language, or treat it as a graph of program properties extracted by dynamic analysis. In order to solve the many shortcomings of the current work, this scheme proposes a deep learning-based vulnerability mining model scheme to achieve interpretable fine-grained vulnerability mining.
School-Enterprise Co-construction Laboratory Scheme Based on AHP-TOPSIS

School-enterprise co-construction of laboratories is a key link in promoting new engineering construction. However, at present, the cooperation is not long-term, and cannot meet the requirements of training new engineering talents. In response to the above problems, we shifted the research perspective from the development strategy to the cooperation preparatory stage and proposed a set of school-enterprise co-construction laboratory schemes based on AHP-TOPSIS. Based on the actual situation, carry out school-enterprise evaluation and provide schools with a scientific and effective selection plan for cooperative enterprises. It provides a set of realistic and effective solutions for school-enterprise assessment and cooperative enterprise selection.
Remote social touch framework: a way to communicate physical interactions across long distances

Nowadays, living away from loved ones is a common practice due to various reasons such as work, study, or certain health-related concerns (e.g., infection diseases). Such practice aggregates certain negative emotions such as depression due to the loss of physical, mental, and emotional awareness about loved ones. Because of the importance of social touch for one’s wellbeing, this paper reports the research focusing on remote social touch (RST), as a way to stimulate the sense of touch remotely to regain some of the lost awareness. The research identified various dimensions of RST and the process of communicating social touch remotely through a product. This is done through an extensive literature survey, online diary keeping, and interviews. The paper also presents the early proposed RST framework that consists of three elements (actors, product, and communication) and their dimensions, that explain the process of how RST communications can be successfully achieved through a product.
Blockchain-Based Services Implemented in a Microservices Architecture Using a Trusted Platform Module Applied to Electric Vehicle Charging Stations

Microservice architectures exploit container-based virtualized services, which rarely use hardware-based cryptography. A trusted platform module (TPM) offers a hardware root for trust in services that makes use of cryptographic operations. The virtualization of this hardware module offers high usability for other types of service that require TPM functionalities. This paper proposes the design of TPM virtualization in a container. To ensure integrity, different mechanisms, such as attestation and sealing, have been developed for the binaries and libraries stored in the container volumes. Through a REST API, the container offers the functionalities of a TPM, such as key generation and signing. To prevent unauthorized access to the container, this article proposes an authentication mechanism based on tokens issued by the Cognito Amazon Web Service. As a proof of concept and applicability in industry, a use case for electric vehicle charging stations using a microservice-based architecture is proposed. Using the EOS.IO blockchain to maintain a copy of the data, the virtualized TPM microservice provides the cryptographic operations necessary for blockchain transactions. Through a two-factor authentication mechanism, users can access the data. This scenario shows the potential of using blockchain technologies in microservice-based architectures, where microservices such as the virtualized TPM fill a security gap in these architectures.
Document-level relation extraction based on sememe knowledge-enhanced abstract meaning representation and reasoning

Abstract Document-level relation extraction is a challenging task in information extraction, as it involves identifying semantic relations between entities that are dispersed throughout a document. Existing graph-based approaches often rely on simplistic methods to construct text graphs, which do not provide enough lexical and semantic information to accurately predict the relations between entity pairs. In this paper, we introduce a document-level relation extraction method called SKAMRR ( S ememe K nowledge-enhanced A bstract M eaning R epresentation and R easoning). First, we generate document-level abstract meaning representation graphs using rules and acquire entity nodes’ features through sufficient information propagation. Next, we construct inference graphs for entity pairs and utilize graph neural networks to obtain their representations for relation classification. Additionally, we propose the global adaptive loss to address the issue of long-tailed data. We conduct extensive experiments on four datasets DocRE, CDR, GDA, and HacRED. Our model achieves competitive results and its performance outperforms previous state-of-the-art methods on four datasets.
Application of Adaptive Algorithms on Ultrasound Imaging

Ultrasound, also known as ultrasonography, plays a major role in the medical imaging field. Ultrasound images are inevitably prone to different kinds of noise and speckle during their acquisition. Adaptive filters show the best performance in removing noise and speckles from images. In this paper, we compared the least mean square algorithm, the quaternion least mean square algorithm, and the normalized least mean square algorithm for ultrasound image processing. It was demonstrated that NLMS displayed the best performance of these algorithms. The results are provided in order to illustrate the performance of algorithms.
A Comparative Study of Various Digital Image Watermarking Techniques: Specific to Hybrid Watermarking

Abstract: Digital security is one of the important aspects of today’s era. Digital content is being grown every day on the internet; therefore, it is essential to guard the copyright of digital content using various techniques. Watermarking has emerged as an important field of study aiming at securing digital content and copyright protection. None of the watermarking techniques can provide well robustness against all the attacks, and algorithms are designed based on required specifications, which means there is a lot of opportunity in this field. Image watermarking is a vast area of research, starting from spatial-based methods to deep learning-based methods, and it has recently gained a lot of popularity due to the involvement of deep learning technology for ensuring the security of digital content. This study aims at exploring important highlights from spatial to deep learning methods of watermarking, which will be helpful for the researchers. In order to accomplish this study, the standard research papers of the last ten years have been obtained from various databases and reviewed to answer the five research questions. Open issues and challenges are identified and listed after reviewing various kinds of literature. Our study reveals that hybrid watermarking performs better in terms of balancing the trade-off between imperceptibility and robustness. Current research trends and future direction is also discussed.