Reviewerly/exHarmony huggingface dataset:
STRATEGIC ALLIANCE FOR BLOCKCHAIN GOVERNANCE GAME

This paper deals with design of an alternative secure Blockchain network framework to prevent damages from an attacker. The alliance concept from the strategic management perspectives is applied on the top of a general stochastic game framework. This new enhanced hybrid theoretical model is designed to find the best strategies toward preparation for preventing a network malfunction from an attacker through strategic alliances with other genuine nodes and it is developed based on the combination of a strategic management framework and a conventional stochastic model based on the Blockchain Governance Game. Analytically, tractable results for decision-making parameters are fully obtained to predict of the moment for operations and also to provide the optimal number of allegiance nodes to protect a Blockchain network. This research helps those whom are considering initial coin offering or launching new Blockchain-based services by enhancing security features through strategic alliances in a decentralized network.
Deep LSTM-RNN with Word Embedding for Sarcasm Detection on Twitter

In the world full of sarcastic people, it’s becoming challenging for the people of 21 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">st</sup> century to detect sarcasm using sentiment analysis efficiently. Sarcasm detection helps us to understand the bitter truth under the sugar coated sentences. It is widely used in various networking sites for understanding the true reviews and taking appropriate actions on the same if needed. Various methods, techniques and algorithms have been applied, although there’s little or much drawback of using the same. For instance, algorithms like logistic regression has been used to detect sarcasm, which has a drawback, can’t be used for continuous datasets. In our paper, we will be discussing about the approach we found appropriate and also provides an increased accuracy. Self designed dataset of sarcastic and non-sarcastic tweets is used for training of proposed model. Use of Recurrent Neural Network, Long Short Term Memory (LSTM) and Word Embeddings can make the sarcasm detection efficient and thereby making the statements from twitter easily classifiable.
Characterizing Types of Smart Contracts in the Ethereum Landscape

After cryptocurrencies, smart contracts are the second major innovation of the blockchain era. Leveraging the immutability and accountability of blockchains, these event-driven programs form the basis of the new digital economy with tokens, wallets, exchanges, and markets, but facilitating also new models of peer-to-peer organizations. To judge the long-term prospects of particular projects and this new technology in general, it is important to understand how smart contracts are used. While public announcements, by their nature, make promises of what smart contracts might achieve, openly available data of blockchains provides a more balanced view on what is actually going on. We focus on Ethereum as the major platform for smart contracts and aim at a comprehensive picture of the smart contract landscape regarding common or heavily used types of contracts. To this end, we unravel the publicly available data of the main chain up to block 9 000 000, in order to obtain an understanding of almost 20 million deployed smart contracts and 1.5 billion interactions. As smart contracts act behind the scenes, their activities are only fully accessible by also considering the execution traces triggered by transactions. They serve as the basis for this analysis, in which we group contracts according to common characteristics, observe temporal aspects and characterize them quantitatively and qualitatively. We use static methods by analyzing the bytecode of contracts as well as dynamic methods by aggregating and classifying the communication between contracts.
RAT - Resilient Allreduce Tree for Distributed Machine Learning

Parameter/gradient exchange plays an important role in large-scale distributed machine learning (DML). However, prior solutions such as parameter server (PS) or ring-allreduce (Ring) fall short since they are not resilient to issues or uncertainties like oversubscription, congestion or failures that may occur in datacenter networks (DCN).
TSCMF: Temporal and social collective matrix factorization model for recommender systems

Abstract In real-world recommender systems, user preferences are dynamic and typically change over time. Capturing the temporal dynamics of user preferences is essential to design an efficient personalized recommender system and has recently attracted significant attention. In this paper, we consider user preferences change individually over time. Moreover, based on the intuition that social influence can affect the users’ preferences in a recommender system, we propose a Temporal and Social Collective Matrix Factorization model called TSCMF for recommendation. We jointly factorize the users’ rating information and social trust information in a collective matrix factorization framework by introducing a joint objective function. We model user dynamics into this framework by learning a transition matrix of user preferences between two successive time periods for each individual user. We present an efficient optimization algorithm based on stochastic gradient descent for solving the objective function. The experiments on a real-world dataset illustrate that the proposed model outperforms the competitive methods. Moreover, the complexity analysis demonstrates that the proposed model can be scaled up to large datasets.
WhatsApp Group for Teaching and Learning in Indonesian Higher Education: What’s Up?

