Reviewerly/exHarmony huggingface dataset:
Reservoir computing and photoelectrochemical sensors: A marriage of convenience

Sensing technology is an important aspect of information processing. Current development in artificial intelligence systems (especially those aimed at medical and environmental applications) requires a lot of data on the chemical composition of biological fluids or environmental samples. These complex matrices require advanced sensing devices, and photoelectrochemical ones seem to have potential to overcome at least some of the obstacles. Furthermore, the development of artificial intelligence (AI) technology for autonomous robotics requires technology mimicking human senses, also those operating at the molecular level, such as gustation and olfaction. Again, photoelectrochemical sensing can provide some suitable solutions. In this review, we introduce the idea of integration of photoelectrochemical sensors with some unconventional computing paradigm – reservoir computing. This approach should not only boost the performance of the sensors itself, but also open new pathways through science. Integration of sensing devices with computing systems will also contribute to a better understanding (or at least mimicking) of the human senses and neuromorphic sensory information processing. Although reservoir systems can be considered magic “black boxes” and their operation is at the same time simple and hard to comprehend, this combination is expected to open a new era of effective information harvesting and processing systems.
A Survey of Text Representation and Embedding Techniques in NLP

Natural Language Processing (NLP) is a research field where a language in consideration is processed to understand its syntactic, semantic, and sentimental aspects. The advancement in the NLP area has helped solve problems in the domains such as Neural Machine Translation, Name Entity Recognition, Sentiment Analysis, and Chatbots, to name a few. The topic of NLP broadly consists of two main parts: the representation of the input text (raw data) into numerical format (vectors or matrix) and the design of models for processing the numerical data. This paper focuses on the former part and surveys how the NLP field has evolved from rule-based, statistical to more context-sensitive learned representations. For each embedding type, we list their representation, issues they addressed, limitations, and applications. This survey covers the history of text representations from the 1970s and onwards, from regular expressions to the latest vector representations used to encode the raw text data. It demonstrates how the NLP field progressed from where it could comprehend just bits and pieces to all the significant aspects of the text over time.
Exploring the capabilities and limitations of GPT and Chat GPT in natural language processing

Natural Language Processing (NLP) has seen tremendous advancements with the development of Generative Pretrained Transformer (GPT) models and their conversational variant, ChatGPT. These language models have been shown to generate contextually appropriate and coherent responses to natural language prompts, making them highly useful for various NLP applications. However, there are still limitations to their performance and understanding these limitations is crucial for their effective utilization. This paper presents a comprehensive analysis of the capabilities and limitations of GPT and ChatGPT, covering their architecture, training processes, and evaluation metrics. The study also evaluates the performance of these models on various NLP tasks, including language translation, question-answering, and text summarization. The results reveal that while these models excel in certain tasks, they still face challenges in understanding context, generating diverse responses, and handling rare or out-of-domain inputs. The study concludes by discussing potential solutions and future research directions for improving the performance of GPT and ChatGPT in NLP applications.
A Flexible Sharding Blockchain Protocol Based on Cross-Shard Byzantine Fault Tolerance

Sharding technology is crucial to achieve decentralization, scalability, and security simultaneously. However, existing sharding blockchain schemes suffer from high cross-shard transaction processing latency, low parallelism, incomplete cross-shard views of shard members, centralized reconfiguration, high overhead of randomness generation, and lack of formalized protocol design and security proofs. This paper proposes a flexible sharding (FS) blockchain protocol. First, a cross-shard Byzantine fault tolerance (CSBFT) protocol is designed to cut down confirmation delays when processing cross-shard transactions. Second, we utilize multiple parallel CSBFT where each node acts not only as a leader but also as multiple ordinary members to break through the performance bottleneck caused by a leader’s bandwidth and computing power, improving the system parallelism. Third, a cross-shard transaction censorship attack is proposed, and a cross-shard view-change mechanism is designed to defend against it. Fourth, a secure and truly decentralized shard reconfiguration method combining proof-of-work, proof-of-possession, and intra-shard BFT is designed. Fifth, we utilize a formal protocol design method and give strict security proof for each protocol. Finally, we evaluate FS from both theoretical and practical perspectives. FS is proven to have lower communication and computation complexity and achieve considerable performance.
Predicting Colorectal Cancer Using Machine and Deep Learning Algorithms: Challenges and Opportunities

