Reviewerly/exHarmony huggingface dataset:
Enhancing the Robustness of Neural Collaborative Filtering Systems Under Malicious Attacks

Recommendation systems have become ubiquitous in online shopping in recent decades due to their power in reducing excessive choices of customers and industries. Recent collaborative filtering methods based on the deep neural network are studied and introduce promising results due to their power in learning hidden representations for users and items. However, it has revealed its vulnerabilities under malicious user attacks. With the knowledge of a collaborative filtering algorithm and its parameters, the performance of this recommendation system can be easily downgraded. Unfortunately, this problem is not addressed well, and the study on defending recommendation systems is insufficient. In this paper, we aim to improve the robustness of recommendation systems based on two concepts - stage-wise hints training and randomness. To protect a target model, we introduce noise layers in the training of a target model to increase its resistance to adversarial perturbations. To reduce the noise layers' influence on model performance, we introduce intermediate layer outputs as hints from a teacher model to regularize the intermediate layers of a student target model. We consider white box attacks under which attackers have the knowledge of the target model. The generalizability and robustness properties of our method have been analytically inspected in experiments and discussions, and the computational cost is comparable to training a standard neural network-based collaborative filtering model. Through our investigation, the proposed defensive method can reduce the success rate of malicious user attacks and keep the prediction accuracy comparable to standard neural recommendation systems.
A Robust Image Watermarking Scheme Using Z-Transform, Discrete Wavelet Transform and Bidiagonal Singular Value Decomposition

Watermarking is a widely used solution to the problems of authentication and copyright protection of digital media especially for images, videos, and audio data. Chaos is one of the emerging techniques adopted in image waterm... | Find, read and cite all the research you need on Tech Science Press
Memory and representativeness.

We explore the idea that judgment by representativeness reflects the workings of memory. In our model, the probability of a hypothesis conditional on data increases in the ease with which instances of that hypothesis are retrieved when cued with the data. Retrieval is driven by a measure of similarity which exhibits contextual interference: a data/cue is less likely to retrieve instances of a hypothesis that occurs frequently in other data. As a result, probability assessments are context dependent. In a new laboratory experiment, participants are shown two groups of images with different distributions of colors and other features. In line with the model's predictions, we find that (a) decreasing the frequency of a given color in one group significantly increases the recalled frequency of that color in the other group; and (b) cueing different features for the same set of images entails different probabilistic assessments, even if the features are normatively irrelevant. A calibration of the model yields a good quantitative fit with the data, highlighting the central role of contextual interference. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
Co-Attention Network With Question Type for Visual Question Answering

Visual Question Answering (VQA) is a challenging multi-modal learning task since it requires an understanding of both visual and textual modalities simultaneously. Therefore, the approaches used to represent the images and questions in a fine-grained manner play key roles in the performance. In order to obtain the fine-grained image and question representations, we develop a co-attention mechanism using an end-to-end deep network architecture to jointly learn both the image and the question features. Specifically, textual attention implemented by a self-attention model will reduce unrelated information and extract more discriminative features for question-level representations, which is in turn used to guide visual attention. We also note that a lot of finished works use complex models to extract feature representations but neglect to use high-level information summary such as question types in learning. Hence, we introduce the question type in our work by directly concatenating it with the multi-modal joint representation to narrow down the candidate answer space. A new network architecture combining the proposed co-attention mechanism and question type provides a unified model for VQA. The extensive experiments on two public datasets demonstrate the effectiveness of our model as compared with several state-of-the-art approaches.
Learning the Traditional Art of Chinese Calligraphy via Three-Dimensional Reconstruction and Assessment

The traditional art of Chinese calligraphy, reflecting the wisdom of the grass-roots community, is the soul of Chinese culture.Just like many other types of craftsmanship, it is part of the historical heritage and is worth conserving, from generation to generation.Since the movements of an ink brush are in a 3D style when Chinese calligraphy is written, they embody "The Power of Beauty", comprising various reflectance properties and rough-surface geometry.To truly understand the powerful significance and beauty of the art of Chinese calligraphy, in this paper, a 3D calligraphy reconstruction method, based on Photometric Stereo, is designed to capture the detailed appearance of the calligraphy's 3D surface geometry.For assessment, an Iterative Closest Point (ICP) algorithm is applied for registration of 3D intrinsic shapes between the Chinese calligraphy and the calligraphy fans' handwriting.Through matching these two sets of calligraphy characters, the designed system can give a score to the handwriting of a user.Experiments have been performed on Chinese calligraphy from different historical dynasties to evaluate the effectiveness of the proposed scheme, and experimental results show that the developed system is useful and provides a convenient method of calligraphy appreciation and assessment.
Semi-quantum secret sharing protocol using W-state

