Reviewerly/exHarmony huggingface dataset:
Forecasting Photovoltaic Power Generation with a Stacking Ensemble Model

Nowadays, photovoltaics (PV) has gained popularity among other renewable energy sources because of its excellent features. However, the instability of the system’s output has become a critical problem due to the high PV penetration into the existing distribution system. Hence, it is essential to have an accurate PV power output forecast to integrate more PV systems into the grid and to facilitate energy management further. In this regard, this paper proposes a stacked ensemble algorithm (Stack-ETR) to forecast PV output power one day ahead, utilizing three machine learning (ML) algorithms, namely, random forest regressor (RFR), extreme gradient boosting (XGBoost), and adaptive boosting (AdaBoost), as base models. In addition, an extra trees regressor (ETR) was used as a meta learner to integrate the predictions from the base models to improve the accuracy of the PV power output forecast. The proposed model was validated on three practical PV systems utilizing four years of meteorological data to provide a comprehensive evaluation. The performance of the proposed model was compared with other ensemble models, where RMSE and MAE are considered the performance metrics. The proposed Stack-ETR model surpassed the other models and reduced the RMSE by 24.49%, 40.2%, and 27.95% and MAE by 28.88%, 47.2%, and 40.88% compared to the base model ETR for thin-film (TF), monocrystalline (MC), and polycrystalline (PC) PV systems, respectively.
Knowledge sharing and achieving competitive advantage in international environments

This study investigates knowledge sharing in achieving competitive advantage in international environments in digital start-ups. In terms of research purpose, it is developmental and mixed. In the quantitative method, the sample consists of 17 experts, while in the qualitative method, the sample consists of executive and international managers of 250 Iranian international digital start-ups in 2020, numbering 500 people. The sample size is estimated to be 217 by using Cochran’s formula. In order to collect data, the authors used a standard 24-item questionnaire with a five-point Likert scale. Finally, the qualitative data is analysed according to the coding process, whereas the quantitative method is analysed by using Smart PLS 3 software. In the qualitative method, in-depth semi-structured interviews were conducted with 17 experts. The coding results led to the identification of knowledge sharing components, including management support, cultural drivers’ management, knowledge sharing performance, Social networks and networking and human resource in the firms. Findings in the quantitative method showed that the management support, cultural drivers’ management, knowledge sharing performance, social networks and networking, and human resource of the firms positively affect the competitive advantage of digital start-ups in international environments. The Conclusion of this study showed that knowledge sharing in digital start-ups improves the organisation’s performance, creating new and innovative ideas in today’s dynamic environment and creating a competitive advantage in international environments.
Availability Attacks Create Shortcuts

Availability attacks, which poison the training data with imperceptible perturbations, can make the data not exploitable by machine learning algorithms so as to prevent unauthorized use of data. In this work, we investigate why these perturbations work in principle. We are the first to unveil an important population property of the perturbations of these attacks: they are almost linearly separable when assigned with the target labels of the corresponding samples, which hence can work as shortcuts for the learning objective. We further verify that linear separability is indeed the workhorse for availability attacks. We synthesize linearly-separable perturbations as attacks and show that they are as powerful as the deliberately crafted attacks. Moreover, such synthetic perturbations are much easier to generate. For example, previous attacks need dozens of hours to generate perturbations for ImageNet while our algorithm only needs several seconds. Our finding also suggests that the shortcut learning is more widely present than previously believed as deep models would rely on shortcuts even if they are of an imperceptible scale and mixed together with the normal features. Our source code is published at https://github.com/dayu11/Availability-Attacks-Create-Shortcuts.
Hybrid Encoding for Generating Large Scale Game Level Patterns With Local Variations

Generative Adversarial Networks (GANs) are a powerful indirect genotype-to-phenotype mapping for evolutionary search. Much previous work applying GANs to level generation focuses on fixed-size segments combined into a whole level, but individual segments may not fit together cohesively. In contrast, segments in human designed levels are often repeated, directly or with variation, and organized into patterns (the symmetric eagle in Level 1 of The Legend of Zelda, or repeated pipe motifs in Super Mario Bros). Such patterns can be produced with Compositional Pattern Producing Networks (CPPNs). CPPNs define latent vector GAN inputs as a function of geometry, organizing segments output by a GAN into complete levels. However, collections of latent vectors can also be evolved directly, producing more chaotic levels. We propose a hybrid approach that evolves CPPNs first, but allows latent vectors to evolve later, combining the benefits of both approaches. These approaches are evaluated in Super Mario Bros. and The Legend of Zelda. We previously demonstrated via a Quality-Diversity algorithm that CPPNs better cover the space of possible levels than directly evolved levels. Here, we show that the hybrid approach (1) covers areas that neither of the other methods can, and (2) achieves comparable or superior QD scores.
A single-qubit position verification protocol that is secure against multi-qubit attacks