One of the three most serious and deadly cancers in the world is colorectal cancer. The most crucial stage, like with any cancer, is early diagnosis. In the medical industry, artificial intelligence (AI) has recently made tremendous strides and showing promise for clinical applications. Machine learning (ML) and deep learning (DL) applications have recently gained popularity in the analysis of medical texts and images due to the benefits and achievements they have made in the early diagnosis of cancerous tissues and organs. In this paper, we intend to systematically review the state-of-the-art research on AI-based ML and DL techniques applied to the modeling of colorectal cancer. All research papers in the field of colorectal cancer are collected based on ML and DL techniques, and they are then classified into three categories: the aim of the prediction, the method of the prediction, and data samples. Following that, a thorough summary and a list of the studies gathered under each topic are provided. We conclude our study with a critical discussion of the challenges and opportunities in colorectal cancer prediction using ML and DL techniques by concentrating on the technical and medical points of view. Finally, we believe that our study will be helpful to scientists who are considering employing ML and DL methods to diagnose colorectal cancer.
B<sup>2</sup>C<sup>3</sup>NetF<sup>2</sup>: Breast cancer classification using an end‐to‐end deep learning feature fusion and satin bowerbird optimization controlled Newton Raphson feature selection

Abstract Currently, the improvement in AI is mainly related to deep learning techniques that are employed for the classification, identification, and quantification of patterns in clinical images. The deep learning models show more remarkable performance than the traditional methods for medical image processing tasks, such as skin cancer, colorectal cancer, brain tumour, cardiac disease, Breast cancer (BrC), and a few more. The manual diagnosis of medical issues always requires an expert and is also expensive. Therefore, developing some computer diagnosis techniques based on deep learning is essential. Breast cancer is the most frequently diagnosed cancer in females with a rapidly growing percentage. It is estimated that patients with BrC will rise to 70% in the next 20 years. If diagnosed at a later stage, the survival rate of patients with BrC is shallow. Hence, early detection is essential, increasing the survival rate to 50%. A new framework for BrC classification is presented that utilises deep learning and feature optimization. The significant steps of the presented framework include (i) hybrid contrast enhancement of acquired images, (ii) data augmentation to facilitate better learning of the Convolutional Neural Network (CNN) model, (iii) a pre‐trained ResNet‐101 model is utilised and modified according to selected dataset classes, (iv) deep transfer learning based model training for feature extraction, (v) the fusion of features using the proposed highly corrected function‐controlled canonical correlation analysis approach, and (vi) optimal feature selection using the modified Satin Bowerbird Optimization controlled Newton Raphson algorithm that finally classified using 10 machine learning classifiers. The experiments of the proposed framework have been carried out using the most critical and publicly available dataset, such as CBIS‐DDSM, and obtained the best accuracy of 94.5% along with improved computation time. The comparison depicts that the presented method surpasses the current state‐of‐the‐art approaches.
Improving fault tolerance in diagnosing power system failures with optimal hierarchical extreme learning machine

Accurate diagnosis of failures has a pivotal role to play in the stable operation of power systems. Neural networks have shown promising fault tolerance in solving this problem. However, the widely used BP and RBF networks have a tedious training process and are difficult to provide approving generalization performance. In this work, an optimal hierarchical extreme learning machine (HELM) via adaptive quadratic interpolation learning differential evolution (AQILDE) is designed to address this issue. HELM has good generalization performance but its optimal structure is hard to achieve. Thus, we present AQILDE to automatically search the structure parameters of HELM, including the number of hidden layers, the number of neurons per hidden layer, and the regularization factor. In addition, individual coding method and improved training target function are proposed to ensure the generalization performance and structural compactness. The size of decision variables can be adjusted during the training process. Both regression loss and classification loss are integrated into the target function. The feasibility of AQILDE-based HELM is evaluated in a 14-bus power system and a practical fault in the Siping power grid, China. Simulation results show that it has better generalization performance and diagnoses varied fault scenarios correctly with higher fault credibility.
Drone Transportation System: Systematic Review of Security Dynamics for Smart Mobility

The intelligence and integrity of a real-time cyber–physical system depend on how trustworthy the data’s legitimacy, appropriation, and authorization are during end-to-end communication between the participating nodes in its network. With the recurrent repugnant global violations of the airspace by drones and their derivatives, there is an urgent need to empirically evaluate the underlying security architectures that govern drone usage operations for priority logistics. This review examines the significant contribution of artificial intelligence models and blockchain to the development of trustworthy and reliable intelligent and secure autonomous systems by integrating cyberspace, intelligence space, and airspace security. PRISMA-SPIDER methodology was adopted for the systematic review of 133 articles based on the inclusion criteria consisting of 91 (68.4%) quantitative studies, 19 qualitative studies (14.2%), and 23 (17.3%) mixed method studies to balance article selection sensitivity and specificity. The review outcome shows a significant disconnect between model proposals and actual implementation. Through the incorporation of zero-trust architecture into the existing blockchain technology and the convergence of newer AI models, dynamic security issues like drone ownership authentication, drone package delivery verification, drone operation authorization, and drone jurisdiction accountability, can be achieved seamlessly for secure smart mobility via drone transportation systems.
Dynamic Contrastive Distillation for Image-Text Retrieval

