Reviewerly/exHarmony huggingface dataset:
The Promise and Perils of Alternative Market-Based Finance: The Case of P2P in the UK

The collapse of the global financial industry in 2008 and the subsequent decay of most Western economies into a period of prolonged economic stagnation have represented a springboard for the progressive growth of alternative channels of financial intermediation. The reluctance and inability of mainstream banks in the post-crisis years to provide credit facilities to the real economy, most critically to start-ups and small and medium-sized enterprises, propelled the latest wave of financial innovation, this time under the guise of FinTech. Much has been written about the rise of FinTech in recent years, but there is still insufficient clarity about the benefits that this phenomenon is bringing to the real economy and the potential risks that can arise from its growth.This paper maps the development of FinTech platforms in the UK and reconceptualises the the rationale for their growth. In doing that, this study focuses on the structure and operation of the main UK platforms, recognising that while some are effectively banks that adopt a different, technology-based business model, many platforms operate under the P2P business model. The question then is to assess the policy and regulatory approach that is relevant to UK P2P platforms. In doing that, this paper takes a critical look into the activities conducted by these platforms with the aim to clarify their specific business model and its associated risks. In particular, the emergence of P2P securitisation raises a number of regulatory and policy questions, because longer intermediation chains typical of securitisation may well defy the social and economic purposes under which the idea of P2P developed. Furthermore, questions of systemic risk inevitably resurface in these types of transactions. Ensuing questions related to the best way to regulate these new channels of financial intermediation lead to critically evaluate the initiatives launched by the UK FCA, initially under the Innovation Hub, and more recently under the consultation for a new regulatory framework.
Comparison of Least Significant Bit, Pixel Value Differencing, and Modulus Function on Steganography to Measure Image Quality, Storage Capacity, and Robustness

Abstract Information security is an important aspect of information. The importance of the value of information in every aspect can result in attempts to transfer access or theft of data and information by unauthorized parties. One technique for securing data and information is by applying the digital image steganography technique. Many methods can be used in steganography, some of which are Least Significant Bit (LSB), Pixel Value Differencing (PVD), and Modulus Function (MF). Good steganography must produce a stego-image with an image quality that is not different from the original image or what is called a cover image and has the storage capacity to store confidential data and is resistant to robustness. The purpose of this study was to compare the LSB, MF, and PVD methods to serve as alternatives to the use of steganography techniques. The results of this study indicate that the LSB method has the best image quality compared to the MF and PVD methods. For storage capacity, the PVD algorithm has a better capacity than the LSB and MF methods. Meanwhile, the robustness of the LSB, PVD, and MF methods has resistance to cropping with a percentage of 10% at the bottom of the stego-image.
Neural Abstractive Text Summarizer for Telugu Language

Abstractive text summarization is the process of constructing semantically relevant shorter sentences which captures the essence of the overall meaning of the source text. It is actually difficult and very time consuming for humans to summarize manually large documents of text. Much of work in abstractive text summarization is being done in English, and almost no significant work has been reported in Telugu abstractive text summarization. So, we would like to propose an abstractive text summarization approach for Telugu language using deep learning. In this paper, we are proposing an abstractive text summarization deep learning model for Telugu language. The proposed architecture is based on encoder–decoder sequential models with attention mechanism. We have applied this model on manually created dataset to generate a one sentence summary of the source text and have got good results measured qualitatively.
Relational Database Watermarking for Data Tracing

Data should be distributed to clients from a centralized database. But distribution may result in a number of problems such as unauthorized copies and alteration of data. As an important branch of information hiding technology, digital watermarking technology is an effective method to realize the protection of copyright of relational database. However, the vast majority of relational digital watermarking solutions have the problem of limited use times of watermarks. In this paper, we propose a watermark scheme based on clustering, along with watermark embedding algorithm, watermark detection algorithm, and data recovery algorithm. By controlling the relevant parameters, we can verify the watermark for several times. We also elaborated how to use this scheme for database traceability. In the end of this paper, the calculation performance experiment of the algorithm shows that the scheme has good feasibility; the anti-attack ability comparison experiment shows that the scheme has good robustness.
Biomedical image classification using deep convolutional neural networks – overview

