Reviewerly/exHarmony huggingface dataset:
Fraud Detection of Credit Card using Data Mining Techniques

A credit card is an essential and daily used component in our life. Credit card fraud is a crime in our society. It causes many problems in our daily online transactions. Various deficiency of the previous method has been researched in the introduction chapter. The establishment of avoiding credit card fraud security is a big issue for small financial institutions. The goal of this paper is to propose a model for detecting credit card fraud and making a false alarm. For this research purpose, we have used four data mining techniques i.e. Decision Tree (DT), Random Forest (RF), Artificial Neural Networks (ANN), and Logistic regression (LR). Our motivation was how to reduce the false alarm so that the cardholder gets into less trouble and also the card provider gets more time for accurate alarming checking. After collecting the data from the Kaggle repository we look into the structure of the large dataset to check the performance. Datasets are filtered by the feature selection algorithms called Pearson correlation and chi-squared. We have introduced this model to get fewer false alarms in credit card fraud. From the proposed system, we can obtain about 99% accuracy (LR) and it also gives fewer false alarms. For tracking credit card fraud in the era of industry 4.0, our research work will ascertain the advancement and effectiveness of sustainable technologies.
Forecasting of Solar Photovoltaic Output Energy using LSTM Machine Learning Algorithm

Due to its appealing qualities, solar photovoltaic (PV) energy generation has received much attention worldwide by the researchers and practitioners. However, the biggest challenge in producing solar energy is the unpredictable intermittent power output of PV systems, which is mostly caused by weather conditions. This variation in power generation usually causes significant economic loss. Accurate forecasting of output power a day ahead or hourly ahead is the efficient way for the management of the power production. This paper offers a dependable short-term, hourly-ahead forecasting method of PV power generation for the delivery and storage mostly for the grid optimization. A long short-term memory (LSTM)- based deep learning method is used for its dependability for the long data along with feature of time series data. The historical dataset in between 1990-2013 in Australian locations were used. By appropriately functioning dependent features with the independent features a correlation between them is developed. This resulted in excellent power output forecast of RMSE 0.27%. The accuracy of this simple system is much better than the recently proposed more complex hybrid system like LSTM+CNN, LSTM+SVM+RNN.
ELWNet: An Extremely Lightweight Approach for Real-Time Salient Object Detection

Existing lightweight salient object detection (SOD) methods aim to solve the problem of high computational costs that is prevalent with heavyweight methods. However, compared with heavyweight methods, the detection accuracy of lightweight methods is greatly reduced while real-time performance is not significantly improved. Therefore, we aim to establish a trade off between computational cost and detection performance by improving the network efficiency. We propose a fast and extremely lightweight end-to-end wavelet neural network (ELWNet) for real-time salient object detection. ELWNet can achieve salient object detection and segmentation at approximately 70FPS (GPU), 19FPS (CPU) with 76K parameters and 0.38G FLOPs. We introduce wavelet transform theory into a neural network, proposing a wavelet transform module (WTM), a wavelet transform fusion module (WTFM), a novel feature residual mechanism, and construct an efficient architecture. The wavelet transform theory is integrated into the neural network to realize the interaction between the features in the frequency and the time domain. Meanwhile, ELWNet does not rely on a pre-trained model, which significantly reduces redundant features. We validate the performance of ELWNet using five well-known datasets, and demonstrate state-of-the-art performance compared with 24 other SOD models in terms of being lightweight, detection accuracy and real-time capabilities. Our method maintains high detection performance while reducing the number of model parameters by approximately 99% compared with heavyweight methods.
Text-Guided Image Manipulation via Generative Adversarial Network With Referring Image Segmentation-Based Guidance

This study proposed a novel text-guided image manipulation method that introduces referring image segmentation into a generative adversarial network.The proposed text-guided image manipulation method aims to manipulate images containing multiple objects while preserving text-unrelated regions.The proposed method assigns the task of distinguishing between text-related and unrelated regions in an image to segmentation guidance based on referring image segmentation.With this architecture, the adversarial generative network can focus on generating new attributes according to the text description and reconstructing text-unrelated regions.For the challenging input images with multiple objects, the experimental results demonstrate that the proposed method outperforms conventional methods in terms of image manipulation precision.
Blockchain in Human Resource Management to Hire the Right Candidate

