Reviewerly/exHarmony huggingface dataset:
Assessment of concentrated solar power generation potential in China based on Geographic Information System (GIS)

Concentrated solar power (CSP) technology can not only match peak demand in power systems but also play an important role in the carbon neutrality pathway worldwide. Actions in China is decisive. Few previous studies have estimated CSP technology’s power generation and CO2 emission reduction potentials in China. To address this knowledge gap, the geographical, technical, and CO2 emission reduction potential of CSP in China was evaluated by province based on a high resolution geographical information system with up-to-date data. A comprehensive framework including geographic and technical constrains was proposed. Exclusion criteria including solar radiation, slope, land-use type, natural reserve, and water resources were adopted to determine the suitability of CSP plant construction. Then, based on the power conversion efficiency difference from various CSP technologies, the technical potential was calculated on suitable land. The results show that approximately 1.02 × 106 km2 of land is available to support CSP development in China. Based on the available solar resource on the suitable land, the geographical potential is 2.13 × 1015 kWh. The potential installed capacity is 2.45 × 107–5.40 × 107 MW, considering four CSP technologies. The corresponding annual energy generation potential is 6.46 × 1013–1.85 × 1014 kWh. Considering the scenario of using the potential of CSP to replace the current power supply to the maximum extent, CO2 emission would have been reduced by 5.19 × 108, 5.61 × 108, and 6.24 × 108 t in 2017, 2018, and 2019, respectively. At the provincial level, more than 99% of China’s technical potential is concentrated in five western provinces, including Xinjiang, Inner Mongolia, Qinghai, Gansu, and Tibet. These results provide policy guidance and serve as a reference for the future development of CSP and site selection for CSP plant construction both in China and worldwide.
Federated Learning Meets Multi-Objective Optimization

Federated learning has emerged as a promising, massively distributed way to train a joint deep model over large amounts of edgedevices while keeping private user data strictly on device. In this work, motivated from ensuring fairness among users and robustness against malicious adversaries, we formulate federated learning as multi-objective optimization and propose a new algorithm <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FedMGDA+</monospace> that is guaranteed to converge to Pareto stationary solutions. <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FedMGDA+</monospace> is simple to implement, has fewer hyperparameters to tune, and refrains from sacrificing the performance of any <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">participating</i> user. We establish the convergence properties of <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FedMGDA+</monospace> and point out its connections to existing approaches. Extensive experiments on a variety of datasets confirm that <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FedMGDA+</monospace> compares favorably against state-of-the-art.
Scaling multi-instance support vector machine to breast cancer detection on the BreaKHis dataset

Breast cancer is a type of cancer that develops in breast tissues, and, after skin cancer, it is the most commonly diagnosed cancer in women in the United States. Given that an early diagnosis is imperative to prevent breast cancer progression, many machine learning models have been developed in recent years to automate the histopathological classification of the different types of carcinomas. However, many of them are not scalable to large-scale datasets.In this study, we propose the novel Primal-Dual Multi-Instance Support Vector Machine to determine which tissue segments in an image exhibit an indication of an abnormality. We derive an efficient optimization algorithm for the proposed objective by bypassing the quadratic programming and least-squares problems, which are commonly employed to optimize Support Vector Machine models. The proposed method is computationally efficient, thereby it is scalable to large-scale datasets. We applied our method to the public BreaKHis dataset and achieved promising prediction performance and scalability for histopathological classification.Software is publicly available at: https://1drv.ms/u/s!AiFpD21bgf2wgRLbQq08ixD0SgRD?e=OpqEmY.Supplementary data are available at Bioinformatics online.
Cross-Domain Recommendation to Cold-Start Users via Variational Information Bottleneck

Recommender systems have been widely deployed in many real-world applications, but usually suffer from the long-standing user cold-start problem. As a promising way, Cross-Domain Recommendation (CDR) has attracted a surge of inter-est, which aims to transfer the user preferences observed in the source domain to make recommendations in the target domain. Previous CDR approaches mostly achieve the goal by following the Embedding and Mapping (EMCDR) idea which attempts to learn a mapping function to transfer the pre-trained user repre-sentations (embeddings) from the source domain into the target domain. However, they pre-train the user/item representations independently for each domain, ignoring to consider both domain interactions simultaneously. Therefore, the biased pre-trained representations inevitably involve the domain-specific information which may lead to negative impact to transfer information across domains. In this work, we consider a key point of the CDR task: what information needs to be shared across domains? To achieve the above idea, this paper utilizes the information bottleneck (IB) principle, and proposes a novel approach termed as CDRIB to enforce the representations encoding the domain-shared information. To derive the unbiased representations, we devise two IB regularizers to model the cross-domain/in-domain user-item interactions simultaneously and thereby CDRIB could consider both domain interactions jointly for de-biasing. With an additional contrastive information regularizer, CDRIB can also capture cross-domain user-user correlations. In this way, those regularizers encourage the representations to encode the domain-shared information, which has the capability to make recommendations in both domains directly. To the best of our knowledge, this paper is the first work to capture the domain-shared information for cold-start users via variational information bottleneck. Empirical experiments illustrate that CDRIB outperforms the state-of-the-art approaches on four real-world cross-domain datasets, demonstrating the effectiveness of adopting the information bottleneck for CDR.
Asynchronous Federated Learning Over Wireless Communication Networks

