Reviewerly/exHarmony huggingface dataset:
High-precision moiré alignment method using centrosymmetric grating for 3D interlayer interconnect

The fabrication of integrated circuits in the future will rely heavily on high-precision three-dimensional interlayer interconnect alignment technology. In this study, we explore a controllable and clear-cut alignment method based on moiré centrosymmetric grating marks that can achieve fine alignment in the X-Y-θ direction simultaneously with only a single alignment mark without additional coarse alignment mark. Compared with the traditional peak-intensity-based detection method, circumferential alignment of the order of 10−4 deg and radial alignment of less than 10 nm can be achieved, and its fine alignment accuracy is improved by nearly two orders of magnitude by using the proposed local moiré phase alignment method. With further optimization of the parameters, sub-nanometer alignment is expected to be achieved. Moreover, our measurement system is simpler and more stable. Both simulation and experimental results verify the feasibility of the proposed method. The uncertainty analysis of the results is also reported.
Spatiotemporal motor learning with reward-modulated Hebbian plasticity in modular reservoir computing

Generation of complex patterns at a specific timing is crucial to most forms of learning and behavior, which are acquired through dopamine-modulated plasticity in the striatum. However, the neural mechanisms of such reward-based spatiotemporal processing remain unknown. Inspired by the cortico-striatal circuits, this study developed a new reservoir computing method, a class of recurrent neural networks, based on reward-modulated Hebbian learning (RMHL) for the spatiotemporal motor learning. We utilized a reservoir of basal dynamics (reBASICS), which generated self-sustained limit cycle oscillations with various frequencies, as a reservoir structure. Then, the oscillations were linearly integrated as readout output, in which readout weights were modulated with RMHL. The simulations showed that reBASICS-based RMHL was able to accomplish both motor timing and pattern drawing tasks, for which existing reservoir-based RMHL failed. Further, introducing an eligibility trace mechanism into RMHL allowed the model to learn motor timing even when reward-based modulation was delayed. In conclusion, this model is proposed as a new computational model of temporal processing of the striatum, where the cortical areas generate stable oscillations. From the oscillatory dynamics, spatiotemporal patterns are learned using RMHL in the striatum.
Gaze Analytics Dashboard for Distributed Eye Tracking

Understanding the focus and visual scanning behavior of users during a collaborative activity in a distributed environment can be helpful in improving users’ engagement. Eye tracking measures can provide informative cues to understanding human visual search behavior. In this study, we present a distributed eye-tracking system with a gaze analytics dashboard. This system extracts eye movements from multiple participants utilizing common off-the-shelf eye trackers, generates real-time traditional positional gaze measures and advanced gaze measures such as ambient-focal coefficient $\mathcal{K}$, and displays them in an interactive dashboard. We evaluate the proposed methodology by developing a gaze analytics dashboard and conducting a pilot study to (1) investigate the relationship between $\mathcal{K}$ with collaborative behavior, and (2) compare it against the User Experience Questionnaire (UEQ) benchmark. Our results show that groups that spent more time had more ambient attention, and our dashboard has a higher overall impression compared to the UEQ benchmark.
OpenFL-XAI: Federated learning of explainable artificial intelligence models in Python

Artificial Intelligence (AI) systems play a significant role in manifold decision-making processes in our daily lives, making trustworthiness of AI more and more crucial for its widespread acceptance. Among others, privacy and explainability are considered key requirements for enabling trust in AI. Building on these needs, we propose a software for Federated Learning (FL) of Rule-Based Systems (RBSs): on one hand FL prioritizes user data privacy during collaborative model training. On the other hand, RBSs are deemed as interpretable-by-design models and ensure high transparency in the decision-making process. The proposed software, developed as an extension to the Intel® OpenFL open-source framework, offers a viable solution for developing AI applications balancing accuracy, privacy, and interpretability.
Fusion of Textural and Visual Information for Medical Image Modality Retrieval Using Deep Learning-Based Feature Engineering