Nowadays, blockchain technology is a great and immersive technology like artificial intelligence, machine learning, and the internet of things. Blockchain is a complex and secure distributed ledger system that stores data with other information such as timestamp, and hash value in a block, and each block is connected with the other. With the help of blockchain technology, many problems can be solved. After conceptualizing blockchain technology, it has changed many sectors, such as health, education, and business, as well as human resource management (HRM). However, still, there are some gaps in this area. So in this paper, we are going to investigate and solve those. Hiring employees is a vital role of the human resource (HR) but it is not so easy to hire the right candidate. To hire the right candidate, checking the performance appraisals of his previous organization is a great method to know about him. HR often needs to know the candidate's performance appraisals from his previous organization as feedback, which is not as easy and trustworthy as the traditional way. A performance appraisal is a systematic way to review or evaluate an employee's organizational performance. This could be an incredible, reliable system for an organization's HR. In this paper, a blockchain-based performance appraisal system will be used to check the performance of an employee of a previous organization. This system will be trustworthy, secure, and anonymous.
NCG-LS: Named Entity Recognition Model Specializing for Analyzing Product Titles

Entity recognition of product titles is essential for retrieving and recommending product information, where product title text has the characteristics of high entity density and fine type granularity. Most of the current studies focus on only one of the two features instead of considering the two challenges together. Our approach, named NCG-LS, proposes NEZHA-CNN-GlobalPointer architecture with the addition of label semantic networks, and uses multi-granularity contextual and label semantic information to fully capture the internal structure and category information of words and texts to improve the entity recognition accuracy. Through a series of experiments, we proved the efficiency of NCG-LS over a dataset of Chinese product titles from JD, improving the F1 value by 5.98%, when compared to the BERT-LSTM-CRF model on the product title corpus.
Oscillation Patterns of A Complex Exponential Neural Network

The paper presents the design and evaluation of a complex exponential neural network model. The development of the model is inspired by the exponential form of general solutions to nonlinear differential equations that describe dynamical systems. The research goal is to develop a mathematical representation for neural oscillation and reduce the amount of computation in the neural network to improve computational efficiency. In particular, the weighted sum of two complex exponential neurons is evaluated to demonstrate that the difference of oscillation frequencies between the two neurons is the dominant parameter that determines the oscillation patterns of the neural network.
Delivering Sensing Technologies for Education and Learning

Pervasive and ubiquitous sensing technologies have been frequently leveraged in experimental settings within the Ubicomp, ISWC, and HCI community. In the recent past, we have seen advances in the use of ubiquitous sensing technologies to understand and support learning. Recent work investigated a wide range of education-related technologies that detect attention, postures, behaviors, and emotions. However, the promise of delivering these technologies to potential users en masse remains difficult to fulfill. At this time it is important to understand the factors hindering the use of these technologies, to critically evaluate methodologies for technological development, and to discuss in what ways developed technologies can be safely delivered to students. In this workshop, we bring together experts from the fields of ubiquitous and pervasive computing, HCI, and education to discuss and develop an agenda for moving sensing technologies for learning and education from research to practice.
LMs go Phishing: Adapting Pre-trained Language Models to Detect Phishing Emails

Despite decades of research, the problem of Phishing in everyday email communication is ever so prevalent. Traditionally viewed as a text-classification task, the task of phishing detection is an active defense against phishing attempts. Mean-while, progress in natural language processing has established the universal usefulness of adapting pre-trained language models to perform downstream tasks, in a paradigm known as pre-train-then-fine-tune. In this work, we build on this paradigm, and propose two language models that are adapted on 725k emails containing phishing and legitimate messages. We use these two models in two ways: 1) by performing classification-based fine-tuning, and 2) by developing a simple priming-based approach. Our approaches achieve empirical gains over a good deal of prior work, achieving near perfect performance on in-domain data, and relative improvements on out-of-domain emails.
Hierarchical Feature Recovery for Robust Human Activity Recognition in Body Sensor Networks

With the advances in Body Sensor Networks (BSNs) and textile-integrated sensing, more sensor data becomes available from which human activities are recognised. However, some sensors may become unavailable unexpectedly in practice. Previous work proposed to complement the features of a missing sensor with regression-based methods but considered only up to one sensor missing and thus lacked a mechanism for selecting relevant sensors when multiple sensors were missing. The number of unique combinations of missing sensors increases exponentially when multiple sensors may be missing. To handle this, we propose a Hierarchical Feature Recovery (HFR) approach. We first assess the dependencies between sensors by comparing the feature mapping accuracy between each sensor and then evaluate the HFR approach on a dataset of activities of daily living with 17 gestures using 14 motion sensors. Our HFR method can alleviate classification performance drop by up to 8.3 pp compared to a baseline method.
Defining Classification Ambiguity to Discover a Potential Bias Applied to Emotion Recognition Data Sets

