Reviewerly/exHarmony huggingface dataset:
Value targets in off-policy AlphaZero: a new greedy backup

This article presents and evaluates a family of AlphaZero value targets, subsuming previous variants and introducing AlphaZero with greedy backups (A0GB). Current state-of-the-art algorithms for playing board games use sample-based planning, such as Monte Carlo Tree Search (MCTS), combined with deep neural networks (NN) to approximate the value function. These algorithms, of which AlphaZero is a prominent example, are computationally extremely expensive to train, due to their reliance on many neural network evaluations. This limits their practical performance. We improve the training process of AlphaZero by using more effective training targets for the neural network. We introduce a three-dimensional space to describe a family of training targets, covering the original AlphaZero training target as well as the soft-Z and A0C variants from the literature. We demonstrate that A0GB, using a specific new value target from this family, is able to find the optimal policy in a small tabular domain, whereas the original AlphaZero target fails to do so. In addition, we show that soft-Z, A0C and A0GB achieve better performance and faster training than the original AlphaZero target on two benchmark board games (Connect-Four and Breakthrough). Finally, we juxtapose tabular learning with neural network-based value function approximation in Tic-Tac-Toe, and compare the effects of learning targets therein.
Credit scoring by leveraging an ensemble stochastic criterion in a transformed feature space

The credit scoring models are aimed to assess the capability of refunding a loan by assessing user reliability in several financial contexts, representing a crucial instrument for a large number of financial operators such as banks. Literature solutions offer many approaches designed to evaluate users’ reliability on the basis of information about them, but they share some well-known problems that reduce their performance, such as data imbalance and heterogeneity. In order to face these problems, this paper introduces an ensemble stochastic criterion that operates in a discretized feature space, extended with some meta-features in order to perform efficient credit scoring. Such an approach uses several classification algorithms in such a way that the final classification is obtained by a stochastic criterion applied to a new feature space, obtained by a twofold preprocessing technique. We validated the proposed approach by using real-world datasets with different data imbalance configurations, and the obtained results show that it outperforms some state-of-the-art solutions.
Towards Effective Adversarial Attack Against 3D Point Cloud Classification

In the domain of 3D point cloud classification, deep learning based classifiers have made significant progress, while they have been also proven to be vulnerable on the adversarial at-tack at the same time. Some recent works employ the attack methods that devised for image classification such as projected gradient descent (PGD) to attack the 3D classifiers, but their performances seem quite limited when faced with statistical operations including point cloud denoising and point cloud upsampling. In this paper, we propose ‘SmoothAttack’, a new attack that can craft adversarial point clouds robust to statistical operations. SmoothAttack can be easily applied in both global constraint and pointwise constraint. Besides, we analyze the directions of perturbations onto the point cloud during the iteration process, where SmoothAttack can some-how stabilize the direction and make full use of the adversarial budgets. Experiments validate that our ‘SmoothAttack’ can raise the attack success rates against statistical defenses up to 98% for untargeted attack and 91% for targeted attack on ModelNet40 database when fooling the classifiers Point-Net and DGCNN.
Design and simulation of handwritten detection via generative adversarial networks and convolutional neural network

In the field of Computer Vision, optical character recognition (OCR) plays a crucial part. In OCR, handwritten document images are transformed into machine-encoded text. Handwritten character recognition is an essential research field of pattern recognition and has attracted broad studies. Convolutional Neural Network (CNN) is a popular choice used with deep learning, in recognizing handwritten characters and many research papers have already been published in this area. Implementation of neural network in different forms is found in most of the work and produces different accuracy levels due to complex letter structure, data unavailability, and different writing styles. We can increase the accuracy of recognising theEnglish handwritten character by our proposed method, producing new training instances from the existing instances using augmentation and Generative Adversarial Networks (GANs). We use a deep learning model in this article, GAN for generating English handwritten character recognition. Furthermore, we illustrate that how generated characters can be used to increase the performance of English handwritten characters classification. With the help of this proposed model, we attained the accuracy of 95.36% for recognising characters.
A framework for modeling structural association among De‐Motivators of scaling agile

