Reviewerly/exHarmony huggingface dataset:
Mapping user interest into hyper-spherical space: A novel POI recommendation method

Point-of-interest (POI) recommendation helps users quickly filter out irrelevant POI by considering the spatio-temporal factor. In this paper, we address the problem of check-in preference modeling in POI recommendation, and propose a novel POI recommendation method that depicts user preference by constructing unique hypersphere interest model for each user. Different from existing works, we have done three innovative work. (1) We build a check-in graph and adopt DeepWalk algorithm to learn POI embedding, further aggregating them to obtain a hypersphere interest space with an interest center and interest radius. (2) We established a stacked neural network module by a bidirectional LSTM, a self-attention and a memory network, to grasp memory features contained in check-in histories. (3) We proposed a novel candidate POI filter method that updates ranking score by evaluating the Euclidean distance between the vectors of candidate POI and interest center. We evaluate the performance of our method on the four real-world check-in datasets constructed from Foursquare. The comparison between our method and six baselines demonstrates the outstanding performance on various measurements. Compared to the best baseline method, our method achieves about 50% performance improvement on NDCG. In terms of MRR, Precision and Recall, our method achieves about 37%, 21% and 9% performance improvement over the best baseline method. Further ablation experiments verified the importance and effectiveness of the hypersphere interest model, as removing this component caused significant performance degradation.
Binocular visual dimension measurement method for rectangular workpiece with a precise stereoscopic matching algorithm

Abstract Binocular vision measurement benefits from high prediction robustness and low structural complexity. However, there are still significant flaws in its accuracy. In this paper, binocular vision measurement of a rectangular workpiece is investigated. A new precise measurement method based on binocular vision is designed to achieve precise measurement of rectangular workpiece dimensions. Firstly, an algorithm for workpiece location based on Zernike moments and corner matching is proposed and employed to precisely locate the workpiece and extract the sub-pixel coordinates of discrete points on an image’s edge. Then, a novel stereoscopic matching algorithm combined with epipolar-geometry and cross-ratio invariance (CMEC) is proposed to improve the accuracy of binocular vision stereoscopic matching. Finally, a projection plane is introduced after the 3D reconstruction of discrete points in the workpiece contours by fitting the plane with least squares. The projection plane limits the coordinate fluctuations of discrete points. Furthermore, the data screening is used to further improve the accuracy of size calculation. The experimental results of the standard checkerboard and actual workpiece show that CMEC’s matching accuracy reached 99%, and the proposed method’s measurement accuracy reached 0.018 mm. This work presents a novel algorithm for stereoscopic matching in binocular vision and machine vision measurement.
A Compact Butterfly-Style Silicon Photonic–Electronic Neural Chip for Hardware-Efficient Deep Learning

The optical neural network (ONN) is a promising hardware platform for next-generation neurocomputing due to its high parallelism, low latency, and low energy consumption. Previous ONN architectures are mainly designed for general matrix multiplication (GEMM), leading to unnecessarily large area cost and high control complexity. Here, we move beyond classical GEMM-based ONNs and propose an optical subspace neural network (OSNN) architecture, which trades the universality of weight representation for lower optical component usage, area cost, and energy consumption. We devise a butterfly-style photonic–electronic neural chip to implement our OSNN with up to 7× fewer trainable optical components compared to GEMM-based ONNs. Additionally, a hardware-aware training framework is provided to minimize the required device programming precision, lessen the chip area, and boost the noise robustness. We experimentally demonstrate the utility of our neural chip in practical image recognition tasks, showing that a measured accuracy of 94.16% can be achieved in handwritten digit recognition tasks with 3 bit weight programming precision.
Why did AI get this one wrong? — Tree-based explanations of machine learning model predictions

Increasingly complex learning methods such as boosting, bagging and deep learning have made ML models more accurate, but harder to interpret and explain, culminating in black-box machine learning models. Model developers and users alike are often presented with a trade-off between performance and intelligibility, especially in high-stakes applications like medicine. In the present article we propose a novel methodological approach for generating explanations for the predictions of a generic machine learning model, given a specific instance for which the prediction has been made. The method, named AraucanaXAI, is based on surrogate, locally-fitted classification and regression trees that are used to provide post-hoc explanations of the prediction of a generic machine learning model. Advantages of the proposed XAI approach include superior fidelity to the original model, ability to deal with non-linear decision boundaries, and native support to both classification and regression problems. We provide a packaged, open-source implementation of the AraucanaXAI method and evaluate its behaviour in a number of different settings that are commonly encountered in medical applications of AI. These include potential disagreement between the model prediction and physician's expert opinion and low reliability of the prediction due to data scarcity.
Potential of residential building integrated photovoltaic systems in different regions of China