This work proposes a criterion for the identification of anomalies derived from potential biases in the labelling of data sets for supervised classification algorithms. Experimentation is carried out in emotion recognition, which is particularly prone to structural and induced biases. Through a preliminary analysis of the results, in this case from the automated classification of images using neural networks, a metric is proposed to identify anomalies and ambiguities in the results, comparing them to a knowledge base.Among the achievements of this work are developing an ambiguity graph; clarifying the mutual relationships among the classes by the correlation of anomalies, ambiguities and biases; setting up two experimental methods to test class robustness by downsampling and class ambiguities by contamination with samples from a neutral class.The same approach can be generalized to any classification application context, with simple adaptations of the target classes.The open problem of identifying bias in a data set is of major relevance and criticality for an ethical approach to automatic classification, especially in all those domains where the results of classification are going to affect the quality of human life.
Smart-Badge: A wearable badge with multi-modal sensors for kitchen activity recognition

Human health is closely associated with their daily behavior and environment. However, keeping a healthy lifestyle is still challenging for most people as it is difficult to recognize their living behaviors and identify their surrounding situations to take appropriate action. Human activity recognition is a promising approach to building a behavior model of users, by which users can get feedback about their habits and be encouraged to develop a healthier lifestyle. In this paper, we present a smart light wearable badge with six kinds of sensors, including an infrared array sensor MLX90640 offering privacy-preserving, low-cost, and non-invasive features, to recognize daily activities in a realistic unmodified kitchen environment. A multi-channel convolutional neural network (MC-CNN) based on data and feature fusion methods is applied to classify 14 human activities associated with potentially unhealthy habits. Meanwhile, we evaluate the impact of the infrared array sensor on the recognition accuracy of these activities. We demonstrate the performance of the proposed work to detect the 14 activities performed by ten volunteers with an average accuracy of 92.44 % and an F1 score of 88.27 %.
Perceptual Modifications in Augmented Reality: A Short Survey

Views on Augmented Reality (AR) and its technology have changed. Rather than being a research area on its own, with a focus on computer vision and computer-generated imagery, we now have to deal with the integration of AR technology with ubiquitous computing where we have sensors, actuators, and processing capabilities beyond what is possible with a single AR device. The plethora of AR applications that follow from this point of view has not yet fully been investigated. In this paper, we review how in reported AR research this issue is dealt with. We survey the attempts from the AR community to integrate perception technology with AR. These attempts should be added to and integrated with augmented human and multisensorial research that nowadays is being done in the context of ubiquitous computing (Internet of Things, Ambient Intelligence, smart environments).
Time-Series Flexible Resampling for Continuous and Real-Time Finger Character Recognition

Sign language is one of the crucial methods of communication for deaf or hearing-impaired people. Finger characters are often used to supplement sign language in conversations, especially when it is difficult to express some words by sign language. However, few people understand sign language. The realization of an automatic translation system for sign language or finger characters will facilitate communication with deaf or hearing-impaired people. To develop a finger character recognition system that is practical in the daily lives for smooth conversations, continuous and real-time recognition is required. This paper presents a novel real-time data processing framework, the time-series flexible resampling. This framework consists of three steps: detection, segmentation, and extraction. This framework enables continuous and real-time recognition for acyclic time-series data, in which stable states and changing states occur repeatedly. In addition, continuous and real-time finger character recognition is realized by applying time-series flexible resampling. The effectiveness of applying time-series flexible resampling to the finger character recognition system was verified in subject experiments.
Exploring Explicitly Disentangled Features for Domain Generalization

Domain generalization (DG) is a challenging task that aims to train a robust model with only labeled source data and can generalize well on unseen target data. The domain gap between the source and target data may degrade the performance. A plethora of methods resort to obtaining domain-invariant features to overcome the difficulties. However, these methods require sophisticated network designs or training strategies, causing inefficiency and complexity. In this paper, we first analyze and reclassify the features into two categories, i.e., implicitly disentangled ones and explicitly disentangled counterparts. Since we aim to design a generic algorithm for DG to alleviate the problems mentioned above, we focus more on the explicitly disentangled features due to their simplicity and interpretability. We find out that the shape features of images are simple and elegant choices based on our analysis. We extract the shape features from two aspects. In the aspect of networks, we propose Multi-Scale Amplitude Mixing (MSAM) to strengthen shape features at different layers of the network by Fourier transform. In the aspect of inputs, we propose a new data augmentation method called Random Shape Warping (RSW) to facilitate the model to concentrate more on the global structures of the objects. RSW randomly distorts the local parts of the images and keeps the global structures unchanged, which can further improve the robustness of the model. Our methods are simple yet efficient and can be conveniently used as plug-and-play modules. They can outperform state-of-the-art (SOTA) methods without bells and whistles.
Improve Immersion in Virtual Reality-Based Basketball Training By Haptic Feedback

