Reviewerly/exHarmony huggingface dataset:
Feature Space Perturbation for Transferable Adversarial Examples in Image Forensics Networks

As we delve deeper into the features learned by networks associated with various computer vision tasks, such as object detection and image classification, we find that they vary from more general to more network-specific. Feature space-based adversarial perturbation methods, which exploit these general features in the earlier layers of the surrogate network, can generate adversarial examples transferable to a target network. Image forensics networks, such as those associated with camera model identification and manipulation detection, are generally shallow and trained from scratch. Such networks are said to be devoid of general features. Recent works in image forensics using various gradient-based adversarial generation methods show the limited transferability of these adversarial examples. A stop condition added to the gradient-based generation algorithms leads to an improvement in transferability. In this work, we exploit a feature space-based perturbation method to examine the generality of the learned features of the image forensics networks and generate transferable adversarial examples. Under various knowledge scenarios, the transferability in terms of the attack success rate is task dependent. The transferability achieved in the median filtering case is significant and shows the generality of the learned features in image forensics networks. The same cannot be said for the resizing detection task, as the transferability is found to be zero under all knowledge scenarios.
Challenges in spoken language diarization in code-switched scenario

Spoken language diarization (SLD) is a task of automatically annotating the monolingual segments in a given code-switched (CS) speech signal. Most of the SLD methods in the literature are linked to the task like automatic speech recognition and no independent frameworks exist. One attractive direction to evolving an independent framework is to explore speaker diarization (SD). However, there are several challenges in doing the same due to the production of the CS speech by a single speaker. This work uses the speaker change detection (SCD) framework of SD to illustrate the challenges. The initial experiments demonstrate that the performance of language change detection (LCD) is significantly poor compared to SCD. The degradation is mostly due to the significant overlapping of the features belonging to two languages in the feature space. Trained adaptation models are used to compute the Gaussian mixture model posterior (GP) features and observed that the overlapping is significantly lower compared to the acoustic features. Hence the GP feature provides a relative improvement of 20% for LCD in terms of identification rate (IDR). Thus using existing SD frameworks, to begin with, and modifying them to increase the discrimination between languages in the CS scenario seems to be a good way to evolve independent frameworks.
Parturition Hindi Speech Dataset for Automatic Speech Recognition

While automatic speech recognition (ASR) technologies have become mature, they are mostly being developed by industry for large scale commercial applications. There are many niche domains that can potentially benefit from ASR. These domains may need ASR for a specific limited vocabulary for a certain population with a distinct language and dialect. This paper details the complete procedure for developing an ASR solution for such an application. It presents Parturition Hindi Speech (PHS) dataset prepared for real-time ASR for a medical application in Bihar, India. The dataset is prepared for childbirth assistance with recordings done by nurses in situ. Finally, several ASR systems are developed for the PHS dataset and their performances are compared. The models are pre-trained on large datasets and are adapted to PHS dataset. In experiments, we find that end-to-end ASR models adapt more effectively as compared to GMM-HMM based models. Moreover, custom language models further boost the performance.
Rooted Mahalanobis distance based Gustafson-Kessel fuzzy C-means

Fuzzy c-means (FCM) is a classic unsupervised clustering algorithm in machine learning fields. Euclidean distance is a frequently used distance metric in FCM, but it is only suitable for data with spherical clusters. Therefore, Mahalanobis distance was introduced into Gustafson-Kessel Fuzzy C-Means (GK-FCM) to help improve the performance on data with ellipsoidal clusters. However, GK-FCM and existing Mahalanobis distance based algorithms only focus on squared Mahalanobis distance, because squared Mahalanobis distance based problems are usually convex and easily solvable. But squared Mahalanobis distance is not a perfect metric, because it tends to exaggerate the influence of outliers and lead to unsatisfying results. In this paper, we propose a rooted Mahalanobis distance based GK-FCM model, which has better clustering performance and superior robustness than traditional GK-FCM. Moreover, owing to the introduction of rooted Mahalanobis distance, the optimization of the proposed model becomes non-trivial and it is not realistic to obtain a closed-form solution as that of traditional GK-FCM. In this paper, by making reference to the re-weighted method, we develop a novel iterative converging algorithm to optimize the proposed model. Finally, extensive experiments are conducted on both synthetic and real-world data sets to manifest the superiority of the proposed model.
Type-2 evolving fuzzy Kalman filter design based on unobservable spectral components space for interval tracking of non-stationary experimental data

