Reviewerly/exHarmony huggingface dataset:
Spatial-Temporal Interval Aware Sequential POI Recommendation

The past flourishing years of sequential point-of-interest (POI) recommendation began with the introduction of Self-Attention Network (SAN), which quickly superseded CNN or RNN as the state-of-the-art backbone. To realize the fine-grained users' behavior patterns modeling, recent works utilize modified attention mechanisms or neural network layers to process spatial-temporal factors. However, due to the significant increase on either model's parameter scale or computational burden, we argue that these methods can be further improved. In this paper, we exploit two lightweight approaches, Time Aware Position Encoder (TAPE) and Interval Aware Attention Block (IAAB), to impel SAN by considering the spatial-temporal intervals among POIs separately, where requiring neither extra parameters nor high computational cost. On the one hand, TAPE, adjusting the positions in sequences based on the timestamps dynamically and generating positional representations with sinusoidal transformation, can enhance sequence representations to reflect both the absolute order and relative temporal proximity among all POIs. On the other hand, IAAB, point-wise adding the scaled spatial-temporal intervals to the attention map, can promote the attention mechanism attaching importance to the spatial relation among all POIs under the constraints of time conditions and providing more explainable recommendation. We integrate these two modules into SAN and propose a Spatial-Temporal Interval-Aware sequential POI recommender, namely STiSAN, as an end-to-end deployment. Experimental results based on three public LBSN datasets and one real-world city transportation dataset demonstrate STiSAN's superior performance (average 13.01% improvement against the strongest baseline). Moreover, we validate the extensibility and interpretability of TAPE and IAAB through metric evaluation and visualization separately.
Feature Augmentation with Reinforcement Learning

Sufficient good features are indispensable to train well-performed machine learning models. However, it is com-mon that good features are not always enough, where feature augmentation is necessary to enrich high-quality features by joining with other tables. There are two main challenges for the problem. Given a set of tables where we can augment features from, the first challenge is that there are a lot of ways of joining multiple tables and deciding which features (or attributes) to use - selecting the best set of features to augment is hard. Moreover, we may need to materialize the join results for different join options, doing full materialization might be time consuming - efficient but approximate methods are needed. In this paper, we first introduce the design space of the feature augmentation problem. Then, to address the above challenges, we propose a reinforcement learning based framework, namely AutoFeature, to augment the features following an exploration-exploitation strategy. AutoFeature keeps exploring the features in tables that have led to performance improvement. At the same time, AutoFeature also exploits the tables (features) that are rarely selected. In this way, the search space of tables (features) to be augmented can be well explored and a subset of good features can be selected. AutoFeature utilizes sampling techniques to achieve high efficiency. We implement two algorithms, one with multi-arm bandit and the other with branch Deep Q Networks (branch DQN), to realize the framework of AutoFeature. We conducted experiments on three real-world datasets School/XuetangE/Air using 16/23/34 candidate tables with 695/204/338 candidate features. Extensive results show that AutoFeature outperforms other methods by 12.4% and 9.8% on AUC values on two classification datasets (School and XuetangE) and by 0.113 on the MSE value on Air in terms of the model performance.
Change Captioning: A New Paradigm for Multitemporal Remote Sensing Image Analysis

Change detection (CD) is among the most important applications in remote sensing that allows identifying the changes that occurred in a given geographical area across different times. Even though CD systems have seen a lot of progress in RS, their output is either a binary map highlighting the changing area or a semantic change map that indicates the type of change for each pixel. The change maps are often difficult to interpret by end-users and they omit important information such are relationships and attributes of the changed areas. Motivated by the recent advancement of image captioning in the RS community, in this article we propose to describe the changes over bi-temporal images through change sentence descriptions. The aim of this article is to provide a user-friendly interpretation of the occurred changes. To this end, we propose two change captioning (CC) systems that take as input bi-temporal images and generate coherent sentence descriptions of the occurred changes. Convolutional neural networks (CNNs) are used to extract discriminative features from the bi-temporal images and recurrent neural networks (RNNs) or support vector machines (SVMs) are exploited to generate coherent change descriptions. Furthermore, in absence of a CC dataset to test our systems, we propose two new datasets. One is based on very high-resolution RGB images and the other one is based on multispectral RS images. The obtained experimental results show promising capabilities of the proposed systems to generate coherent change descriptions from the bi-temporal images. The datasets are available at the following link: https://disi.unitn.it/&#x007E;melgani/datasets.html.
Enhanced unconventional photon-blockade effect in one- and two-qubit cavities interacting with nonclassical light