Although the vision-and-language pretraining (VLP) equipped cross-modal image-text retrieval (ITR) has achieved remarkable progress in the past two years, it suffers from a major drawback: the ever-increasing size of VLP models restrict its deployment to real-world search scenarios (where the high latency is unacceptable). To alleviate this problem, we present a novel plug-in dynamic contrastive distillation (DCD) framework to compress the large VLP models for the ITR task. Technically, we face the following two challenges: 1) the typical uni-modal metric learning approach is difficult to directly apply to cross-modal task, due to the limited GPU memory to optimize too many negative samples during handling cross-modal fusion features. 2) it is inefficient to static optimize the student network from different hard samples, which have different effects on distillation learning and student network optimization. We try to overcome these challenges from two points. First, to achieve multi-modal contrastive learning, and balance the training costs and effects, we propose to use a teacher network to estimate the difficult samples for students, making the students absorb the powerful knowledge from pre-trained teachers, and master the knowledge from hard samples. Second, to dynamic learn from hard sample pairs, we propose dynamic distillation to dynamically learn samples of different difficulties, from the perspective of better balancing the difficulty of knowledge and students' self-learning ability. We successfully apply our proposed DCD strategy on two state-of-the-art vision-language pretrained models, i.e. ViLT and METER. Extensive experiments on MS-COCO and Flickr 30 K benchmarks show the effectiveness and efficiency of our DCD framework. Encouragingly, we can speed up the inference at least 129 × compared to the existing ITR models. We further provide in-depth analyses and discussions that explain where the performance improvement comes from. We hope our work can shed light on other tasks that require distillation and contrastive learning.
Underwater Attentional Generative Adversarial Networks for Image Enhancement

In this article, to exclusively suppress unuseful underwater noise feature and effectively avoid overenhancement, simultaneously, an underwater attentional generative adversarial network (UAGAN) is innovatively established. Main contributions are as follows: combining dense concatenation with global maximum and average pooling techniques, a cascade dense-channel attention (CDCA) module is devised to adaptively distinguish noise feature and recalibrate channel weight, simultaneously, such that low-contribution feature map can be effectively suppressed; to sufficiently capture long-range dependence between any two nonlocal spatial patches, the position attention (PA) module is created such that the deviation among independent patches can be sufficiently eliminated, thereby avoiding overenhancement; and in conjunction with CDCA and PA modules, the entire UAGAN framework is eventually developed in an end-to-end manner. Comprehensive experiments conducted on underwater image enhancement benchmark (UIEB) and underwater robot professional contest (URPC) datasets demonstrate remarkable effectiveness and superiority of the proposed UAGAN scheme by comparing with typical underwater image enhancement approaches including unsupervised color correction method, image blurriness and light absorption, underwater dark channel prior, underwater generative adversarial network, underwater convolutional neural network, and WaterNet in terms of peak signal-to-noise ratio, underwater color image quality evaluation, underwater image quality measures, etc.
Succinct Vector, Polynomial, and Functional Commitments from Lattices

Vector commitment schemes allow a user to commit to a vector of values $$\textbf{x}\in \{0,1\}^\ell $$ and later, open up the commitment to a specific set of positions. Both the size of the commitment and the size of the opening should be succinct (i.e., polylogarithmic in the length $$\ell $$ of the vector). Vector commitments and their generalizations to polynomial commitments and functional commitments are key building blocks for many cryptographic protocols. We introduce a new framework for constructing non-interactive lattice-based vector commitments and their generalizations. A simple instantiation of our framework yields a new vector commitment scheme from the standard short integer solution (SIS) assumption that supports private openings and large messages. We then show how to use our framework to obtain the first succinct functional commitment scheme that supports openings with respect to arbitrary bounded-depth Boolean circuits. In this scheme, a user commits to a vector $$\textbf{x}\in \{0,1\}^\ell $$ , and later on, open the commitment to any function $$f(\textbf{x})$$ . Both the commitment and the opening are non-interactive and succinct: namely, they have size $$\textsf{poly}(\lambda , d, \log \ell )$$ , where $$\lambda $$ is the security parameter and d is the depth of the Boolean circuit computing f. Previous constructions of functional commitments could only support constant-degree polynomials, or require a trusted online authority, or rely on non-falsifiable assumptions. The security of our functional commitment scheme is based on a new falsifiable family of "basis-augmented" SIS assumptions ( $$\textsf{BASIS}$$ ) we introduce in this work. We also show how to use our vector commitment framework to obtain (1) a polynomial commitment scheme where the user can commit to a polynomial $$f \in \mathbb {Z}_{q}[x]$$ and subsequently open the commitment to an evaluation $$f(x) \in \mathbb {Z}_{q}$$ ; and (2) an aggregatable vector (resp., functional) commitment where a user can take a set of openings to multiple indices (resp., function evaluations) and aggregate them into a single short opening. Both of these extensions rely on the same $$\textsf{BASIS}$$ assumption we use to obtain our succinct functional commitment scheme.
Adaptive memetic differential evolution-back propagation-fuzzy neural network algorithm for robot control