Medical image retrieval is essential to modern medical treatment because it enables doctors to diagnose and treat a variety of illnesses. In this study, we present an innovative technique for selecting the methodology of medical images by combining textural and visual information. Knowing the imaging process behind an idea, such as a chest X-ray, skin dermatology, or breast histopathology image, may be extremely helpful to healthcare professionals since it can aid in image investigation and provide important information about the imaging technique used. We use deep learning-based feature engineering to do this, using both the textural and visual components of healthcare images. We extract detailed visual information from the images using a predefined Convolutional Neural Network (CNN). The Global-Local Pyramid Pattern (GLPP), Zernike moments, and Haralick are also used to physically separate the pertinent parts from the images’ other visual and factual aspects. These essential characteristics, such as image modality and imaging technique-specific characteristics, provide additional information about the technology. We employ a feature fusion method that incorporates the depictions obtained from the two modalities in order to combine the textural and visual elements. This fusion process, which improves the discrimination capacity of the feature vectors, makes accurate modality classification possible. We conducted trials on a sizable dataset consisting of various medical images to assess the effectiveness of our proposed method. The results indicate that, in comparison to conventional methods, our technique outperforms modality retrieval, with a precision of 95.89 and a recall of 96.31. The accuracy and robustness of the classification task are greatly creased by the combination of textural and visual data. Through the integration of textural and visual information, our work offers a unique method for recovering the modality of medical images. This method has the potential to greatly improve the speed and accuracy of medical image processing and diagnosis by helping experts rapidly and accurately identify the imaging technology being utilized.
Non-Uniform Illumination Underwater Image Enhancement via Minimum Weighted Error Entropy Loss

An effective enhanced unsupervised network based on minimum weighted error entropy (MWEE) loss is proposed for underwater image enhancement, which is one of the most challenging issues in computer vision. First, by the inspiration of Retinex theory, an underwater image is decomposed into non-uniform illumination and reflectance with shot noise. Then non-uniform illumination is modeled as an independent and piecewise identical (IPI) distribution, and shot noise in reflectance is seen as a single non-Gaussian distribution. Next, taking advantage of these two distributions, the MWEE criterion and its special case as training losses are embedded into a generative adversarial network (GAN) for piecewise uniformization of illumination and reflectance denoising. Experiments on underwater image enhancement datasets show the network enhanced by the proposed method obtains superior performance, and exhibits higher naturalness and better visual quality than several existing approaches.
Cloud-Edge-Client Collaborative Learning in Digital Twin Empowered Mobile Networks

Digital twin (DT) has emerged as a key enabler for the intelligent-oriented evolution of mobile networks. With the rise of privacy concerns for enabling intelligent applications in DT-empowered mobile networks (DTMNs), federated learning has garnered wide attention due to its potential on breaking down data silos. However, the data privacy of federated learning is greatly threatened by emerging gradient leakage attacks, and the need for frequent knowledge exchange limits its training efficiency over resource-constrained DTMNs. To circumvent such dilemmas, this work first proposes a privacy-enhanced federated learning framework based on cloud-edge-client collaborations. Particularly, model splitting between clients and edge servers makes gradient leakage attacks computationally prohibitive, and cloud-side partial model aggregation provides hierarchical data utility. To improve the training efficiency of the proposed learning framework, we further establish its communication and computation cost models, and develop a DT-assisted multi-agent deep reinforcement learning-based resource scheduler for joint client association and channel assignment. Finally, as a case study of intelligent applications in DTMNs, a human-robot collaborative nursing task is designed to evaluate the practical performance of our proposed scheduler. Experimental results show its superiority in saving training costs and preserving learning accuracy.
The Automatic Definition of the Intuitive Linguistic Heuristics Set to Recognize the Elements of UML Analysis and Design Models in English

Elicitation of the elements of Unified Modelling Language (UML) analysis and design models from sentences written in scripted English is essential in the production of analysis and design models. The correct elicitation of these elements depends on the intuitive, manually defined set of linguistic heuristics, which is used to map a word in the sentence to its correct semantics in the domain of UML analysis and design models. This paper proposes a Genetic Algorithm-based classification rule discovery approach and a developed Enhanced Intuitive Linguistic Heuristics (EILH) dataset to automate the definition of the intuitive linguistic heuristics set to elicit five elements of UML analysis and design models from English sentences. These elements are the use case, the actor, the sender, the receiver, and the message. The automatically defined intuitive linguistic heuristics set was evaluated by developing an Artificial Neural Network (ANN) to recognize the elements of the UML analysis and design models using both manually defined and automatically defined sets. This comparison shows the superiority of the automatically defined set over the manually defined one.
A surrogate-assisted highly cooperative coevolutionary algorithm for hyperparameter optimization in deep convolutional neural networks