The conventional federated learning (FL) framework usually assumes synchronous reception and fusion of all the local models at the central aggregator and synchronous updating and training of the global model at all the agents as well. However, in a wireless network, due to limited radio resource, inevitable transmission failures and heterogeneous computing capacity, it is very hard to realize strict synchronization among all the involved user equipments (UEs). In this paper, we propose a novel asynchronous FL framework, which well adapts to the heterogeneity of users, communication environments and learning tasks, by considering both the possible delays in training and uploading the local models and the resultant staleness among the received models that has heavy impact on the global model fusion. A novel centralized fusion algorithm is designed to determine the fusion weight during the global update, which aims to make full use of the fresh information contained in the uploaded local models while avoiding the biased convergence by enforcing the impact of each UE’s local dataset to be proportional to its sample share. Numerical experiments validate that the proposed asynchronous FL framework can achieve fast and smooth convergence and enhance the training efficiency significantly.
<i>DL-GuesS</i>: Deep Learning and Sentiment Analysis-Based Cryptocurrency Price Prediction

Cryptocurrencies are peer-to-peer-based transaction systems where the data exchanges are secured using the secure hash algorithm (SHA)-256 and message digest (MD)-5 algorithms. The prices of cryptocurrencies are highly volatile and follow stochastic moments and have reached their unpredictable limits. They are commonly used for investment and have become a substitute for other types of investment like metals, estates, and the stock market. Their importance in the market raises the strict requirement for a sturdy forecasting model. However, cryptocurrency price prediction is quite challenging due to its dependency on other cryptocurrencies. Many researchers have used machine learning and deep learning models, and other market sentiment-based models to predict the price of cryptocurrencies. As all the cryptocurrencies belong to a specific class, we can infer that the increase in the price of one cryptocurrency can lead to a price change for other cryptocurrencies. Researchers had also utilized the sentiments from tweets and other social media platforms to increase the performance of their proposed system. Motivated by these, in this paper, we propose a hybrid and robust framework, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DL-Gues</i> , for cryptocurrency price prediction, that considers its interdependency on other cryptocurrencies and also on market sentiments. We have considered price prediction of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Dash</i> carried out using price history and tweets of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Dash</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Litecoin</i> , and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Bitcoin</i> for various loss functions for validation. Further, to check the usability of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DL-GuesS</i> on other cryptocurrencies, we have also inferred results for price prediction of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Bitcoin-Cash</i> with the price history and tweets of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Bitcoin-Cash</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Litecoin</i> , and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Bitcoin</i> .
Quantum Optimization via Four-Body Rydberg Gates

A large ongoing research effort focuses on obtaining a quantum advantage in the solution of combinatorial optimization problems on near-term quantum devices. A particularly promising platform implementing quantum optimization algorithms are arrays of trapped neutral atoms, laser coupled to highly excited Rydberg states. However, encoding combinatorial optimization problems in atomic arrays is challenging due to limited interqubit connectivity of the native finite-range interactions. Here, we present a four-body Rydberg parity gate, enabling a direct and straightforward implementation of the parity architecture, a scalable architecture for encoding arbitrarily connected interaction graphs. Our gate relies on adiabatic laser pulses and is fully programmable by adjusting two hold times during operation. We numerically demonstrate implementations of the quantum approximate optimization algorithm (QAOA) for small-scale test problems. Variational optimization steps can be implemented with a constant number of system manipulations, paving the way for experimental investigations of QAOA beyond the reach of numerical simulations.
ASS-GAN: Asymmetric semi-supervised GAN for breast ultrasound image segmentation

