Reviewerly/exHarmony huggingface dataset:
A Deep Learning Based Hybrid Model for Sales Prediction of E-commerce with Sentiment Analysis

The market of E-commerce has developed rapidly since the emergence of Internet. Many companies or shops of E-commerce want to seek a way to predict the sales of their products. The prediction of sales can help the merchants to formulate a sales strategy, in order to obtain a bigger profit and attract more investment. However, many studies simply use the daily sales or some very basic daily information of the product to predict the sales, without considering the effects of reviews. In this paper, a hybrid network including Bi-directional Long Short-Term Memory (BiLSTM) and Convolutional Neural Network (CNN) is proposed to solve this prediction task. Through careful selection, several attributes and comments of the products are utilized. Feature engineering is used to normalize the different kinds of data. BiLSTM is conducted to analyze all the comments. CNN is utilized to make predictions by using the data provided by feature engineering. Analysis is described to show the advantages and effectiveness of this network. By using the attributes of the product, along with the polarization of comments, CNN can predict the sales of the product.
Multi-objective scheduling technique based on hybrid hitchcock bird algorithm and fuzzy signature in cloud computing

As the number of requests in the cloud increases, providers face more problems (e.g., task scheduling and resource management). The exhaustive search for determining the optimal solutions of scheduling problem is usually impractical and hence metaheuristic algorithms have been widely used to evolve solutions for task scheduling. In this paper, we firstly propose a hybrid meta-heuristic algorithm called Hybrid Fuzzy Hitchcock Bird (HFHB) and second, a multi-objective form of HFHB (MOHFHB) is introduced to solve multi-objective problems (e.g., task scheduling). The HFHB algorithm consists of three main enhancements: (a) The first random population of birds is improved, (b) The attack regulator parameter is set with a fuzzy Sugeno-signature, and (c) The dead birds are replaced with new birds. In multi-objective form (MOHFHB), two concepts (i.e., crowding distance and ranking non-dominated solution) are added to determine the optimal Pareto front. In the first part, HFHB and MOHFHB are evaluated as two global optimizers. In the second part, HFHB and MOHFHB are evaluated for a task scheduling problem against Moth Search Algorithm with DE (MSDE), Enhanced Multi-Verse Optimizer (EMVO), Fuzzy Modified Particle Swarm Optimization (FMPSO), and Simulated-annealing-based Bees Algorithm (SBA). The results indicate that HFHB improves makespan by 12.57%, 38.61%, and 35.75%, and resource utilization by 1.14%, 7.30%, and 5.25% compared to FMPSO, MSDE, and EMVO, respectively.
How virtual avatar experience interplays with self-concepts: the use of anthropometric 3D body models in the visual stimulation process

Abstract We explored how viewing one’s anthropometric virtual avatar would affect the viewer’s self-body perception through the comparative evaluation of self-concepts—self-esteem and self-compassion, within the framework of allocentric lock theory. We recruited 18 female adults, aged 18–21, who identified themselves to have some level of body image concerns, and who had had no clinical treatment for their body image. Participants were randomly assigned either to the experimental or control group. The experimental group participated in both body positivity program and virtual avatar program, whereas the control group attended the body positivity program, only. The results affirmed that the body positivity program served as a psychological buffer prior to the virtual avatar stimulus. After the virtual avatar experience, the participants demonstrated self-acceptance by lowering their expectation on how they should look like. The findings from exit interviews enriched the quantitative results. This study verified the mechanism of the altered processing of the stored bodily memory by the egocentric sensory input of virtual avatars, and offered practical potential of the study outcomes to be applied in various emerging fields where novel applications of virtual 3D technology are sought, such as fashion e-commerce.
Structural relational inference actor-critic for multi-agent reinforcement learning

Multi-agent reinforcement learning (MARL) is essential for a wide range of high-dimensional scenarios and complicated tasks with multiple agents. Many attempts have been made for agents with prior domain knowledge and predefined structure. However, the interaction relationship between agents in a multi-agent system (MAS) in general is usually unknown, and previous methods could not tackle dynamical activities in an ever-changing environment. Here we propose a multi-agent Actor-Critic algorithm called Structural Relational Inference Actor-Critic (SRI-AC), which is based on the framework of centralized training and decentralized execution. SRI-AC utilizes the latent codes in variational autoencoder (VAE) to represent interactions between paired agents, and the reconstruction error is based on Graph Neural Network (GNN). With this framework, we test whether the reinforcement learning learners could form an interpretable structure while achieving better performance in both cooperative and competitive scenarios. The results indicate that SRI-AC could be applied to complex dynamic environments to find an interpretable structure while obtaining better performance compared to baseline algorithms.
Towards Good Practices for Efficiently Annotating Large-Scale Image Classification Datasets

