Reviewerly/exHarmony huggingface dataset:
Low-Resource Dialogue Summarization with Domain-Agnostic Multi-Source Pretraining

With the rapid increase in the volume of dialogue data from daily life, there is a growing demand for dialogue summarization.Unfortunately, training a large summarization model is generally infeasible due to the inadequacy of dialogue data with annotated summaries.Most existing works for low-resource dialogue summarization directly pretrain models in other domains, e.g., the news domain, but they generally neglect the huge difference between dialogues and conventional articles.To bridge the gap between out-of-domain pretraining and indomain fine-tuning, in this work, we propose a multi-source pretraining paradigm to better leverage the external summary data.Specifically, we exploit large-scale in-domain nonsummary data to separately pretrain the dialogue encoder and the summary decoder.The combined encoder-decoder model is then pretrained on the out-of-domain summary data using adversarial critics, aiming to facilitate domain-agnostic summarization.The experimental results on two public datasets show that with only limited training data, our approach achieves competitive performance and generalizes well in different dialogue scenarios.Recon.
Forecasting of solar radiation in photovoltaic power station based on ground‐based cloud images and BP neural network

The solar radiation near the surface is the main reason that affects photovoltaic power generation. Accurate ultra-short-term solar radiation prediction is the premise of photovoltaic power generation prediction. Here the cloud movement prediction method based on the ground-based cloud images is presented. The cloud recognition, cloud matching, cloud area correction and cloud movement prediction are performed to predict the drift trajectory of the clouds that will block the sun. Then, using digital image technology, 13 kinds of feature information are extracted from the ground-based cloud images. Then, these feature information are input into BP neural network, and the parameters of BP neural network are optimized by genetic algorithm. Through a large number of data training, a new ultra-short-term prediction model of solar radiation is established. Finally, through experimental comparison, the results show that the prediction accuracy of the model with the feature information of ground-based cloud images can reach 96%, compared with the model without the feature information of ground-based cloud images, the accuracy is improved by 5%. The proposed ultra-short-term solar radiation prediction model can effectively predict the radiation jumping process caused by cloud occlusion, and greatly improve the prediction accuracy, especially in cloudy weather.
Learning Neural Templates for Recommender Dialogue System

Though recent end-to-end neural models have shown the promising progress on Conversational Recommender System (CRS), two key challenges still remain.First, the recommended items cannot be always incorporated into the generated replies precisely and appropriately.Second, only the items mentioned in the training corpus have a chance to be recommended in the conversation.To tackle these challenges, we introduce a novel framework called NTRD for recommender dialogue system that decouples the dialogue generation from the item recommendation.NTRD has two key components, i.e., response template generator and item selector.The former adopts an encoder-decoder model to generate a response template with slot locations tied to target items, while the latter fills in slot locations with the proper items using a sufficient attention mechanism.Our approach combines the strengths of both classical slot filling approaches (that are generally controllable) and modern neural NLG approaches (that are generally more natural and accurate).Extensive experiments on the benchmark RE-DIAL show our NTRD significantly outperforms the previous state-of-the-art methods.Besides, our approach has the unique advantage to produce novel items that do not appear in the training set of dialogue corpus.
Multitask Healthcare Management Recommendation System Leveraging Knowledge Graph

In this paper, a novel multitask healthcare management recommendation system leveraging the knowledge graph is proposed, which is based on deep neural network and 5G network, and it can be applied in mobile and terminal device to free up medical resources and provide treatment programs. The technique we applied is referred to as KG-based recommendation system. When several experiments have been carried out, it is demonstrated that it is more intelligent and precise in disease prediction and treatment recommendation, similar to the state of the art. Also, it works well in the accuracy and comprehension, which is much higher and highly consistent with the predictions of the theoretical model. The fact that our work involves studies of multitask healthcare management recommendation system, which can contribute to the smart healthcare development, proves to be promising and encouraging.
An Optimized Face Recognition System Using Cuckoo Search

Abstract The development of an effective and efficient face recognition system has always been a challenging task for researchers. In a face recognition system, feature selection is one of the most vital processes to achieve maximum accuracy by removing irrelevant and superfluous data. Many optimization techniques, such as particle swarm optimization (PSO), genetic algorithm (GA), ant colony optimization, etc., have been implemented in face recognition systems mainly based on two feature extraction methods: discrete cosine transform (DCT) and principal component analysis (PCA). In this research, a nature-inspired well-known algorithm, namely cuckoo search, has been implemented for face recognition. Further, a hybrid method consisting of DCT and PCA is applied to extract the various features by which recognition can be made with a high rate of accuracy. To validate the proposed methodology, the results are also compared with the existing methodologies, such as PSO, differential evolution, and GA.
Automatic Normalization of Word Variations in Code-Mixed Social Media Text