Ultrasound imaging is considered to be one of the important methods for diagnosing breast cancers, and lesion segmentation is an essential step in automatic computer-aided ultrasonic diagnosis. However, the high cost of ultrasound image labeling and the small amount of data in a single dataset hinder the progress of breast ultrasound (BUS) image segmentation algorithms. In this paper, we propose a novel asymmetric semi-supervised GAN (ASSGAN), which employs two generators and a discriminator for adversarial learning. The two generators can supervise each other, i.e., they can generate reliable segmentation predicted masks as guidance for each other without labels. Therefore, the unlabeled cases can be used to effectively promote model training. To verify the proposed method, we compared it with fully supervised and semi-supervised methods on three public BUS datasets (DBUI, OASBUI, SPDBUI) and one dataset (SDBUI) that we collected. DBUI, OASBUI, SPDBUI and SDBUI contain 647, 200, 320 and 1805 cases respectively. The experimental results show that the proposed method has excellent performance under the condition of having a small number of labeled images. Compared with fully supervised methods, our method is higher by 4.16%∼13.94% in IoU.
Transferable SAR Image Classification Crossing Different Satellites Under Open Set Condition

For synthetic aperture radar (SAR) image classification problem, we need to take into account unlabeled datasets containing unknown classes crossing different satellites. In this letter, a spherical space domain adaptation (DA) network under open set condition is proposed to solve this problem. First, we transform the prior Euclidean feature space into the spherical space to construct a classification network such that features of the same class of SAR images are clustered together and features of different or unknown classes are separated on the hypersphere. Second, a correction module is designed to increase the accuracy of the pseudo-label obtained by the classifier. Then, based on the adversarial learning strategy, we introduce the gradient alignment module to achieve better alignment of the source and target domains. Finally, tests on two SAR benchmark datasets from distinct satellites show that the proposed network outperforms state-of-the-art (SOTA) approaches in terms of classification accuracy.
PipAttack: Poisoning Federated Recommender Systems for Manipulating Item Promotion

Due to the growing privacy concerns, decentralization emerges rapidly in personalized services, especially recommendation. Also, recent studies have shown that centralized models are vulnerable to poisoning attacks, compromising their integrity. In the context of recommender systems, a typical goal of such poisoning attacks is to promote the adversary's target items by interfering with the training dataset and/or process. Hence, a common practice is to subsume recommender systems under the decentralized federated learning paradigm, which enables all user devices to collaboratively learn a global recommender while retaining all the sensitive data locally. Without exposing the full knowledge of the recommender and entire dataset to end-users, such federated recommendation is widely regarded 'safe' towards poisoning attacks. In this paper, we present a systematic approach to backdooring federated recommender systems for targeted item promotion. The core tactic is to take advantage of the inherent popularity bias that commonly exists in data-driven recommenders. As popular items are more likely to appear in the recommendation list, our innovatively designed attack model enables the target item to have the characteristics of popular items in the embedding space. Then, by uploading carefully crafted gradients via a small number of malicious users during the model update, we can effectively increase the exposure rate of a target (unpopular) item in the resulted federated recommender. Evaluations on two real-world datasets show that 1) our attack model significantly boosts the exposure rate of the target item in a stealthy way, without harming the accuracy of the poisoned recommender; and 2) existing defenses are not effective enough, highlighting the need for new defenses against our local model poisoning attacks to federated recommender systems.
Efficient, Revocable, and Privacy-Preserving Fine-Grained Data Sharing With Keyword Search for the Cloud-Assisted Medical IoT System

The cloud-assisted medical Internet of Things (MIoT) has played a revolutionary role in promoting the quality of public medical services. However, the practical deployment of cloud-assisted MIoT in an open healthcare scenario raises the concern on data security and user's privacy. Despite endeavors by academic and industrial community to eliminate this concern by cryptographic methods, resource-constrained devices in MIoT may be subject to the heavy computational overheads of cryptographic computations. To address this issue, this paper proposes an efficient, revocable, privacy-preserving fine-grained data sharing with keyword search (ERPF-DS-KS) scheme, which realizes the efficient and fine-grained access control and ciphertext keyword search, and enables the flexible indirect revocation to malicious data users. A pseudo identity-based signature mechanism is designed to provide the data authenticity. We analyze the security properties of our proposed scheme, and via the theoretical comparison and experimental results we demonstrate that for the resource-constrained devices in the patient and doctor side of MIoT, in comparison with other related schemes, ERPF-DS-KS just consumes the lightweight and constant size communication/storage as well as computational time cost. For the keyword search, compared with related schemes, the cloud can quickly check whether a ciphertext contains the specified keyword with slight computations in the online phase. This further demonstrates that ERPF-DS-KS is efficient and practical in the cloud-assisted MIoT scenario.
Visual Dialog