Abstract Usage of agile methods for software development has increased in recent times. Rapid delivery of software products is ensured by these methods with less expense and high user gratification. Application of these methods on a large scale causes many De‐Motivators or challenges as initially these methods were aimed at those developmental teams that worked on small scale. However, no effort has been made yet to hand out the various correlations among the challenges. To fill the space, the Interpretive Structural Model (ISM) approach is used to reconnoiter the correlations among the De‐Motivators regarding Scaling Agile Software Development Methodologies (ASDM) from the management perspective. Creation of a framework for structural modeling association among the De‐Motivators is the aim of this study. To attain the goal, a hybrid methodology was applied based on a Systematic literature review (SLR), empirical survey, and ISM. First, we pointed out 15 De‐Motivators of Scaling ASDM from management perspectives through SLR study. Second, with the help of 59 experts from different countries, a questionnaire survey was conducted to empirically examine the correlations among the detected De‐Motivators. Further, correlations among the challenges are discovered with the usage of ISM through review of the panel, and they accomplish the categorization through CrossImpact Matrix Multiplication Applied Approach.
Self-Supervised Saliency Estimation for Pixel Embedding in Road Detection

Road detection is an important task inthe signal processing field. Although self-supervised learning has the potential to learn rich and effective visual representations that avoid tedious labeling, the current approaches learn from object-centered images, which leads to ambiguous results in complex traffic scenarios. We introduce saliency estimation to extend the self-supervised segmentation beyond object-center images, with spatial-temporal information and ensemble learning employed to improve the robustness. Then, we also design a quadruple loss for the pixel embedding learning and optimize the affinity between different categories, while exploring structural information in negative pixels. Experiments on the public datasets show that our approach is competitive with state-of-the-art approaches.
Anonymization of Daily Activity Data by Using ℓ-diversity Privacy Model

In the age of IoT, collection of activity data has become ubiquitous. Publishing activity data can be quite useful for various purposes such as estimating the level of assistance required by older adults and facilitating early diagnosis and treatment of certain diseases. However, publishing activity data comes with privacy risks: Each dimension, i.e., the activity of a person at any given point in time can be used to identify a person as well as to reveal sensitive information about the person such as not being at home at that time. Unfortunately, conventional anonymization methods have shortcomings when it comes to anonymizing activity data. Activity datasets considered for publication are often flat with many dimensions but typically not many rows, which makes the existing anonymization techniques either inapplicable due to very few rows, or else either inefficient or ineffective in preserving utility. This article proposes novel multi-level clustering-based approaches using a non-metric weighted distance measure that enforce ℓ-diversity model. Experimental results show that the proposed methods preserve data utility and are orders more efficient than the existing methods.
Interpreting Internal Activation Patterns in Deep Temporal Neural Networks by Finding Prototypes

Deep neural networks have demonstrated competitive performance in classification tasks for sequential data. However, it remains difficult to understand which temporal patterns the internal channels of deep neural networks capture for decision-making in sequential data. To address this issue, we propose a new framework with which to visualize temporal representations learned in deep neural networks without hand-crafted segmentation labels. Given input data, our framework extracts highly activated temporal regions that contribute to activating internal nodes and characterizes such regions by prototype selection method based on Maximum Mean Discrepancy. Representative temporal patterns referred to here as Prototypes of Temporally Activated Patterns (PTAP) provide core examples of subsequences in the sequential data for interpretability. We also analyze the role of each channel by Value-LRP plots using representative prototypes and the distribution of the input attribution. Input attribution plots give visual information to recognize the shapes focused on by the channel for decision-making.
Techniques for Maintaining Situational Awareness During Large-Scale Electric Grid Simulations

Simulations of the dynamic response of high voltage, large-scale electric grids often generate large amounts of data. This can make it difficult for engineers to understand the overall system behavior. This paper presents various techniques to help with gaining situational awareness for electric grid simulations in the time frame of milliseconds to minutes. These techniques include the use of time-domain graphs, geographic data views in which geographic information embedded in the electric grid model is leveraged to create visualizations, contouring, animation loops, machine learning and modal analysis. Results are demonstrated on a 10,000 bus synthetic grid model and an actual electric grid model with 110,000 buses.
SwitchNet: Learning to switch for word-level language identification in code-mixed social media text

Abstract Word-level language identification is an essential prerequisite for extracting useful information from code-mixed social media content. Previous studies in word-level language identification show two important observations. First, the local context is an important indicator of the language of a word when a word is valid in multiple languages. Second, considering the word in isolation from its context leads to more effective language classification when a word is borrowed or embedded into sentences of other languages. In this paper, we propose a framework for language identification that makes use of a dynamic switching mechanism for effective language classification of both words that are borrowed or embedded from other languages as well as words that are valid in multiple languages. For a given input, the proposed switching mechanism makes a dynamic decision to bias its prediction either towards the prediction obtained by the contextual information or that obtained by the word in isolation. In contrast to existing studies that rely upon large amounts of annotated data for robust performance in a multilingual environment, the proposed approach uses minimal annotated resources and no external resources, making it easily extendible to newer languages. Evaluation over a corpus of transliterated Facebook comments shows that the proposed approach outperforms its baseline counterparts: classification based on the contextual information, classification based on the word in isolation, as well as an ensemble of the two classifiers.
A phishing Mitigation Solution using Human Behaviour and Emotions that Influence the Success of Phishing Attacks