The position of a device or agent is an important security credential in today’s society, both online and in the real world. Unless in direct proximity, however, the secure verification of a position is impossible without further assumptions. This is true classically1, but also in any future quantum-equipped communications infrastructure2. We show in this work that minimal quantum resources, in the form of a single qubit, combined with classical communication are sufficient to thwart quantum adversaries that pretend to be at a specific position and have the ability to coordinate their action with entanglement. More precisely, we show that adversaries using an increasing amount of entanglement can be combatted solely by increasing the number of classical bits used in the protocol. The presented protocols are noise-robust and within reach of current quantum technology. Unless you are nearby, it is difficult to verify where someone is. Access to a single qubit and classical computation and communication makes it possible to securely check someone’s position as long as adversaries’ quantum resources are limited.
Blockchain for electronic medical record: assessing stakeholders’ readiness for successful blockchain adoption in health-care

Purpose This paper aims to provide a helpful tool for those who plan to implement blockchain-based solutions for the governance of the electronic medical record (EMR) in health-care settings. The goals are to identify each type of stakeholders involved in these projects and to clarify the relevance, to achieve success, of their readiness, intended as availability and ability to adopt blockchain. Design/methodology/approach The chosen methodology is a multiple case study on three initiatives that used blockchain to manage EMRs. This study relied on multiple sources of evidence. The primary data consisted of two rounds of semi-structured interviews with different informants. This study followed a grounded theory approach and performed within- and cross-case analyses. Findings This study identified the types of stakeholders – nodes and not-nodes – of the network and how their readiness level affects the implementation of blockchain-based projects applied to EMR. The nodes (e.g. patients and doctors) are pivotal in making the network working once this has been constructed. Out of the four readiness dimensions suggested by literature, motivational readiness, has the higher impact. Not-nodes stakeholders play a pivotal role in the project’s pre-implementation phase. For them, structural readiness is the dimension with the higher relevance. Originality/value To the best of the authors’ knowledge, it is the first time that a paper analyses the differences between nodes and not-nodes stakeholders of the blockchain network, in terms not only of type but also of readiness. Identifying the readiness level to implement successful projects is a fundamental step that has never been analysed in the health field.
VGNMN: Video-grounded Neural Module Networks for Video-Grounded Dialogue Systems

Neural module networks (NMN) have achieved success in image-grounded tasks such as Visual Question Answering (VQA) on synthetic images.However, very limited work on NMN has been studied in the video-grounded dialogue tasks.These tasks extend the complexity of traditional visual tasks with the additional visual temporal variance and language cross-turn dependencies.Motivated by recent NMN approaches on image-grounded tasks, we introduce Videogrounded Neural Module Network (VGNMN) to model the information retrieval process in video-grounded language tasks as a pipeline of neural modules.VGNMN first decomposes all language components in dialogues to explicitly resolve any entity references and detect corresponding action-based inputs from the question.The detected entities and actions are used as parameters to instantiate neural module networks and extract visual cues from the video.Our experiments show that VGNMN can achieve promising performance on a challenging video-grounded dialogue benchmark as well as a video QA benchmark.
Learnable Privacy-Preserving Anonymization for Pedestrian Images

This paper studies a novel privacy-preserving anonymization problem for pedestrian images, which preserves personal identity information (PII) for authorized models and prevents PII from being recognized by third parties. Conventional anonymization methods unavoidably cause semantic information loss, leading to limited data utility. Besides, existing learned anonymization techniques, while retaining various identity-irrelevant utilities, will change the pedestrian identity, and thus are unsuitable for training robust re-identification models. To explore the privacy-utility trade-off for pedestrian images, we propose a joint learning reversible anonymization framework, which can reversibly generate full-body anonymous images with little performance drop on person re-identification tasks. The core idea is that we adopt desensitized images generated by conventional methods as the initial privacy-preserving supervision and jointly train an anonymization encoder with a recovery decoder and an identity-invariant model. We further propose a progressive training strategy to improve the performance, which iteratively upgrades the initial anonymization supervision. Experiments further demonstrate the effectiveness of our anonymized pedestrian images for privacy protection, which boosts the re-identification performance while preserving privacy. Code is available at https://github.com/whuzjw/privacy-reid.
The formation of specific and gist associative episodic memory representations during encoding: Effects of rate of presentation.