This study established an adaptive memetic differential evolution-back propagation-fuzzy neural network (AMDE-BP-FNN) control method to achieve high-efficiency and precise control of robots with complex dynamic characteristics while reducing control costs. The adaptive differential evolution (ADE) method was applied to search the optimal parameters in the global scope and delimited the pseudo-global search scope. The memetic differential evolution (MDE) method was used to search for optimal parameters in the pseudo-global scope, and the probability factor was set to decide whether to use the back propagation (BP) algorithm for online optimization. Finally, simulations, experiments, and real-world applications were conducted. The results indicated the high efficiency, high precision, and viability of the proposed AMDE-BP-FNN method.
An adaptive neural network with nonlinear FOPID design of underwater robotic vehicle in the presence of disturbances, uncertainty, and obstacles

An adaptive neural network with nonlinear fractional-order PID (ANNFOPID) controller design is proposed for underwater robotic vehicle (URV) to solve the path tracking problem. The path tracking problem is caused by disturbances and unknown uncertainties of the underwater vehicle dynamic model. The disturbances were presented with the URV model to evaluate the performance of the ANNFOPID controller to reject the disturbances. At the same time, an obstacle avoidance model has been presented with ANNFOPID controller to evaluate the controller ability to maneuver around different obstacle locations. A radial base function (RBF) has been used to estimate both of the disturbances and the unknown uncertainties of the dynamic model. An improved slime mould algorithm (ISMA) is presented to invent a new trajectories for the underwater vehicle and enhance the ability of the URV to overcome the obstacles problems. At the end, the results obtained show that the ANNFOPID controller present an outstanding performance in comparison with other existing works to overcome the disturbances, unknown uncertainties, and obstacles problems effectively.
(Why) Are Open Research Practices the Future for the Study of Language Learning?

Abstract Open research practices are relevant to all stages of research, from conceptualization through dissemination. Here, we discuss key facets of open research, highlighting its rationales, infrastructures, behaviors, and challenges. Part I conceptualizes open research and its rationales. Part II identifies challenges such as the speed and cost of open research, the usability of open data and materials, the difficulties of conducting replication research, and the economics and sustainability of open access and open research generally. In discussing these challenges, we have sought to provide examples of good practice, describe and evaluate emerging innovations, and envision change. Part III considers ongoing coevolutions of culture, infrastructure, and behaviors and acknowledges the limitations of our review and of open research practices. We argue that open research is indeed a large part of our future, and most—if not all—challenges are surmountable, but doing so requires significant changes for many aspects of the research process.
Semantic segmentation guided pseudo label mining and instance re-detection for weakly supervised object detection in remote sensing images

Weakly supervised object detection (WSOD) in remote sensing images (RSIs) has good practical value because it only requires the image-level annotations. The existing methods usually have two problems. The first problem is that many methods mine the pseudo ground truth (PGT) instances solely depending on the class confidence score (CCS), however, the reliability of CCS is not enough because of the inter-class similarity and intra-class diversity in RSIs, consequently, the reliability of corresponding PGT instances is limited, in addition, the most discriminative part with high CCS rather than the whole object is easily selected as the PGT instance. The second problem is that the object localization solely relies on the candidate proposals generated by the selective search or edge boxes algorithm, however, the localization accuracy of the candidate proposals is not enough because of the cluttered background in RSIs. To address the first problem, a semantic segmentation guided pseudo label mining (SGPLM) module is proposed, which uses a novel metric named class-specific object confidence score (COCS) to mine high-quality PGT instances. The COCS is made up of the CCS and class-specific object overlap score (COOS) which is calculated through the weakly supervised semantic segmentation. The mined PGT instances are more robust and incline to cover the whole object by combining the COOS. To handle the second problem, an instance re-detection (IR) module is proposed for improving the localization accuracy of the WSOD model, in which an enhanced PGT instance generation strategy is designed to obtain the enhanced PGT instances on the basis of the candidate proposals, and the enhanced PGT instances are used to train the instance re-classification and re-localization branches which are jointly utilized to infer the final results. The ablation studies validate the effectiveness of the SGPLM and IR modules. The comprehensive comparisons with other advanced methods show that the performance of the proposed method is state-of-the-art on two RSI datasets.