In this paper, an interval type-2 evolving fuzzy Kalman filter is designed for processing of unobservable spectral components of uncertain experimental data. The adopted methodology consider the following steps: an initial model of the interval type-2 fuzzy Kalman filter, which is off-line identified from an initial window of the experimental data; the updating of antecedent proposition of interval type-2 fuzzy Kalman filter by using an interval type-2 formulation of evolving Takagi-Sugeno (eTS) clustering algorithm and the updating of consequent proposition by using a type-2 fuzzy formulation of Observer/Kalman Filter Identification (OKID) algorithm, taking into account the multivariable recursive Singular Spectral Analysis of the experimental data. The computational results for tracking the Mackey-Glass chaotic time series illustrate the efficiency of proposed methodology as compared to relevant approaches from literature, and the experimental results for tracking a 2DoF helicopter demonstrate its applicability.
Developing and hosting web data apps in R programming for official statistics

Official statistics place particular emphasis on communication and dissemination of surveys’ results to citizens and stakeholders. This is typically done through the publication of press releases and presentation of aggregated data of statistical surveys. The use of web services and software that allow users to interact with the results of official statistics comes to further enhance communication, dissemination, literacy and overall quality of official statistics. This paper is related to the objectives and context of reaching a wider audience through engaging users and explains how an NSO (National Statistical Office) member without specialized knowledge of frontend-backend programming techniques can create such web services in R programming environment through “Shiny” library. The paper also reviews the issue of hosting “Shiny” apps and presents existing approaches. For demonstration purposes, an experimental version of such an application was constructed that presents in an interactive way the quarterly results of the new statistical product of the Hellenic Statistical Authority (ELSTAT) on Greek business demography.
A deep learning approach to detect phishing websites using CNN for privacy protection

Nowadays, with the variety of internet frauds, every web user while browsing the net is vulnerable to being a target of various attacks. The phishing attack is one of the largest and most effective cyber threats; it is a sort of social engineering technique employed by web hackers, with the aim of deceiving users and stealing their credentials for financial gain. The continuous growth and the rising volume of phishing websites have led researchers to propose several anti-phishing solutions to fight against this cyber-attack such as visual similarity-based approaches, list-based approaches, machine learning, heuristics-based techniques … etc, moreover deep learning in recent years has gained increasing interest in several areas, especially in the phishing detection area. In this paper, we propose a deep learning approach to detect phishing websites using convolutional neural networks testing both 1D CNN &amp; 2D CNN with three feature types, URL-based features, content-based features, and third-party services-based features. The experimental results show that 1D CNN is more adequate for phishing detection and it achieves a high accuracy value of 96.76%. Moreover, it reduces the training time compared to other deep learning-based works.
Self-attention generative adversarial capsule network optimized with atomic orbital search algorithm based sentiment analysis for online product recommendation

Self-Attention based Generative Adversarial Capsule Network optimized with Atomic orbital search algorithm based Sentiment Analysis is proposed in this manuscript for Online Product Recommendation (SFA-AGCN-AOSA-SA-OPR). Here, Collaborative filtering (CF) and product-product (P-P) similarity method is utilized for designing the new recommendation system. CF is employed for predicting the best shops and P-P similarity method is employed to predict the better product. Initially, the datas are gathered via Amazon Product recommendation dataset. After that, the datas are given to pre-processing. During pre-processing, Markov chain random field (MCRF) co-simulation is used to remove the unwanted content and filtering relevant text. The preprocessing output is fed to feature extraction. The features, like manufacturing date, Manufacturing price, discounts, offers, quality ratings, and suggestions or reviews are extracted using Gray level co-occurrence matrix (GLCM) window adaptive algorithm based feature extraction method. Finally, Self-Attention based Generative Adversarial Capsule Network (SFA-AGCN) categorizes the product recommendation as excellent, good, very good, bad, very bad. Atomic orbital search algorithm optimizes the SFA-AGCN weight parameters. The performance metrics, like accuracy, precision, sensitivity, recall, F-measure, mean absolute error (MAE), mean squared error (MSE), mean absolute percentage error (MAPE) is examined. The efficiency of the proposed method provides higher mean absolute percentage error 98.23%, 88.34%, 90.35% and 78.96% and lower Mean squared error 92.15%, 90.25%, 89.64% and 92.48% compared to the existing methods, such as sentiment analysis of online product reviews using DLMNN and future prediction of online product using IANFIS (DLMNN-IANFIS-SA-OPR), intelligent sentiment analysis approach using edge computing based deep learning technique (DCNN-SA-OPR), sentiment analysis for online product reviews in Chinese depending on sentiment lexicon and deep learning (CNN-BiGRU-SA-OPR) and sentiment analysis on product reviews depending on weighted word embedding and deep neural networks (CNN-LSTM-SA-OPR) respectively.
Automated detection of vehicles with anomalous trajectories in traffic surveillance videos