Assessing the time course under which underlying memory representations can be formed is an important question for understanding memory. Several studies assessing item memory have shown that gist representations of items are laid out more rapidly than verbatim representations. However, for associations among items/components, which form the core of episodic memory, it is unclear whether gist representations form more quickly than, or at least in parallel with, verbatim representations, as fuzzy-trace theory predicts, or whether gist is extracted more slowly from inferring the meaning of verbatim representations, as in gist macroprocessor theories. To test these contrasting possibilities, we used a novel associative recognition task in which participants studied face-scene pairs for .75, 1.5, or 4 seconds each, and were later tested on their ability to discriminate intact pairs from foils which varied in how similar they were to originally studied pairs. Across 2 experiments, we found that verbatim memory for associations, measured using a multinomial-processing-tree model, improved from .75 to 1.5 to 4 seconds of presentation time. Paralleling these effects of encoding time on verbatim memory, for gist memory, there were improvements from .75 seconds to 1.5 seconds in both experiment 1 and 2, while improvements from 1.5 seconds to 4 seconds were only evident when the retention interval between study and test was increased (experiment 2). These results provide strong support for the parallel processing framework of fuzzy-trace theory over the slow gist extraction framework of an alternative gist macroprocessor theory. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
Computational pathology in ovarian cancer

Histopathologic evaluations of tissue sections are key to diagnosing and managing ovarian cancer. Pathologists empirically assess and integrate visual information, such as cellular density, nuclear atypia, mitotic figures, architectural growth patterns, and higher-order patterns, to determine the tumor type and grade, which guides oncologists in selecting appropriate treatment options. Latent data embedded in pathology slides can be extracted using computational imaging. Computers can analyze digital slide images to simultaneously quantify thousands of features, some of which are visible with a manual microscope, such as nuclear size and shape, while others, such as entropy, eccentricity, and fractal dimensions, are quantitatively beyond the grasp of the human mind. Applications of artificial intelligence and machine learning tools to interpret digital image data provide new opportunities to explore and quantify the spatial organization of tissues, cells, and subcellular structures. In comparison to genomic, epigenomic, transcriptomic, and proteomic patterns, morphologic and spatial patterns are expected to be more informative as quantitative biomarkers of complex and dynamic tumor biology. As computational pathology is not limited to visual data, nuanced subvisual alterations that occur in the seemingly "normal" pre-cancer microenvironment could facilitate research in early cancer detection and prevention. Currently, efforts to maximize the utility of computational pathology are focused on integrating image data with other -omics platforms that lack spatial information, thereby providing a new way to relate the molecular, spatial, and microenvironmental characteristics of cancer. Despite a dire need for improvements in ovarian cancer prevention, early detection, and treatment, the ovarian cancer field has lagged behind other cancers in the application of computational pathology. The intent of this review is to encourage ovarian cancer research teams to apply existing and/or develop additional tools in computational pathology for ovarian cancer and actively contribute to advancing this important field.
Translation-invariant context-retentive wavelet reflection removal network

It has been widely investigated for images taken through glass to remove unwanted reflections in deep learning. However, none of these methods have bad effects, but they all remove reflections in specific situations, and validate the results with their own datasets, e.g., several local places with strong reflections. These limitations will result in situations where real reflections in the world cannot be effectively eliminated. In this study, a novel Translation-invariant Context-retentive Wavelet Reflection Removal Network is proposed to address this issue. In addition to context and background, low-frequency sub-images still have a small amount of reflections. To enable background context retention and reflection removal, the low-frequency sub-images at each level are performed on the Context Retention Subnetwork (CRSn) after wavelet transform. Novel context level blending and inverse wavelet transform are proposed to remove reflections in low frequencies and retain background context recursively, which is of great help in restoring clean images. High-frequency sub-images with reflections are performed on the Detail-enhanced Reflection layer removal Subnetwork to complete reflection removal. In addition, in order to further separate the reflection layer and the transmission layer better, we also propose Detail-enhanced Reflection Information Transmission, through which the extracted features of reflection layer in high-frequency images can help the CRSn effectively separate the transmission layer and the reflection layer, so as to achieve the effects of removing reflection. The quantitative and visual experimental results on benchmark datasets demonstrate that the proposed method performs better than the state-of-the-art approaches.
The effect of preservice teachers’ experiences in a flipped course on digital competencies related to educational technology and innovativeness