Hyperparameter optimization in convolutional neural networks (CNNs) plays a vital role in ensuring the effectiveness of the models. However, with the depth of the existing CNN expanding, this task becomes very challenging due to the high-dimensional and computationally expensive characteristics. Given these difficulties, this study proposes a surrogate-assisted highly cooperative hyperparameter optimization (SHCHO) algorithm for large-scale chain-styled CNNs. SHCHO tackles the original hyperparameter optimization problem by cooperatively optimizing its subproblems. Specifically, it first decomposes the whole CNN into several overlapping sub-CNNs, which conforms to the inherent overlapping interaction structure among hyperparameters and significantly reduces the search space. The resulting hyperparameter optimization subproblems on these sub-CNNs are then coevolved collaboratively and competitively, facilitating a proper hyperparameter configuration for the whole CNN. Besides, SHCHO also employs the computationally efficient surrogate technique to assist in each subproblem optimization. As a result, the expensive computational cost can be greatly reduced. Extensive experimental results on three widely-used image classification datasets indicate that SHCHO shows competitive performance when compared with several state-of-the-art algorithms in terms of both hyperparameter superiority and computational cost.
MRHN: Hypergraph Convolutional Network for Web API Recommendation

With the development of service-oriented computing, Mashup technology has emerged that uses web API as reusable components to create new products. How to achieve efficient and accurate service recommendation has attracted the attention of researchers in the field of service computing. The call relationship between mashups and APIs in real service data is intricate, and the information carried by the service further increases the complexity of the relationship between them. Most existing mashup recommendation models hardly mine such complex relationships effectively. To this end, this paper proposes the MRHN method. This method uses motifs to extract the hypergraph structure from services. While studying the complex relationship between service data, it also solves the problem of data sparsity, and uses the hypergraph convolutional network to extract the features of Mashup. Further, the weights of channels are adjusted using channel error attention mechanism. Finally, the performance of the proposed method is comprehensively evaluated. The experimental results show that compared with the existing service recommendation methods, the proposed method has significantly improved in terms of evaluation indicators such as NDCG and HR.
Completeness of Natural Language Requirements: A Comparative Study of User Stories and Feature Descriptions

Checking the completeness of requirements is critical for software validation, as incomplete requirements can adversely affect the delivery of high-quality software within budget. Many existing methods rely on the domain model that defines the correct and relevant constructs, against which the requirements completeness is checked. However, building accurate and updated domain models requires considerable human effort, which is often challenging in practical settings. To operate in the absence of domain models, we propose to measure a textual requirement's completeness based on a universal linguistic theory, namely Fillmore's frame semantics. Our approach treats the frame elements (FEs) associated with a requirement's verb as the roles that should participate in the syntactic structure evoked by the verb. The FEs thus give rise to a linguistic measure of completeness, through which we compute a requirement's actual completeness. Using our linguistic-theoretic approach allows for a fully automatic completeness check of different real-world requirements. The comparisons show that our studied feature descriptions are more complete than user stories.
Adaptive evidential K-NN classification: Integrating neighborhood search and feature weighting

The number of nearest neighbors K and the utilized distance measure considerably impact the performance of the K-nearest neighbor (K-NN) algorithm. The information provided by neighbors may be perplexing due to the set presumption of K for every test sample without any prior knowledge, which might result in incorrect classification results. The hypothesis of locally constant class conditional probabilities relied on by K-NN, is violated by high dimensions, which also bring out the dimensionality curse. Notably, in the case of imperfect data, training samples may be noise-corrupted or located in substantially overlapping regions, thus impairing the effectiveness of the K-NN rule. In contrast to existing research, which frequently focuses on just one of the problems above, this paper presents an adaptive evidential K-NN classification (AEK-NN) algorithm that synchronizes neighborhood search with feature weighting. In order to jointly learn the adaptive neighborhood for each sample and various feature weights, AEK-NN maps the Euclidean space into a rebuilt space consisting of similarity coefficients through a two-stage training process. Within the evidence theory framework, AEK-NN produces classification results for data with imperfect labels. The advantages of applying evidence theory, incorporating adaptive neighborhood search, and feature weighting are shown through ablation studies. We also use simulated and real-world datasets to demonstrate the state-of-the-art performance of AEK-NN.
Streamlining streaming videos: moving towards an efficient streaming video workflow in Alma/Primo

AbstractAbstractThis case study intends to demonstrate the lessons learned as an academic library went through several changes to accommodate an increase in streaming video requests on their campus. A historical outline of the changing landscape of streaming video procedures and platforms will be provided for context. The iterative changes in vendor relations, internal workflows, and resources allocated to adapt to the changing nature of streaming videos as well as lessons learned during implementation will be presented.Keywords: Streaming videoelectronic resource managementacquisitionsworkflow
Information Security System Using Blockchain Technology Implementation

