Reviewerly/exHarmony huggingface dataset:
A top-k POI recommendation approach based on LBSN and multi-graph fusion

POI(Point of Interest) recommendation is a basic and on-going issue in LBSN (Location-based Social Network) services. In this paper, a novel POI recommendation approach which is based on LBSN and multi-graph fusion is proposed. First, we take advantages of the graph neural network to construct user-POI interaction graph based on the rating data of users and construct user social graph based on the user social networks. First-order friends and high-order friends will be considered simultaneously in the user social graph. And then, we present a spectral cluster-based algorithm to gain the latent vector of the POI in location space. After this, the graph neural network is used to learn the information above. Lastly, we predict the score based on the aforementioned information and pick out the top-k POIs with the highest scores to form a recommendation list. Extensive experiments conducted on real datasets demonstrated that the method proposed in this paper can effectively generate the embedding vectors of users and POIs, and can achieve high recommendation accuracy as well.
Advanced dwarf mongoose optimization for solving CEC 2011 and CEC 2017 benchmark problems

This paper proposes an improvement to the dwarf mongoose optimization (DMO) algorithm called the advanced dwarf mongoose optimization (ADMO) algorithm. The improvement goal is to solve the low convergence rate limitation of the DMO. This situation arises when the initial solutions are close to the optimal global solution; the subsequent value of the alpha must be small for the DMO to converge towards a better solution. The proposed improvement incorporates other social behavior of the dwarf mongoose, namely, the predation and mound protection and the reproductive and group splitting behavior to enhance the exploration and exploitation ability of the DMO. The ADMO also modifies the lifestyle of the alpha and subordinate group and the foraging and seminomadic behavior of the DMO. The proposed ADMO was used to solve the congress on evolutionary computation (CEC) 2011 and 2017 benchmark functions, consisting of 30 classical and hybrid composite problems and 22 real-world optimization problems. The performance of the ADMO, using different performance metrics and statistical analysis, is compared with the DMO and seven other existing algorithms. In most cases, the results show that solutions achieved by the ADMO are better than the solution obtained by the existing algorithms.
KEPT: Knowledge Enhanced Prompt Tuning for event causality identification

Event causality identification (ECI) aims to identify causal relations of event mention pairs in text. Despite achieving certain accomplishments, existing methods are still not effective due to the following two issues: (1) the lack of causal reasoning ability, imposing restrictions on recognizing implicit causal relations; (2) the significant gap between fine-tuning and pre-training, which hinders the utilization of pre-trained language models (PLMs). In this paper, we propose a novel Knowledge Enhanced Prompt Tuning (KEPT) framework for ECI to address the issues mentioned above. Specifically, this method leverages prompt tuning to incorporate two kinds of knowledge obtained from external knowledge bases (KBs), including background information and relational information, for causal reasoning. To introduce external knowledge into our model, we first convert it to textual descriptions, then design an interactive attention mechanism and a selective attention mechanism to fuse background information and relational information, respectively. In addition, to further capture implicit relations between events, we adopt the objective from knowledge representation learning to jointly optimize the representations of causal relations and events. Experiment results on two widely-used benchmarks demonstrate that the proposed method outperforms the state-of-the-art models.
Ultrafast Demagnetization Control in Magnetophotonic Surface Crystals

Magnetic memory combining plasmonics and magnetism is poised to dramatically increase the bit density and energy efficiency of light-assisted ultrafast magnetic storage, thanks to nanoplasmon-driven enhancement and confinement of light. Here we devise a new path for that, simultaneously enabling light-driven bit downscaling, reduction of the required energy for magnetic memory writing, and a subtle control over the degree of demagnetization in a magnetophotonic surface crystal. It features a regular array of truncated-nanocone-shaped Au-TbCo antennas showing both localized plasmon and surface lattice resonance modes. The ultrafast magnetization dynamics of the nanoantennas show a 3-fold resonant enhancement of the demagnetization efficiency. The degree of demagnetization is further tuned by activating surface lattice modes. This reveals a platform where ultrafast demagnetization is localized at the nanoscale and its extent can be controlled at will, rendering it multistate and potentially opening up so-far-unforeseen nanomagnetic neuromorphic-like systems operating at femtosecond time scales controlled by light.
Moving visual world experiments online? A web-based replication of Dijkgraaf, Hartsuiker, and Duyck (2017) using PCIbex and WebGazer.js

