Reviewerly/exHarmony huggingface dataset:
Analogue and Physical Reservoir Computing Using Water Waves: Applications in Power Engineering and Beyond

More than 3.5 billion people live in rural areas, where water and water energy resources play an important role in ensuring sustainable and productive rural economies. This article reviews and critically analyses the recent advances in the field of analogue and reservoir computing that have been driven by the unique physical properties and energy of water waves. It also demonstrates that analogue and physical reservoir computing, taken as an independent research field, holds the potential to bring artificial intelligence closer to people living outside large cities, thus enabling them to enjoy the benefits of novel technologies that are already in place in large cities but are not readily available or suitable for regional communities. In particular, although the physical reservoir computing systems discussed in the main text are universal in terms of processing input data and making forecasts, they can be used to design and optimise power grid networks and forecast energy consumption, both at local and global scales. Thus, this review article will be of interest to a broad readership interested in novel concepts of artificial intelligence and machine learning and their innovative practical applications in diverse areas of science and technology.
SP-YOLOv8s: An Improved YOLOv8s Model for Remote Sensing Image Tiny Object Detection

An improved YOLOv8s-based method is proposed to address the challenge of accurately recognizing tiny objects in remote sensing images during practical human-computer interaction. In detecting tiny targets, the accuracy of YOLOv8s is low because the downsampling module of the original YOLOv8s algorithm causes the network to lose fine-grained feature information, and the neck network feature information needs to be sufficiently fused. In this method, the strided convolution module in YOLOv8s is replaced with the SPD-Conv module. By doing so, the feature map undergoes downsampling while preserving fine-grained feature information, thereby improving the learning and expressive capabilities of the network and enhancing recognition accuracy. Meanwhile, the path aggregation network is substituted with the SPANet structure, which facilitates the acquisition of more prosperous gradient paths. This substitution enhances the fusion of feature maps at various scales, reduces model parameters, and further improves detection accuracy. Additionally, it enhances the network’s robustness to complex backgrounds. Experimental verification is conducted on the following two intricate datasets containing tiny objects: AI-TOD and TinyPerson. A comparative analysis with the original YOLOv8s algorithm reveals notable enhancements in recognition accuracy. Specifically, under real-time performance constraints, the proposed method yields a 4.9% and 9.1% improvement in mAP0.5 recognition accuracy for AI-TOD and TinyPerson datasets, respectively. Moreover, the recognition accuracy for mAP0.5:0.95 is enhanced by 3.4% and 3.2% for the same datasets, respectively. The results indicate that the proposed method enables rapid and accurate recognition of tiny objects in complex backgrounds. Furthermore, it demonstrates better recognition precision and stability than other algorithms, such as YOLOv5s and YOLOv8s.
Compatible Remediation on Vulnerabilities from Third-Party Libraries for Java Projects

With the increasing disclosure of vulnerabilities in open-source software, software composition analysis (SCA) has been widely applied to reveal third-party libraries and the associated vulnerabilities in software projects. Beyond the revelation, SCA tools adopt various remediation strategies to fix vulnerabilities, the quality of which varies substantially. However, ineffective remediation could induce side effects, such as compi-lation failures, which impede acceptance by users. According to our studies, existing SCA tools could not correctly handle the concerns of users regarding the compatibility of remediated projects. To this end, we propose Compatible Remediation of Third-party libraries (CORAL) for Maven projects to fix vulnerabilities without breaking the projects. The evaluation proved that Coralnot only fixed 87.56% of vulnerabilities which outperformed other tools (best 75.32%) and achieved a 98.67% successful compilation rate and a 92.96% successful unit test rate. Furthermore, we found that 78.45% of vulnerabilities in popular Maven projects could be fixed without breaking the compilation, and the rest of the vulnerabilities (21.55%) could either be fixed by upgrades that break the compilations or even be impossible to fix by upgrading.
SkCoder: A Sketch-based Approach for Automatic Code Generation

Recently, deep learning techniques have shown great success in automatic code generation. Inspired by the code reuse, some researchers propose copy-based approaches that can copy the content from similar code snippets to obtain better performance. Practically, human developers recognize the content in the similar code that is relevant to their needs, which can be viewed as a code sketch. The sketch is further edited to the desired code. However, existing copy-based approaches ignore the code sketches and tend to repeat the similar code without necessary modifications, which leads to generating wrong results. In this paper, we propose a sketch-based code generation approach named Skcoderto mimic developers' code reuse behavior. Given a natural language requirement, Skcoderretrieves a similar code snippet, extracts relevant parts as a code sketch, and edits the sketch into the desired code. Our motivations are that the extracted sketch provides a well-formed pattern for telling models “how to write”. The post-editing further adds requirement-specific details into the sketch and outputs the complete code. We conduct experiments on two public datasets and a new dataset collected by this work. We compare our approach to 20 baselines using 5 widely used metrics. Experimental results show that (1) Skcodercan generate more correct programs, and outperforms the state-of-the-art -CodeT5-base by 30.30%, 35.39%, and 29.62% on three datasets. (2) Our approach is effective to multiple code generation models and improves them by up to 120.1% in Pass@l. (3) We investigate three plausible code sketches and discuss the importance of sketches. (4) We manually evaluate the generated code and prove the superiority of our Skcoderin three aspects.
CSCIM_FS: Cosine similarity coefficient and information measurement criterion-based feature selection method for high-dimensional data