Quantum secret sharing protocol, which lets a master share a secret with his/her agents and the agents can recover the master’s secret when they collaborate, is an important research issue in the quantum information field. In order to make the quantum protocol more practical, the concept of semi-quantum protocol is advanced by Boyer et al. Based on this concept, many semi-quantum secret sharing protocols have been proposed. The various entanglement states (including Bell state, GHZ state and so on) were used to be the quantum resources in these SQSS protocols, except for W-state which is the other multi-qubit entanglement state and different from GHZ states. Therefore, this study wants to use the entanglement property of W-state to propose the first three-party SQSS protocol and analyze the proposed protocol is free from the well-known attacks.
Modeling Personalization in Continuous Space for Response Generation via Augmented Wasserstein Autoencoders

Variational autoencoders (VAEs) and Wasserstein autoencoders (WAEs) have achieved noticeable progress in open-domain response generation.Through introducing latent variables in continuous space, these models are capable of capturing utterance-level semantics, e.g., topic, syntactic properties, and thus can generate informative and diversified responses.In this work, we improve the WAE for response generation.In addition to the utterance-level information, we also model user-level information in latent continue space.Specifically, we embed user-level and utterance-level information into two multimodal distributions, and combine these two multimodal distributions into a mixed distribution.This mixed distribution will be used as the prior distribution of WAE in our proposed model, named as PersonaWAE.Experimental results on a large-scale real-world dataset confirm the superiority of our model for generating informative and personalized responses, where both automatic and human evaluations outperform state-of-the-art models.
An Ontology-based Approach for Failure Classification in Predictive Maintenance Using Fuzzy C-means and SWRL Rules

Within manufacturing processes, anomalies such as machinery faults and failures may lead to the outage situation of production lines. The outage of production lines is detrimental for the availability of production systems and may cause severe economic loss. To avoid the economic loss that may be caused by the outage situation, the prediction of anomalies on production lines is a crucial concern for manufacturers. Recently, data mining techniques have been applied to the manufacturing domain for predicting occurrence time of anomalies, such as the moment of machinery failure. However, existing predictive maintenance approaches have been limited to the prediction of the time of occurrence of machinery failures, while lacking the capability for identifying the criticality of the failures. This may lead to inappropriate maintenance plans and strategies. In this context, in this paper, we introduce a novel ontology-based approach to facilitate predictive maintenance in industry. The proposed approach is a combination use of fuzzy clustering and semantic technologies, where fuzzy clustering techniques are used to learn the criticality of failures based on machine historical data, and semantic technologies use the results of fuzzy clustering to predict the time of failures and the criticality of them. As results, a domain ontology for modeling predictive maintenance knowledge is developed, and a set of Semantic Web Rule Language (SWRL) predictive rules are proposed to reason about the time and criticality of machinery failures. A case study on a real-world industrial data set is followed to evaluate the usefulness and effectiveness of the proposed approach.
The Attack of the Clones Against Proof-of-Authority

The vulnerability of traditional blockchains have been demonstrated at multiple occasions.Various companies are now moving towards Proof-of-Authority (PoA) blockchains with more conventional Byzantine fault tolerance, where a known set of n permissioned sealers, among which no more than t are Byzantine, seal blocks that include user transactions.Despite their wide adoption, these protocols were not proved correct.In this paper, we present the Cloning Attack against the two mostly deployed PoA implementations of Ethereum, namely Aura and Clique.The Cloning Attack consists of one sealer cloning its pair of public-private keys into two distinct Ethereum instances that communicate with distinct groups of sealers.To identify their vulnerabilities, we first specify the corresponding algorithms.We then deploy one testnet for each protocol and demonstrate the success of the attack with only one Byzantine sealer.Finally, we propose counter-measures that prevent an adversary from double spending and introduce the necessary number of sealers needed to decide a block depending on n and t for both Aura and Clique to be safe.
Election Algorithm for Random k Satisfiability in the Hopfield Neural Network