Abstract Deep Learning is an advanced area of machine learning which gained much interest in the past decades. It has been widely used in a variety of applications and has proved to be an effective machine learning method for many complicated issues. Especially when it comes to the medical field, the classification of biomedical images is a complex task to identify and classify the images manually by the doctors. So, Deep Learning is a key to enhance the classification of biomedical images using various architectures. The biomedical picture classification aims to identify and classify biomedical characteristics efficiently, which have significant advantages to numerous study and development fields. In this paper, the framework focused on the different architectures that were used to classify the medical images along with their performances.
A Fuzzy-Based Approach to Enhance Cyber Defence Security for Next-Generation IoT

In the modern era, the Cognitive Internet of Things (CIoT) in conjunction with IoT evolves which provides the intelligence power of sensing and computation for next-generation IoT (Nx-IoT) networks. The data scientists have discovered a large amount of techniques for knowledge discovery from processed data in CIoT. This task is accomplished successfully and data proceeds for further processing. The major cause for the failure of IoT devices is due to the attacks, in which Web spam is more prominent. There seems a requirement of a technique which can detect the Web spam before it enters into a device. Motivated from these issues, in this article, a cognitive spammer framework (CSF) for Web spam detection is proposed. CSF detects the Web spam by fuzzy rule-based classifiers along with machine learning classifiers. Each classifier produces the quality score of the webpage. These quality scores are then ensembled to generate a single score, which predicts the spamicity of the webpage. For ensembling, the fuzzy voting approach is used in CSF. The experiments were performed using a standard data set WEBSPAM-UK 2007 with respect to accuracy and overhead generated. From the results obtained, it has been demonstrated that CSF improves the accuracy by 97.3%, which is comparatively high in comparison to the other existing approaches in the literature.
Integrating Genetic Algorithm with Random Forest for Improving the Classification Performance of Web Log Data

Web mining is an important approach to retrieve and analyse the information from web server log data. In the internet-driven information age, a lot of data is present on the web in many ways and analysing such data using the web mining methods cam result in some novel insights. Such data can be extracted from the server log files and can be preprocessed to be used for various web mining functionalities. In this paper authors used the data from web server log files, preprocessed it and then applied various classification algorithms such as Naïve bayes,KNN,decision tree,random forest and analysed the results. The best approach was then chosen to further improve the performance of the classifier by integrating it with genetic algorithm. In this context, a hybrid approach, namely RFGA was used integrating Random forest and genetic algorithm on the dataset and the results of different machine learning classifiers were compared with RFGA in terms of the predictive accuracy.
Robust particle filtering with enhanced outlier resilience and real-time disturbance compensation

This paper is concerned with the state estimation problem for nonlinear/non-Gaussian systems suffered from both time-varying disturbances (TVD) and measurement outliers. Conventional particle filtering (PF) approach can be used to track the non-Gaussian probability density functions, but its sampling efficiency is degraded in the presence TVD. To address this problem, we propose a disturbance observer based PF (DOBPF) method where the knowledge on the dynamics of TVD are fully exploited and real-time disturbance compensation is achieved. Furthermore, to enhance the resilience of our method against outliers, we adopt the skew-t distribution to characterize both skewness and heavy-tailedness of the measurement noise. On this basis, the variational Bayes approach is incorporated into the DOBPF under the marginalization PF framework to infer the noise statistics. Compared with conventional PF approaches, the proposed outlier-resilient DOBPF method exhibits improved resilience against measurement outliers and increased sampling efficiency in the presence of TVD. Simulation and experimental results confirm the effectiveness of the proposed algorithm.
Writer Identification System Based on Offline Handwritten Text in Gurumukhi Script

Writer Identification (WI) based on handwriting is an amazing application under the wide spectrum of machine learning and document based identification and recognition. Its most acknowledged applications include forgery detection, investigating forensic crimes and resolving other suspects. This paper is concerned with the development of WIS based on the handwritten text in Gurumukhi script. Through this paper, authors presented a contemporary and experimental move in Gurumukhi script, its framework, characteristics of handwriting modality, differences between writer identification and verification, text independent and text dependent systems, script dependent V s script independent, character set of Gurumukhi script, latest survey on WIS based on offline handwritten text in Gurumukhi (Punjabi) script and experimental findings. A dataset of 100 writers i.e. 100 x53 x 10=53000 Gurumukhi characters has been taken for the experimental study. Feature extraction techniques such as Zoning, Transition, Diagonal and Peak Extent based were used and for the classification part Multi Layered Perceptron (MLP), Artificial Neural Network (ANN), and Random Forest (RF) classifiers were implemented. The proposed experiment reported 93.06% writer identification accuracy rates along with performance evaluation metrics with 93.2% True Positive Rate (TPR) and 0.39% False Positive Rate (FPR). The reported result outperforms in comparison to the literature survey and also poses futuristic and upcoming directions to the researchers such as age, gender, handedness, physiological autopsy, personality, stress and even nationality identification based on handwriting.
Boosting Quality Education with Inclusive Human Development: Empirical Evidence from Sub-Saharan Africa