Feature selection (FS) based on mutual information (MI) metrics needs to discretize the data in preprocessing, which is a convenient way to identify correlation between features. However, information loss often occurs in data discretization. In order to solve this information loss problem, this paper proposes a FS algorithm based on cosine similarity coefficient and information measurement criterion (CSCIM_FS). First, the MI between features and tags is calculated, and features are sorted out according to the MI calculated. Then, a feature matrix is constructed to transform the one-dimensional feature sequence into a two-dimensional square matrix. Next, cosine transform is adopted to obtain the high-frequency components of the feature matrix, and sampling is conducted to derive the hash fingerprint of the feature matrix. After that, the similarity between every two features is calculated on the basis of the hash fingerprints of different features. Finally, the feature weight is calculated according to tags, the MI and similarity between features, and a key feature subset is obtained and used to conduct feature selection from the data. The experimental results on several UCI public datasets show that CSCIM_FS algorithm selected a feature subset with high accuracy, and that this algorithm performs better than MIM, CMIM, mRMR and other algorithms.
Information Retrieval: Recent Advances and Beyond

This paper provides an extensive and thorough overview of the models and techniques utilized in the first and second stages of the typical information retrieval processing chain. Our discussion encompasses the current state-of-the-art models, covering a wide range of methods and approaches in the field of information retrieval. We delve into the historical development of these models, analyze the key advancements and breakthroughs, and address the challenges and limitations faced by researchers and practitioners in the domain. By offering a comprehensive understanding of the field, this survey is a valuable resource for researchers, practitioners, and newcomers to the information retrieval domain, fostering knowledge growth, innovation, and the development of novel ideas and techniques.
Industrial anomaly detection with domain shift: A real-world dataset and masked multi-scale reconstruction

Industrial anomaly detection (IAD) is crucial for automating industrial quality inspection. The diversity of the datasets is the foundation for developing comprehensive IAD algorithms. Existing IAD datasets focus on diversity of data categories, overlooking the diversity of domains within the same data category. In this paper, to bridge this gap, we propose the Aero-engine Blade Anomaly Detection (AeBAD) dataset, consisting of two sub-datasets: the single-blade dataset and the video anomaly detection dataset of blades. Compared to existing datasets, AeBAD has the following two characteristics: (1.) The target samples are not aligned and at different scales. (2.) There is a domain shift between the distribution of normal samples in the test set and the training set, where the domain shifts are mainly caused by the changes in illumination and view. Based on this dataset, we observe that current state-of-the-art (SOTA) IAD methods exhibit limitations when the domain of normal samples in the test set undergoes a shift. To address this issue, we propose a novel method called masked multi-scale reconstruction (MMR), which enhances the model’s capacity to deduce causality among patches in normal samples by a masked reconstruction task. MMR achieves superior performance compared to SOTA methods on the AeBAD dataset. Furthermore, MMR achieves competitive performance with SOTA methods to detect the anomalies of different types on the MVTec AD dataset. Code and dataset are available at https://github.com/zhangzilongc/MMR.
HAR-DeepConvLG: Hybrid deep learning-based model for human activity recognition in IoT applications

Smartphones and wearable devices have built-in sensors that can collect multivariant time-series data that can be used to recognize human activities. Research on human activity recognition (HAR) has gained significant attention in recent years due to its growing demand in various application domains. As wearable sensor-aided devices and the Internet of Things (IoT) became more common, great attention has been paid to the HAR ubiquitous computing and mobile computing. To infer human activity data from a massive amount of multivariant data generated by different wearable devices, in this study an innovative deep learning–based model named HAR-DeepConvLG is proposed. It includes three convolution layers and a squeezing and excitation (SE) block, which are employed to precisely learn and extract the spatial representation data from the collected raw sensor data. The extracted features are used as input of three parallel paths, each of which includes a long short-term memory (LSTM) layer connected in sequence with a gated recurrent unit (GRU) layer to learn temporal representation. The three paths are connected in parallel to avoid the vanishing gradient problem. Finally, to evaluate the effectiveness of the proposed model, experiments were conducted on four widely utilized HAR datasets. Additionally, the model’s performance was compared to several state-of-the-art deep learning models, which further validated its effectiveness. The experimental results show that the proposed HAR-DeepConvLG model performs better than the existing HAR deep learning–based models, achieving a competitive classification accuracy.
A Deep Learning Network with Aggregation Residual Transformation for Human Activity Recognition Using Inertial and Stretch Sensors

