Reviewerly/exHarmony huggingface dataset:
Investigating the impact of weather parameters selection on the prediction of solar radiation under different genera of cloud cover: A case-study in a subtropical location

In the present study, a classification system of four meteorological indices were introduced to classify the sky types from sunny to cloudy. These meteorological indices are; cloud cover (Cc), sunshine hour (S), clearness index (Kt) and diffuse fraction (K). Frequency of occurrence and cumulative frequency distribution of each sky indices were established to interpret the prevailing sky conditions in eight cases, each case investigates one day, these days were chosen according to the type of clouds that were overwhelming that day at a subtropical location in Turkey (Izmir). The impact of Cc, on measured global solar radiation (GSR) intensity were also analyzed. In order to predict GSR in each selected case and to making it more and more precise, NARX, FFNN and GRNN weather parameters-based models were evaluated and compared. To achieve accuracy of estimation, ten different input configurations were used to train the models. In addition global solar radiation in a cloudless skies (GSR’) is evaluated as a new input parameter. NARX models had the best performance for estimation of GSR in the first case, with GPI of 0.936 (NARX-C1M1). Results are also indicating that all NARX artificial neural network models provided better estimations than GRNN and FFNN models in case 2 with GPI value of 0.872 (NARX-C2M1). As for case 3, NARX was considered as the best models (NARX-C3M1, GPI = 0.957), followed by NARX (NARX-C3M6, GPI = 0.956) and then FFNN (FF-C3M1, GPI = 0.949). In case 4, NARX models had the highest GPI value (NARX-C4M1, GPI = 0.957) compared with FFNN and GRNN indicated models. Regarding case 5, NARX was considered as the best network (NARX-C5M4, GPI = 0.748), followed by NARX-C5M9 (GPI = 0.549), while FFNN and GRNN model couldn't provide positive results. Likewise, the NARX models did provide better results in case 6 with best GPI value up to 0.64. Furthermore, only NARX and FFNN models provided the better estimation in case 7 (best model, NARX-C7M1 with GPI = 0.826), whereas, GRNN models did not give such acceptable results. Similar to the mentioned above and in comparison with FFNN and GRNN, we have deducated that NARX models offered a better accuracy results compared with the other models (best model, NARX-C8M4 with GIP = 0.364) in case8.
Experimental quantum homomorphic encryption

Abstract Quantum computers promise not only to outperform classical machines for certain important tasks, but also to preserve privacy of computation. For example, the blind quantum computing protocol enables secure delegated quantum computation, where a client can protect the privacy of their data and algorithms from a quantum server assigned to run the computation. However, this security comes with the practical limitation that the client and server must communicate after each step of computation. A practical alternative is homomorphic encryption, which does not require any interactions, while providing quantum-enhanced data security for a variety of computations. In this scenario, the server specifies the computation to be performed, and the client provides only the input data, thus enabling secure noninteractive computation. Here, we demonstrate homomorphic-encrypted quantum computing with unitary transformations of individual qubits, as well as multi-qubit quantum walk computations using single-photon states and non-birefringent integrated optics. The client encrypts their input in the photons’ polarization state, while the server performs the computation using the path degree of freedom. Our demonstration using integrated quantum photonics underlines the applicability of homomorphic-encrypted quantum computations, and shows the potential for delegated quantum computing using photons.
Mobile Applications to Fight against COVID-19 Pandemic: The Case of Saudi Arabia

In order to fight against the COVID-19 pandemic the government of Saudi Arabia launched three mobile applications namely Tetamman, Tabaud &amp; Tawakkalna to keep the public aware about the corona virus and ensure monitoring of the suspected cases. The objective of this study is to curb the spread of COVID-19 by enhancing social measures and investigate the role of mobile applications in achievement of this objective. Comprehensive review of the role of mobile applications was made and a survey approach was used to evaluate the effectiveness of these applications. Findings of the study indicate that the users perceive that the applications are successful in achievement of the objectives for which these applications were launched. The key performance indicators (KPIs) included in the survey were efficiency, ease of use, satisfaction of users, fulfilment of purpose, usefulness and helpfulness etc. The average number of respondents, who agreed that these applications are performing according to the mentioned KPIs, is 86.6% for Tetamman, 80.5% for Tabaud and 90% for Tawakkalna. More awareness campaigns are needed so that more people adopt the use of these applications, which could significantly help in identification of new casesand enhance telehealth and teleconsultations. Moreover, the applications should be upgraded for self-triage.
Task Aligned Generative Meta-learning for Zero-shot Learning

Zero-shot learning (ZSL) refers to the problem of learning to classify instances from novel classes (unseen) that are absent in the training set (seen). Most ZSL methods infer the correlation between visual features and attributes to train the classifier for unseen classes. They may have a strong bias towards seen classes during training. Meta-learning has been introduced to mitigate the basis, but meta-ZSL methods are inapplicable when tasks used for training are sampled from diverse distributions. In this regard, we propose a novel Task-aligned Generative Meta-learning model for Zero-shot learning (TGMZ), aiming to mitigate the potentially biased training and to enable meta-ZSL to accommodate real-world datasets that contain diverse distributions. Specifically, TGMZ incorporates an attribute-conditioned task-wise distribution alignment network that projects tasks into a unified distribution to deliver an unbiased model. Our experiments show TGMZ achieves a relative improvement of 2.1%, 3.0%, 2.5%, and 7.6% over state-of-the-art algorithms on AWA1, AWA2, CUB, and aPY datasets, respectively. Overall, TGMZ outperforms competitors by 3.6% in the generalized zero-shot learning (GZSL) setting and 7.9% in our proposed fusion-ZSL setting.
Bloom Filter Encryption and Applications to Efficient Forward-Secret 0-RTT Key Exchange