We introduce the task of Visual Dialog, which requires an AI agent to hold a meaningful dialog with humans in natural, conversational language about visual content. Specifically, given an image, a dialog history, and a question about the image, the agent has to ground the question in image, infer context from history, and answer the question accurately. Visual Dialog is disentangled enough from a specific downstream task so as to serve as a general test of machine intelligence, while being sufficiently grounded in vision to allow objective evaluation of individual responses and benchmark progress. We develop a novel two-person real-time chat data-collection protocol to curate a large-scale Visual Dialog dataset (VisDial). VisDial v0.9 has been released and consists of dialog question-answer pairs from 10-round, human-human dialogs grounded in images from the COCO dataset.
Association Rules Extraction for Customer Segmentation in the SMEs Sector Using the Apriori Algorithm

Data Mining applied to the field of commercialization allows, among other aspects, to discover patterns of behavior in clients, which companies can use to create marketing strategies addressed to their different types of clients. This research focused on a database, the CRISP-DM methodology was applied for the Data Mining process. The database used was that corresponding to the sector of SMEs and referring to customers and sales, the analysis was made based on the PFM model (Presence, Frequency, Monetary Value), and on this model the grouping algorithms were applied: k -means, k-medoids, and SelfOrganizing Maps (SOM). To validate the result of the grouping algorithms and select the one that provides the best quality groups, the cascade evaluation technique has been used applying a classification algorithm. Finally, the Apriori algorithm was used to find associations between products for each group of customers.
Confidential Assets

Bitcoin is an online distributed ledger in which coins are distributed according to the unspent transaction output (UTXO) set, and transactions describe changes to this set. Every UTXO has associated to it an amount and signature verification key, representing the quantity that can be spent and the entity authorized to do so, respectively. Because the ledger is distributed and publicly verifiable, every UTXO (and the history of all changes) is publicly available and may be used for analysis of all users’ payment history. Although this history is not directly linked to users in any way, it exposes enough structure that even small amounts of personally identifiable information may completely break users’ privacy. Further, the ability to trace coin history creates a market for “clean” coins, harming the fungibility of the underlying asset. In this paper we describe a scheme, confidential transactions, which blinds the amounts of all UTXOs, while preserving public verifiability that no transaction creates or destroys coins. This removes a significant amount of information from the transaction graph, improving privacy and fungibility without a trusted setup or exotic cryptographic assumptions. We further extend this to confidential assets, a scheme in which a single blockchain-based ledger may track multiple asset types. We extend confidential transactions to blind not only output amounts, but also their asset type, improving the privacy and fungibility of all assets.
Good Visual Guidance Make A Better Extractor: Hierarchical Visual Prefix for Multimodal Entity and Relation Extraction

Multimodal named entity recognition and relation extraction (MNER and MRE) is a fundamental and crucial branch in information extraction.However, existing approaches for MNER and MRE usually suffer from error sensitivity when irrelevant object images incorporated in texts.To deal with these issues, we propose a novel Hierarchical Visual Prefix fusion NeTwork (HVPNeT) for visual-enhanced entity and relation extraction, aiming to achieve more effective and robust performance.Specifically, we regard visual representation as pluggable visual prefix to guide the textual representation for error insensitive forecasting decision.We further propose a dynamic gated aggregation strategy to achieve hierarchical multiscaled visual features as visual prefix for fusion.Extensive experiments on three benchmark datasets demonstrate the effectiveness of our method, and achieve state-of-the-art performance 1 .
Deploying an Inter‐European Quantum Network

Abstract Around 40 years have passed since the first pioneering works introduced the possibility of using quantum physics to enhance communications safety. Nowadays, quantum key distribution (QKD) exited the physics laboratories to become a mature technology, triggering the attention of States, military forces, banks, and private corporations. This work takes on the challenge of bringing QKD closer to a consumer technology: deployed optical fibers by telecommunication companies of different States have been used to realize a quantum network, the first‐ever connecting three different countries. This work also emphasizes the necessity of networks where QKD can come up besides classical communications, whose coexistence currently represents the main limitation of this technology. This network connects Trieste to Rijeka and Ljubljana via a trusted node in Postojna. A key rate of over 3 kbps in the shortest link and a 7‐hour‐long measurement demonstrate the system's stability and reliability. The network has been used to present the QKD at the G20 Digital Ministers' Meeting in Trieste. The experimental results, together with the interest that one of the most important events of international politics has attracted, showcase the maturity of the QKD technology bundle, placing it in the spotlight for consumer applications in the near term.