The visual world paradigm is one of the most influential paradigms to study real-time language processing. The present study tested whether visual world studies can be moved online, using PCIbex software (Zehr & Schwarz, 2018) and the WebGazer.js algorithm (Papoutsaki et al., 2016) to collect eye-movement data. Experiment 1 was a fixation task in which the participants looked at a fixation cross in multiple positions on the computer screen. Experiment 2 was a web-based replication of a visual world experiment by Dijkgraaf et al. (2017). Firstly, both experiments revealed that the spatial accuracy of the data allowed us to distinguish looks across the four quadrants of the computer screen. This suggest that the spatial resolution of WebGazer.js is fine-grained enough for most visual world experiments (which typically involve a two-by-two quadrant-based set-up of the visual display). Secondly, both experiments revealed a delay of roughly 300 ms in the time course of the eye movements, possibly caused by the internal processing speed of the browser or WebGazer.js. This delay can be problematic in studying questions that require a fine-grained temporal resolution and requires further investigation.
Qubit Mapping and Routing via MaxSAT

Near-term quantum computers will operate in a noisy environment, without error correction. A critical problem for near-term quantum computing is laying out a logical circuit onto a physical device with limited connectivity between qubits. This is known as the qubit mapping and routing (QMR) problem, an intractable combinatorial problem. It is important to solve QMR as optimally as possible to reduce the amount of added noise, which may render a quantum computation useless. In this paper, we present a novel approach for optimally solving the QMR problem via a reduction to maximum satisfiability (MAXSAT). Additionally, we present two novel relaxation ideas that shrink the size of the MAXSAT constraints by exploiting the structure of a quantum circuit. Our thorough empirical evaluation demonstrates (1) the scalability of our approach compared to state-of-the-art optimal QMR techniques (solves more than 3x benchmarks with 40x speedup), (2) the significant cost reduction compared to state-of-the-art heuristic approaches (an average of ~5x swap reduction), and (3) the power of our proposed constraint relaxations.
Exploring the antecedents of customers’ willingness to use service robots in restaurants

Abstract This study examines the willingness of customers to accept, and their intention to recommend, the services provided by service robots in restaurants. A mixed-methods research approach was taken to evaluate a theoretical model based on behavioural reasoning theory (BRT). The results demonstrated the important influence of positive attitudes and objections to the use of service robots on consumers’ willingness to use service robots, as well as their intention to recommend restaurants that use them. Among the main aspects that affect attitudes and objections, we found hedonic perceptions, perceived safety, interaction quality perception and anthropomorphism.
A robustness-improved image encryption scheme utilizing Life-liked cellular automaton

Image encryption is considered as an effective method to protect image against revealing. As a discrete dynamic system, the Life-liked cellular automaton (CA) has good chaotic performance and has been applied for image encryption. This paper proposes a robust block encryption scheme, based on reversible Life-liked CA with balanced rule. The proposed method adopts classic confusion–diffusion structure on block level and reversible Life-liked CA within block. An effect permutation method is developed to reduce the iteration rounds of whole system, while the diffusion module adopts reversible Life-liked CA with balanced rule to encrypt the blocks to noise-like ones. Performance analyses show the proposed scheme have good cryptographic features, satisfactory security for defeating common attacks and robustness to resist data loss and random noise.
LARO: Opposition-Based Learning Boosted Artificial Rabbits-Inspired Optimization Algorithm with Lévy Flight

The artificial rabbits optimization (ARO) algorithm is a recently developed metaheuristic (MH) method motivated by the survival strategies of rabbits with bilateral symmetry in nature. Although the ARO algorithm shows competitive performance compared with popular MH algorithms, it still has poor convergence accuracy and the problem of getting stuck in local solutions. In order to eliminate the effects of these deficiencies, this paper develops an enhanced variant of ARO, called Lévy flight, and the selective opposition version of the artificial rabbit algorithm (LARO) by combining the Lévy flight and selective opposition strategies. First, a Lévy flight strategy is introduced in the random hiding phase to improve the diversity and dynamics of the population. The diverse populations deepen the global exploration process and thus improve the convergence accuracy of the algorithm. Then, ARO is improved by introducing the selective opposition strategy to enhance the tracking efficiency and prevent ARO from getting stuck in current local solutions. LARO is compared with various algorithms using 23 classical functions, IEEE CEC2017, and IEEE CEC2019 functions. When faced with three different test sets, LARO was able to perform best in 15 (65%), 11 (39%), and 6 (38%) of these functions, respectively. The practicality of LARO is also emphasized by addressing six mechanical optimization problems. The experimental results demonstrate that LARO is a competitive MH algorithm that deals with complicated optimization problems through different performance metrics.
Design and implementation of real-time object detection system based on single-shoot detector and OpenCV