Data is the engine of modern computer vision, which necessitates collecting large-scale datasets. This is expensive, and guaranteeing the quality of the labels is a major challenge. In this paper, we investigate efficient annotation strategies for collecting multi-class classification labels for a large collection of images. While methods that exploit learnt models for labeling exist, a surprisingly prevalent approach is to query humans for a fixed number of labels per datum and aggregate them, which is expensive. Building on prior work on online joint probabilistic modeling of human annotations and machine-generated beliefs, we propose modifications and best practices aimed at minimizing human labeling effort. Specifically, we make use of advances in self-supervised learning, view annotation as a semi-supervised learning problem, identify and mitigate pitfalls and ablate several key design choices to propose effective guidelines for labeling. Our analysis is done in a more realistic simulation that involves querying human la-belers, which uncovers issues with evaluation using existing worker simulation methods. Simulated experiments on a 125k image subset of the ImageNet100 show that it can be annotated to 80% top-1 accuracy with 0.35 annotations per image on average, a 2.7x and 6.7x improvement over prior work and manual annotation, respectively. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
Fairness-aware Methods in Rankings and Recommenders

We increasingly depend on a variety of data-driven algorithmic systems to assist us in many aspects of life. Search engines and recommender systems amongst others are used as sources of information and to help us in making all sort of decisions from selecting restaurants and books, to choosing friends and careers. This has given rise to important concerns regarding the fairness of such systems. In this tutorial, we aim at presenting a toolkit of methods used for ensuring fairness in rankings and recommendations. Our objectives are two-fold: (a) to present related methods of this novel, quickly evolving and impactful domain, and put them into perspective, and (b) to highlight open challenges and research paths for future work.
Reliable Policy Updating Under Efficient Policy Hidden Fine-Grained Access Control Framework for Cloud Data Sharing

Ciphertext-Policy Attribute-Based Encryption (CP-ABE) is one of the potent encryption paradigms in protecting data confidentiality in the cloud data sharing scenario. However, the access policy of the traditional CP-ABE is in plaintext form that reveals significant sensitive information of data owners and data visitors. To mitigate this problem, two approaches have been proposed in the literature. One is partially hidden, where the attributes in the access policy are divided into two parts: the plaintext attribute names and the hidden attribute values. The other approach fully hides the attributes in the access policy which, unfortunately, hinders efficient and correct decryption as well as dynamic policy-updating. In this article, we design a security-enhanced Attribute Cuckoo Filter (se-ACF) to hide the access policy and propose a new CP-ABE system, called Privacy-Preserving Policy Updating ABE (3PU-ABE), which effectively integrates policy hiding and policy updating. We conduct rigorous security analysis and performance evaluation of 3PU-ABE. The results indicate that 3PU-ABE completely hides the access policy without affecting the decryption, and entails better policy-updating efficiency than similar works.
Data Augmentation Techniques on Arabic Data for Named Entity Recognition

Much research has been conducted to apply data augmentation techniques (DA), i.e., transformations to a given training set to produce more data synthetically. While DA is often used in computer vision and speech recognition, it is not very common in Natural Language Processing, especially in the Named Entity Recognition (NER) task, i.e., identifying named entities. To the best of our knowledge, it is also not applied in NER on the challenging Code-Switching (CS) data, which is text containing more than one language in the same sentence. This paper presents several practical and easy-to-implement data augmentation techniques to improve the Arabic NER and especially on CS data based on word embedding substitution, a modified version of the Easy Data Augmentation technique, and back-translation. We demonstrate that the proposed methods boost the performance of the NER task on CS data through several experiments on an available AR-EN CS data-set with an increase of F-score equal to 1.5%. The proposed DA methods also eliminate the time and effort of collecting and labeling new data for low resources NER tasks.
Understanding and predicting COVID-19 clinical trial completion vs. cessation

As of March 30 2021, over 5,193 COVID-19 clinical trials have been registered through Clinicaltrial.gov. Among them, 191 trials were terminated, suspended, or withdrawn (indicating the cessation of the study). On the other hand, 909 trials have been completed (indicating the completion of the study). In this study, we propose to study underlying factors of COVID-19 trial completion vs . cessation, and design predictive models to accurately predict whether a COVID-19 trial may complete or cease in the future. We collect 4,441 COVID-19 trials from ClinicalTrial.gov to build a testbed, and design four types of features to characterize clinical trial administration, eligibility, study information, criteria, drug types, study keywords, as well as embedding features commonly used in the state-of-the-art machine learning. Our study shows that drug features and study keywords are most informative features, but all four types of features are essential for accurate trial prediction. By using predictive models, our approach achieves more than 0.87 AUC (Area Under the Curve) score and 0.81 balanced accuracy to correctly predict COVID-19 clinical trial completion vs . cessation. Our research shows that computational methods can deliver effective features to understand difference between completed vs . ceased COVID-19 trials. In addition, such models can also predict COVID-19 trial status with satisfactory accuracy, and help stakeholders better plan trials and minimize costs.
A possibilistic fuzzy Gath-Geva clustering algorithm using the exponential distance