Social media platforms such as Twitter and Facebook are becoming popular in multilingual societies. This trend induces portmanteau of South Asian languages with English. The blend of multiple languages as code-mixed data has recently become popular in research communities for various NLP tasks. Code-mixed data consist of anomalies such as grammatical errors and spelling variations. In this paper, we leverage the contextual property of words where the different spelling variation of words share similar context in a large noisy social media text. We capture different variations of words belonging to same context in an unsupervised manner using distributed representations of words. Our experiments reveal that preprocessing of the code-mixed dataset based on our approach improves the performance in state-of-the-art part-of-speech tagging (POS-tagging) and sentiment analysis tasks.
Data Subset Selection With Imperfect Multiple Labels

We study the problem of selecting a subset of weakly labeled data where the labels of each data instance are redundant and imperfect. In real applications, less-than-expert labels are obtained at low cost in order to acquire many labels for each instance and then used for estimating the ground truth. However, on one side, preparing and processing data itself sometimes can be even more expensive than labeling. On the other side, noisy labels also decrease the performance of supervised learning methods. Thus, we introduce a new quality control mechanism on labels for each instance and use it to select an optimal subset of data. Based on the quality control mechanism, in which the labeling quality of each instance is estimated, it provides a way to know which instance has enough reliable labels or how many labels still need to be collected for a data instance. In this paper, first, we consider the data subset selection problem under the probably approximately correct model. Then, we show how to find an $\epsilon $ -optimal labeled instance based on expected labeling quality. Furthermore, we propose new algorithms to select the best $k$ quality instances that have high expected labeling quality. Using a reliable subset of data provides substantial benefit over using all data with imperfect multiple labels, and the expected labeling quality is a good indicator of where to allocate labeling effort. It shows how many labels should be acquired for an instance and which instances are qualified to be selected comparing with others. Both the theoretical guarantees and the comprehensive experiments demonstrate the effectiveness and efficiency of our algorithms.
On the Generalization Gap in Reparameterizable Reinforcement Learning

Understanding generalization in reinforcement learning (RL) is a significant challenge, as many common assumptions of traditional supervised learning theory do not apply. We focus on the special class of reparameterizable RL problems, where the trajectory distribution can be decomposed using the reparametrization trick. For this problem class, estimating the expected return is efficient and the trajectory can be computed deterministically given peripheral random variables, which enables us to study reparametrizable RL using supervised learning and transfer learning theory. Through these relationships, we derive guarantees on the gap between the expected and empirical return for both intrinsic and external errors, based on Rademacher complexity as well as the PAC-Bayes bound. Our bound suggests the generalization capability of reparameterizable RL is related to multiple factors including smoothness of the environment transition, reward and agent policy function class. We also empirically verify the relationship between the generalization gap and these factors through simulations.
An Efficient Separable Reversible Data Hiding Using Paillier Cryptosystem for Preserving Privacy in Cloud Domain

Reversible data hiding in encrypted image (RDHEI) is advantageous to scenarios where complete recovery of the original cover image and additional data are required. In some of the existing RDHEI schemes, the image pre-processing step involved is an overhead for the resource-constrained devices on the sender’s side. In this paper, an efficient separable reversible data hiding scheme over a homomorphically encrypted image that assures privacy preservation of the contents in the cloud environment is proposed. This proposed scheme comprises three stakeholders: content-owner, data hider, and receiver. Initially, the content-owner encrypts the original image and sends the encrypted image to the data hider. The data hider embeds the encrypted additional data into the encrypted image and then sends the marked encrypted image to the receiver. On the receiver’s side, both additional data and the original image are extracted in a separable manner, i.e., additional data and the original image are extracted independently and completely from the marked encrypted image. The present scheme uses public key cryptography and facilitates the encryption of the original image on the content-owner side, without any pre-processing step involved. In addition, our experiment used distinct images to demonstrate the image-independency and the obtained results show high embedding rate where the peak signal noise ratio (PSNR) is +∞ dB for the directly decrypted image. Finally, a comparison is drawn, which shows that the proposed scheme is an optimized approach for resource-constrained devices as it omits the image pre-processing step.
On the Downstream Performance of Compressed Word Embeddings.