With the rise of artificial intelligence, sensor-based human activity recognition (S-HAR) is increasingly being employed in healthcare monitoring for the elderly, fitness tracking, and patient rehabilitation using smart devices. Inertial sensors have been commonly used for S-HAR, but wearable devices have been demanding more comfort and flexibility in recent years. Consequently, there has been an effort to incorporate stretch sensors into S-HAR with the advancement of flexible electronics technology. This paper presents a deep learning network model, utilizing aggregation residual transformation, that can efficiently extract spatial–temporal features and perform activity classification. The efficacy of the suggested model was assessed using the w-HAR dataset, which included both inertial and stretch sensor data. This dataset was used to train and test five fundamental deep learning models (CNN, LSTM, BiLSTM, GRU, and BiGRU), along with the proposed model. The primary objective of the w-HAR investigations was to determine the feasibility of utilizing stretch sensors for recognizing human actions. Additionally, this study aimed to explore the effectiveness of combining data from both inertial and stretch sensors in S-HAR. The results clearly demonstrate the effectiveness of the proposed approach in enhancing HAR using inertial and stretch sensors. The deep learning model we presented achieved an impressive accuracy of 97.68%. Notably, our method outperformed existing approaches and demonstrated excellent generalization capabilities.
Postdigital Literacies

Literacy researchers across various perspectives have demonstrated a notable agility in responding to the emergence of digital technologies and their impacts on literacy practices. As digital technologies permeate human existence, writing and other literacy practices occur in postdigital literacy ecologies. Examining how literacy is bound up postdigitally in everyday life offers opportunities to comprehend the complex relationships between technology, society, and individuals. It also reflects ongoing efforts in Literacy Studies to understand the social, epistemological, and ideological nature of mediatized representation as occurring at the level of everyday literacy.
How does anthropomorphism improve human-AI interaction satisfaction: a dual-path model

With the development of artificial intelligence technology, the application of anthropomorphism has been established as a good design principle. However, the effectiveness of anthropomorphism in smart home assistants is still unclear, as is the mechanism influencing interaction satisfaction. Therefore, we have categorized the anthropomorphic cues of smart home assistants into four dimensions: visual, identity, emotional, and auditory, and constructed a dual-path influence mechanism model of anthropomorphism on interaction satisfaction. Furthermore, we also consider the different influences of different usage contexts. Through two experimental studies, the results show that, in the intimacy influence path, the emotional cue and auditory cue can directly improve interaction satisfaction, and they can further improve interaction satisfaction by affecting intimacy. However, the visual cue has no significant direct effect on interaction satisfaction. The visual cue mainly improves interaction satisfaction through the intermediate effect of intimacy. In the privacy invasion influence path, the emotional cue and auditory cue also further affect interaction satisfaction by affecting privacy invasion. However, the impact of the visual cue and identity cue on privacy invasion was not valid. The study is expected to provide practical guidance for product designers to realize the long-term feasibility and sustainability of products.
A Blockchain-Based Architecture and Framework for Cybersecure Smart Cities

A smart city is one that uses digital technologies and other means to improve the quality of life of its citizens and reduce the cost of municipal services. Smart cities primarily use IoT to collect and analyze data to interact directly with the city’s infrastructure and monitor city assets and community developments in real time to improve operational efficiency and proactively respond to potential problems and challenges. Today, cybersecurity is considered one of the main challenges facing smart cities. Over the past few years, the cybersecurity research community has devoted a great deal of attention to this challenge. Among the different technologies proposed to address this challenge, Blockchain appears to offer the security and data privacy needed to enhance smart cities security. In this paper, we propose a comprehensive framework and architecture based on Blockchain, big data and artificial intelligence to improve smart cities cybersecurity. For the sake of illustration of the proposed framework, simulation results are presented for a smart grid dataset from the UCI Machine Learning Repository, demonstrating its potential and efficiency to deal with cybersecurity challenges in smart cities.
Randomized Benchmarking Using Nondestructive Readout in a Two-Dimensional Atom Array