Video feeds from traffic cameras can be useful for many purposes, the most critical of which are related to monitoring road safety. Vehicle trajectory is a key element in dangerous behavior and traffic accidents. In this respect, it is crucial to detect those anomalous vehicle trajectories, that is, trajectories that depart from usual paths. In this work, a model is proposed to automatically address that by using video sequences from traffic cameras. The proposal detects vehicles frame by frame, tracks their trajectories across frames, estimates velocity vectors, and compares them to velocity vectors from other spatially adjacent trajectories. From the comparison of velocity vectors, trajectories that are very different (anomalous) from neighboring trajectories can be detected. In practical terms, this strategy can detect vehicles in wrong-way trajectories. Some components of the model are off-the-shelf, such as the detection provided by recent deep learning approaches; however, several different options are considered and analyzed for vehicle tracking. The performance of the system has been tested with a wide range of real and synthetic traffic videos.
A picture paints a thousand words: supporting organizational learning in the emergency services with data visualization

Purpose This study aims to examine the use of data visualization as a tool to support practitioner-led organizational learning within the emergency services. The authors investigate how data visualization can support visual communication and the analysis of emergency response data to promote system improvement. Design/methodology/approach The authors investigate if communication data, presented as node-link diagrams, can be understood and evaluated by firefighters. Objective understanding of the communication network is measured quantitatively, while subject judgement of the emergency response system is measured qualitatively and compared to prior system evaluation outcomes. The authors compare different data visualization layouts and assess their value in supporting practitioner evaluation of emergency response systems. Findings The authors find that while firefighters are largely unfamiliar with their use, data visualizations function as a tool for visual communication and analysis. The authors identify the importance of visualization design and the difficulty in representing characteristics of a dynamic network within static diagrams. The authors also find some correlation between layout design and how respondents interpret visual data. Originality/value Results demonstrate the value of data visualization to support practitioner-led organizational learning and suggest future work to support the development of emergency response management.
Exploring influencing mechanism of herd behavior in academic information use: The perspective of cognitive load

In general, when users search in academic databases, the search result page directly shows the download count and citation count next to each paper. The download count and citation count are likely to produce information cascade effects. Based on the cognitive load theory, this study employs the semi-structured interview and grounded theory methods to explore herd behavior in academic information use in the university digital academic platform context, and develops a theoretical model of influencing mechanism of herd behavior in academic information use through three levels of coding. The results show that intrinsic cognitive load, extraneous cognitive load, and germane cognitive load all have direct impacts on herd behavior in academic information use. Germane cognitive load has moderating effects on the links from intrinsic cognitive load and extraneous cognitive load to herd behavior in academic information use. Meanwhile, information quality, source reliability, reputation, and convenience have direct impacts on information usefulness. Scientific research literacy and prior experience are elicited as germane cognitive load. It is recommended that researchers' scientific research literacy should be cultivated as far as possible, and researchers should be greatly encouraged to become familiar with scientific research tasks and become familiar with academic databases.
Effect of Ukraine-Russia Conflict on the Cryptocurrency Market: an Event Study Perspective

Russia massively invaded Ukraine on February 24, 2022, unavoidably having an effect on the world economy and finance. This paper uses the event study to research the short-term response of the February 2022 top 5 variable-price cryptocurrencies (BTC, ETH, BNB, XRP, SOL) to the Russia-Ukrainian war under the constant mean model. The cryptocurrency volatility was dramatic during the event window, and cryptocurrencies did not show the characteristics of safe haven. Overall, the result of the effect of the Russia-Ukraine war on the cryptocurrency market was negative, with the least negative impact on SOL and the most negative impact on BNB, XRP. Finally, Using the different event window analysis, it shows the cryptocurrency market return volatility rebounded, but it does not sufficiently indicate there is a positive trend in the cryptocurrency market after the event. The analysis of this paper can provide some help for cryptocurrency investors in the event of unforeseen circumstances. And in the data selection, this paper doesn’t consider stablecoins.
Factors Impacts on Online Shopping Consumers' Purchasing Decisions: Evidence of Women dresses from Amazon