The antibunching effect generated in a cavity containing first a single two-level atomic system and then two identical atoms is explored for off-resonant interactions, in strong- and weak-coupling regimes. The cavity is driven by coherent and squeezed sources. Using the second-order correlation function analytically derived in the weak-excitation regime, we show that squeezed light achieves and improves strong antibunching, leading to a photon blockade. This enhancement is due to a destructive quantum interference mechanism induced by the nonclassical light, creating a supplementary transition pathway. The best effect is obtained for equal frequency detunings in the strong-coupling regime. More interestingly, our investigation reveals that the two-atom-cavity system can further improve the antibunching effect compared to the single-atom cavity. It turns out that the additional couplings appearing in the cavity due to the implementation of the two atoms, combined with the squeezed light, could substantially enhance the photon-blockade effect.
FINDEAS: A FinTech-Based Approach on Designing and Assessing IoT Systems

This article proposes a new approach to the design and assessment of Internet of Things (IoT) systems. Specifically, it identifies the principles that can be derived from the financial technology ecosystem and should be considered when designing an IoT system in order for it to be technically sound, economically efficient, and have the best market acceptance prospects. Considering a specific area of IoT systems, that of the smart agriculture, we then examine whether the proposed design principles are followed by the current state-of-the-art systems. Furthermore, we claim that our proposal can also be utilized as a more comprehensive basis for assessment in comparison to the technology readiness level, which indicates only the technological readiness of a project without grading its market potential.
A conceptual model and case study of blockchain-enabled social media platform

Nowadays, with the emergence of Web 3.0 and the metaverse, we collectively witnessed the explosive development of the decentralised autonomous organisation and the blockchain business model. Particularly, the advancement of technologies has further given birth to a novel form of social platform as blockchain-enabled social media (i.e., SocialFi), which is growing both in size and number of users. Accordingly, the rapid development of these blockchain-enabled social media firms illustrates the requirement to better understand the reasons behind this increase and the innovative practices and strategies of firms in this emerging field. Using the case of Pixie – the world’s first fully functional decentralised photo and video sharing social network based on blockchain technology, this insight paper identifies a conceptual model of blockchain-enabled social media that is useful for illustrating the successful business strategy and operations of firms. Particularly, the identified model employs four pillars of innovation as fundamental technologies, governance and operations, incentive mechanism design, and organisational structure and performance. Based on this crypto economy social media model, the study further presents the main challenges, discusses the implications based on agency theory, as well as highlights several directions for future research associated with blockchain-enabled social media.
On the 4-adic complexity of the two-prime quaternary generator

R. Hofer and A. Winterhof proved that the 2-adic complexity of the two-prime (binary) generator of period pq with two odd primes $$p\ne q$$ is close to its period and it can attain the maximum in many cases. When the two-prime generator is applied to producing quaternary sequences, we need to determine the 4-adic complexity. It is proved that there are only two possible values of the 4-adic complexity for the two-prime quaternary generator, which are at least $$pq-1-\max \{\log _4(pq^2),\log _4(p^2q)\}$$ . Examples for primes p and q with $$5\le p, q <10000$$ illustrate that the 4-adic complexity only takes one value larger than $$pq-\max \{\log _4(p),\log _4(q)\}$$ , which is close to its period. So it is good enough to resist the attack of the rational approximation algorithm.
HiTDL: High-Throughput Deep Learning Inference at the Hybrid Mobile Edge

Deep neural networks (DNNs) have become a critical component for inference in modern mobile applications, but the efficient provisioning of DNNs is non-trivial. Existing mobile- and server-based approaches compromise either the inference accuracy or latency. Instead, a hybrid approach can reap the benefits of the two by splitting the DNN at an appropriate layer and running the two parts separately on the mobile and the server respectively. Nevertheless, the DNN throughput in the hybrid approach has not been carefully examined, which is particularly important for edge servers where limited compute resources are shared among multiple DNNs. This article presents HiTDL, a runtime framework for managing multiple DNNs provisioned following the hybrid approach at the edge. HiTDL's mission is to improve edge resource efficiency by optimizing the combined throughput of all co-located DNNs, while still guaranteeing their SLAs. To this end, HiTDL first builds comprehensive performance models for DNN inference latency and throughout with respect to multiple factors including resource availability, DNN partition plan, and cross-DNN interference. HiTDL then uses these models to generate a set of candidate partition plans with SLA guarantees for each DNN. Finally, HiTDL makes global throughput-optimal resource allocation decisions by selecting partition plans from the candidate set for each DNN via solving a fairness-aware multiple-choice knapsack problem. Experimental results based on a prototype implementation show that HiTDL improves the overall throughput of the edge by <inline-formula><tex-math notation="LaTeX">$4.3\times$</tex-math></inline-formula> compared with the state-of-the-art.
Advancing hybrid quantum–classical computation with real-time execution