Building integrated photovoltaic (BIPV) is a promising solution for providing building energy and realizing net-zero energy buildings. Based on the developed mathematical model, this paper assesses the solar irradiation resources and BIPV potential of residential buildings in different climate zones of China. It is found that roofs are the first choice for BIPV installation, followed by south façades, especially in high-latitude cities, and then east and west facades. North façades could be recommended in some cities with frequent clouds. The photovoltaic (PV) contribution of a combined rooftop and south façade BIPV system to building energy is highlighted, where the PV covers 50 % of the roof and 40 % of the south façade area. The system can meet the net load of a 4, 8, 7, 6, 4, and 4-storey building in Harbin, Urumqi, Beijing, Shanghai, Chengdu, and Guangzhou, respectively. In areas with sufficient irradiation and low demand, such as Lhasa and Kunming, the proposed system is most likely to achieve net-zero energy consumption in high-rise residential buildings. Different from previous study, this study proposes a comprehensive research framework for assessing the potential of residential BIPV system in various regions of China, considering partial shading effects. Since many provinces have issued relevant policies to promote distributed PVs, the findings can provide guidance for BIPV deployment in China.
Stable and efficient resource management using deep neural network on cloud computing

Resource management autoscaling in a cloud computing service guarantees the high availability and extensibility of applications and services. Horizontal pod autoscaling (HPA) does not affect the executed tasks but also has the disadvantage that it cannot provide immediate scaling. Furthermore, scale down is not possible if excess resources are allocated, because it is difficult to identify the amount of resources required for applications and services; thus resources are wasted. Therefore, this study proposes Proactive Hybrid Pod Autoscaling (ProHPA), which immediately responds to irregular workloads and reduces resource overallocation. ProHPA uses a bidirectional long short-term memory (Bi-LSTM) model applied with an attention mechanism for forecasting future CPU and memory usage that has similar or different patterns. Reducing excessive resource usage with vertical pod autoscaling (ReVPA) adjusts the overallocation of resources within a pod by forecasted resource usage. Lastly, prevention overload with HPA (PoHPA) immediately performs resource scaling by using forecasted resource usage and pod information. When the performance of ProHPA was evaluated, CPU and memory average utilization were improved by 23.39% and 42.52%, respectively, compared with conventional HPA when initial resources were overallocated. In addition, ProHPA did not exhibit overload compared to conventional HPA when resources are insufficiently allocated.
Artificial Intelligence in Intensive Care Medicine: Bibliometric Analysis

Interest in critical care-related artificial intelligence (AI) research is growing rapidly. However, the literature is still lacking in comprehensive bibliometric studies that measure and analyze scientific publications globally.The objective of this study was to assess the global research trends in AI in intensive care medicine based on publication outputs, citations, coauthorships between nations, and co-occurrences of author keywords.A total of 3619 documents published until March 2022 were retrieved from the Scopus database. After selecting the document type as articles, the titles and abstracts were checked for eligibility. In the final bibliometric study using VOSviewer, 1198 papers were included. The growth rate of publications, preferred journals, leading research countries, international collaborations, and top institutions were computed.The number of publications increased steeply between 2018 and 2022, accounting for 72.53% (869/1198) of all the included papers. The United States and China contributed to approximately 55.17% (661/1198) of the total publications. Of the 15 most productive institutions, 9 were among the top 100 universities worldwide. Detecting clinical deterioration, monitoring, predicting disease progression, mortality, prognosis, and classifying disease phenotypes or subtypes were some of the research hot spots for AI in patients who are critically ill. Neural networks, decision support systems, machine learning, and deep learning were all commonly used AI technologies.This study highlights popular areas in AI research aimed at improving health care in intensive care units, offers a comprehensive look at the research trend in AI application in the intensive care unit, and provides an insight into potential collaboration and prospects for future research. The 30 articles that received the most citations were listed in detail. For AI-based clinical research to be sufficiently convincing for routine critical care practice, collaborative research efforts are needed to increase the maturity and robustness of AI-driven models.
StorSeismic: A New Paradigm in Deep Learning for Seismic Processing