Online reviews have become an important way for consumers to understand product quality and merchant services with the prosperity of online shopping. This paper adopts the method of natural language learning to conduct sentiment analysis on Amazon women's online shopping reviews, then establishes a bag of words model to vectorizes the text. Finally, a prediction model is established based on the Naive Bayes classifier, which predicts consumers' recommendation willingness by analyzing the review text. Through training and testing, the accuracy of the prediction model reaches 0.87, and improved to 0.92 by TF-IDF algorithm. This study analyzes the key influencing factors of women's online shopping which has a high reference value for merchants to improve services in a targeted manner to obtain more consumer recommendations.
Long-term Changes in Ethereum Prices: A Normalized Pandemic Framework

As the pandemic, Covid-19, spreading across the world from 2020, it changes the habits of people. It helped the development of the online movement. Cryptocurrency investment was one of them. Ethereum is one of the most significant blockchain-based platforms and the second largest proportion of the cryptocurrency market. The price of Ethereum was examined from the last 3 years. The result shows that the price of Ethereum increases drastically at the beginning of the pandemic due to different influences of Covid-19. However, it is decreasing as Covid-19 has become a normal illness to handle recently. In summary, Ethereum is in a strong correlation with Covid-19 and still can fluctuate by illness or movement that increases the interaction of people on the internet. In this paper, vector autoregression model and ARMA-GARCHX model was constructed where VAR model helped to find the relationship between the new infections of COVID-19 in China and Overseas and the return rate of Ethereum and ARMA-GARCHX model was applied to analyze the volatility of the return and predict the future return rate. The models suggest that the return rate can be affected if the number of new infections increases in a short period. However, the number of new infections is not significant to the volatility of the return rate of Ethereum.
The Analysis of NPV and IRR and Its Alternative Methods

Based on the analysis of the net present value method and the internal rate of return method, this paper lists the pros and cons of these two different investment rules and gives some alternative approaches. With improvement and optimization, these two investment rules can be more efficient convenient, and widely used than before. This essay further enriched and developed the internal rate of return and net present value rules by analyzing the advantages and disadvantages of the measurement methods in detail. In addition, it provides implications for investors to better evaluate the corporate or project value.
The Causes, Consequences and Countermeasures of Financial Fraud Based on the Case of Luckin Coffee

Muddy waters made an 89 pages’ report on Luckin coffee, it said Luckin Coffee had been making fictitious trades since the third quarter of 2019. The massive financial fraud at Luckin Coffee has dealt a blow to the Chinese company's reputation.This article through the relevant theories of financial fraud and the actual condition of financial fraud, rui xing coffee GONE theory analysis of the financial fraud motivation, and on the basis of the use of event study research shares for rui xing coffee fraud exception response, in the end, through to the audit institutions, enterprise itself and the government and regulators all three Suggestions, To guard against the consequences of governance failure.
Using SWOT Analysis Of Jellycat's Explosion In China's Youth Market

This article was based on the fact that previous scholars have mostly studied the healing function of plush toys in postoperative rehabilitation, but there is little discussion of the healing economy. So, this paper aimed to examine why Jellycat is so popular among young Chinese in the 21st century, amidst rising stress and loneliness. The qualitative data were collected through websites. The analysis was developed based on the Marketing Mix Theory, where a SWOT analysis was conducted for the product, price, price, and promotion of Jellycat. The research resulted reveal that product and promotion have a huge impact on consumers' purchase intention, whereas price has little impact on it, which is reliant on whether the price is equal to quality. This paper suggested that Jellycat should protect its IP property rights and adopt the emerging sales method - live sales, lower the selling price of large-size dolls and use combination selling prices for pop-up dolls.
Patent Phrase to Phrase Matching Based on Bert

Due to a large US patent archive, it is necessary to introduce a similarity matching system to judge if an invention has been granted a patent so that people just focus on high similarity patent items and ignore low similarity ones. First, the large-scale corpus is pre-trained using the Bert language model to acquire the semantic characteristics of general language. The pre-training Bert language model is used to tune the text data set of patent phrases to acquire the semantic features of the certain text and the specific meaning of the keywords to match similarity, given certain parameters according to the task, such as MSE as loss function and certain number as learning rate and so on. The validation results are good whether it is according to MSE loss or the Pearson correlation coefficient. Finally, applying this model to the test dataset and the results show that the Pearson correlation of all the variables is significant, and the model fits well.