Election Algorithm (EA) is a novel variant of the socio-political metaheuristic algorithm, inspired by the presidential election model conducted globally. In this research, we will investigate the effect of Bipolar EA in enhancing the learning processes of a Hopfield Neural Network (HNN) to generate global solutions for Random k Satisfiability (RANkSAT) logical representation. Specifically, this paper utilizes a bipolar EA incorporated with the HNN in optimizing RANkSAT representation. The main goal of the learning processes in our study is to ensure the cost function of RANkSAT converges to zero, indicating the logic function is satisfied. The effective learning phase will affect the final states of RANkSAT and determine whether the final energy is a global minimum or local minimum. The comparison will be made by adopting the same network and logical rule with the conventional learning algorithm, namely, exhaustive search (ES) and genetic algorithm (GA), respectively. Performance evaluation analysis is conducted on our proposed hybrid model and the existing models based on the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Sum of Squared Error (SSE), and Mean Absolute Error (MAPE). The result demonstrates the capability of EA in terms of accuracy and effectiveness as the learning algorithm in HNN for RANkSAT with a different number of neurons compared to ES and GA.
Finding Structure in Time: Visualizing and Analyzing Behavioral Time Series

The temporal structure of behavior contains a rich source of information about its dynamic organization, origins, and development. Today, advances in sensing and data storage allow researchers to collect multiple dimensions of behavioral data at a fine temporal scale both in and out of the laboratory, leading to the curation of massive multimodal corpora of behavior. However, along with these new opportunities come new challenges. Theories are often underspecified as to the exact nature of these unfolding interactions, and psychologists have limited ready-to-use methods and training for quantifying structures and patterns in behavioral time series. In this paper, we will introduce four techniques to interpret and analyze high-density multi-modal behavior data, namely, to: (1) visualize the raw time series, (2) describe the overall distributional structure of temporal events (Burstiness calculation), (3) characterize the nonlinear dynamics over multiple timescales with Chromatic and Anisotropic Cross-Recurrence Quantification Analysis (CRQA), (4) and quantify the directional relations among a set of interdependent multimodal behavioral variables with Granger Causality. Each technique is introduced in a module with conceptual background, sample data drawn from empirical studies and ready-to-use Matlab scripts. The code modules showcase each technique’s application with detailed documentation to allow more advanced users to adapt them to their own datasets. Additionally, to make our modules more accessible to beginner programmers, we provide a “Programming Basics” module that introduces common functions for working with behavioral timeseries data in Matlab. Together, the materials provide a practical introduction to a range of analyses that psychologists can use to discover temporal structure in high-density behavioral data.
Intelligible and Explainable Machine Learning

Learning methods such as boosting and deep learning have made ML models harder to understand and interpret. This puts data scientists and ML developers in the position of often having to make a tradeoff between accuracy and intelligibility. Research in IML (Interpretable Machine Learning) and XAI (Explainable AI) focus on minimizing this trade-off by developing more accurate interpretable models and by developing new techniques to explain black-box models. Such models and techniques make it easier for data scientists, engineers and model users to debug models and achieve important objectives such as ensuring the fairness of ML decisions and the reliability and safety of AI systems. In this tutorial, we present an overview of various interpretability methods and provide a framework for thinking about how to choose the right explanation method for different real-world scenarios. We will focus on the application of XAI in practice through a variety of case studies from domains such as healthcare, finance, and bias and fairness. Finally, we will present open problems and research directions for the data mining and machine learning community. What audience will learn: When and how to use a variety of machine learning interpretability methods through case studies of real-world situations. The difference between glass-box and black-box explanation methods and when to use them. How to use open source interpretability toolkits that are now available
Survey of Automatic Spelling Correction

Automatic spelling correction has been receiving sustained research attention. Although each article contains a brief introduction to the topic, there is a lack of work that would summarize the theoretical framework and provide an overview of the approaches developed so far. Our survey selected papers about spelling correction indexed in Scopus and Web of Science from 1991 to 2019. The first group uses a set of rules designed in advance. The second group uses an additional model of context. The third group of automatic spelling correction systems in the survey can adapt its model to the given problem. The summary tables show the application area, language, string metrics, and context model for each system. The survey describes selected approaches in a common theoretical framework based on Shannon’s noisy channel. A separate section describes evaluation methods and benchmarks.
A new SLA-aware method for discovering the cloud services using an improved nature-inspired optimization algorithm