Computer vision (CV) and human-computer interaction (HCI) are essential in many technological fields. Researchers in CV are particularly interested in real-time object detection techniques, which have a wide range of applications, including inspection systems. In this study, we design and implement real-time object detection and recognition systems using the single-shoot detector (SSD) algorithm and deep learning techniques with pre-trained models. The system can detect static and moving objects in real-time and recognize the object's class. The primary goals of this research were to investigate and develop a real-time object detection system that employs deep learning and neural systems for real-time object detection and recognition. In addition, we evaluated the free available, pre-trained models with the SSD algorithm on various types of datasets to determine which models have high accuracy and speed when detecting an object. Moreover, the system is required to be operational on reasonable equipment. We tried and evaluated several deep learning structures and techniques during the coding procedure and developed and proposed a highly accurate and efficient object detection system. This system utilizes freely available datasets such as MS Common Objects in Context (COCO), PASCAL VOC, and Kitti. We evaluated our system's accuracy using various metrics such as precision and recall. The proposed system achieved a high accuracy of 97% while detecting and recognizing real-time objects.
Hyperspectral imagery reveals large spatial variations of heavy metal content in agricultural soil - A case study of remote-sensing inversion based on Orbita Hyperspectral Satellites (OHS) imagery

The widespread heavy metal contamination in soil induced by extensive human disturbance has been a global significant issue because of its chronic toxic effects on human health. However, the establishment of effective monitoring and assessing methods for heavy metal content in the soil remain a long-term challenge due to the intrinsic limitation of the current multi-band remote sensing technology and field measurement methods. Prompted by this significant technique gap, we represent an implementation of remote-sensing inversion models based on hyperspectral imagery to reconstruct soil heavy metal contents within an experimental farmland located in Mianzhu city, Sichuan, China. We collected soil samples at the pre-defined sites, and measured soil heavy metal contents and soil spectrum in the laboratory condition. Meanwhile, we obtained Orbita Hyperspectral Satellites (OHS) imagery and quantified the associated vegetation indexes. The measured soil spectrum, bands of OHS, and the generated vegetation indexes were mathematically transformed to better represent their relations with soil heavy metal contents. Consequently, the most sensitive variables were selected as potent predictors of soil heavy metal contents in the inversion models. After evaluating the optimal inversion models for each heavy metal element, we implemented them to reconstruct the spatial patterns of soil heavy metal contents over the study landscape. We found obvious benefits of the remote sensing inversion model in predicting the spatial heterogeneity of heavy metal content within this small landscape patch. Specifically, the inversion model unraveled a normal distribution of heavy metal contents within the landscape, while the traditional spatial interpolation based on field measurements may suggest a largely skewed distribution. Compared with airborne-based studies, this study represents an application of spaceborne satellite data, which can be easily applied to a large spatial scale and long-term monitoring.
A Unified Multi-task Learning Framework for Multi-goal Conversational Recommender Systems

Recent years witnessed several advances in developing multi-goal conversational recommender systems (MG-CRS) that can proactively attract users’ interests and naturally lead user-engaged dialogues with multiple conversational goals and diverse topics. Four tasks are often involved in MG-CRS, including Goal Planning, Topic Prediction, Item Recommendation, and Response Generation. Most existing studies address only some of these tasks. To handle the whole problem of MG-CRS, modularized frameworks are adopted where each task is tackled independently without considering their interdependencies. In this work, we propose a novel Unified MultI-goal conversational recommeNDer system (UniMIND). Specifically, we unify these four tasks with different formulations into the same sequence-to-sequence paradigm. Prompt-based learning strategies are investigated to endow the unified model with the capability of multi-task learning. Finally, the overall learning and inference procedure consists of three stages, including multi-task learning, prompt-based tuning, and inference. Experimental results on two MG-CRS benchmarks (DuRecDial and TG-ReDial) show that UniMIND achieves state-of-the-art performance on all tasks with a unified model. Extensive analyses and discussions are provided for shedding some new perspectives for MG-CRS.
Effects of service robots' anthropomorphism on consumers' attribution toward and forgiveness of service failure

