Reviewerly/exHarmony huggingface dataset:
Large Language Models Demonstrate the Potential of Statistical Learning in Language

To what degree can language be acquired from linguistic input alone? This question has vexed scholars for millennia and is still a major focus of debate in the cognitive science of language. The complexity of human language has hampered progress because studies of language-especially those involving computational modeling-have only been able to deal with small fragments of our linguistic skills. We suggest that the most recent generation of Large Language Models (LLMs) might finally provide the computational tools to determine empirically how much of the human language ability can be acquired from linguistic experience. LLMs are sophisticated deep learning architectures trained on vast amounts of natural language data, enabling them to perform an impressive range of linguistic tasks. We argue that, despite their clear semantic and pragmatic limitations, LLMs have already demonstrated that human-like grammatical language can be acquired without the need for a built-in grammar. Thus, while there is still much to learn about how humans acquire and use language, LLMs provide full-fledged computational models for cognitive scientists to empirically evaluate just how far statistical learning might take us in explaining the full complexity of human language.
Magnetic/conductive/elastic multi-material 3D-printed self-powered sensing gloves for underwater/smoke environmental Human-Computer Interaction

Current Human-Computer Interaction (HCI) devices allow interactive communications between the users’ gestures and the computers. However, extreme environments beyond atmosphere, such as underwater or smoke, will greatly interrupt the normal operation of existing HCI devices. Here we demonstrated the fabrication of 3D-printed self-powered sensing gloves that can be used for underwater/smoke environmental HCI. A home-made multi-extrusion-head manipulator, which can simultaneously print three kinds of functional inks including magnetic, conductive and elastic materials, has been developed to generate the self-powered sensing gloves. The magnetic part was printed on the polymeric thumb region while four liquid metal coils were printed on the other four fingers, yielding the self-powered gesture recognition by clicking the fingers or nine-key input method keys of alphabetic typing. Our work offers a design of self-powered sensing glove for gesture recognition and therefore provides a pathway to realize HCI in extreme environments.
Blockchain Meets Federated Learning in Healthcare: A Systematic Review With Challenges and Opportunities

Recently, innovations in the Internet of Medical Things (IoMT), information and communication technologies, and machine learning (ML) have enabled smart healthcare. Pooling medical data into a centralized storage system to train a robust ML model, on the other hand, poses privacy, ownership, and regulatory challenges. Federated learning (FL) overcomes the prior problems with a centralized aggregator server and a shared global model. However, there are two technical challenges: 1) FL members need to be motivated to contribute their time and effort and 2) the centralized FL server may not accurately aggregate the global model. Therefore, combining the blockchain and FL can overcome these issues and provide high-level security and privacy for smart healthcare in a decentralized fashion. This study integrates two emerging technologies, blockchain and FL, for healthcare. We describe how blockchain-based FL plays a fundamental role in improving competent healthcare, where edge nodes manage the blockchain to avoid a single point of failure, while IoMT devices employ FL to use dispersed clinical data fully. We discuss the benefits and limitations of combining both technologies based on a content analysis approach. We emphasize three main research streams based on a systematic analysis of blockchain-empowered: 1) IoMT; 2) electronic health records (EHRs) and electronic medical records (EMRs) management; and 3) digital healthcare systems (internal consortium/secure alerting). In addition, we present a novel conceptual framework of blockchain-enabled FL for the digital healthcare environment. Finally, we highlight the challenges and future directions of combining blockchain and FL for healthcare applications.
Quantum detectable Byzantine agreement for distributed data trust management in blockchain

No system entity within a contemporary distributed cyber system can be entirely trusted. Hence, the classic centralized trust management method cannot be directly applied to it. Blockchain technology is essential to achieving decentralized trust management, its consensus mechanism is useful in addressing large-scale data sharing and data consensus challenges. Herein, an n-party quantum detectable Byzantine agreement (DBA) based on the GHZ state to realize the data consensus in a quantum blockchain is proposed, considering the threat posed by the growth of quantum information technology on the traditional blockchain. Relying on the nonlocality of the GHZ state, the proposed protocol detects the honesty of nodes by allocating the entanglement resources between different nodes. The GHZ state is notably simpler to prepare than other multi-particle entangled states, thus reducing preparation consumption and increasing practicality. When the number of network nodes increases, the proposed protocol provides better scalability and stronger practicability than the current quantum DBA. In addition, the proposed protocol has the optimal fault-tolerant found and does not rely on any other presumptions. A consensus can be reached even when there are n−2 traitors. The performance analysis confirms viability and effectiveness through exemplification. The security analysis also demonstrates that the quantum DBA protocol is unconditionally secure, effectively ensuring the security of data and realizing data consistency in the quantum blockchain.
Subtraction-Average-Based Optimizer: A New Swarm-Inspired Metaheuristic Algorithm for Solving Optimization Problems