Compressing word embeddings is important for deploying NLP models in memory-constrained settings. However, understanding what makes compressed embeddings perform well on downstream tasks is challenging-existing measures of compression quality often fail to distinguish between embeddings that perform well and those that do not. We thus propose the eigenspace overlap score as a new measure. We relate the eigenspace overlap score to downstream performance by developing generalization bounds for the compressed embeddings in terms of this score, in the context of linear and logistic regression. We then show that we can lower bound the eigenspace overlap score for a simple uniform quantization compression method, helping to explain the strong empirical performance of this method. Finally, we show that by using the eigenspace overlap score as a selection criterion between embeddings drawn from a representative set we compressed, we can efficiently identify the better performing embedding with up to 2× lower selection error rates than the next best measure of compression quality, and avoid the cost of training a model for each task of interest.
Phase Matching Quantum Key Distribution based on Single-Photon Entanglement

Two time-reversal quantum key distribution (QKD) schemes are the quantum entanglement based device-independent (DI)-QKD and measurement-device-independent (MDI)-QKD. The recently proposed twin field (TF)-QKD, also known as phase-matching (PM)-QKD, has improved the key rate bound from O(η) to O[Formula: see text] with η the channel transmittance. In fact, TF-QKD is a kind of MDI-QKD but based on single-photon detection. In this paper, we propose a different PM-QKD based on single-photon entanglement, referred to as single-photon entanglement-based phase-matching (SEPM)-QKD, which can be viewed as a time-reversed version of the TF-QKD. Detection loopholes of the standard Bell test, which often occur in DI-QKD over long transmission distances, are not present in this protocol because the measurement settings and key information are the same quantity which is encoded in the local weak coherent state. We give a security proof of SEPM-QKD and demonstrate in theory that it is secure against all collective attacks and beam-splitting attacks. The simulation results show that the key rate enjoys a bound of O[Formula: see text] with respect to the transmittance. SEPM-QKD not only helps us understand TF-QKD more deeply, but also hints at a feasible approach to eliminate detection loopholes in DI-QKD for long-distance communications.
Audio Metric Learning by Using Siamese Autoencoders for One-Shot Human Fall Detection

In the recent years, several supervised and unsupervised approaches to fall detection have been presented in the literature. These are generally based on a corpus of examples of human falls that are, though, hard to collect. For this reason, fall detection algorithms should be designed to gather as much information as possible from the few available data related to the type of events to be detected. The one-shot learning paradigm for expert systems training seems to naturally match these constraints, and this inspired the novel Siamese Neural Network (SNN) architecture for human fall detection proposed in this contribution. Acoustic data are employed as input, and the twin convolutional autoencoders composing the SNN are trained to perform a suitable metric learning in the audio domain and, thus, extract robust features to be used in the final classification stage. A large acoustic dataset has been recorded in three real rooms with different floor types and human falls performed by four volunteers, and then adopted for experiments. Obtained results show that the proposed approach, which only relies on two real human fall events in the training phase, achieves a F$_1$-Measure of 93.58% during testing, remarkably outperforming the recent supervised and unsupervised state-of-art techniques selected for comparison.
Hybrid Deep Framework for Group Event Recommendation

Group recommender systems suggesting items for a group of users have received many attentions recently.Some aggregation-based and model-based group recommendation methods have been proposed.However, the cold-start problem in group recommendation has not been well studied, which limits the application of group recommendation in many important domains, such as recommending offline events for a group of users.In this paper, we propose a new hybrid deep framework to solve cold-start problem of group event recommendation.Our framework incorporates multiple Restricted Boltzmann Machines (RBM) and conditional RBM.The former extracts high latent group preference from user feedback and group feedback.The latter obtains latent event features based on contextual information, such as location and organizer of events.Thus, the hybrid deep framework can utilize user feedback and contextual information of events to overcome cold-start problem.We conduct exhaustive experiments on two real-world datasets and the results show that our proposed framework outperforms the baseline group recommendation methods and alleviates the cold-start problem of group event recommendation effectively.
General Confidentiality and Utility Metrics for Privacy-Preserving Data Publishing Based on the Permutation Model