Technology plays a pivotal role in today’s teaching and learning process in terms of facilitating learning and improving students’ performance. Exploring an avant-garde technology to be integrated into the classroom is critical for finding new ways of learning. This study aimed at investigating university students’ usage, perception, attitude, and readiness towards the WhatsApp group as a teaching and learning platform. This is an exploratory study engaged in one class of university students after joining a course conducting by integrating a WhatsApp group for teaching and learning platform. The result revealed that students actively use WhatsApp in daily life, but unfortunately passively use it for learning in a group. Students recognise that the WhatsApp group is of potential for ubiquitous learning, and they have a good attitude to this app as a teaching and learning platform. However, it found that students are not ready yet to learn actively, collaboratively, and independently through the WhatsApp group. The academic and practical implications of these findings are discussed, and avenues for future research outlined.
Explanations for Negative Query Answers under Existential Rules

Ontology-mediated query answering is an extensively studied paradigm, where the conceptual knowledge provided by an ontology is leveraged towards more enhanced querying of data sources. A major advantage of ontological reasoning is its interpretability, which allows one to derive explanations for query answers. Indeed, explanations have a long history in knowledge representation, and have also been investigated for ontology languages based on description logics and existential rules. Existing works on existential rules, however, merely focus on understanding why a query is entailed, i.e., explaining positive query answers. In this paper, we continue this line of research and address another important problem, namely, explaining why a query is not entailed under existential rules, i.e., explaining negative query answers. We consider various problems related to explaining non-entailments from the abduction literature, and also introduce new problems. For all considered problems, we give a detailed complexity analysis for a wide range of existential rule languages and complexity measures.
Masked Face Recognition for Secure Authentication

With the recent world-wide COVID-19 pandemic, using face masks have become an important part of our lives. People are encouraged to cover their faces when in public area to avoid the spread of infection. The use of these face masks has raised a serious question on the accuracy of the facial recognition system used for tracking school/office attendance and to unlock phones. Many organizations use facial recognition as a means of authentication and have already developed the necessary datasets in-house to be able to deploy such a system. Unfortunately, masked faces make it difficult to be detected and recognized, thereby threatening to make the in-house datasets invalid and making such facial recognition systems inoperable. This paper addresses a methodology to use the current facial datasets by augmenting it with tools that enable masked faces to be recognized with low false-positive rates and high overall accuracy, without requiring the user dataset to be recreated by taking new pictures for authentication. We present an open-source tool, MaskTheFace to mask faces effectively creating a large dataset of masked faces. The dataset generated with this tool is then used towards training an effective facial recognition system with target accuracy for masked faces. We report an increase of 38% in the true positive rate for the Facenet system. We also test the accuracy of re-trained system on a custom real-world dataset MFR2 and report similar accuracy.
3D shape measurement of diffused/specular surface by combining fringe projection and direct phase measuring deflectometry

The three-dimensional (3D) data of object surfaces, like a precision machine part, play an important role in the fields of aerospace, automotive industry, augmented reality, heritage preservation, smart city, etc. The existing fringe projection profilometry and deflectometry can only measure the 3D shape of diffused and specular surfaces, respectively. However, there are many components having both diffused and specular surfaces. This paper presents a novel method for measuring the 3D shape of diffused/specular surfaces by combining fringe projection profilometry and direct phase measuring deflectometry. The principle and calibration method of the proposed method are elaborated. Experimental studies are conducted with an artificial diffused/specular step having diffused/specular surfaces to verify the measurement accuracy. The results on several objects show that the proposed method can measure diffused/specular surfaces effectively with certain accuracy. Error sources are also analyzed to improve the measurement accuracy.
DuCaGAN: Unified Dual Capsule Generative Adversarial Network for Unsupervised Image-to-Image Translation

