Reviewerly/exHarmony huggingface dataset:
Heterogeneous Domain Adaptation With Adversarial Neural Representation Learning: Experiments on E-Commerce and Cybersecurity

Learning predictive models in new domains with scarce training data is a growing challenge in modern supervised learning scenarios. This incentivizes developing domain adaptation methods that leverage the knowledge in known domains (source) and adapt to new domains (target) with a different probability distribution. This becomes more challenging when the source and target domains are in heterogeneous feature spaces, known as heterogeneous domain adaptation (HDA). While most HDA methods utilize mathematical optimization to map source and target data to a common space, they suffer from low transferability. Neural representations have proven to be more transferable; however, they are mainly designed for homogeneous environments. Drawing on the theory of domain adaptation, we propose a novel framework, Heterogeneous Adversarial Neural Domain Adaptation (HANDA), to effectively maximize the transferability in heterogeneous environments. HANDA conducts feature and distribution alignment in a unified neural network architecture and achieves domain invariance through adversarial kernel learning. Three experiments were conducted to evaluate the performance against the state-of-the-art HDA methods on major image and text e-commerce benchmarks. HANDA shows statistically significant improvement in predictive performance. The practical utility of HANDA was shown in real-world dark web online markets. HANDA is an important step towards successful domain adaptation in e-commerce applications.
A professional-driven blockchain framework for sharing E-Portfolio in the context of Industry 4.0

The journey towards Industry 4.0 have driven the workforce with the need for continuous competency development to stay on top of their profession and remain attractive to their employers. In this context, E-Portfolio has been conceived as a promising tool for both professionals and employers, in assessing and providing guidance for further workplace learning. Nevertheless, the challenges in implementing E-Portfolio are daunting, particularly with regard to lack of interoperability while collecting and sharing the competency evidences across multiple organizations and institutions. The increasing move towards professional (learner)-centered interoperability poses additional challenges that needs to be addressed to realize the full potential of E-Portfolio in the context of Industry 4.0. This work puts emphasize on realization of professional-centered interoperability and proposes a framework leveraging the advantage of blockchain, decentralized storage system interplanetary file system, smart contract and session management for E-Portfolio creation and sharing evidences across multiple organizations without comprising the confidentiality, integrity and availability of the shared evidences. The proof of concept simulation on Ethereum official test network Rinkeby demonstrates the feasibility of proposed framework in practice. In addition, the evaluation of the proposed framework demonstrates its effectiveness from the aspects of cost and security as a potential solution for implementing E-Portfolio and helping the professional to remain competitive in global labor market.
Academic Aggregated Search Approach Based on BERT Language Model

Academic Search concerns the indexing and retrieval of information objects like tutorials, blog posts, papers, books, etc. in the domain of academic research. When looking for a specific information, many researchers can't find all the information needed on the topic for which they are searching in the same academic search engine, which pushes them to search in each one separately. Therefore, we propose to exploit the task of aggregating search results from various sources in an academic domain. Exploiting the advantage of such systems may improve retrieval performance of academic search results by selecting the most appropriate vertical results according to the user's information need, and presenting them in a single page. Our proposed approach consists of exploiting BERT contextual embeddings in unsupervised fashion and taking advantage of their rich semantic features to improve the quality of the retrieved results. To the best of our knowledge, this is the first time when aggregated search paradigm is investigated in an in academic environment using BERT embeddings. Experimental results show that by only using BERT algorithm as a core component in our system, has given excellent results for our task and helped us achieved our objective in this work. In addition, a comparison with other state-of-the-art language models such as ELMo, USE and XLNet was made. The findings showed that the best performance was obtained using the BERT language model, which proved that using BERT model in our academic aggregated search system is a good choice.
Handling Missing and Imbalanced Data to Improve Generalization Performance of Machine Learning Classifier

Exploratory data analysis (EDA) is an important process for creating a machine learning model. Through data preprocessing, we can see the characteristics of the data and how to handle them. Data preprocessing is part of EDA that handles problems in the data before creating training and testing datasets. As a study case, this research uses the air pollution dataset published by the Ministry of Environment and Forestry of the Republic of Indonesia. The dataset has missing value and imbalanced class problems in which the previous research used complete case analysis for missing value and neglected imbalanced class problems within the dataset. The dataset has more than 5% missing values and has a significant amount of imbalance. The class that has the most amount of data has around 64% of the data, but the least amount of data only has 0.6% of the data. On the other hand, the dataset&#x2019;s missing values are randomly scattered among the data. This research proposed a preprocessing process to combine Multiple Imputation by Chained Equations (MICE) and Synthetic Minority Oversampling Technique (SMOTE) and tested it with three machine learning methods such as Random Forest, Support Vector Machine, and K-Nearest Neighbour. As a result, the g-mean metrics for those three machine learning approaches improved from 0.2 percent to 3.8 percent.
Canonical DPA Attack on HMAC-SHA1/SHA2

