Reviewerly/exHarmony huggingface dataset:
Technical potential for rooftop solar photovoltaic in Commercial and Residential Areas in Saudi Arabia

The economic and social development of the Kingdom of Saudi Arabia (KSA) has led to a significant increase in its electricity consumption. The KSA is highly dependent on non-renewable sources of energy, but the government has recently formulated a new strategy to encourage an increase in the use of renewable energy sources. The purpose of this plan is to diminish the KSA’s reliance on fossil fuels and to reduce the air pollution. Currently, the global cumulative installed capacity of solar photovoltaic (PV) technology is expanding at an exponential rate. For residential and commercial installations, the saturation threshold for distributed PV installations is directly proportional to the availability of the physical roof area. This research assessed how rooftop PV generation in residential and commercial buildings can be used to reduce the electricity import from the national grid. This paper presents the methodology that was used to assess the rooftop PV potential in the residential and commercial areas of Abha city, KSA using real data. The results show that the PV production on the rooftop of a commercial building can meet more than 50% of the building’s total electricity consumption. Also, it was found that PV can meet the whole demand of residential buildings and there is surplus energy from the PV generation that could be stored for future use.
Analysis of E Store Business Using ARAS Method

E-Store India is India's revolutionary retail platform that caters to all the needs of shoppers.State is different.From our technology to our community and the way we do business.We are changing the way the world makes products.Alternative: Product availability, ease of finding, overall look/design, reviews Quantity, relative price, different types of shipping, on-time delivery.Evaluation Option: Authorization Required, Information Search Brand, Selection/Purchase, Selection/Purchase, Met expectations.Shows that from the result it is seen that On-time delivery and is got the first rank whereas is the Ease of finding got is having the lowest rank.The value of the dataset for E-store business in ARAS method shows that it results in on-time delivery and top ranking.
Understanding the AR-VR Based Architectural Design Workflow among Selected Danish Architecture Practices

Virtual reality (VR) and augmented reality (AR) have been proposed to be additional architectural design mediums for at least 25 years (Dagit, 1993).Despite rapid technical and technological development, it has not been adopted into architectural design practices as compared to academia and research.Surveys from the American Institute of Architects (AIA) and Royal Institutes of British Architects (RIBA) demonstrate the state of architectural practices; 72% of architects and 65% of architects respectively are not using any kind of virtual, augmented, or mixed reality in their practices (RIBA and Microsoft, 2018;Hampson, 2020).In this paper, the authors investigate the state of practices, issues, challenges, and opportunities of the utilization of virtual, augmented, and mixed realities in six architectural practices in the Danish context.Three of the practices are large architectural practices, one medium-sized practice specializing in institutional, healthcare and cultural architecture, and one firm designing private family houses, kindergartens, daycares and places for people with disability and, one experimental design studio.All these practices have used VR/AR in their projects to various degrees.In recent years Danish architectural practices have been involved in various VR/AR-based exhibitions, demonstrations, and tool developments to promote the usage of the same in design practice.Through a set of qualitative interviews with personnel from key architectural practices, the authors would like to demonstrate the present state of practices.The investigation explores the usage of VR and AR in Danish architecture practices by identifying challenges and opportunities regarding skill levels, architectural typology, use cases, toolchains, and workflow and shows similarities and differences between traditional and VR-based design processes.The main findings show how VR/AR-based visualization helps architects to perceive spatiality and also ushers creativity through immersion and overlays.
An Overview on ARIES: Reliable European Identity Ecosystem

Identity-theft, fraud and other related cyber-crimes are continually evolving, causing important damages and problems for European citizens in both virtual and physical places. To meet this challenge, ARIES has devised and implemented a reliable identity management framework endowed with new processes, biometric features, services and security modules that strengthen the usage of secure identity credentials, thereby ensuring a privacy-respecting identity management solution for both physical and online processes. The framework is intended to reduce levels of identity-related crimes by tackling emerging patterns in identity-fraud, from a legal, ethical, socio-economic, technological and organization perspective. This chapter summarizes the main goals, approach taken, achievements and main research challenges in H2020 ARIES project.
A Cognitive Agent's Infrastructure for Smart Mobility

Cognitive agents are artificial intelligence systems that are able to communicate in a way that is as much as possible acceptable to humans. Technologies like natural language processing (NLP), text to speech (TTS), speech to text (STT) and motion capture (MoCap) are usually applied to provide such an interface. In this work-in-progess paper we present the current state of the Beautiful ARtificial Intelligence Cognitive Agent (B.A.R.I.C.A.) infrastructure that we have developed. This infrastructure allows for the implementation of open source cognitive agents in a wide spectrum of domains that are able to communicate using the Croatian language. The aim of this study is to analyze possible applications of B.A.R.I.C.A. to smart mobility in order to highlight possible gaps to be overcome, applications that are possible as well as provide guidelines for implementation.
Design and Development of UI/UX on Company Profile Web with Design Thinking Method