Machine learned tasks on seismic data are often trained sequentially and separately, even though they utilize the same features (i.e. geometrical) of the data. We present StorSeismic, as a dataset centric framework for seismic data processing, which consists of neural network pre-training and fine-tuning procedures. We, specifically, utilize a neural network as a preprocessing tool to extract and store seismic data features of a particular dataset for any downstream tasks. After pre-training, the resulting model can be utilized later, through a fine-tuning procedure, to perform different tasks using limited additional training. Used often in Natural Language Processing (NLP) and lately in vision tasks, BERT (Bidirectional Encoder Representations from Transformer), a form of a Transformer model, provides an optimal platform for this framework. The attention mechanism of BERT, applied here on a sequence of traces within the shot gather, is able to capture and store key geometrical features of the seismic data. We pre-train StorSeismic on field data, along with synthetically generated ones, in the self-supervised step. Then, we use the labeled synthetic data to fine-tune the pre-trained network in a supervised fashion to perform various seismic processing tasks, like denoising, velocity estimation, first arrival picking, and NMO (normal moveout). Finally, the fine-tuned model is used to obtain satisfactory inference results on the field data.
XRR: Extreme multi-label text classification with candidate retrieving and deep ranking

Extreme Multi-label Text Classification (XMTC) is a key task of finding the most relevant labels from a large label set for a document. Although some deep learning-based methods have shown great success in XMTC, they still suffer from the following drawbacks. First, although several methods have improved the precision by clustering labels and combining several sub-models to train and predict for one dataset, they were not ideal in terms of computational efficiency. Second, most of those methods need a low dimensional bottleneck layer before the output layer to compress the feature representations to fit the GPU memory, which results in information loss of original features. In this paper, we proposed a novel two-stage XMTC framework with candidate Retrieving and deep Ranking (XRR) to address those drawbacks. In the retrieving stage, we designed two retrieval strategies, including an aligning Point Mutual Information (aPMI) method, and a Unified Label-Semantic Embedding (ULSE) method, to extract hundreds of candidates from massive labels. In the ranking stage, we presented a deep ranking model using a pre-trained transformer to distinguish the true labels from candidates. Extensive experiments show that XRR outperforms the state-of-the-art methods on five widely used multi-label datasets.
A MPC Combined Decision Making and Trajectory Planning for Autonomous Vehicle Collision Avoidance

Increasing focus is being paid to ensuring safety in autonomous driving. The current paper addresses the challenge of collision avoidance with dynamic surrounding vehicles in different driving situations. The established solution formulated utilizing Model Predictive Control (MPC) includes decision making and trajectory planning. A simplified prediction model is used, which takes into account the relative positions and velocities of the surrounding vehicles and the ego vehicle. Depending on traffic conditions, which are stated as constraints in the MPC formulation, the ego vehicle may perform lane keeping, lane shift, overtaking or braking to avoid collision with the road participants. The decision making constraints are included into the MPC in a mixed integer formulation-like manner. The safety constraints are defined using the Sigmoid function and the braking barrier to define the navigable zone of the ego vehicle. The proposed algorithm has been evaluated through simulation, with different scenarios revealing its effectiveness.
Running city game optimizer: a game-based metaheuristic optimization algorithm for global optimization

Abstract As science and technology improve, more and more complex global optimization difficulties arise in real-life situations. Finding the most perfect approximation and optimal solution using conventional numerical methods is intractable. Metaheuristic optimization approaches may be effective in achieving powerful global optimal solutions for these complex global optimization situations. Therefore, this paper proposes a new game-based algorithm called the running city game optimizer (RCGO), which mimics the game participant’s activity of playing the running city game. The RCGO is mathematically established by three newfangled search strategies: siege, defensive, and eliminated selection. The performance of the proposed RCGO algorithm in optimization is comprehensively evaluated on a set of 76 benchmark problems and 8 engineering optimization scenarios. Statistical and comparative results show that RCGO is more competitive with other state-of-the-art competing approaches in terms of solution quality and convergence efficiency, which stems from a proper balance between exploration and exploitation. Additionally, in the case of engineering optimization scenarios, the proposed RCGO is able to deliver superior fitting and occasionally competitive outcomes in optimization applications. Thus, the proposed RCGO is a viable optimization tool to easily and efficiently handle various optimization problems.
Machine Learning Benchmarking for Secured IoT Smart Systems

Smartness and IoT along with machine learning (ML) lead the research directions nowadays. Smart city, smart campus, smart home, smart vehicle, etc; or if we call it “Smart x” will change how the world entities interact among themselves. This paper provides an ML benchmarking as well as a taxonomy that divides its models into linear and non-linear ones based on the problem type (classification or regression), the targeted security issue, the kind of IoT network, and the used evaluation measure. On the other hand, security algorithms enhanced with ML play a significant role to govern the new era of communication. This paper also provides a case study to apply the ML methods to IoT smart campus (SC) as a model to reach a secured IoT system for data collection and manipulation with guided research directions.
OAB-YOLOv5: One-Anchor-Based YOLOv5 for Rotated Object Detection in Remote Sensing Images