We present a new DPA attack on the secret initial state in the “outer” hash of HMAC-SHA1/SHA2. This attack only requires a suitable leakage of the SHA1/SHA2 working variables a and e. Due to the fact that each value of a working variable a or e is used as an input to copy, add, shift, and logical operations in multiple successive SHA1/SHA2 rounds, they are the primary potential sources of side channel leakage. Our attack is different but equivalent in this aspect to a known attack [9] on the secret initial state in the “inner” hash of HMAC-SHA1/SHA2. The combination of the two attacks provides the first full-fledged attack on HMAC-SHA1/SHA2 that does not depend on leakage assumptions on any ephemeral intermediates. As a proof-of-concept, we present a full key disclosure of the battery authentication key of a BQ27Z561 fuel gauge.
Acoustic features correlated to perceived urgency in evacuation announcements

To encourage prompt evacuation behavior during disasters, evacuation announcement systems are required to adjust the perceived urgency of announcements to the danger of a situation while maintaining voice clarity. In this study, we aimed to understand acoustic features correlated to the perceived urgency in speech when clarity is maintained. For this purpose, we used a speech synthesis tool to manipulate the key acoustic features: the time duration, F0 (instantaneous fundamental frequency), and spectrum. Specifically, we replaced these features in five real evacuation announcements that were spoken clearly by a TV announcer during an actual disaster and had different magnitudes of urgency. We found quantitatively and qualitatively that the perceived urgency was mostly influenced by the F0 of speech. Furthermore, by manipulating the F0 time average and variation, we compared the influence of the F0′s static constant feature and its dynamic fluctuation pattern on the magnitude of perceived urgency. The results indicated that both types of F0 information influenced the magnitude of perceived urgency in real Japanese speech. Our results suggest that the sense of urgency in evacuation announcements can be controlled by adjusting the F0 while maintaining voice clarity.
E-Commerce Recommendation Technology Based on Collaborative Filtering Algorithm and Mobile Cloud Computing

Personalized recommendation technology, as one of the core technologies of an E-commerce platform, has attracted a lot of attention with the rise of E-commerce in the Internet industry. Mobile cloud computing-based E-commerce has also exploded in popularity. You can buy whatever you want without leaving the house. Consumers are becoming increasingly receptive to online shopping as a result of this convenience; the E-commerce model demonstrates great modern business value. With its convenient and quick characteristics, online shopping has become fashionable and trendy; however, the popularity of the Internet and the rapid development of E-commerce has resulted in information overload, making it difficult for users to find the goods they require among a vast amount of product information. As a result, the E-commerce recommendation system was born. However, there is currently very little in-depth research on personalized recommendation technology in the field of o2o E-commerce, and most existing recommendation algorithms need to be improved in terms of accuracy and recommendation efficiency.
One class SVM model based on neural tangent kernel for anomaly detection task on small-scale data

Recent studies have shown that the evolution of infinitely wide neural networks satisfying certain conditions can be described by a kernel function called neural tangent kernel (NTK). We introduce NTK into a one-class support vector machine model and select data from different domains in UCI for a small-sample outlier detection task, demonstrate that NTK-OCSVM generally outperforms a variety of commonly used classification models, with more than 20% improvement in accuracy for similar models. When the kernel function parameters are varied, the experiments show that the model has strong robustness within a certain parameter range. Finally, we experimentally compare the time complexity of different models and the decision boundaries, and demonstrate that NTK-OCSVM improves accuracy at the expense of operational efficiency and has linear decision boundaries.
DWT Lifting Scheme for Image Compression with Cordic-Enhanced Operation

This paper proposes an innovative image compression scheme by utilizing the Adaptive Discrete Wavelet Transform-based Lifting Scheme (ADWT-LS). The most important feature of the proposed DWT lifting method is splitting the low-pass and high-pass filters into upper and lower triangular matrices. It also converts the filter execution into banded matrix multiplications with an innovative lifting factorization presented with fine-tuned parameters. Further, optimal tuning is the most important contribution that is achieved via a new hybrid algorithm known as Lioness-Integrated Whale Optimization Algorithm (LI-WOA). The proposed algorithm hybridizes the concepts of both the Lion Algorithm (LA) and Whale Optimization Algorithm (WOA). In addition, innovative cosine evaluation is initiated in this work under the CORDIC algorithm. Also, this paper defines a single objective function that relates multi-constraints like the Peak Signal-to-Noise Ratio (PSNR) as well as Compression Ratio (CR). Finally, the performance of the proposed work is compared over other conventional models regarding certain performance measures.
Virtual sample generation for few-shot source camera identification

