Reviewerly/exHarmony huggingface dataset:
Cost efficient management of complex financial energy trading systems: Knowledge-based blockchain technique

Industry 4.0 has led to the growth of smart cities utilizing the Internet of Things (IoT). Wireless sensor network (WSN) has drawn considerable interest as an important element of the IoT. The Energy Internet (EI) is growing in importance as a significant component of constructing the intelligent city, especially in terms of its reliability and security. EI is moving in a new direction of energy trading advancement where distributed energy transaction models are replacing the conventional centralized layout. Blockchain technology, which is the underlying support, has gained interest as a result of its benefits, namely integrity, and nonrepudiation. Privacy disclosure, despite the benefits, is a concern for many blockchain-enabled trading layouts. The paper presents encryption according to the cryptographic text properties as the main scheme for reconstructing the dealing layout in order to resolve the issue in the electricity market. In particular, the blockchain energy trade plan with privacy (BETPWP) has been developed to manage the distributed transaction. Transaction arbitration in ciphertext form is employed for achieving precise access control. As a result of this method, the transaction layout could be made considerably more secure and reliable, as well as maximizing the security of private data. BETPWP also proposes a credibility-enabled equity proof consensus method that would significantly improve operational performance. A study of security and the test assessment of the suggested method are performed in order to demonstrate its effectiveness.
Type-Preserving, Dependence-Aware Guide Generation for Sound, Effective Amortized Probabilistic Inference

In probabilistic programming languages (PPLs), a critical step in optimization-based inference methods is constructing, for a given model program, a trainable guide program. Soundness and effectiveness of inference rely on constructing good guides, but the expressive power of a universal PPL poses challenges. This paper introduces an approach to automatically generating guides for deep amortized inference in a universal PPL. Guides are generated using a type-directed translation per a novel behavioral type system. Guide generation extracts and exploits independence structures using a syntactic approach to conditional independence, with a semantic account left to further work. Despite the control-flow expressiveness allowed by the universal PPL, generated guides are guaranteed to satisfy a critical soundness condition and moreover, consistently improve training and inference over state-of-the-art baselines for a suite of benchmarks.
A Quantum-Computing-Based Method for Solving Quantum Confinement Problem in Semiconductor

Technology computer-aided design (TCAD) of semiconductor devices relies on the numerical solution of differential equations in devices. Recent advances in quantum computing provide a new opportunity for TCAD simulations to be performed on a quantum computer. Based on a variational quantum algorithm, we develop a quantum-computing-based method to solve quantum confinement problems in semiconductor nanostructures. As the number of numerical discretization grid points for solving the Schrödinger equation increases, the number of qubits needed scales only logarithmically, ~ <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${O}[\log(N)]$ </tex-math></inline-formula> . The method is applied to solve quantum confinement problems at all dimensions, which are related to confinement in a quantum well, semiconductor nanowire, and semiconductor quantum dot structures. The method can treat an anisotropic band structure and electrostatic potential in semiconductor nanostructures. We further show that the design of ansatz plays an important role in the performance of the method in terms of solution accuracy. The quantum-computing-based method can compute the energies and wave functions of both the ground and excited states with high accuracy.
Part-of-Speech and Pragmatic Tagging of a Corpus of Film Dialogue: A Pilot Study

Abstract This article presents how a pilot study for automatically POS-tagging a corpus of orthographic transcriptions of film dialogues (Pavia Corpus of Film Dialogue) was dealt with and the related issues solved. The software CLAWS4, which is freely available on UCREL’s website, was used for the sake of comparability with reference corpora such as the BNC (both 1994 and 2014) and all the English corpora available on english-corpora.org (former BYU interface). The study highlights that automatic POS-tagging needs readjusting when applied to film dialogue and the accuracy of the tagging greatly benefits from the introduction of tags for pragmatic categories. This integrated approach of grammatical and pragmatic automatic tagging was realised through the writing of a Python script which post-processes the data output of CLAWS4.
Statically Resolvable Ambiguity