Currently, the human need for documents in this modern world is increasing, which was originally in the form of paper now turned into a digital file. Businesspeople think this is an opportunity, because there are so many people who need to create or understand documents but can't do it all. Be it due to limited time or lack of knowledge. Currently a graduate student from a well-known university in Yogyakarta, has taken the initiative to create a startup in the field of document typing and editing services. His business was named the Ngetikapasaja business. The meaning of the word “Password” is that this startup offers any type of typing service according to the user's needs. As a new startup, business owners experience difficulties related to the business they run. Starting from how to set up complex services, how to convert all the testimonials that have been obtained into easy-to-view sections, order flows, and profiles, to links that take users to order directly. One solution to overcome some of these problems is to create a system, one of which is a website. A website is a collection of pages that can be accessed via the internet. With the website, users can access information about Ngetikapaja and directly order services at Ngetikapaja. The design of this website is made using the Design Thinking method, which is a problem-solving method that focuses on the user. The design thinking method consists of 5 stages: Empathize, Define, Ideate, Prototype, and Test. After the design is complete, testing is carried out with two test models, namely Usability Testing and User Interface Attractiveness. Test results say that this invention is interesting and easy to use.
Using Regression to Predict Number of Tourism in Indonesia based of Global COVID-19 Cases

COVID-19 has majorly impacted the world and has spread to every corner of the world. As a result, the tourism industry suffered greatly with many tourist sites having to close. Previous research has used regression models to predict the impact of COVID-19, though few has linked it to the number of tourists. This paper uses five different regression models to predict tourism rates based on multiple country's COVID-19 data. Regression models include linear regression, polynomial regression, K-Nearest Neighbors regression, random forest regression, and support vector regression. The datasets that we use are COVID-19 data that contains the number of cases and Indonesia's tourism data that contains the monthly number of incoming tourists to Indonesia from different countries. The dataset will be processed by selecting the countries with the most amount of tourist. The preprocessed dataset is divided into two for training and testing the models with an 8:2 ratio. The result from the evaluation showed that random forest regression has the highest accuracy with a R2 score of 0.9. Our research is limited to the number of datasets that are used as there might be other variables that are not considered.
Underwater Object Detection with Swin Transformer

Underwater target detection algorithms are of great significance in exploration of the oceans, as well as search and rescue operations. The photographs that were taken underwater are very distinct from those that were taken on land because of the effect that water has on the way light travels through it. Images captured underwater are frequently distinguished by a color palette that ranges from blue to green, as well as by varying degrees of distortion and blurriness. In addition, marine organisms frequently have distinctive ways of life, which presents significant difficulties when attempting to locate objects sub-merged below the surface. The application of Natural Language Processing (NLP) Transformer to the visual field has become increasingly popular as of late. In particular, the use of the straightforward and efficient Vision Transformer (ViT) enables us to see the potential of Transformer in the visual field. On the foundation of the conventional Transformer, Swin Transformer takes the concept of feature extraction from a convolutional neural network and applies it to the task of obtaining multi-scale features of images in a hierarchical fashion. It is possible to employ it as a generic backbone network to overturn the irreplaceable status of convolutional networks in the visual field, where it has a good influence on all downstream tasks in the visual field. As a result, we attempt to use Swin Transformer in the field of underwater object detection, and the results of our experiments indicate that Swin-B has a similar detection effect to Cascade R-CNN with ResNeXt101 backbone.
A Scalable RF-XGBoost Framework for Financial Fraud Mitigation

Machine learning offers the ability to analyze large financial data and extract hidden knowledge for anomalous patterns. In the current study, we propose an ensemble machine learning framework using eXtreme Gradient Boosting (XGBoost) and random forest for the detection of financial frauds. The model used an adaptive synthetic (ADASYN) algorithm over sampling technique with out-of-bag scoring on a grid search algorithm for the 11 input features. We tested our framework with IEEE-CIS fraud detection benchmark dataset from Kaggle. The performance metrics were F1-Score <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$=$</tex-math> </inline-formula> 0.9982, area under curve <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$=$</tex-math> </inline-formula> 0.9999, recall <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$=$</tex-math> </inline-formula> 1.0, precision <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$=$</tex-math> </inline-formula> 0.9965, and accuracy <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$=$</tex-math> </inline-formula> 0.9999 A detailed comparison with state-of-the-art techniques, such as decision tree, logistic regression, gradient boosting, and particle swarm optimization models, shows scalability and robustness of the framework.
RTN-GNNR: Fusing Review Text Features and Node Features for Graph Neural Network Recommendation