The Source Camera Identification (SCI) has achieved remarkable success. However, existing approaches require sufficiently large training sets for high performance on accuracy and robustness. For maintaining high performance given small training sets, we propose a semi-supervised, Mega-Trent-Diffusion (MTD) method to generate virtual samples, such that the training sets can be expanded and unlabeled samples can be fully utilized as well. The stability of our method is improved using ensemble learning. Our theoretical analysis and experiments corroborate the effectiveness of our method beyond others when few-shot is given.
Understanding the potential of server-driven edge video analytics

The proliferation of edge video analytics applications has given rise to a new breed of streaming protocols which stream aggressively compressed videos to remote servers for compute-intensive DNN inference. One popular design paradigm of such protocols is to leverage the server-side DNN to extract useful feedback (e.g. based on a low-quality-encoded stream sent to the server) and use the feedback to inform how the camera should encode and stream the video in the future. In this server-driven approach, an ideal form of feedback should (1) be derived from minimum information from the video sensor (2) incur minimum bandwidth usage to obtain (3) indicate the optimal video streaming/encoding scheme (e.g. the minimum frames/regions that require high encoding quality). However, our preliminary study shows that these idealized requirements are far from being met. Using object detection as an example use case, we demonstrate significant yet untapped room for improvement by considering a broader design space, in terms of how the feedback should be derived from the DNN, how often it should be extracted, and how to determine the encoding quality of the video on which we extract the feedback.
Green Cloud Computing-To Build A Sustainable Tomorrow

Cloud computing is an excellent alternative to desk computing. Cloud computing technology has a massive number of users as they provide a range of scalability, trustworthiness, and high performance with comparatively low cost. As cloud computing is growing day by day, the energy use in the cloud is also increasing. There is also a need for energy use control in the segment of technology (information and communication). In the case of energy consumption, data centres have attracted more exposure. Green cloud computing corresponds to the environmentally sustainable use of computers and their IT-related tools and facilities. The development of cloud infrastructure redesigns traditional networks and provides both technical and economic benefits and exciting sustainable environmental initiatives. Such technology promises to increase energy efficiency and reduce carbon footprints as well as e-waste. Energy-efficient solutions requiring the best use of the available resources are indeed a standard. The service providers need to implement and redesign the cloud technology to green computing to protect the environment from adverse cloud effects. Cloud architecture with green features includes power management, load balancing, green data centres, virtualisation, high-performance computing, renewability, reuse, recycling, etc. This paper provides a thorough research study on green cloud computing and its features. This paper highlights the challenges of green cloud computing.
Research on Audit Opinion Prediction of Listed Companies Based on Sparse Principal Component Analysis and Kernel Fuzzy Clustering Algorithm

The prediction of audit opinions of listed companies plays a significant role in the security market risk prevention. By introducing machine learning methods, many innovations can be implemented to improve audit quality, lift audit efficiency, and cultivate the keen insight of auditors. However, in a realistic environment, category imbalance and critical feature selection exist in the prediction model of company audit opinions. This paper firstly combines batched sparse principal component analysis (BSPCA) with kernel fuzzy clustering algorithm (KFCM) and proposes a sparse-kernel fuzzy clustering undersampling method (S-KFCM) to deal with the imbalance of sample categories. This method adopts the kernel fuzzy clustering algorithm to down-sample the normal samples, and their features are extracted from abnormal sample sets based on the group sparse component method. The sparse normal sample set can maintain the original distribution space structure and highlight the classification boundary samples. Secondly, considering the company’s characteristic attributes and data sources, 448 original variables are grouped, and then BSPCA is used for feature screening. Finally, the support vector machine (SVM) is adopted to complete the classification prediction. According to the empirical results, the SKFCM-SVM model has the highest prediction accuracy.
Semi-Supervised Fake Reviews Detection based on AspamGAN