This study examines the importance of inclusive human development in promoting education quality in a panel of 49 sub-Saharan African countries for the period 2000–2012. The empirical evidence is based on ordinary least squares (OLS), fixed effects (FE), and quantile regression (QR) estimations. It is apparent from the OLS and FE findings that inclusive human development has a negative effect on the outcome variable. This negative effect implies that inclusive human development improves education quality. This result should be understood in the light of the fact that the adopted education variable is a negative economic signal given that it is computed as the ratio of pupils to teachers. Therefore, a higher ratio reflects diminishing education quality. From QR, with the exception of the highest quantile, the tendency of inclusive human development in reducing poor quality education is consistent throughout the conditional distribution of poor education quality. Policy implications are discussed.
Experience

Spatio-temporal data can be used to study and simulate the movement and behavior of objects and natural phenomena. However, the use of real-world data raises several challenges related to its acquisition, representation, and quality. This article presents a data cleaning process, based on consistency rules and checks, that uses geometric operations to detect and remove outliers or inaccurate data in a spatio-temporal series. The proposal consists of selecting key frames and applying the process iteratively until the data have the desired quality. The case study consists of extracting and cleaning spatio-temporal data from a video tracking the propagation of a controlled fire captured using drones. The source data was generated using segmentation techniques to obtain the regions representing the burned area across time. The main issues concern noisy data (e.g., the height of flames is highly variable) and occlusion due to smoke. The results show that the quality assessment and improvement method proposed in this work can identify and remove inconsistencies from a dataset of more than 22,500 polygons in just a few iterations. The quality of the corrected dataset is verified using metrics and graph analysis.
Measuring Information Systems Project Complexity: A Structural Equation Modelling Approach

Complexity has emerged as the new norm in the 21st century, and IS projects play a significant role in organisations to address various socio-political concerns. The purpose of this paper is to understand what are the relevant constructs for measuring IS project complexity. A model for measuring IS project complexity is developed using PLS-SEM. The model reveals that organisational complexity, technical complexity, and uncertainty underpin IS project complexity. Organisational complexity in terms of project team, stakeholder management, and strategic drive should be managed by the project manager. Technical complexity was established in terms of project goals, requirements management, technology management, and norms and standards. Uncertainty in IS projects exists in terms of skills management, the triple constraint, and activity management. Suggestions were provided to guide IS project managers on how to manage each construct and alleviate the level of project complexity. This paper presents an updated and different perspective on measuring and managing IS project complexity. The findings would serve as additional building blocks to further elucidate IS project complexity understanding and assist with improving the value of these projects. Furthermore, the suggestions for IS project managers can lead discussions around how IS projects should be managed to ensure complexity is under control.
Combining of Extraction Butterfly Image using Color, Texture and Form Features

Combining the extraction results in digital image processing is an innovation in research. Previous research stated that the extraction was carried out separately for each feature, including color, shape, and pattern. Previous research for color feature extraction was used for koi fish, for shape feature extraction in flowers, and pattern feature extraction on batik. In this study, feature extraction results were combined using a dataset of insects, namely butterflies. The number of datasets used was 890 types of butterflies, which were divided into 10 classes. Before feature extraction and merging, the normalization process is carried out with the aim that the data used is clean from noise. The color feature extraction process uses the RGB and HSV methods by maintaining the color quality of the objects owned by faster computation and color quantization. The extraction of shape features using the LBPROT (local binary pattern rotational excess) method allows the dataset to be in multiple positions to obtain maximum results. The extraction of pattern features with region props allows the region of each object to be obtained. All of the feature extraction results are then tested to get an accuracy value of 75%, while the precision, recall, and f-measure are 76.4 for precision and 75 for recall, and 75.5 for f-measure. This research will continue to be tested with different methods and data.
Contact Tracing: A Game of Big Numbers in the Time of COVID-19