Abstract We investigated the effects of service robots' anthropomorphism on consumers' attribution and forgiveness of service failure. By manipulating the levels of anthropomorphism in service robots in three experiments, we evaluated the relationship norms and involvement levels of consumers. Three contributions of our study are as follows. First, most human–robot interaction studies have focused on consumers' dissatisfaction with service robots' service failures. Unfortunately, few studies have investigated the influence of service robots' anthropomorphism levels on consumers' internal attribution and forgiveness of service failure. In the present study, we found a positive correlation between the former and the latter. The findings of this study indicate marketing managers should design their service robots to be less anthropomorphic to reduce the likelihood of consumers making an internal attribution when a service failure occurs and increase their forgiveness of service failure. In addition, to reduce the likelihood of consumers' internal attribution upon service failure, managers may ensure that their service robots explain to the consumers the cause of service failure (e.g., inability to understand consumers' requirements and robots' design‐related limitations). Second, consumers' relationship norms moderate the effect of anthropomorphism level on internal attribution. Third, robot anthropomorphism is reportedly effective only when anthropomorphization occurs subconsciously.
Optimal Design of Convolutional Neural Network Architectures Using Teaching–Learning-Based Optimization for Image Classification

Convolutional neural networks (CNNs) have exhibited significant performance gains over conventional machine learning techniques in solving various real-life problems in computational intelligence fields, such as image classification. However, most existing CNN architectures were handcrafted from scratch and required significant amounts of problem domain knowledge from designers. A novel deep learning method abbreviated as TLBOCNN is proposed in this paper by leveraging the excellent global search ability of teaching–learning-based optimization (TLBO) to obtain an optimal design of network architecture for a CNN based on the given dataset with symmetrical distribution of each class of data samples. A variable-length encoding scheme is first introduced in TLBOCNN to represent each learner as a potential CNN architecture with different layer parameters. During the teacher phase, a new mainstream architecture computation scheme is designed to compute the mean parameter values of CNN architectures by considering the information encoded into the existing population members with variable lengths. The new mechanisms of determining the differences between two learners with variable lengths and updating their positions are also devised in both the teacher and learner phases to obtain new learners. Extensive simulation studies report that the proposed TLBOCNN achieves symmetrical performance in classifying the majority of MNIST-variant datasets, displays the highest accuracy, and produces CNN models with the lowest complexity levels compared to other state-of-the-art methods due to its promising search ability.
Camera-aware representation learning for person re-identification

Person re-identification (ReID) aims to associate the same person across non-overlapping cameras. However, most of existing works neglect the issue of camera-imbalanced data distribution. Consequently, pedestrian representation learning gives preference to the head cameras, which have comparatively more training data, and disregards the tail cameras, which have relatively less training data. In this paper, we propose a novel framework for camera-aware representation learning to overcome this issue, named CARL. On the proxy level representation learning, CARL presents a multiple-center softmax loss to correct the head camera bias and presents a hard sub-center mining strategy to improve the discrimination of tail camera samples. On the pair-wise level representation learning, CARL builds a camera-balanced memory bank (CBM) to re-balance the sample pair distribution and proposes a camera-paired loss for pair-wise metric learning. Extensive experiments and ablation studies on MSMT17, the current largest ReID dataset with massive camera-imbalanced data distribution, demonstrate that our CARL is superior to previous metric learning based ReID methods and achieves state-of-the-art performance.
Big Data Pipelines on the Computing Continuum: Tapping the Dark Data

The computing continuum enables new opportunities for managing big data pipelines concerning efficient management of heterogeneous and untrustworthy resources. We discuss the big data pipelines lifecycle on the computing continuum and its associated challenges, and we outline a future research agenda in this area.
Understanding, Idealization, and Explainable AI

Abstract Many AI systems that make important decisions are black boxes: how they function is opaque even to their developers. This is due to their high complexity and to the fact that they are trained rather than programmed. Efforts to alleviate the opacity of black box systems are typically discussed in terms of transparency, interpretability, and explainability. However, there is little agreement about what these key concepts mean, which makes it difficult to adjudicate the success or promise of opacity alleviation methods. I argue for a unified account of these key concepts that treats the concept of understanding as fundamental. This allows resources from the philosophy of science and the epistemology of understanding to help guide opacity alleviation efforts. A first significant benefit of this understanding account is that it defuses one of the primary, in-principle objections to post hoc explainable AI (XAI) methods. This “rationalization objection” argues that XAI methods provide mere rationalizations rather than genuine explanations. This is because XAI methods involve using a separate “explanation” system to approximate the original black box system. These explanation systems function in a completely different way than the original system, yet XAI methods make inferences about the original system based on the behavior of the explanation system. I argue that, if we conceive of XAI methods as idealized scientific models, this rationalization worry is dissolved. Idealized scientific models misrepresent their target phenomena, yet are capable of providing significant and genuine understanding of their targets.