As a famous clustering algorithm, Gath-Geva (GG) clustering has been widely applied in many fields. Unlike fuzzy c-means (FCM) clustering, GG clustering uses the exponential distance with the fuzzy covariance matrix instead of Euclidean distance to cluster hyperellipsoidal data accurately. Due to the existence of probabilistic constraint, GG clustering is sensitive to noise data. For possibilistic fuzzy c-means (PFCM), the type of clustering data limits its application. To solve the noise sensitivity problem of GG clustering and extend PFCM for clustering hyperellipsoidal data, a possibilistic fuzzy Gath-Geva (PFGG) clustering algorithm is proposed. The PFGG clustering is derived from GG clustering in combination with PFCM clustering. FCM, GG, PFCM and PFGG are run on the several datasets to compare their clustering results. The experimental results show that the performance of PFGG is significantly better than the other clustering algorithms.
Emergence of structural bias in differential evolution

Heuristic optimisation algorithms are in high demand due to the overwhelming amount of complex optimisation problems that need to be solved. The complexity of these problems is well beyond the boundaries of applicability of exact optimisation algorithms and therefore require modern heuristics to find feasible solutions quickly. These heuristics and their effects are almost always evaluated and explained by particular problem instances. In previous works, it has been shown that many such algorithms show structural bias, by either being attracted to a certain region of the search space or by consistently avoiding regions of the search space, on a special test function designed to ensure uniform 'exploration' of the domain. In this paper, we analyse the emergence of such structural bias for Differential Evolution (DE) configurations and, specifically, the effect of different mutation, crossover and correction strategies. We also analyse the emergence of the structural bias during the run-time of each algorithm. We conclude with recommendations of which configurations should be avoided in order to run DE unbiased.
Data Disclosure With Non-Zero Leakage and Non-Invertible Leakage Matrix

We study a statistical signal processing privacy problem, where an agent observes useful data <inline-formula> <tex-math notation="LaTeX">$Y$ </tex-math></inline-formula> and wants to reveal the information to a user. Since the useful data is correlated with the private data <inline-formula> <tex-math notation="LaTeX">$X$ </tex-math></inline-formula>, the agent employs a privacy mechanism to generate data <inline-formula> <tex-math notation="LaTeX">$U$ </tex-math></inline-formula> that can be released. We study the privacy mechanism design that maximizes the revealed information about <inline-formula> <tex-math notation="LaTeX">$Y$ </tex-math></inline-formula> while satisfying a strong <inline-formula> <tex-math notation="LaTeX">$\ell _{1}$ </tex-math></inline-formula>-privacy criterion. When a sufficiently small leakage is allowed, we show that the optimizer distributions of the privacy mechanism design problem have a specific geometry, i.e., they are perturbations of fixed vector distributions. This geometrical structure allows us to use a local approximation of the conditional entropy. By using this approximation the original optimization problem can be reduced to a linear program so that an approximate solution for the optimal privacy mechanism can be easily obtained. The main contribution of this work is to consider a non-invertible leakage matrix with non-zero leakage. In our first example, inspired by a watermark application, we first demonstrate the accuracy of the approximation. Then, we employ different measures for utility and privacy leakage to compare the privacy-utility trade-off using our approach with other methods. In particular, we show that by allowing small leakage, significant utility can be achieved using our method compared to the case where no leakage is allowed. In the second and third examples which are based on the MNIST data set and medical applications, we illustrate the suggested design for disclosed data <inline-formula> <tex-math notation="LaTeX">$U$ </tex-math></inline-formula>. It has been shown that the letters of <inline-formula> <tex-math notation="LaTeX">$Y$ </tex-math></inline-formula> which are disclosing more information about <inline-formula> <tex-math notation="LaTeX">$X$ </tex-math></inline-formula> are combined (randomized) to produce a new letter of <inline-formula> <tex-math notation="LaTeX">$U$ </tex-math></inline-formula>.
Adaptive Progressive Continual Learning