Neutral atoms are a promising platform for scalable quantum computing, however, prior demonstration of high fidelity gates or low-loss readout methods have employed restricted numbers of qubits. Using randomized benchmarking of microwave-driven single-qubit gates, we demonstrate average gate errors of 7(2)×10^{-5} on a 225 site atom array using conventional, destructive readout. We further demonstrate a factor of 1.7 suppression of the primary measurement errors via low-loss, nondestructive, and state-selective readout on 49 sites while achieving gate errors of 2(9)×10^{-4}.
Deep Learning for Medical Image Cryptography: A Comprehensive Review

Electronic health records (EHRs) security is a critical challenge in the implementation and administration of Internet of Medical Things (IoMT) systems within the healthcare sector’s heterogeneous environment. As digital transformation continues to advance, ensuring privacy, integrity, and availability of EHRs become increasingly complex. Various imaging modalities, including PET, MRI, ultrasonography, CT, and X-ray imaging, play vital roles in medical diagnosis, allowing healthcare professionals to visualize and assess the internal structures, functions, and abnormalities within the human body. These diagnostic images are typically stored, shared, and processed for various purposes, including segmentation, feature selection, and image denoising. Cryptography techniques offer a promising solution for protecting sensitive medical image data during storage and transmission. Deep learning has the potential to revolutionize cryptography techniques for securing medical images. This paper explores the application of deep learning techniques in medical image cryptography, aiming to enhance the privacy and security of healthcare data. It investigates the use of deep learning models for image encryption, image resolution enhancement, detection and classification, encrypted compression, key generation, and end-to-end encryption. Finally, we provide insights into the current research challenges and promising directions for future research in the field of deep learning applications in medical image cryptography.
UAV Path Planning Based on an Improved Chimp Optimization Algorithm

Path planning is one of the key issues in the research of unmanned aerial vehicle technology. Its purpose is to find the best path between the starting point and the destination. Although there are many research recommendations on UAV path planning in the literature, there is a lack of path optimization methods that consider both the complex flight environment and the performance constraints of the UAV itself. We propose an enhanced version of the Chimp Optimization Algorithm (TRS-ChOA) to solve the UAV path planning problem in a 3D environment. Firstly, we combine the differential mutation operator to enhance the search capability of the algorithm and prevent premature convergence. Secondly, we use improved reverse learning to expand the search range of the algorithm, effectively preventing the algorithm from missing high-quality solutions. Finally, we propose a similarity preference weight to prevent individuals from over-assimilation and enhance the algorithm’s ability to escape local optima. Through testing on 13 benchmark functions and 29 CEC2017 complex functions, TRS-ChOA demonstrates superior optimization capability and robustness compared to other algorithms. We apply TRS-ChOA along with five well-known algorithms to solve path planning problems in three 3D environments. The experimental results reveal that TRS-ChOA reduces the average path length/fitness value by 23.4%/65.0%, 8.6%/81.0%, and 16.3%/41.7% compared to other algorithms in the three environments, respectively. This indicates that the flight paths planned by TRS-ChOA are more cost-effective, smoother, and safer.
Replacing academic journals

Replacing traditional journals with a more modern solution is not a new idea. Here, we propose ways to overcome the social dilemma underlying the decades of inaction. Any solution needs to not only resolve the current problems but also be capable of preventing takeover by corporations: it needs to replace traditional journals with a decentralized, resilient, evolvable network that is interconnected by open standards and open-source norms under the governance of the scholarly community. It needs to replace the monopolies connected to journals with a genuine, functioning and well-regulated market. In this new market, substitutable service providers compete and innovate according to the conditions of the scholarly community, avoiding sustained vendor lock-in. Therefore, a standards body needs to form under the governance of the scholarly community to allow the development of open scholarly infrastructures servicing the entire research workflow. We propose a redirection of money from legacy publishers to the new network by funding bodies broadening their minimal infrastructure requirements at recipient institutions to include modern infrastructure components replacing and complementing journal functionalities. Such updated eligibility criteria by funding agencies would help realign the financial incentives for recipient institutions with public and scholarly interest.
Analog Photonics Computing for Information Processing, Inference, and Optimization

Abstract This review presents an overview of the current state‐of‐the‐art in photonics computing, which leverages photons, photons coupled with matter, and optics‐related technologies for effective and efficient computational purposes. It covers the history and development of photonics computing and modern analogue computing platforms and architectures, focusing on optimization tasks and neural network implementations. The authors examine special‐purpose optimizers, mathematical descriptions of photonics optimizers, and their various interconnections. Disparate applications are discussed, including direct encoding, logistics, finance, phase retrieval, machine learning, neural networks, probabilistic graphical models, and image processing, among many others. The main directions of technological advancement and associated challenges in photonics computing are explored, along with an assessment of its efficiency. Finally, the paper discusses prospects and the field of optical quantum computing, providing insights into the potential applications of this technology.