Traditionally, a grammar defining the syntax of a programming language is typically both context free and unambiguous. However, recent work suggests that an attractive alternative is to use ambiguous grammars,thus postponing the task of resolving the ambiguity to the end user. If all programs accepted by an ambiguous grammar can be rewritten unambiguously, then the parser for the grammar is said to be resolvably ambiguous. Guaranteeing resolvable ambiguity statically---for all programs---is hard, where previous work only solves it partially using techniques based on property-based testing. In this paper, we present the first efficient, practical, and proven correct solution to the statically resolvable ambiguity problem. Our approach introduces several key ideas, including splittable productions, operator sequences, and the concept of a grouper that works in tandem with a standard parser. We prove static resolvability using a Coq mechanization and demonstrate its efficiency and practical applicability by implementing and integrating resolvable ambiguity into an essential part of the standard OCaml parser.
Selective Feature Bagging of one-class classifiers for novelty detection in high-dimensional data

Novelty detection in high-dimensional data is a challenging task due to the masking effect of irrelevant attributes. A common solution is to discover feature subspace, of which attributes are relevant to novelties. Due to the high uncertainty of novelties in practical applications, ensemble models that combine results from multiple subspaces are proved to be more effective than single models. According to the theory of bias–variance tradeoff, existing ensembles are often developed based on variance reduction. However, it is argued that the combination of poor detectors will deteriorate the performance of ensembles. To this end, this paper proposes an ensemble detector that takes into account variance and bias reduction simultaneously. Our ensemble is referred to as Selective Feature Bagging (SFB) since it is developed on the basis of Feature Bagging (FB). In order to improve the accuracy without deterioration of diversity of base detectors in FB, we resort to the notion of dynamic classifier selection which is proved be effective in classification. During the ensemble generation phase, base detectors are produced and categorized into different groups that are distinguished by the dimensionality of subspace used for training. The purpose of such a design is to maintain the diversity. During the generation phase, the most competent base detector from each of groups is dynamically selected and used to make decision on the test pattern. The purpose of such a design is to enhance the accuracy. We verify the effectiveness of SFB on 15 data sets from KEEL repository. Experimental results have shown that SFB can statistically outperform FB. In addition, several state-of-the-art have also been outperformed by SFB.
Diversity-Boosted Generalization-Specialization Balancing for Zero-Shot Learning

Zero-Shot Learning (ZSL) aims to transfer classification capability from seen to unseen classes. Recent methods have proved that generalization and specialization are two essential abilities to achieve good performance in ZSL. However, focusing on only one of the abilities may result in models that are either too general with degraded classification ability or too specialized to generalize to unseen classes. In this paper, we propose an end-to-end network, termed as BGSNet, which equips and balances generalization and specialization abilities at the instance and dataset level. Specifically, BGSNet consists of two branches: the Generalization Network (GNet), which applies episodic meta-learning to learn generalized knowledge, and the Balanced Specialization Network (BSNet), which adopts multiple attentive extractors to extract discriminative features and achieve instance-level balance. A novel self-adjusted diversity loss is designed to optimize BSNet with redundancy reduced and diversity boosted. We further propose a differentiable dataset-level balance and update the weights in a linear annealing schedule to simulate network pruning and thus obtain the optimal structure for BSNet with dataset-level balance achieved. Experiments on four benchmark datasets demonstrate our model's effectiveness. Sufficient component ablations prove the necessity of integrating and balancing generalization and specialization abilities.
DuCL: Dual-stage contrastive learning framework for Chinese semantic textual matching

Chinese semantic textual matching is a fundamental yet challenging task in natural language processing (NLP). How to accurately capture the features in a single piece of text and the interactive features between pieces of text is the core problem of the task. Although pretrained language models (PLMs) and contrastive learning (CL) have been applied to address the problem to some extent, the existing works usually just utilize contrastive learning to finetune the PLMs on one single perspective, such as the sentence or pair level, which neglects to capture the semantic features from the other perspective, leading to inefficient learning and suboptimal performance. To tackle the problem, we propose a novel dual-stage contrastive learning framework (DuCL) for Chinese semantic textual matching. Specifically, DuCL consists of two stages sequentially, i.e., CL on the sentence level and CL on the pair level, each of which is responsible to finetune PLMs from the corresponding perspective. Besides, DuCL introduces a block-enhanced interaction module to integrate token-level and block-level interactive features to generate a semantic matching representation for two pieces of text. Extensive experimental results on two real-world public datasets demonstrate that our method can achieve better performance than the representative and state-of-the-art methods.
An angular shrinkage BERT model for few-shot relation extraction with none-of-the-above detection