This paper presents a new evolutionary-based approach called a Subtraction-Average-Based Optimizer (SABO) for solving optimization problems. The fundamental inspiration of the proposed SABO is to use the subtraction average of searcher agents to update the position of population members in the search space. The different steps of the SABO's implementation are described and then mathematically modeled for optimization tasks. The performance of the proposed SABO approach is tested for the optimization of fifty-two standard benchmark functions, consisting of unimodal, high-dimensional multimodal, and fixed-dimensional multimodal types, and the CEC 2017 test suite. The optimization results show that the proposed SABO approach effectively solves the optimization problems by balancing the exploration and exploitation in the search process of the problem-solving space. The results of the SABO are compared with the performance of twelve well-known metaheuristic algorithms. The analysis of the simulation results shows that the proposed SABO approach provides superior results for most of the benchmark functions. Furthermore, it provides a much more competitive and outstanding performance than its competitor algorithms. Additionally, the proposed approach is implemented for four engineering design problems to evaluate the SABO in handling optimization tasks for real-world applications. The optimization results show that the proposed SABO approach can solve for real-world applications and provides more optimal designs than its competitor algorithms.
Learning-imitation strategy-assisted alpine skiing optimization for the boom of offshore drilling platform

The boom of a semi submersible platform has large moment of inertia and high operating cost. How to effectively achieve the optimal solution of the boom to minimize the design and operation cost is a dynamic problem. In this study, a learning-imitation strategy-assisted alpine skiing optimization (LISASO) is proposed to find the optimal solution of the semi submersible platform boom. Firstly, the optimization model of the boom of the semi submersible platform is established. Secondly, the learning-imitation strategy (LIS) is implemented to improve the performance of the alpine skiing optimization (ASO). In LIS, the learning ability of individuals and the imitation of competitions are introduced to strengthen the association between individuals and the first individual. The performance of the LISASO is verified by three truss examples. The statistical results demonstrate that the LISASO is more competitive compared with other state-of-the-art optimization algorithms. Finally, the LISASO is applied to solve the optimal structural parameters of the boom. Results show that the energy consumption is reduced by 18.32% compared with the initial design.
Will Affective Computing Emerge From Foundation Models and General Artificial Intelligence? A First Evaluation of ChatGPT

ChatGPT has shown the potential of emerging general artificial intelligence capabilities, as it has demonstrated competent performance across many natural language processing tasks. In this work, we evaluate the capabilities of ChatGPT to perform text classification on three affective computing problems, namely, big-five personality prediction, sentiment analysis, and suicide tendency detection. We utilize three baselines, a robust language model (RoBERTa-base), a legacy word model with pretrained embeddings (Word2Vec), and a simple bag-of-words (BoW) baseline. Results show that the RoBERTa model trained for a specific downstream task generally has a superior performance. On the other hand, ChatGPT provides decent results and is relatively comparable to the Word2Vec and BoW baselines. ChatGPT further shows robustness against noisy data, where the Word2Vec model achieves worse results due to noise. Results indicate that ChatGPT is a good generalist model that is capable of achieving good results across various problems without any specialized training; however, it is not as good as a specialized model for a downstream task.
Integrated Inertial-LiDAR-Based Map Matching Localization for Varying Environments

Localization is critical for automated vehicles as it provides essential position, velocity, and heading angle information to perform object tracking, trajectory prediction, motion planning, and control. However, model/environmental uncertainties (including road friction) and noises in sensor measurements have a significant effect on the accuracy of the localization and vehicle state estimation, specially for perceptually degraded conditions. In this paper, an integrated localization method based on the fusion of an inertial dead reckoning model and 3D LiDAR-based map matching is proposed and experimentally verified in an urban environment with varying environmental conditions. Leveraging a global navigation satellite system (GNSS), inertial navigation system (INS), and 3D LiDAR point clouds, a novel light point cloud map generation method, which only keeps the necessary point clouds (i.e., buildings and roads regardless of vegetation varying with seasonal change), is proposed. Subsequently, based on the onboard sensors and the pre-built light point cloud map, a dead reckoning model is derived and integrated with the normal distribution transformation (NDT) based map matching algorithm by an error-state-constrained Kalman filter to limit the localization error. On top of the Kalman filter, the stability analysis of the estimator is presented. Finally, the performance of the algorithm is validated in real road experiments under various environmental conditions. Thorough experiments in winter and summer and associated results confirm the advantages of integrating the proposed light point cloud map generation with the dead reckoning model in terms of accuracy and reduced computational complexity.
A verified, efficient embedding of a verifiable assembly language