In today's digital era, information security is a very important thing to pay attention to. There are many methods to maintain information security, but there are still many vulnerabilities that can be exploited by irresponsible parties. Blockchain technology provides a solution by providing a strong and reliable information security system. Blockchain technology can be used to ensure the integrity and validity of stored data. This can be done by storing data in blocks that are connected to one another and using encryption to maintain privacy. In addition, the decentralized system that exists in blockchain technology makes it difficult for irresponsible parties to change or delete it. The application of blockchain technology to information security systems can be carried out in various sectors, such as banking, commerce, and government. In the banking sector, blockchain technology can be used to maintain the integrity of transaction data and ensure that transactions are truly valid. In the trade sector, blockchain technology can be used to ensure that the goods being traded are genuine and have a verifiable source. In the government sector, blockchain technology can be used to store and verify election data, and ensure that election results are truly valid and representative.
mpiPython: Prospects for Node Performance

Python as an interpreted language is limited in performance by its ability to optimize code. With it being a high-level programming language, it’s still a strong choice for data scientists to learn and use. If Python could be optimized for parallel programming, its full potential in parallel and cloud computing environments could be achieved. mpiPython is a message-passing module that gives Python the ability to be used in SPMD (Single Program Multiple Data) environments. In this paper, we review basic features of mpiPython, including its runtime communication libraries and design strategies. mpiPython also has new features to help mpiPython programmers, that includes simplifying traditional MPI initialization. During the development of mpiPython, we realized that individual node performance of mpiPython is uncertain and critical. mpiPython node performance will be analyzed within the benchmarks.
Uncovering the Risks and Drawbacks Associated With the Use of Synthetic Data for Grammatical Error Correction

In a Data-Centric AI paradigm, the model performance is enhanced without altering the model architecture, as evidenced by real-world and benchmark dataset demonstrations. With the advancements of large language models (LLM), it has become increasingly feasible to generate high-quality synthetic data, while considering the need to construct fully synthetic datasets for real-world data containing numerous personal information. However, in-depth validation of the solely synthetic data setting has yet to be conducted, despite the increased possibility of models trained exclusively on fully synthetic data emerging in the future. Therefore, we examined the question, "Do data quality control techniques (known to positively impact Data-Centric AI) consistently aid models trained exclusively on synthetic datasets?". To explore this query, we performed detailed analyses using synthetic datasets generated for speech recognition postprocessing using the BackTranScription (BTS) approach. Our study primarily addressed the potential adverse effects of data quality control measures (e.g., noise injection and balanced data) and training strategies in the context of synthetic-only experiments. As a result of the experiment, we observed the negative effect that the Data-Centric methodology drops by a maximum of 44.03 points in the fully synthetic data setting.
Distributed Matrix Computations With Low-Weight Encodings

Straggler nodes are well-known bottlenecks of distributed matrix computations which induce reductions in computation/communication speeds. A common strategy for mitigating such stragglers is to incorporate Reed-Solomon based MDS (maximum distance separable) codes into the framework; this can achieve resilience against an optimal number of stragglers. However, these codes assign dense linear combinations of submatrices to the worker nodes. When the input matrices are sparse, these approaches increase the number of non-zero entries in the encoded matrices, which in turn adversely affects the worker computation time. In this work, we develop a distributed matrix computation approach where the assigned encoded submatrices are random linear combinations of a small number of submatrices. In addition to being well suited for sparse input matrices, our approach continues to have the optimal straggler resilience in a certain range of problem parameters. Moreover, compared to recent sparse matrix computation approaches, the search for a “good” set of random coefficients to promote numerical stability in our method is much more computationally efficient. We show that our approach can efficiently utilize partial computations done by slower worker nodes in a heterogeneous system which can enhance the overall computation speed. Numerical experiments conducted through Amazon Web Services (AWS) demonstrate up to 30% reduction in per worker node computation time and 100× faster encoding compared to the available methods.
Deep Reinforcement Learning Method of Target Hunting for Multi-agents with Flocking Property

A multi-agent deep deterministic policy gradient method is proposed for the target hunting problem in this paper. The behaviours of target hunting and flocking are trained with the action experiences under random environment while the multi-agents explore the environment randomly. A danger zone model is designed to construct a reward function module for the hunting behaviour of the target, and an improved artificial potential energy function is designed to impel the agents to evolve to the flocking state. To evaluate the proposed method, experiment is simulated with gym. The experimental results show that the multi-agent hunting of dynamic targets with flocking property is successfully realized.