Cloud computing is one of the most important computing patterns that use a pay-as-you-go manner to process data and execute applications. Therefore, numerous enterprises are migrating their applications to cloud environments. Not only do intensive applications deal with enormous quantities of data, but they also demonstrate compute-intensive properties very frequently. The dynamicity, coupled with the ambiguity between marketed resources and resource requirement queries from users, remains important issues that hamper efficient discovery in a cloud environment. Cloud service discovery becomes a complex problem because of the increase in network size and complexity. Complexity and network size keep increasing dynamically, making it a complex NP-hard problem that requires effective service discovery approaches. One of the most famous cloud service discovery methods is the Ant Colony Optimization (ACO) algorithm; however, it suffers from a load balancing problem among the discovered nodes. If the workload balance is inefficient, it limits the use of resources. This paper solved this problem by applying an Inverted Ant Colony Optimization (IACO) algorithm for load-aware service discovery in cloud computing. The IACO considers the pheromones’ repulsion instead of attraction. We design a model for service discovery in the cloud environment to overcome the traditional shortcomings. Numerical results demonstrate that the proposed mechanism can obtain an efficient service discovery method. The algorithm is simulated using a CloudSim simulator, and the result shows better performance. Reducing energy consumption, mitigate response time, and better Service Level Agreement (SLA) violation in the cloud environments are the advantages of the proposed method.
Learning Layout and Style Reconfigurable GANs for Controllable Image Synthesis

With the remarkable recent progress on learning deep generative models, it becomes increasingly interesting to develop models for controllable image synthesis from reconfigurable structured inputs. This paper focuses on a recently emerged task, layout-to-image, whose goal is to learn generative models for synthesizing photo-realistic images from a spatial layout (i.e., object bounding boxes configured in an image lattice) and its style codes (i.e., structural and appearance variations encoded by latent vectors). This paper first proposes an intuitive paradigm for the task, layout-to-mask-to-image, which learns to unfold object masks in a weakly-supervised way based on an input layout and object style codes. The layout-to-mask component deeply interacts with layers in the generator network to bridge the gap between an input layout and synthesized images. Then, this paper presents a method built on Generative Adversarial Networks (GANs) for the proposed layout-to-mask-to-image synthesis with layout and style control at both image and object levels. The controllability is realized by a proposed novel Instance-Sensitive and Layout-Aware Normalization (ISLA-Norm) scheme. A layout semi-supervised version of the proposed method is further developed without sacrificing performance. In experiments, the proposed method is tested in the COCO-Stuff dataset and the Visual Genome dataset with state-of-the-art performance obtained.
Read, Listen, and See: Leveraging Multimodal Information Helps Chinese Spell Checking

Chinese Spell Checking (CSC) aims to detect and correct erroneous characters for usergenerated text in Chinese language.Most of the Chinese spelling errors are misused semantically, phonetically or graphically similar characters.Previous attempts notice this phenomenon and try to utilize the similarity relationship for this task.However, these methods use either heuristics or handcrafted confusion sets to predict the correct character.In this paper, we propose a Chinese spell checker called REALISE, by directly leveraging the multimodal information of the Chinese characters.The REALISE model tackles the CSC task by (1) capturing the semantic, phonetic and graphic information of the input characters, and (2) selectively mixing the information in these modalities to predict the correct output.Experiments 1 on the SIGHAN benchmarks show that the proposed model outperforms strong baselines by a large margin.
DRB-GAN: A Dynamic ResBlock Generative Adversarial Network for Artistic Style Transfer

The paper proposes a Dynamic ResBlock Generative Adversarial Network (DRB-GAN) for artistic style transfer. The style code is modeled as the shared parameters for Dynamic ResBlocks connecting both the style encoding network and the style transfer network. In the style encoding network, a style class-aware attention mechanism is used to attend the style feature representation for generating the style codes. In the style transfer network, multiple Dynamic ResBlocks are designed to integrate the style code and the extracted CNN semantic feature and then feed into the spatial window Layer-Instance Normalization (SW-LIN) decoder, which enables high-quality synthetic images with artistic style transfer. Moreover, the style collection conditional discriminator is designed to equip our DRB-GAN model with abilities for both arbitrary style transfer and collection style transfer during the training stage. No matter for arbitrary style transfer or collection style transfer, extensive experiments strongly demonstrate that our proposed DRB-GAN outperforms state-of-the-art methods and exhibits its superior performance in terms of visual quality and efficiency. Our source code is available at https://github.com/xuwenju123/DRB-GAN.
Model-free reinforcement learning from expert demonstrations: a survey

Reinforcement learning from expert demonstrations (RLED) is the intersection of imitation learning with reinforcement learning that seeks to take advantage of these two learning approaches. RLED uses demonstration trajectories to improve sample efficiency in high-dimensional spaces. RLED is a new promising approach to behavioral learning through demonstrations from an expert teacher. RLED considers two possible knowledge sources to guide the reinforcement learning process: prior knowledge and online knowledge. This survey focuses on novel methods for model-free reinforcement learning guided through demonstrations, commonly but not necessarily provided by humans. The methods are analyzed and classified according to the impact of the demonstrations. Challenges, applications, and promising approaches to improve the discussed methods are also discussed.