Recent recommendation systems have achieved good results by applying Graph Neural Network (GNN) to user-item interaction graphs. However, these recommendation systems can only handle structured interaction data and cannot handle unstructured review text data well. Based on the user-item interaction graph, combining review text can effectively solve the problem of data sparsity and improve recommendation quality. Most of the current recommendation methods combining review texts stitch the data from different modalities, leading to insufficient interactions and degrading the recommendations’ performance. A model called RTN-GNNR to fuse Review Text feature and Node feature for Graph Neural Network Recommendation is proposed to solve these problems and get better item recommendations. RTN-GNNR consists of four modules. The review text feature extraction module proposes a Bi-directional Gated Recurrent Unit (Bi-GRU) text analysis method that combines Bidirectional Encoder Representation from Transformers (BERT) and attention mechanism to enable the model to focus on more valuable reviews. The node feature extraction module proposes a GNN combined with the attention mechanism for the interactive node extraction method, which enables the model to have better higher-order feature extraction capability. The feature fusion module proposes the method of tandem Factorization Machine (FM) and Multilayer Perceptron (MLP) to realize interactive learning among multi-source features. The prediction module inner-products the fused higher-order features to achieve recommendation effect. We conducted experiments on five publicly available datasets from Amazon, showing that RTN-GNNR outperforms state-of-the-art personalized recommendation methods in both RMSE and MSE, especially in the sparser two datasets. The effectiveness of each module of the model is also demonstrated by a comparison of the ablation experiments.
Nonparametric Prediction for Spatial Dependent Functional Data Under Fixed Sampling Design

In this work, we consider a nonparametric prediction of a spatiofunctional process observed under a non-random sampling design. The proposed predictor is based on functional regression and depends on two kernels, one of which controls the spatial structure and the other measures the proximity between the functional observations. It can be considered, in particular, as a supervised classification method when the variable of interest belongs to a predefined discrete finite set. The mean square error and almost complete (or sure) convergence are obtained when the sample considered is a locally stationary α-mixture sequence. Numerical studies were performed to illustrate the behavior of the proposed predictor. The finite sample properties based on simulated data show that the proposed prediction method outperformsthe classical predictor which not taking into account the spatial structure.
GreedyNASv2: Greedier Search with a Greedy Path Filter

Training a good supernet in one-shot NAS methods is difficult since the search space is usually considerably huge <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(\mathrm{e}.\mathrm{g}.,\ 13^{21})$</tex> . In order to enhance the supernet's evaluation ability, one greedy strategy is to sample good paths, and let the supernet lean towards the good ones and ease its evaluation burden as a result. However, in practice the search can be still quite inefficient since the identification of good paths is not accurate enough and sampled paths still scatter around the whole search space. In this paper, we leverage an explicit path filter to capture the characteristics of paths and directly filter those weak ones, so that the search can be thus implemented on the shrunk space more greedily and efficiently. Concretely, based on the fact that good paths are much less than the weak ones in the space, we argue that the label of “weak paths” will be more confident and reliable than that of “good paths” in multi-path sampling. In this way, we thus cast the training of path filter in the positive and unlabeled (PU) learning paradigm, and also encourage a path embedding as better path/operation representation to enhance the identification capacity of the learned filter. By dint of this embedding, we can further shrink the search space by aggregating similar operations with similar embeddings, and the search can be more efficient and accurate. Extensive experiments validate the effectiveness of the proposed method GreedyNASv2. For example, our obtained GreedyNASv2-L achieves 81.1% Top-1 accuracy on ImageNet dataset, significantly outperforming the ResNet-50 strong baselines.
Super-Fibonacci Spirals: Fast, Low-Discrepancy Sampling of SO(3)

Super-Fibonacci spirals are an extension of Fibonacci spirals, enabling fast generation of an arbitrary but fixed number of 3D orientations. The algorithm is simple and fast. A comprehensive evaluation comparing to other meth-ods shows that the generated sets of orientations have low discrepancy, minimal spurious components in the power spectrum, and almost identical Voronoi volumes. This makes them useful for a variety of applications, in partic-ular Monte Carlo sampling.
A Methodology for Evaluating the Robustness of Anomaly Detectors to Adversarial Attacks in Industrial Scenarios