Developing the digital competencies and innovativeness of preservice teachers enables them to utilize technology more effectively and efficiently in their future professional life. However, poor technology training can lead preservice teachers to potentially misuse technology in the classroom. Moreover, little is known about how to design effective technology training courses in order to develop the digital competencies and innovativeness of preservice teachers. Therefore, this study examined a course designed with the flipped learning approach and its impact on preservice teachers’ digital competencies and innovativeness. 58 students participated in the current study. Using a mixed-methods sequential explanatory design, changes in digital competencies and innovativeness were analyzed before and after the flipped course. Findings revealed statistically significant gains in preservice teachers’ digital competencies and innovativeness, and qualitative findings revealed the reasons for these gains. The most common themes were using Web 2.0 tools for teaching, acceptance of flipped learning, doing research, and gaining different perspectives. Based on these findings, it is argued that the use of flipped learning approach removed the time barrier in classes and enabled teachers to gain experience on effective technology usage through actual physical application. The study’s results provided recommendations for further research on the connection between flipped learning and the development of preservice teachers’ digital competencies and innovativeness.
Deterministic and Entanglement-Efficient Preparation of Amplitude-Encoded Quantum Registers

Quantum computing promises to provide exponential speed-ups to certain classes of problems. In many such algorithms, a classical vector $\mathbf{b}$ is encoded in the amplitudes of a quantum state $|b⟩$. However, efficiently preparing $|b⟩$ is known to be a difficult problem because an arbitrary state of $Q$ qubits generally requires approximately ${2}^{Q}$ entangling gates, which results in significant decoherence on today's noisy intermediate-scale quantum (NISQ) computers. We present a deterministic (nonvariational) algorithm that allows us to flexibly reduce the quantum resources required for state preparation in an entanglement-efficient manner. Although this comes at the expense of reduced theoretical fidelity, actual fidelities on current NISQ computers might actually be higher due to reduced decoherence. We show this to be true for various cases of interest, such as the normal and log-normal distributions. For low-entanglement states, our algorithm can prepare states with more than an order of magnitude fewer entangling gates as compared with isometric decomposition.
Spatio-Temporal Vehicle Trajectory Recovery on Road Network Based on Traffic Camera Video Data

Large-scale vehicle trajectories bring great benefits in understanding urban mobility, and can be used to promote a wide range of applications in building intelligent transportation systems. Traditional approaches cannot recover the trajectories of all the vehicles on the roads since they are based on partial trajectory data. To address it, we study the all-vehicle trajectory recovery based on traffic camera video data. However, there are two challenges in this study. First, the quality of the images captured by traffic cameras is unbalanced, so it is hard to identify the same vehicles. Second, the traffic camera observation data are sparse due to the incompleteness of the traffic cameras and possible vehicle miss from the traffic cameras. To deal with these challenges, we design a novel system to recover the vehicle trajectory with the granularity of the road intersection. In this system, we propose an iterative framework to jointly optimize the vehicle re-identification and trajectory recovery tasks. In the vehicle re-identification task, we propose an effective strategy to guide the vehicle clustering based on visual features and the spatio-temporal constraint features updated by the trajectory discovery task. In the trajectory recovery task, we model the spatial and temporal relations as well as the vehicle miss problem by a probabilistic approach to recover the trajectories. Extensive experiments demonstrate that our framework outperforms the existing state-of-art solutions. Finally, our system is deployed in practical applications of SenseTime, China, including traffic congestion analysis and traffic signal control.
Detecting Cash-out Users via Dense Subgraphs

Cash-out fraud refers to the withdrawal of cash from a credit card by illegitimate payments with merchants. Conventional data-driven approaches for cash-out detection commonly construct a classifier with domain specific feature engineering. To further spot cash-out behaviors in complex scenarios, recent efforts adopt graph models to exploit the interaction relations rich in financial transactions. However, most existing graph-based methods are proposed for online payment activities in internet financial institutions. Moreover, these methods commonly rely on a large amount of online user data, which are not well suitable for the traditional credit card services in commercial banks. In this paper, we focus on discerning fraudulent cash-out users by taking advantage of only the personal credit card data from banks. To alleviate the scarcity of available labeled data, we formulate the cash-out detection problem as identifying dense blocks. First, we define a bipartite multigraph to hold transactions between users and merchants, where cash-out activities generate cyclically intensive and high-volume flows. Second, we give a formal definition of cash-out behaviors from four perspectives: time, capital, cyclicity, and topotaxy. Then, we develop ANTICO, with a class of metrics to capture suspicious signals of the activities and a greedy algorithm to spot suspicious blocks by optimizing the proposed metric. Theoretical analysis shows a provable upper bound of ANTICO on the effectiveness of detecting cash-out users. Experimental results show that ANTICO outperforms state-of-the-art methods in accurately detecting cash-out users on both synthetic and real-world banking data.