High-performance cryptographic libraries often mix code written in a high-level language with code written in assembly. To support formally verifying the correctness and security of such hybrid programs, this paper presents an embedding of a subset of x64 assembly language in F* that allows efficient verification of both assembly and its interoperation with C code generated from F*. The key idea is to use the computational power of a dependent type system's type checker to run a verified verification-condition generator during type checking. This allows the embedding to customize the verification condition sent by the type checker to an SMT solver. By combining our proof-by-reflection style with SMT solving, we demonstrate improved automation for proving the correctness of assembly-language code. This approach has allowed us to complete the first-ever proof of correctness of an optimized implementation of AES-GCM, a cryptographic routine used by 90% of secure Internet traffic.
Verify-Your-Vote: A Verifiable Blockchain-Based Online Voting Protocol

Blockchain provides the possibility to design new types of applications and systems that allow their users to store data in a secure and transparent way. In this paper, we design a fully verifiable online electronic voting protocol using a blockchain. Our e-voting protocol, called VYV for Verify-Your-Vote, involves cryptographic primitives based on Elliptic-Curve Cryptography (ECC), pairings and Identity Based Encryption (IBE). It ensures the following privacy and security properties: only eligible voter can vote, authentication of the voter, vote privacy, receipt-freeness, fairness, individual and universal verifiability. Furthermore, we formally prove the security of our protocol, using ProVerif tool.
High Capacity Data Hiding in Encrypted Image Based on Compressive Sensing for Nonequivalent Resources

To fulfill the requirements of data security in environments with nonequivalent resources, a high capacity data hiding scheme in encrypted image based on compressive sensing (CS) is proposed by fully utilizing the adaptability of CS to nonequivalent resources.The original image is divided into two parts: one part is encrypted with traditional stream cipher; the other part is turned to the prediction error and then encrypted based on CS to vacate room simultaneously.The collected non-image data is firstly encrypted with simple stream cipher.For data security management, the encrypted non-image data is then embedded into the encrypted image, and the scrambling operation is used to further improve security.Finally, the original image and non-image data can be separably recovered and extracted according to the request from the valid users with different access rights.Experimental results demonstrate that the proposed scheme outperforms other data hiding methods based on CS, and is more suitable for nonequivalent resources.
A Joint Framework for Athlete Tracking and Action Recognition in Sports Videos

Sports video analysis has received increasing attention in recent years. Athlete tracking and action recognition are its two major issues that are highly related to each other; however, they are individually considered and processed in the existing studies. In this paper, we propose a joint framework for athlete tracking and action recognition in sports videos. In athlete tracking, we propose a scaling and occlusion robust tracker, named scaling and occlusion robust compressive tracking (CT), to localize the position of specific athlete in each frame. It follows the approach of CT but extends it in two aspects, i.e., scale refinement as well as occlusion recovery. For the former, an objectness method, edge box, is adopted to generate proposals, which replace the fixed sampling boxes in CT and better fit the scales of the candidate objects. For the latter, a candidate obstruction-based solution is presented, which brings in additional trackers to detect possible obstructions and to relocate the target as occlusion ends. Regarding action recognition, we propose a long-term recurrent region-guided convolutional network, which recognizes pre-defined actions by modeling discriminative temporal cues of the tracking results. We employ SPP-net to extract the robust feature of the tracked region of each frame. The features of all the frames are then fed into a stack of recurrent sequence models to capture the long-term region-level information. We extensively evaluate the proposed approach on a newly collected sports video benchmark and on the off-the-shelf UIUC2 dataset, and the experimental results clearly show its effectiveness.
New European Union Regulations Related to Whole Slide Image Scanners and Image Analysis Software

Whole slide imaging (WSI) scanners and automatic image analysis algorithms, in order to be used for clinical applications, including primary diagnosis in pathology, are subject to specific regulatory frameworks in each country. Until May 25, 2018, in the European Union (EU), in vitro diagnostic (IVD) medical devices were regulated by directive 98/79/EC (in vitro diagnostic medical device directive [IVDD]). Main scanner vendors have obtained a Conformité Européenne mark of their products that in Europe were classified as General Class IVDD, so that conformity is only based on a self-declaration of the manufacturer. This contrasts with the initial classification of the US Food and Drug Administration (FDA) of WSI system as Class III medical devices, although the first digital pathology WSI system to be cleared by FDA was classified as Class II, with special controls. Other digital pathology solutions (automated cervical cytology slide reader) are considered of higher risk by US and European regulations. There is also some disparity in the classification of image analysis solutions between Europe and the United States. All IVD-MDs must be approved under the new European regulation (in vitro diagnostic medical device regulation) 2017/746 after May 26, 2024. This means the need of a performance evaluation, including a scientific validity report, an analytical performance report, and a clinical performance report. According to its clinical use (e.g., screening, diagnosis, or staging of cancer), a WSI slide scanner can be now classified as Class C device. A special regulation is applied to companion diagnostics. The new EU regulation 2017/746 contemplates the use of standard unique identifiers for medical devices and the creation of a European database on medical devices (Eudamed). Existing validation studies and clinical guidelines already available in the literature are a sound basis to avoid that this new regulation becomes a barrier for digital pathology development in Europe.
Segmentation of deformed kidneys and nephroblastoma using Case-Based Reasoning and Convolutional Neural Network