Anomaly Detection systems based on Machine and Deep learning are the most promising solutions to detect cyberattacks in the industry.However, these techniques are vulnerable to adversarial attacks that downgrade predictions performance.Several techniques haven been proposed to measure the robustness of Anomaly Detection in the literature.However, they do not consider that, although a small perturbation in an anomalous sample belonging to an attack, i.e., Denial of Service, could cause it to be misclassified as normal while retaining its ability to damage, an excessive perturbation might also transform it into a truly normal sample, with no real impact on the industrial system.This paper presents a methodology to calculate the robustness of Anomaly Detection models in industrial scenarios.The methodology comprises four steps and use a set of additional models called support models to determine if an adversarial sample remains anomalous.We carried out the validation using the Tennessee Eastman process, a simulated testbed of a chemical process.In such scenario, we applied the methodology to both a Long-Short Term Memory (LSTM) neural network and 1-dimensional Convolutional Neural Network (1D-CNN) focused on detecting anomalies produced by different cyberattacks.The experiments showed that 1D-CNN is significantly more robust than LSTM for our testbed.Specifically, a perturbation of 60% (empirical robustness of 0.6) of the original sample is needed to generate adversarial samples for LSTM, whereas in 1D-CNN the perturbation required increases up to 111% (empirical robustness of 1.11).
An Adaptive Hybrid Attention Based Convolutional Neural Net for Intelligent Transportation Object Recognition

The rapid development of communication transmission, including 6G technology, is creating increasing challenges for real-world object recognition tasks in transportation, which now must operate within complex external environments and the requirement of time efficiency. Although machine learning-based hybrid intelligence has attracted significant attention and achieved much success in recent years, the current models are often ineffective and have poor generalization in extreme weather. This is because the training of a deep learning model is often uncourteous, meaning that the models can easily fail, even during the feature extraction step. An adaptive hybrid attention-based convolutional neural network (AHA-CNN) framework is proposed in this paper to address these shortcomings. First, fuzzy c-means and maximum entropy algorithms are utilized for image feature pre-extraction. A heuristic search-based adaptive attention mechanism is then presented, which adaptively combines the previously extracted features and generates fused images. By applying this mechanism, the key areas of an image are reinforced in a more intelligent and interpretable way, and less important areas are ignored. The processed images are then transferred into a modified region-CNN for further training. Finally, four real-world experiments on traffic sign detection, vehicle license plate recognition, road surface condition monitoring, and pavement disease detection are carried out. Results show that the proposed framework has high testing accuracy compared with other existing methods. The features fused with the cognition mechanism are also easier to interpret.
Driver Locations Harvesting Attack on pRide

Privacy preservation in Ride-Hailing Services (RHS) is intended to protect privacy of drivers and riders. pRide, published in IEEE Trans. Vehicular Technology 2021, is a prediction based privacy-preserving RHS protocol to match riders with an optimum driver. In the protocol, the Service Provider (SP) homomorphically computes Euclidean distances between encrypted locations of drivers and rider. Rider selects an optimum driver using decrypted distances augmented by a new-ride-emergence prediction. To improve the effectiveness of driver selection, the paper proposes an enhanced version where each driver gives encrypted distances to each corner of her grid. To thwart a rider from using these distances to launch an inference attack, the SP blinds these distances before sharing them with the rider. In this work, we propose a passive attack where an honest-but-curious adversary rider who makes a single ride request and receives the blinded distances from SP can recover the constants used to blind the distances. Using the unblinded distances, rider to driver distance and Google Nearest Road API, the adversary can obtain the precise locations of responding drivers. We conduct experiments with random on-road driver locations for four different cities. Our experiments show that we can determine the precise locations of at least 80% of the drivers participating in the enhanced pRide protocol.
Parallel processing proposal by clustering integration of low-cost microcomputers

Regarding parallel processing, a study brings a parallel computing approach by integrating low-cost microcomputers. In the given research, the main aspects linked to parallel processing and its motivation are exposed, widespread today by the need for data mining integrated with large-scale databases, prominent data characteristics, large volumes of data, lack of agile processing for real-time analysis, and high variability of data. For a given proposal, a case study scenario is addressed, where the database composed of more than twelve years of continuous records was processed, requiring processing and processing of the data present for the feasibility of a machine learning project. In the study, the main gains of the implemented system are presented, mainly the use of only 18% of the time required if it was considered a traditional model in the processing of the base. At the end of the article, considerations under the research and proposals for future works are presented.