With the appearance of Generative Adversarial Network (GAN), image-to-image translation based on a new unified framework has attracted growing interests.As a new technique, it can generate synthesizing images for various requirements in both computer vision and image processing.However, the cycle consistent structure adopted in some common models, such as cycle generative adversarial network (CycleGAN), is usually unable to learn more abundant image features.In this work, we developed a novel model based on GAN, named as dual capsule generative adversarial network (DuCaGAN), by utilizing the distinctive characteristic of view angle invariance and rotation equivariance in capsule network.Firstly, two capsule networks were introduced into the traditional CycleGAN model as discriminators to form our proposed model with six agents.To improve the feature capturing performance, we modified the full objective by combining the margin loss and the original adversarial loss.Furthermore, the Routing Algorithm in the capsule network was optimized by changing its compression function.Finally, experimental results on conventional visual tasks with paired and unpaired datasets demonstrated the superiority and effectiveness of the proposed approach compared to both deep convolutional generative adversarial network (DCGAN) and CycleGAN methods.More importantly, the proposed DuCaGAN was applied for the first time to augment the surface defect data from the real industrial field, and exhibited better performance than those methods available.
Class-Variant Margin Normalized Softmax Loss for Deep Face Recognition

In deep face recognition, the commonly used softmax loss and its newly proposed variations are not yet sufficiently effective to handle the class imbalance and softmax saturation issues during the training process while extracting discriminative features. In this brief, to address both issues, we propose a class-variant margin (CVM) normalized softmax loss, by introducing a true-class margin and a false-class margin into the cosine space of the angle between the feature vector and the class-weight vector. The true-class margin alleviates the class imbalance problem, and the false-class margin postpones the early individual saturation of softmax. With negligible computational complexity increment during training, the new loss function is easy to implement in the common deep learning frameworks. Comprehensive experiments on the LFW, YTF, and MegaFace protocols demonstrate the effectiveness of the proposed CVM loss function.
Attack Analysis Framework for Cyber-Attack and Defense Test Platform

In 2012, Google first proposed the knowledge graph and applied it in the field of intelligent searching. Subsequently, knowledge graphs have been used for in-depth association analysis in different fields. In recent years, composite attacks have been discovered through association analysis in the field of cyber security. This paper proposes an attack analysis framework for cyber-attack and defense test platforms, which stores prior knowledge in a cyber security knowledge graph and attack rule base as data that can be understood by a computer, sets the time interval of analysis on the Spark framework, and then mines attack chains from massive data with spatiotemporal constraints, so as to achieve the balance between automated analysis and real-time accurate performance. The experimental results show that the analysis accuracy depends on the completeness of the cyber security knowledge graph and the precision of the detection results from security equipment. With the rational expectation about more exposure of attacks and faster upgrade of security equipment, it is necessary and meaningful to constantly improve the cyber security knowledge graph in the attack analysis framework.
Development of Android-Based Mathematics Learning Media

The purpose of this research is to develop an Android-based math learning media that can be accessed using smartphones.This research is research development and using ADDIE model development with 5 stages of development, namely analysis, design, develop, implement, and evaluation.This research has successfully developed an Android-based math learning media with basic principles 1) the product is developed for one school year, 2) the formula is available or a quick way of calculation, 3) The practice questions with scores, 4) the Discussion is available for each question exercise, 5 there is an evaluation every chapter, 6) There are preparations for midterm and final exam, and 7) available help and about functions.The resulting product has also been through revision and expert tests, with satisfactory results, so that the media product of mathematics learning is feasible to be used as an alternative learning media for students.
Permutation Equivariant Document Interaction Network for Neural Learning to Rank

How to leverage cross-document interactions to improve ranking performance is an important topic in information retrieval research. The recent developments in deep learning show strength in modeling complex relationships across sequences and sets. It thus motivates us to study how to leverage cross-document interactions for learning-to-rank in the deep learning framework. In this paper, we formally define the permutation equivariance requirement for a scoring function that captures cross-document interactions. We then propose a self-attention based document interaction network that extends any univariate scoring function with contextual features capturing cross-document interactions. We show that it satisfies the permutation equivariance requirement, and can generate scores for document sets of varying sizes.
A Hybrid Conditional Variational Autoencoder Model for Personalised Top-n Recommendation