Remote sensing images are widely distributed, small in object size, and complex in background, resulting in low accuracy and slow speed of remote sensing image detection. Existing remote sensing object detection is generally based on the detector with anchors. With the proposal of a feature pyramid network (FPN) and focal loss, an anchorless detector emerges, however, the accuracy of anchorless detection is often low. First, this study analyzes the differences and characteristics of the intersection of union (IoU) and shape matchings based on anchors in mainstream algorithms and indicates that in dense or complex scenes, some labels are not easily assigned to positive samples, which leads to detection failure. Subsequently, we proposean one-anchor-based (OAB) object detection algorithm based on the idea of central point sampling in the anchor-free detector. The positive samples and negative samples are defined according to the central point sampling and distance constraint, and an anchor box is preset for each positive sample to accelerate its convergence. It reduces the complexity of the anchor-based detector, improves the inference speed, and reduces the setting of hyperparameters in the traditional matching strategy, rendering the model more flexible. Finally, in order to suppress background noise in remote sensing images, the vision transformer (ViT) is adopted to connect the neck and head, making it easier for the network to pay attention to key information. Thus, it is not easy to lose in the training process. Experiments on challenging public dataset—DOTA dataset- verified the effectiveness of the proposed algorithm. The experimental results show that the mAP of the optimized OAB-YOLOv5 method is improved by 2.79%, the number of parameters is reduced by 13.2%, and the inference time is reduced by 11% compared with the YOLOv5 baseline.
Perceptron: Learning, Generalization, Model Selection, Fault Tolerance, and Role in the Deep Learning Era

The single-layer perceptron, introduced by Rosenblatt in 1958, is one of the earliest and simplest neural network models. However, it is incapable of classifying linearly inseparable patterns. A new era of neural network research started in 1986, when the backpropagation (BP) algorithm was rediscovered for training the multilayer perceptron (MLP) model. An MLP with a large number of hidden nodes can function as a universal approximator. To date, the MLP model is the most fundamental and important neural network model. It is also the most investigated neural network model. Even in this AI or deep learning era, the MLP is still among the few most investigated and used neural network models. Numerous new results have been obtained in the past three decades. This survey paper gives a comprehensive and state-of-the-art introduction to the perceptron model, with emphasis on learning, generalization, model selection and fault tolerance. The role of the perceptron model in the deep learning era is also described. This paper provides a concluding survey of perceptron learning, and it covers all the major achievements in the past seven decades. It also serves a tutorial for perceptron learning.
A multi-population immune plasma algorithm for path planning of unmanned combat aerial vehicle

Autonomous flight of an unmanned aerial vehicle (UAV) or its weaponized variant named unmanned combat aerial vehicle (UCAV) requires a route or path determined carefully by considering the optimization objectives about the enemy threats and fuel consumption of the system being operated. Immune Plasma algorithm (IP algorithm or IPA) is one of the most recent optimization techniques and directly models the fundamental steps of a medical method also used for the COVID-19 disease and known as convalescent or immune plasma treatment. In this study, IP algorithm for which a promising performance has already been validated with a single population was first extended to a multi-population domain supported by a migration schema. Moreover, the usage of the donor as a source of plasma for the treatment operations of a receiver was remodeled. The new variant of the IPA empowered with the multi-population and modified donor usage approach was called Multi-IP algorithm or MULIPA. For investigating the solving capabilities of the MULIPA as a UCAV path planner, different battlefield scenarios and algorithm specific parameter configurations were used. The results obtained by the MULIPA were compared with the results of other meta-heuristic based path planners. The comparative studies between MULIPA and other techniques showed that newly proposed IPA variant is capable of finding more secure and fuel efficient paths for a UCAV system.
Parameters estimation of three-diode photovoltaic model using fractional-order Harris Hawks optimization algorithm

Effective and efficient systems that calculate the parameters of PV models to be optimal have attracted great interest from many researchers. In this study, a novel algorithm approach is proposed to improve the search performance of the Harris Hawks optimization (HHO) algorithm based on fractal maps. The random parameter that is effective in besiege of the prey in the exploration and exploitation stages of the HHO algorithm is adjusted using the fractal henon chaotic map. Unimodal, multimodal and fixed-dimension benchmark functions were used to verify the quality and efficiency of the proposed Fractional Henon Chaotic Harris Hawks Optimization (FCHHHO) algorithms and variants in providing optimal solutions. Moreover, RTC France photovoltaic cell and Photowatt-PWP 201 photovoltaic models with single, double and three-diodes have been determined accurately, stably and reliably. Comparative and statistical results obtained from the developed algorithms showed that the algorithms have superior performance with regard to accuracy and robustness in comparison with their variants and compared with other metaheuristic algorithms.