Few-shot relation extraction aims to solve the problem of insufficient annotated data in relation extraction tasks. Through the comparison between samples, few-shot relation extraction achieves lower-cost relation classification. However, most existing methods only do classification within the scope of enumerated relations. For one of the main challenges faced by the application of few-shot relation extraction-the recognition of the none-of-the-above instances, there has been few works on it. In this paper, we propose an angular shrinkage BERT model for the few-shot relation extraction task with none-of-the-above detection, which uses an additive angular loss to enlarge the margins of different classes in the feature space, and obtain highly discriminative features to improve the recognition ability for none-of-the-above instances. Meanwhile, we present a two-stage training strategy to enhance the stability of the performance. We evaluate our model on the most used few-shot relation extraction dataset FewRel. Experimental results show that our approach outperforms previous sentence-pair methods in scenarios containing none-of-the-above instances, and also achieves improvements on the traditional few-shot relation extraction task compared with our baseline model.
Overfitting-avoiding goal-guided exploration for hard-exploration multi-goal reinforcement learning

In hard-exploration multi-goal reinforcement learning tasks, the agent faces challenges to achieve a series of distant goals with sparse rewards. Directly exploring to pursue these hard goals can hardly succeed, because the agent is unable to acquire learning signals applicable to these goals. To progressively enhance agent ability and promote exploration, goal-guided exploration methods generate easier auxiliary goals that gradually approach the original hard goals for the agent to pursue. However, due to the neglect of the growth of agent generalizability, the goal generation region of the previous methods is limited, which causes overfitting and traps the exploration for further goals. In this paper, after modeling the multi-goal RL as a distribution-matching process, we propose an overfitting-avoiding goal-guided exploration method (OGE), where the generation of auxiliary goals follows the Wasserstein-distance-based optimal transport geodesic, and the generation region is in the Lipschitz-constant-delimited generalizability margin. Our OGE is compared with state-of-the-art methods in hard-exploration multi-goal robotic manipulation tasks. Apart from showing the highest learning efficiency, in those tasks where all the prior methods meet overfitting and fail, our method can still successfully guide the agent to achieve the hard goals.
Machine learning applications for urban photovoltaic potential estimation: A survey

A growing worldwide consensus agrees that a global energy transition to renewable energy sources is urgent to avoid the direst consequences of rapid climate change. This transition is a substantial challenge facing humanity, which requires cooperation and innovation across disciplines and nations. In this context, the precise estimation of the renewable potential of a given area is valuable for decision-makers. Cities will play an essential role in this transition through distributed photovoltaic generation as evidenced by the UN 11th sustainable development goal. However, the complex nature of cities makes this estimation a difficult problem. Recently, several machine learning approaches have successfully contributed to different aspects of the urban photovoltaic potential estimation problem. In the present manuscript, these proposals are summarized, following a hierarchical framework usually described in the literature, including the latest available research. Input and target variables involved in the discussed works are reclassified using a novel categorization. This categorization highlights interesting trends in the field, for each sub-problem in the hierarchical approach, which allows the identification of knowledge gaps and possible future lines of research. The present work presents other unexplored avenues and lists concisely the techniques and variables used for each estimation problem, facilitating improvements on already explored techniques or innovation on not yet explored avenues.
Forgery face detection via adaptive learning from multiple experts

As an important and challenging problem, Face Forgery Detection has gained considerable attention. Usually, it suffers from the diversity of forgery patterns in forgery images, which requires a detection model to have capability of capturing various patterns in the challenging scenarios. To address this problem, we present a divide-and-aggregate learning framework to build multi-expert models and integrate them into a unified model. Firstly, the built multi-expert models are pre-trained to capture and preserve the specific forgery pattern produced by each manipulation method separately. Secondly, to transfer diverse knowledge of experts, we propose an integrating approach based on knowledge distillation. However, the difference of manipulation-aware knowledge among these experts concerns the way of distillation when the knowledge is combined in the only student model. Thus, to determine the importance of each expert, we propose a sample-aware Adaptive Learning from Experts strategy (ALFE) to assign adaptive expert distillation weights for each fake sample based on the predictions of each expert. Experiments show that our method achieves SOTA performances on ACC/AUC in the benchmark of FaceForensics++, demonstrating the effectiveness of our proposed method.
Wse-MF: A weighting-based student exercise matrix factorization model