The use of mid-circuit measurement and qubit reset within quantum programs has been introduced recently and several applications demonstrated that perform conditional branching based on these measurements. In this work, we go a step further and describe a next-generation implementation of classical computation embedded within quantum programs that enables the real-time calculation and adjustment of program variables based on the mid-circuit state of measured qubits. A full-featured Quantum Intermediate Representation (QIR) model is used to describe the quantum circuit including its embedded classical computation. This integrated approach eliminates the need to evaluate and store a potentially prohibitive volume of classical data within the quantum program in order to explore multiple solution paths. It enables a new type of quantum algorithm that requires fewer round-trips between an external classical driver program and the execution of the quantum program, significantly reducing computational latency, as much of the classical computation can be performed during the coherence time of quantum program execution. We review practical challenges to implementing this approach along with developments underway to address these challenges. An implementation of this novel and powerful quantum programming pattern, a random walk phase estimation algorithm, is demonstrated on a physical quantum computer with an analysis of its benefits and feasibility as compared to existing quantum computing methods.
Robotic employees vs. human employees: Customers’ perceived authenticity at casual dining restaurants

Cost-saving and sanitation considerations and the challenge of labor shortages have catalyzed the application of service robots in restaurants. Although service robots can perform multiple roles and functions, more research attention is needed in hospitality contexts on how different combinations of using robots and humans at different product/service layers may influence customers' experiences and behavioral intentions. Building on the literature of product level theory and authenticity, this study empirically investigated this issue with data collected from 364 customers in China. The results show that the use of robots in core and facilitating product levels is less effective in improving consumers' perceived service and brand authenticity. Consumers' perceived service authenticity positively influences their brand authenticity and repurchase intention. Consumers' perceived brand authenticity only positively affects their repurchase intention. Both theoretical and managerial implications are discussed in this paper.
The Robustness of Counterfactual Explanations Over Time

Counterfactual explanations are a prominent example of post-hoc interpretability methods in the explainable Artificial Intelligence (AI) research domain.Differently from other explanation methods, they offer the possibility to have recourse against unfavourable outcomes computed by machine learning models.However, in this paper we show that retraining machine learning models over time may invalidate the counterfactual explanations of their outcomes.We provide a formal definition of this phenomenon and we introduce a method, namely counterfactual data augmentation, to help improving the robustness of counterfactual explanations over time.We test our method in an empirical study where we simulate different model retraining scenarios.Our results show that counterfactual data augmentation improves the robustness of counterfactual explanations over time, therefore contributing to their use in real-world machine learning applications.
Reordering and Partitioning of Distributed Quantum Circuits

A new approach to reduce the teleportation cost and execution time in Distributed Quantum Circuits (DQCs) was proposed in the present paper. DQCs, a well-known solution, have been applied to solve the problem of maintaining a large number of qubits next to each other. In the distributed quantum system, the qubits are transferred to another subsystem by a quantum protocol like teleportation. Hence, a novel method was proposed to optimize the number of teleportation and to reduce the execution time for generating DQC. To this end, first, the quantum circuit was reordered according to the qubits placement to improve the computational execution time, and then the quantum circuit was modeled as a graph. Finally, we combined the genetic algorithm (GA) and the modified tabu search algorithm (MTS) to partition the graph model in order to obtain a distributed quantum circuit aimed at reducing the number of teleportation costs. A significant reduction in teleportation cost (TC) and execution time (ET) was obtained in benchmark circuits. In particular, we performed a more accurate optimization than the previous approaches, and the proposed approach yielded the best results for several benchmark circuits.
LDC: Lightweight Dense CNN for Edge Detection

This paper presents a Lightweight Dense Convolutional (LDC) neural network for edge detection.The proposed model is an adaptation of two state-of-the-art approaches, but it requires less than 4% of parameters in comparison with these approaches.The proposed architecture generates thin edge maps and reaches the highest score (i.e., ODS) when compared with lightweight models (models with less than 1 million parameters), and reaches a similar performance when compare with heavy architectures (models with about 35 million parameters).Both quantitative and qualitative results and comparisons with stateof-the-art models, using different edge detection datasets, are provided.The proposed LDC does not use pre-trained weights and requires straightforward hyper-parameter settings.The source code is released at https://github.com/xavysp/LDC.
SolCMC: Solidity Compiler’s Model Checker