Many studies have investigated the feasibility of using real sport equipment in VR-based sport training. However, providing haptic feedback to mimic real-world ball interaction (passing and catching) is still less explored. In this paper, a VR-based basketball training system comprising vibrotactile gloves is proposed. With the aid of haptic feedback, the immersive feelings related to interaction with a basketball in a virtual court are improved. A within-subject experiment which compared with/without haptic feedback conditions was conducted. The improvement of presence was objectively measured by an electroencephalography (EEG) approach using a portable, low-cost EEG headset in a virtual catching scenario. Furthermore, the passing reaction time was assessed in a VR-based tactic training scenario to explore the effect of haptic feedback on the performance during training. The results demonstrated the possibility of using low-cost EEG headset to assess the presence. Because the perception of catching was mainly affected by visual, haptic feedback did not make significant effect on the passing reaction time. Nevertheless, the proposed gloves was commented to improve the experience of passing and catching.
Personalized Video Fragment Recommendation

In the mass market, users’ attention span over video contents is agonizingly short (e.g., 15 seconds for music/entertainment videos, 6 minutes for lecture videos, etc.), from a video producer’s or platform provider’s point of view. Given the huge amounts of existing and new videos that are significantly longer than such attention spans, a formidable research challenge is to design and implement a system for recommending just the specific fragments within a long video to match the profiles of the users.In this paper, we propose to meet this challenge based on three major insights. First, we propose to apply Self-Attention Blocks in our deep-learning framework to capture the fragment-level contextual effect. Second, we design a Video-Level Representation Module to take video-level preference into consideration when generating recommendations. Third, we propose a simple yet effective loss function for the video fragment recommendation task. Extensive experiments are conducted to evaluate the effectiveness of the proposed method. Experiment results show that our proposed framework outperforms state-of-the-art approaches in both NDCG@K and Recall@K, demonstrating judicious exploitation of fragment-level contextual effect and video-level preference. Moreover, empirical experiments are also conducted to analyze the key components and parameters in the proposed framework.
What Can You Do For Me? The Discoverability of Intelligent Assistant Skills

A wide discrepancy exists between the range of available Intelligent Personal Assistant (IPA) skills and the range of skills users engage with regularly. One reason for this is the issue of low skill discoverability. From the literature, we understand that there are factors that can potentially enhance discoverability, such as context of use. The literature also signals that current discoverability strategies, which leverage such factors, are being challenged by users' privacy concerns, and rapid skill growth. Mindful of these challenges, we explore the ways users naturally group IPA skills as a possible springboard for discoverability. Our preliminary findings suggest that grouping skills based on users' functionality needs in different areas of life may be a viable direction toward advancing discoverability.
The Journey to Making ‘Digital Technology’ Education a Community Learning Venture

Technology has become an integral part of our educational systems, and its importance in our schools cannot be overstated. However, digital skills, unlike other literacy skills, such as reading, writing, and numeracy, still have many discontinuities between how children use them at home versus in school. Therefore, in Wales (UK), digital skills are being promoted as part of the Digital Competence Framework (DCF) and feature highly in the new Curriculum for Wales (2022). Moreover, the new Digital Technology General Certificate of Secondary Education (GCSE) in Wales has been introduced to provide learners with the opportunity to gain a qualification that builds digital skills, knowledge, and understanding. However, this also brings many challenges for teachers, such as a lack of confidence, knowledge, and training, as well as a lack of resources and fear of change, to name a few. These challenges, in turn, have an impact on pupils’ motivation and performance, as well as parents’ ability to support their children. This paper presents a qualitative case study on the development of a new digital technology learning community for primary and secondary school pupils, their teachers, and parents in Blaenau Gwent, Wales (UK). Firstly, the paper will provide insight into what was required to establish an effective learning community, including ensuring engagement and buy-in from all stakeholders. Secondly, through the description, analysis, and interpretation of findings from two studies, the paper will highlight the impact of the DTLSN learning community on teachers and pupils in Blaenau Gwent, especially in terms of their learning and teaching.