Students who have been taught new ideas need to develop their skills by carrying out further work in their own time. This often consists of a series of exercises which must be completed. While students can choose exercises themselves from online sources, they will learn more quickly and easily if the exercises are specifically tailored to their needs. A good teacher will always aim to do this, but with the large groups of students who typically take advantage of open online courses, it may not be possible. Exercise prediction, working with large-scale matrix data, is a better way to address this challenge, and a key stage within such prediction is to calculate the probability that a student will answer a given question correctly. Therefore, this paper presents a novel approach called Weighting-based Student Exercise Matrix Factorization (Wse-MF) which combines student learning ability and exercise difficulty as prior weights. In order to learn how to complete the matrix, we apply an iterative optimization method that makes the approach practical for large-scale educational deployment. Compared with eight models in cognitive diagnosis and matrix factorization, our research results suggest that Wse-MF significantly outperforms the state-of-the-art on a range of real-world datasets in both prediction quality and time complexity. Moreover, we find that there is an optimal value of the latent factor K (the inner dimension of the factorization) for each dataset, which is related to the relationship between skills and exercises in that dataset. Similarly, the optimal value of hyperparameter c0 is linked to the ratio between exercises and students. Taken as a whole, we demonstrate improvements to matrix factorization within the context of educational data.
Satellite continuous-variable quantum key distribution systems using code-division multiple access

In this paper, we propose a novel architecture of the satellite continuous-variable quantum key distribution (CV-QKD) system that can not only support multiple users but also enhance the security. The satellite CV-QKD system is based on a code-division multiple access (CDMA) technique to simultaneously transmit multiple key streams from a trusted satellite to a ground station, which passively forwards these key streams to multiple users nearby via optical fibers. Our proposed architecture avoids using multiple ground stations that result in high costs. We analyze the performance of the proposed system under the negative impacts of atmospheric attenuation and atmospheric turbulence, as well as beam spreading and pointing error loss. Especially, the impact of multiple user interference is also considered in our analysis. The numerical results show that the proposed QKD system using CDMA can support multiple users, whose performance metrics such as quantum bit-error rate (QBER), sifted probability, and secret key rate meet the designed targets. The eavesdroppers suffer high QBER and low sifted probability, thus the security is guaranteed.
Developing a wearable human activity recognition (WHAR) system for an outdoor jacket

Purpose The emergence of smart wearables using clothing as a technology platform is a significant milestone with considerable implications for industrial convergence, creating new value for fashion. This paper aimed to present a premeditated prototype to integrate a human activity recognition (HAR) system into outdoor clothing. Design/methodology/approach For the development of wearable HAR (WHAR) clothing, this paper explored three subject areas: fashion design related to the structural feature of the clothing platform, electronics related to wearable circuits and modules design and graphic user interface design related to smartphone application development. Findings For WHAR functions in outdoor terrains, the coexistence of accelerometer–gyroscope sensing and distance-sensing could be practical to surpass the technological limitation of activity and posture recognition with gyro sensors highly depending on the changes of acceleration and angles. Research limitations/implications Through the vital sign check and physical activity–change recognition function, this study's WHAR system allows users to check their health by themselves and avoid overwork. A quick rescue is possible manually and automatically in a dangerous situation by notifying others. Thus, it can help protect users' health and safety (life). Originality/value This study designed the modularization of HAR functions generally installed in indoor medical spaces. Through the approach, smart clothing–embracing WHAR systems optimized for health and safety care for outdoor environments was pursued to diversify expensive roles of clothing for technological applications.
Application of Intelligent Fuzzy Decision Tree Algorithm in English Translation Education

Fuzziness is the basic characteristic and conventional phenomenon of the real world. As a language describing the objective world, it is bound to have fuzziness. Fuzziness, as one of the basic characteristics of language, widely exists in any language in the world and frequently appears in people's daily communication and literary works. It can express infinite meaning with limited language means. The accurate use of fuzzy language can enhance the flexibility of language and improve the expression effect of language. Fuzzy language is used at all levels of language use, resulting in a humorous, funny and vivid language charm. Although fuzziness is common to all languages, due to different life and cultural backgrounds, English and Chinese have great differences in Fuzziness and fuzzy beauty. These differences have brought many challenges and difficulties to translation.
Application of Intelligent Water Conservancy Information System in Agricultural Water-Saving Irrigation

Water conservancy project is not only the infrastructure of national economic development, but also an important factor related to the coordinated development of social natural environment, economic environment and cultural environment. Especially with the continuous emergence of Internet information technology and Internet of things technology, a variety of advanced technologies and sensing test instruments are used to monitor regional water environment, flood disaster and agricultural irrigation, and build an information monitoring system for water quality and water regime covering regional water systems, which can realize the modern and intelligent management of agricultural water-saving irrigation.