With the popularization of social software and e-business in recent years, more and more consumers like to share their consumption experiences on social networks and refer to other consumers' reviews and opinions when making consumption decisions. Online reviews have become an essential part of browsing on websites such as shopping, and people's reliance on informative reviews have contributed to the rise of fake reviews. The traditional classification method is affected by the label dataset, which is not only time-consuming, laborious, and subjective, but also the extraction of artificial features also affects the classification accuracy. Due to the relative length of the online text, the possibility of the classifier losing important information increases, this weakens the model’s detection capability. To solve this aforementioned problem, a semi-supervised Generative Adversarial Network (AspamGAN) fake reviews detection method incorporating an attention mechanism is proposed. Using labeled and unlabeled data to correctly learn input distributions, the features required for classification are automatically discovered using deep neural networks, providing better prediction accuracy for online reviews. The approach includes attention mechanisms in the classifier to obtain an adequate semantic representation and relies on a limited dataset of labeled data to detect false reviews, and is applied on the TripAdvisor dataset. Experimental results show that the proposed algorithm outperforms state-of-the-art semi-supervised fake review detection techniques when the label dataset is limited.
Decoherence on staggered quantum walks

Decoherence phenomenon has been widely studied in different types of quantum walks. In this work we show how to model decoherence inspired by percolation on staggered quantum walks. Two models of unitary noise are described: breaking polygons and breaking vertices. The evolution operators subject to these noises are obtained and the equivalence to the coined quantum walk model is presented. Further, we numerically analyze the effect of these decoherence models on the two-dimensional grid of $4$-cliques. We examine how these perturbations affect the quantum walk based search algorithm in this graph and how expanding the tessellations intersection can make it more robust against decoherence.
Sine (B): A single randomized Population-based algorithm for solving optimization problems

• Variants of JAYA and RAO-1 optimization algorithms are proposed and analysed. • Removing the absolute terms from JAYA algorithm does not affect the results. • Two randomized algorithms, Sine (B) and Cosine (B) are proposed and analysed. • Thirty benchmark functions are used. • Wilcoxon signed-rank and Friedman tests are conducted. • Results show that Sine (B) is a fair competitor to JAYA and RAO-1 algorithms. Heuristics are widely used in solving both constrained and unconstrained optimization problems. The population-based “JAYA” and “Rao-1” algorithms proposed in 2016 and 2020 respectively are popular among researchers due to their simplicity and easy adaptability. The absence of any tuning parameter makes them popular and is used effectively for solving real-world engineering problems also. This paper analyses these algorithms and proposes new algorithms, Sine (B) and Cosine (B). The performances of the algorithms are compared using thirty unconstrained benchmark functions available in the literature. Wilcoxon signed-rank test and Friedman test are conducted for statistical comparison. The results demonstrate that the new algorithms perform well and are comparable with JAYA and Rao-1 in terms of solution quality and speed. They can be suitably modified to solve constrained optimization problems also.
An Analysis Model of College English Classroom Patterns Using LSTM Neural Networks

With the advent of the information age, IT represented by mobile computing and neural network has been widely used in various fields of education, and IT has become an indispensable part of university classrooms. Mobile devices can be used to promote teacher-student interaction, and neural networks can be used to update teaching methods. The goal of CET is to help students develop their listening, speaking, reading, and writing skills. Although some studies have discovered a certain level of poor learning adaptability in the practice of AI-supported College English Teaching (CET), the majority of the studies only describe the phenomenon without delving deeper into the causes. The deep integration of AI and education is promoting the growth of a new college English education ecosystem. The research is aimed at understanding the current situation of college students’ English learning adaptability supported by AI, clarifying the influence effect of various influencing factors on learning adaptability and the relationship between learning adaptability and various factors, and proposing strategies to improve students’ learning adaptability through investigation and analysis of college students’ English learning adaptability supported by AI.
Application of a back propagation neural network model based on genetic algorithm to in situ analysis of marine sediment cores by X-ray fluorescence core scanner

The use of core scanners to perform X-ray fluorescence (XRF) spectroscopic analysis can only obtain the intensities of the target elements, which is not conducive for application in marine geology research. In this paper, using a core scanner, in situ measurements were performed on 15 components: Al2O3, SiO2, K2O, CaO, TiO2, MnO, Fe2O3, V, Cr, Zn, Rb, Sr, Y, Zr, and Ba. We explored the feasibility of reducing the effect of interstitial water by normalizing the original intensity using the intensity of Ca and the ratio of coherent to incoherent and attempted to introduce a genetic algorithm-back propagation neural network model and use its nonlinear fitting capability to correct the matrix effect. The prediction precision of this method was 0.6-15.4%. The proposed method is suitable for the rapid analysis of major and minor components in marine sediment core samples, while taking full advantage of the high-resolution of the XRF core scanner.