Continual learning paradigm learns from a continuous stream of tasks in an incremental manner and aims to overcome the notorious issue: the catastrophic forgetting. In this work, we propose a new adaptive progressive network framework including two models for continual learning: Reinforced Continual Learning (RCL) and Bayesian Optimized Continual Learning with Attention mechanism (BOCL) to solve this fundamental issue. The core idea of this framework is to dynamically and adaptively expand the neural network structure upon the arrival of new tasks. RCL and BOCL employ reinforcement learning and Bayesian optimization to achieve it, respectively. An outstanding advantage of our proposed framework is that it will not forget the knowledge that has been learned through adaptively controlling the architecture. We propose effective ways of employing the learned knowledge in the two methods to control the size of the network. RCL employs previous knowledge directly while BOCL selectively utilizes previous knowledge (e.g., feature maps of previous tasks) via attention mechanism. The experiments on variants of MNIST, CIFAR-100 and Sequence of 5-Datasets demonstrate that our methods outperform the state-of-the-art in preventing catastrophic forgetting and fitting new tasks better under the same or less computing resource.
Robust frame synchronization for free-space continuous-variable quantum key distribution

Free-space continuous-variable quantum key distribution (CV-QKD) is an important technology that enables all-day quantum key distribution. Precise frame synchronization is a prerequisite for establishing a correlation between legitimate users of CV-QKD. In free-space CV-QKD, channel transmittance fluctuation caused by atmospheric turbulence increases the difficulty of synchronization. Also, as the channel transmittance is monitored in many reported experiments, the transmittance data also needs to be synchronized. We propose a novel method to solve the above problems by inserting two kinds of synchronization frames, i.e., data synchronization frames and transmittance synchronization frames. The performance of the proposed method is analyzed and Monte Carlo simulation is conducted to test its performance. The results demonstrate the feasibility and efficiency of this method. The proposed method paves the way for the realization of free-space CV-QKD.
Twee: An Equational Theorem Prover

Abstract Twee is an automated theorem prover for equational logic. It implements unfailing Knuth-Bendix completion with ground joinability testing and a connectedness-based redundancy criterion. It came second in the UEQ division of CASC-J10, solving some problems that no other system solved. This paper describes Twee’s design and implementation.
Using Appearance to Predict Pedestrian Trajectories Through Disparity-Guided Attention and Convolutional LSTM

Reasoning about the motions of other objects is important for moving platforms such as intelligent vehicles. In this paper, we propose a pedestrian trajectory prediction approach that utilizes pedestrian appearance as well as historical locations and camera ego-motion to predict future trajectories of pedestrians for vehicles. The proposed model consists of three encoders and a decoder, namely: appearance encoder, location encoder, and ego-motion encoder; and a Long Short-term Memory (LSTM) decoder. The appearance encoder employs an embedding Convolutional Neural Network (CNN) and a Convolutional LSTM (ConvLSTM) to encode pedestrian appearance. A disparity-guided attention mechanism is designed in the appearance encoder to attend to salient dynamic regions in pedestrian appearance. The location encoder and ego-motion encoder employ LSTMs to encode pedestrian historical locations and camera ego-motion. The outputs of all three encoders are concatenated as a representation of input information, which is decoded by the decoder to finally generate the future trajectories of observed pedestrians. We evaluated the proposed model on two public datasets with several baselines and the results showed improvement of about 20% compared with the baselines. We also visualized the predicted trajectories and heat maps generated by disparity-guided attention. Both the visualization and quantitative results validated that our proposed model outperforms the baselines on predicting accuracy and that the model is feasible to capture salient spatiotemporal regions in pedestrian appearance.
Review on the Application of Metalearning in Artificial Intelligence

In recent years, artificial intelligence supported by big data has gradually become more dependent on deep reinforcement learning. However, the application of deep reinforcement learning in artificial intelligence is limited by prior knowledge and model selection, which further affects the efficiency and accuracy of prediction, and also fails to realize the learning ability of autonomous learning and prediction. Metalearning came into being because of this. Through learning the information metaknowledge, the ability to autonomously judge and select the appropriate model can be formed, and the parameters can be adjusted independently to achieve further optimization. It is a novel method to solve big data problems in the current neural network model, and it adapts to the development trend of artificial intelligence. This article first briefly introduces the research process and basic theory of metalearning and discusses the differences between metalearning and machine learning and the research direction of metalearning in big data. Then, four typical applications of metalearning in the field of artificial intelligence are summarized: few-shot learning, robot learning, unsupervised learning, and intelligent medicine. Then, the challenges and solutions of metalearning are analyzed. Finally, a systematic summary of the full text is made, and the future development prospect of this field is assessed.