Most often, image segmentation is not fully automated and a user is required to lead the process in order to obtain correct results. In a medical context, segmentation can furnish much information to surgeons, but this task is rarely executed. Artificial Intelligence (AI) is a powerful approach for devising a viable solution to fully automated treatment. In this paper, we have focused on kidneys deformed by nephroblastoma. However, a frequent medical constraint is encountered which is a lack of sufficient data with which to train our system. In function of this constraint, two AI approaches were used to segment these structures. First, a Case Based Reasoning (CBR) approach was defined which can enhance the growth of regions for segmentation of deformed kidneys using an adaptation phase to modify coordinates of recovered seeds. This CBR approach was confronted with manual region growing and a Convolutional Neural Network (CNN). The CBR system succeeded in performing the best segmentation for the kidney with a mean Dice of 0.83. Deep Learning was then examined as a possible solution, using the latest performing networks for image segmentation. However, for relevant efficiency, this method requires a large data set. An option would be to manually segment only certain representative slices from a patient and then use them to train a Convolutional Neural Network (CNN) how to segment. In this article the authors propose an evaluation of a CNN for medical image segmentation following different training sets with a variable number of manual segmentations. To choose slices to train the CNN, an Overlearning Vector for Valid Sparse SegmentatIONs (OV2 ASSION) was used, with the notion of gap between two slices from the training set. This protocol made it possible to obtain reliable segmentations of per patient with a small data set and to determine that only 26% of initial segmented slices are required to obtain a complete segmentation of a patient with a mean Dice of 0.897.
Crowdsourcing triggers rapid, reliable earthquake locations

Detection of earthquakes by seismic networks has been accelerated on a global scale using crowdsourced data.
Laplacian Eigenmaps Network-Based Nonlocal Means Method for MR Image Denoising

Magnetic resonance (MR) images are often corrupted by Rician noise which degrades the accuracy of image-based diagnosis tasks. The nonlocal means (NLM) method is a representative filter in denoising MR images due to its competitive denoising performance. However, the existing NLM methods usually exploit the gray-level information or hand-crafted features to evaluate the similarity between image patches, which is disadvantageous for preserving the image details while smoothing out noise. In this paper, an improved nonlocal means method is proposed for removing Rician noise in MR images by using the refined similarity measures. The proposed method firstly extracts the intrinsic features from the pre-denoised image using a shallow convolutional neural network named Laplacian eigenmaps network (LEPNet). Then, the extracted features are used for computing the similarity in the NLM method to produce the denoised image. Finally, the method noise of the denoised image is utilized to further improve the denoising performance. Specifically, the LEPNet model is composed of two cascaded convolutional layers and a nonlinear output layer, in which the Laplacian eigenmaps are employed to learn the filter bank in the convolutional layers and the Leaky Rectified Linear Unit activation function is used in the final output layer to output the nonlinear features. Due to the advantage of LEPNet in recovering the geometric structure of the manifold in the low-dimension space, the features extracted by this network can facilitate characterizing the self-similarity better than the existing NLM methods. Experiments have been performed on the BrainWeb phantom and the real images. Experimental results demonstrate that among several compared denoising methods, the proposed method can provide more effective noise removal and better details preservation in terms of human vision and such objective indexes as peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM).
Machine Learning Explainability Through Comprehensible Decision Trees

The role of decisions made by machine learning algorithms in our lives is ever increasing. In reaction to this phenomenon, the European General Data Protection Regulation establishes that citizens have the right to receive an explanation on automated decisions affecting them. For explainability to be scalable, it should be possible to derive explanations in an automated way. A common approach is to use simpler, more intuitive decision algorithms to build a surrogate model of the black-box model (for example a deep learning algorithm) used to make a decision. Yet, there is a risk that the surrogate model is too large for it to be really comprehensible to humans. We focus on explaining black-box models by using decision trees of limited size as a surrogate model. Specifically, we propose an approach based on microaggregation to achieve a trade-off between comprehensibility and representativeness of the surrogate model on the one side and privacy of the subjects used for training the black-box model on the other side.