Abstract Formally verifying smart contracts is important due to their immutable nature, usual open source licenses, and high financial incentives for exploits. Since 2019 the Ethereum Foundation’s Solidity compiler ships with a model checker. The checker, called SolCMC, has two different reasoning engines and tracks closely the development of the Solidity language. We describe SolCMC’s architecture and use from the perspective of developers of both smart contracts and tools for software verification, and show how to analyze nontrivial properties of real life contracts in a fully automated manner.
Quantifying the photovoltaic potential of highways in China

Installing photovoltaic (PV) modules on highways is considered a promising way to support carbon neutrality in China. However, collecting the area of the highway, and precisely assessing the shadow area of the highway under complex terrain remain challenges. That severely hinders the assessment of highway PV potential. To address these challenges, a spatiotemporal model is developed in this study to estimate the annual solar PV potential on highways over the whole Chinese territory. First, the areas of different highway segments are calculated based on highway network and highway toll stations. Second, hourly shadow area on highways created by nearby terrain is estimated based on a digital elevation model (DEM). When calculating the highway PV potential, the solar irradiation received in these shadow areas is regarded as zero. Finally, the PV potential of all lanes and emergency lanes was estimated at the prefecture-level city scale using surface radiation data and radiation assessment models. Based on the highway data with a total mileage of 143,684 km at the end of 2020, the results show that the annual PV potential is 3,932 TW and that the corresponding installed capacity is 700.85 GW, which can generate clean electricity at a rate of up to 629.06 TWh. The annual PV potential of highways in the southeast is greater than that in the northwest owing to the higher highway density in the southeast. This study provides a reference basis for highway PV construction planning and suitably assessment in each region of China for PV highway development.
A New Perspective on Stabilizing GANs Training: Direct Adversarial Training

Generative Adversarial Networks (GANs) are the most popular image generation models that have achieved remarkable progress on various computer vision tasks. However, training instability is still one of the open problems for all GAN-based algorithms. Quite a number of methods have been proposed to stabilize the training of GANs, the focuses of which were respectively put on the loss functions, regularization and normalization technologies, training algorithms, and model architectures. Different from the above methods, in this paper, a new perspective on stabilizing GANs training is presented. It is found that sometimes the images produced by the generator act like adversarial examples of the discriminator during the training process, which may be part of the reason causing the unstable training of GANs. With this finding, we propose the Direct Adversarial Training (DAT) method to stabilize the training process of GANs. Furthermore, we prove that the DAT method is able to minimize the Lipschitz constant of the discriminator adaptively. The advanced performance of DAT is verified on multiple loss functions, network architectures, hyper-parameters, and datasets. Specifically, DAT achieves significant improvements of 11.5% FID on CIFAR-100 unconditional generation based on SSGAN, 10.5% FID on STL-10 unconditional generation based on SSGAN, and 13.2% FID on LSUN-Bedroom unconditional generation based on SSGAN. <xref ref-type="fn" rid="fn1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><sup/></xref>
Detecting Rotated Objects as Gaussian Distributions and Its 3-D Generalization

Existing detection methods commonly use a parameterized bounding box (BBox) to model and detect (horizontal) objects and an additional rotation angle parameter is used for rotated objects. We argue that such a mechanism has fundamental limitations in building an effective regression loss for rotation detection, especially for high-precision detection with high IoU (e.g., 0.75). Instead, we propose to model the rotated objects as Gaussian distributions. A direct advantage is that our new regression loss regarding the distance between two Gaussians e.g., Kullback-Leibler Divergence (KLD), can well align the actual detection performance metric, which is not well addressed in existing methods. Moreover, the two bottlenecks i.e., boundary discontinuity and square-like problem also disappear. We also propose an efficient Gaussian metric-based label assignment strategy to further boost the performance. Interestingly, by analyzing the BBox parameters' gradients under our Gaussian-based KLD loss, we show that these parameters are dynamically updated with interpretable physical meaning, which help explain the effectiveness of our approach, especially for high-precision detection. We extend our approach from 2-D to 3-D with a tailored algorithm design to handle the heading estimation, and experimental results on twelve public datasets (2-D/3-D, aerial/text/face images) with various base detectors show its superiority.