Phishing is a social engineering scam that can cause financial and reputational damage to people and organisations. Studies have demonstrated the effects of human behaviour and emotions on people's security behaviour, such as falling into a phishing scam. Moreover, several studies show the effects of the COVID-19 outbreak on human emotions, impacting phishing attempts' success. In this study, we have developed a solution using previous studies' results to identify vulnerable users (i.e., those at risk of clicking on phishing links) in organisations. The solution assigns proper mitigation actions to those high-risk users. The system contains behaviour measurement, risk scoring, and mitigation modules that can mature and develop accuracy over time. Furthermore, situations similar to a pandemic are considered in the solution. The proposed solution will help organisations focus more on protecting high-risk users and reducing successful phishing attacks. This solution should be used in combination with technical anti-phishing and cybersecurity awareness training campaigns to achieve better results.
Weakly-Supervised Online Hashing

With the rapid development of social websites, recent years have witnessed an explosive growth of social images with user-provided tags. Most existing hashing methods for social image retrieval are batch-based which may violate the nature of social images, i.e., social images are usually generated periodically or collected in a stream fashion. Although there exist many online hashing methods, they either adopt unsupervised learning which ignore the relevant tags, or are designed in the supervised manner which needs high-quality labels. In this paper, to overcome the above limitations, we propose a new method named Weakly-supervised Online Hashing (WOH). In order to learn high-quality hash codes, WOH exploits the weak supervision, i.e., tags, by considering the semantics of tags and removing the noise. Besides, we develop a discrete online optimization algorithm, which is efficient and scalable. Extensive experiments conducted on two real-world datasets demonstrate the superiority of WOH.
16.8 Tb/s True Random Number Generator Based on Amplified Spontaneous Emission

We report a 16.8 Tb/s true random number generator (TRNG) based on amplified spontaneous emission (ASE) of Erbium-doped fiber amplifier (EDFA), which is sliced into 42 parallel channels by an arrayed waveguide grating (AWG). The true randomness of ASE is enhanced and guaranteed by a novel extraction approach utilizing two orthogonal polarized components of ASE. Consequently, the random number generation rate (RNGR) can be significantly increased via higher sampling rate. The experimental results show that, the single-channel RNGR of 400 Gb/s is achieved, while the overall RNGR is 16.8 Tb/s for the full ASE spectrum. The proposed TRNG is of great significance in the field of secret key distribution and confidential communication.
Sequential Recommendation Based on Multivariate Hawkes Process Embedding With Attention

Recommender systems are important approaches for dealing with the information overload problem in the big data era, and various kinds of auxiliary information, including time and sequential information, can help improve the performance of retrieval and recommendation tasks. However, it is still a challenging problem how to fully exploit such information to achieve high-quality recommendation results and improve users' experience. In this work, we present a novel sequential recommendation model, called multivariate Hawkes process embedding with attention (MHPE-a), which combines a temporal point process with the attention mechanism to predict the items that the target user may interact with according to her/his historical records. Specifically, the proposed approach MHPE-a can model users' sequential patterns in their temporal interaction sequences accurately with a multivariate Hawkes process. Then, we perform an accurate sequential recommendation to satisfy target users' real-time requirements based on their preferences obtained with MHPE-a from their historical records. Especially, an attention mechanism is used to leverage users' long/short-term preferences adaptively to achieve an accurate sequential recommendation. Extensive experiments are conducted on two real-world datasets (lastfm and gowalla), and the results show that MHPE-a achieves better performance than state-of-the-art baselines.
Coarse and Fine-Grained Hostility Detection in Hindi Posts Using Fine Tuned Multilingual Embeddings