The interactions of users with a recommendation system are in general sparse, leading to the well-known cold-start problem. Side information, such as age, occupation, genre and category, have been widely used to learn latent representations for users and items in order to address the sparsity of users' interactions. Conditional Variational Autoencoders (CVAEs) have recently been adapted for integrating side information as conditions to constrain the learned latent factors and to thereby generate personalised recommendations. However, the learning of effective latent representations that encapsulate both user (e.g. demographic information) and item side information (e.g. item categories) is still challenging. In this paper, we propose a new recommendation model, called Hybrid Conditional Variational Autoencoder (HCVAE) model, for personalised top-n recommendation, which effectively integrates both user and item side information to tackle the cold-start problem. Two CVAE-based methods -- using conditions on the learned latent factors, or conditions on the encoders and decoders -- are compared for integrating side information as conditions. Our HCVAE model leverages user and item side information as part of the optimisation objective to help the model construct more expressive latent representations and to better capture attributes of the users and items (such as demographic, category preferences) within the personalised item probability distributions. Thorough and extensive experiments conducted on both the MovieLens and Ta-feng datasets demonstrate that the HCVAE model conditioned on user category preferences with conditions on the learned latent factors can significantly outperform common existing top-n recommendation approaches such as MF-based and VAE/CVAE-based models.
Coverless image steganography based on DenseNet feature mapping

Abstract Since the concept of coverless information hiding was proposed, it has been greatly developed due to its effectiveness of resisting the steganographic tools. Most existing coverless image steganography (CIS) methods achieve excellent robustness under non-geometric attacks. However, they do not perform well under some geometric attacks. Towards this goal, a CIS algorithm based on DenseNet feature mapping is proposed. Deep learning is introduced to extract high-dimensional CNN features which are mapped into hash sequences. For the sender, a binary tree hash index is built to accelerate index speed of searching hidden information and DenseNet hash sequence, and then, all matched images are sent. For the receiver, the secret information can be recovered successfully by calculating the DenseNet hash sequence of the cover image. During the whole steganography process, the cover images remain unchanged. Experimental results and analysis show that the proposed scheme has better robust compared with the state-of-the-art methods under geometric attacks.
RF-PCA: A New Solution for Rapid Identification of Breast Cancer Categorical Data Based on Attribute Selection and Feature Extraction

Breast cancer is one of the most common cancer diseases in women. The rapid and accurate diagnosis of breast cancer is of great significance for the treatment of cancer. Artificial intelligence and machine learning algorithms are used to identify breast malignant tumors, which can effectively solve the problems of insufficient recognition accuracy and long time-consuming in traditional breast cancer diagnosis methods. To solve these problems, we proposed a method of attribute selection and feature extraction based on random forest (RF) combined with principal component analysis (PCA) for rapid and accurate diagnosis of breast cancer. Firstly, RF was used to reduce 30 attributes of breast cancer categorical data. According to the average importance of attributes and out of bag error, 21 relatively important attribute data were selected for feature extraction based on PCA. The seven features extracted from PCA were used to establish an extreme learning machine (ELM) classification model with different activation functions. By comparing the classification accuracy and training time of these different models, the activation function of the hidden layer was determined as the sigmoid function. When the number of neurons in the hidden layer was 27, the accuracy of the test set was 98.75%, the accuracy of the training set was 99.06%, and the training time was only 0.0022 s. Finally, in order to verify the superiority of this method in breast cancer diagnosis, we compared with the ELM model based on the original breast cancer data and other intelligent classification algorithm models. The algorithm used in this article has a faster recognition time and a higher recognition accuracy than other algorithms. We also used the breast cancer data of breast tissue reactance features to verify the reliability of this method, and ideal results were obtained. The experimental results show that RF-PCA combined with ELM can significantly reduce the time required for the diagnosis of breast cancer, which has the ability of rapid and accurate identification of breast cancer and provides a theoretical basis for the intelligent diagnosis of breast cancer.
Self-Supervised Scale Recovery for Monocular Depth and Egomotion Estimation

The self-supervised loss formulation for jointly training depth and egomotion neural networks with monocular images is well studied and has demonstrated state-of-the-art accuracy. One of the main limitations of this approach, however, is that the depth and egomotion estimates are only determined up to an unknown scale. In this paper, we present a novel scale recovery loss that enforces consistency between a known camera height and the estimated camera height, generating metric (scaled) depth and egomotion predictions. We show that our proposed method is competitive with other scale recovery techniques that require more information. Further, we demonstrate that our method facilitates network retraining within new environments, whereas other scale-resolving approaches are incapable of doing so. Notably, our egomotion network is able to produce more accurate estimates than a similar method which recovers scale at test time only.