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Virtual Reality Integrated Multi-Depth-Camera-System for Real-Time Telepresence and Telemanipulation in Caregiving

Telepresence and telemanipulation robotics are suitable solutions to relieve humans from direct health risks and repetitive or unhealthy work. Through demographic changes in western countries and the COVID-19 pandemic, this relief is also considered for healthcare workers, especially caregivers, as the demands for them rises. The requirements are intuitively usable telerobotic and telepresence systems for remote assistance, to cut the high physical strain in manual patient transfers and the reduction of contact with infected patients. To ensure this, key technologies like 3D imaging and perception systems are essential. In this work, we present a novel, lightweight telepresence and telemanipulation system, specialized for caregiving. It allows an operator, wearing a virtual reality headset, to immerse into a sensor system captured scene on a distant location in real-time, with low latency of 250 ms and up to 30 fps refresh rate. Extensive measurement shows that 97.1% of the relevant point cloud data is below 1 cm error and 99.5 % is below 1.6 cm, making the system suitable for the application.
A machine learning approach for detecting CNAME cloaking-based tracking on the Web

Various in-browser privacy protection techniques have been designed to protect end-users from third-party tracking. In an arms race against these counter-measures, the tracking providers developed a new technique called CNAME cloaking based tracking to avoid issues with browsers that block third-party cookies and requests. To detect this tracking technique, browser extensions require on-demand DNS lookup APIs. This feature is however only supported by the Firefox browser.In this paper, we propose a supervised machine learning-based method to detect CNAME cloaking-based tracking without the on-demand DNS lookup. Our goal is to detect both sites and requests linked to CNAME cloaking-related tracking. We crawl a list of target sites and store all HTTP/HTTPS requests with their attributes. Then we label all instances automatically by looking up CNAME record of subdomain, and applying wildcard matching based on well-known tracking filter lists. After extracting features, we build a supervised classification model to distinguish site and request related to CNAME cloaking-based tracking. Our evaluation shows that the proposed approach outperforms well-known tracking filter lists: F1 scores of 0.790 for sites and 0.885 for requests. By analyzing the feature permutation importance, we demonstrate that the number of scripts and the proportion of XMLHttpRequests are discriminative for detecting sites, and the length of URL request is helpful in detecting requests. Finally, we analyze concept drift by using the 2018 dataset to train a model and obtain a reasonable performance on the 2020 dataset for detecting both sites and requests using CNAME cloaking-based tracking.
State Representation Learning With Adjacent State Consistency Loss for Deep Reinforcement Learning

Through well-designed optimization paradigm and deep neural networks as feature extractor, deep reinforcement learning (DRL) algorithms learn optimal policy on discrete and continuous action space. However, such capability is restricted by the low sampling efficiency. By inspecting the importance of feature extraction in DRL, we find that state feature learning is one of the key obstacles for sampling efficiently. To this end, we propose a new state representation learning scheme with adjacent state consistency loss (ASC loss). The loss is based on the hypothesis that the distance between adjacent states is smaller than that of far apart ones since scenes in videos generally evolve smoothly. We exploit ASC loss as an assistant of RL loss in the training phase to boost the state feature learning, and make evaluation on existing DRL algorithms as well as behavioral cloning algorithm. Experiments on Atari games and MuJoCo continuous control tasks demonstrate the effectiveness of our scheme.
SlowFast-GCN: A Novel Skeleton-Based Action Recognition Framework

Human action recognition plays an important role in video surveillance, human-computer interaction, video understanding, and virtual reality. Different from two-dimensional object recognition, human action recognition is a dynamic object recognition with a time series relationship, and it faces many challenges from complex environments, such as color shift, light and shadow changes, and sampling angles. In order to improve the accuracy of human action recognition, many studies have proposed skeleton-based action recognition methods that are not affected by the background, but the current framework does not have much discussion on the integration of the time dimension.In this paper, we propose a novel SlowFast-GCN framework which combines the advantages of ST-GCN and SlowFastNet with dynamic human skeleton to improve the accuracy of human action recognition. The proposed framework uses two streams, one stream captures fine-grained motion changes, and the other stream captures static semantics. Through these two streams, we can merge the human skeleton features from two different time dimensions. Experimental results show that the proposed framework outperforms to state-of-the-art approaches on the NTU-RGBD dataset.