Due to the wide adoption of social media platforms like Facebook, Twitter, etc., there is an emerging need of detecting online posts that can go against the community acceptance standards. The hostility detection task has been well explored for resource-rich languages like English, but is unexplored for resource-constrained languages like Hindi due to the unavailability of large suitable data. We view this hostility detection as a multi-label multi-class classification problem. We propose an effective neural network-based technique for hostility detection in Hindi posts. We leverage pre-trained multilingual Bidirectional Encoder Representations of Transformer (mBERT) to obtain the contextual representations of Hindi posts. We have performed extensive experiments including different pre-processing techniques, pre-trained models, neural architectures, hybrid strategies, etc. Our best performing neural classifier model includes One-vs-the-Rest approach where we obtained 92.60%, 81.14%, 69.59%, 75.29% and 73.01% F1 scores for hostile, fake, hate, offensive, and defamation labels respectively. The proposed model (https://github.com/Arko98/Hostility-Detection-in-Hindi-Constraint-2021) outperformed the existing baseline models and emerged as the state-of-the-art model for detecting hostility in the Hindi posts.
Efficient Fuzzy Similarity-Based Text Classification with SVM and Feature Reduction

With the generation of enormous data day by day, the need of feature reduction has tremendously increased in the field of text classification. In this direction, this paper presents two text classification systems, called concept-based mining model using threshold (CMMT) and fuzzy similarity-based concept mining model using feature clustering (FSCMM-FC). Both systems aim to classify the English text documents into pre-defined mutually exclusive categories. These systems preprocess the documents at the sentence, document, and integrated corpora levels; apply feature extraction and reduction; train the classifier; and finally, classify the documents using support vector machine. CMMT cuts off the less frequent features by applying threshold on the extracted features, whereas FSCMM-FC reduces the features by finding the feature points using fuzzy C-means. The experimental results obtained 95.8% and 94.695% feature reduction in CMMT and FSCMM-FC, respectively, and also the 85.41% and 93.43% classification accuracy in CMMT and FSCMM-FC, respectively. Therefore, these results state that FSCMM-FC outperformed CMMT greatly with effective memory usage and efficient classification accuracy.
A privacy-preserving framework for cross-domain recommender systems

User privacy in the recommender systems have received much attention over the years. However, much of this attention has been on privacy protection in single-domain recommender systems and not on cross-domain recommender systems. The privacy-preserving cross-domain recommender systems not only encourages collaboration of data between different domains to solve the problem of data sparsity but also ensures the users' privacy and secure transfer of auxiliary information between domains. However, existing studies are not suitable for privacy protection in a cross-domain scenario. To this end, we propose a novel privacy-preserving framework for cross-domain recommender systems that provides a generic template for other secure cross-domain recommender systems. Employing a homomorphic encryption scheme, the framework consists of two protocols for users' privacy in cross-domain recommender systems. We mathematically described every step involved in each protocol, proved that the two protocols are secure against a semi-honest adversary, and compared the complexity of the protocols.
Topic-based sentiment analysis of hotel reviews

We developed a system for sentiment analysis of hotel reviews. The system uses topics to give accurate reports related to some qualities specified by the end-user. This system permits users to evaluate a given hotel services quality according to multiple criteria. Besides, a user can make different detailed comparisons between hotels. To achieve this goal, the proposed system decomposes complex opinions into sub-comments to extract the specified concept. Further, it translates non-English comments into the English language. After having tested the system by using two datasets, the obtained results are judged to be satisfactory.
Learning from Imbalanced and Incomplete Supervision with Its Application to Ride-Sharing Liability Judgment

In multi-label tasks, sufficient and class-balanced label is usually hard to obtain, which makes it challenging to train a good classifier. In this paper, we consider the problem of learning from imbalanced and incomplete supervision, where only a small subset of labeled data is available and the label distribution is highly imbalanced. This setting is of importance and commonly appears in a variety of real applications. For instance, considering the ride-sharing liability judgment task, liability disputes usually due to a variety of reasons, however, it is expensive to manually annotate the reasons, meanwhile, the distribution of reason is often seriously imbalanced. In this paper, we present a systemic framework Limi consisting of three sub-steps, that is, Label Separating, Correlation Mining and Label Completion. Specifically, we propose an effective two-classifier strategy to separately tackle head and tail labels so as to alleviate the performance degradation on tail labels while maintaining high performance on head labels. Then, a novel label correlation network is adopted to explore the label relation knowledge with flexible aggregators. Moreover, the Limi framework completes the label on unlabeled instances in a semi-supervised fashion. The framework is general, flexible, and effective. Extensive experiments on diverse applications, such as the ride-sharing liability judgment task from Didi and various benchmark tasks, demonstrate that our solution is clearly better than many competitive methods.