Anonymization for privacy-preserving data publishing, also known as statistical disclosure control (SDC), can be viewed under the lens of the permutation model. According to this model, any SDC method for individual data records is functionally equivalent to a permutation step plus a noise addition step, where the noise added is marginal, in the sense that it does not alter ranks. Here, we propose metrics to quantify the data confidentiality and utility achieved by SDC methods based on the permutation model. We distinguish two privacy notions: in our work, anonymity refers to subjects and hence mainly to protection against record re-identification, whereas confidentiality refers to the protection afforded to attribute values against attribute disclosure. Thus, our confidentiality metrics are useful even if using a privacy model ensuring an anonymity level ex ante. The utility metric is a general-purpose metric that can be conveniently traded off against the confidentiality metrics, because all of them are bounded between 0 and 1. As an application, we compare the utility-confidentiality trade-offs achieved by several anonymization approaches, including privacy models (k-anonymity and ε-differential privacy) as well as SDC methods (additive noise, multiplicative noise and synthetic data) used without privacy models.
The Psychological Expectation of New Project Income Under the Influence of the Entrepreneur’s Sentiment From the Perspective of Information Asymmetry

The phenomenon of high first-day income and high break-up rate in China's capital market has long attracted the attention of investors. Based on the disagreement model, in combination with the information asymmetry theory and behavioral finance, a mathematical model was put forward to analyze the reasons and mechanisms for the excess return of the initial public offering (IPO) on the first-day under the influence of investors' sentiments. The analysis shows that the IPO first-day income is a function of the disagreement between investors, which generally presents an asymmetric U shape. Information asymmetry affects the degree of valuation deviation. The sentiment of investors reflects the psychological state of the investor at the time, which makes the income increase or decrease under the influence of investor sentiment and market sentiment. In turn, it leads to the emergence of excess returns or break-up rates on the first-day. The research results help deepen the understanding of the IPO pricing mechanism and explore the impact of investor psychology on its pricing, putting forward suggestions for the issue pricing mechanism of the Sci-Tech Innovation Board.
Application of improved ant colony optimization in mobile robot trajectory planning

Under the condition of known static environment and dynamic environment, an improved ant colony optimization is proposed to solve the problem of slow convergence, easily falling into local optimal solution, deadlock phenomenon and other issues when the ant colony optimization is constructed. Based on the traditional ant colony optimization, the ant colony search ability at the initial moment is strengthened and the range is expanded to avoid falling into the local optimal solution by adaptively changing the volatility coefficient. Secondly, the roulette operation is used in the state transition rule which improves the quality of the solution and the convergence speed of the algorithm effectively. Finally, through the elite selection and the node crossover operation of the better path, the global search efficiency and convergence speed of the algorithm are effectively improved. Several experimental results have also been obtained by applying the improved ant colony optimization to obstacle avoidance. The experimental results demonstrate the feasibility and effectiveness of the algorithm.
An improved agglomerative hierarchical clustering anomaly detection method for scientific data

Summary Anomaly detection tries to find out the data that disobeys the rule of majority data or expected patterns. The traditional hierarchical clustering algorithms have been adopted to detect anomaly, but have the disadvantages of low effectiveness and unstability. So we propose an improved agglomerative hierarchical clustering method for anomaly detection. It dynamically adjusts the optimum clustering number according to the self‐defined criterion to save the trouble of manually picking clustering number, and determines the optimum clustering distance mode according to cophenetic correlation coefficient to reduce the procedure of manually testing the suitable distance mode in each iteration. The performances of proposed method are verified on tensile test, HTRU2 and credit card dataset. Compared with the traditional methods, our method possesses the most comprehensive performance (the highest F ‐measure with less iterations), which shows effectiveness of anomaly detection. And compared with the traditional methods (single, complete, average, and centroid mode), our method achieves the best performance on tensile test and HTRU2 dataset, showing stronger generalization. Compared with other methods (Decision + Gradient Boosted Tree, Decision Trees + Decision Stump, etc) on credit card dataset, our method obtains similar accuracy, and ranks in the top level in the aspect of sensitivity.
PathQG: Neural Question Generation from Facts

Existing research for question generation encodes the input text as a sequence of tokens without explicitly modeling fact information. These models tend to generate irrelevant and uninformative questions. In this paper, we explore to incorporate facts in the text for question generation in a comprehensive way. We present a novel task of question generation given a query path in the knowledge graph constructed from the input text. We divide the task into two steps, namely, query representation learning and query-based question generation. We formulate query representation learning as a sequence labeling problem for identifying the involved facts to form a query and employ an RNN-based generator for question generation. We first train the two modules jointly in an end-to-end fashion, and further enforce the interaction between these two modules in a variational framework. We construct the experimental datasets on top of SQuAD and results show that our model outperforms other state-of-the-art approaches, and the performance margin is larger when target questions are complex. Human evaluation also proves that our model is able to generate relevant and informative questions.