Abstract Forward secrecy is considered an essential design goal of modern key establishment (KE) protocols, such as TLS 1.3, for example. Furthermore, efficiency considerations such as zero round-trip time (0-RTT), where a client is able to send cryptographically protected payload data along with the very first KE message, are motivated by the practical demand for secure low-latency communication. For a long time, it was unclear whether protocols that simultaneously achieve 0-RTT and full forward secrecy exist. Only recently, the first forward-secret 0-RTT protocol was described by Günther et al. ( Eurocrypt , 2017). It is based on puncturable encryption. Forward secrecy is achieved by “puncturing” the secret key after each decryption operation, such that a given ciphertext can only be decrypted once (cf. also Green and Miers, S&amp;P 2015). Unfortunately, their scheme is completely impractical, since one puncturing operation takes between 30 s and several minutes for reasonable security and deployment parameters, such that this solution is only a first feasibility result, but not efficient enough to be deployed in practice. In this paper, we introduce a new primitive that we term Bloom filter encryption (BFE), which is derived from the probabilistic Bloom filter data structure. We describe different constructions of BFE schemes and show how these yield new puncturable encryption mechanisms with extremely efficient puncturing. Most importantly, a puncturing operation only involves a small number of very efficient computations, plus the deletion of certain parts of the secret key, which outperforms previous constructions by orders of magnitude. This gives rise to the first forward-secret 0-RTT protocols that are efficient enough to be deployed in practice. We believe that BFE will find applications beyond forward-secret 0-RTT protocols.
VIPPrint: Validating Synthetic Image Detection and Source Linking Methods on a Large Scale Dataset of Printed Documents

The possibility of carrying out a meaningful forensic analysis on printed and scanned images plays a major role in many applications. First of all, printed documents are often associated with criminal activities, such as terrorist plans, child pornography, and even fake packages. Additionally, printing and scanning can be used to hide the traces of image manipulation or the synthetic nature of images, since the artifacts commonly found in manipulated and synthetic images are gone after the images are printed and scanned. A problem hindering research in this area is the lack of large scale reference datasets to be used for algorithm development and benchmarking. Motivated by this issue, we present a new dataset composed of a large number of synthetic and natural printed face images. To highlight the difficulties associated with the analysis of the images of the dataset, we carried out an extensive set of experiments comparing several printer attribution methods. We also verified that state-of-the-art methods to distinguish natural and synthetic face images fail when applied to print and scanned images. We envision that the availability of the new dataset and the preliminary experiments we carried out will motivate and facilitate further research in this area.
Development of Augmented Reality (AR) for Innovative Teaching and Learning in Engineering Education

Engineering education in particular involves laboratory equipment and apparatus which requires safety procedures and substantial financial cost. Recent pandemic has also affected engineering education teaching and learning processes as all the classes and laboratory sessions need to be conducted via online distance learning (ODL) method. Therefore, this research selects Augmented Reality (AR) Method for innovative teaching and learning process. AR is known for its ability to overlay rich media into the real world by viewing through web-enabled devices such as phones and tablets, making it accessible anywhere anytime. In addition to improving interest and motivation of students during the learning process, AR could also address the financial and space constraints pertaining to science and technology laboratory equipment and apparatus. In this research, an AR has been developed according to system development method by using Assemblr application. The developed AR system is then given to selected educators to be evaluated in terms of their awareness and the system’s usability. The educators’ responses are obtained through a questionnaire survey and the findings are then analysed and presented in this paper. The responses are taken into account for improving the AR system and the development process will be continued in parallel with more testing. The final prototype will then be implemented in real classes in the future.&#x0D; &#x0D; Keywords: Augmented Reality, Education, Engineering
Conditional Invertible Neural Networks for Diverse Image-to-Image Translation

We introduce a new architecture called a conditional invertible neural network (cINN), and use it to address the task of diverse image-to-image translation for natural images. This is not easily possible with existing INN models due to some fundamental limitations. The cINN combines the purely generative INN model with an unconstrained feed-forward network, which efficiently preprocesses the conditioning image into maximally informative features. All parameters of a cINN are jointly optimized with a stable, maximum likelihood-based training procedure. Even though INN-based models have received far less attention in the literature than GANs, they have been shown to have some remarkable properties absent in GANs, e.g. apparent immunity to mode collapse. We find that our cINNs leverage these properties for image-to-image translation, demonstrated on day to night translation and image colorization. Furthermore, we take advantage of our bidirectional cINN architecture to explore and manipulate emergent properties of the latent space, such as changing the image style in an intuitive way. Code: github.com/VLL-HD/conditional_INNs .
Dynamic Segmentation for Physical Activity Recognition Using a Single Wearable Sensor

Data segmentation is an essential process in activity recognition when using machine learning techniques. Previous studies on physical activity recognition have mostly relied on the sliding window approach for segmentation. However, choosing a fixed window size for multiple activities with different durations may affect recognition accuracy, especially when the activities belong to the same category (i.e., dynamic or static). This paper presents and verifies a new method for dynamic segmentation of physical activities performed during the rehabilitation of individuals with spinal cord injuries. To adaptively segment the raw data, signal characteristics are analyzed to determine the suitable type of boundaries. Then, the algorithm identifies the time boundaries to represent the start- and endpoints of each activity. To verify the method and build a predictive model, an experiment was conducted in which data were collected using a single wrist-worn accelerometer sensor. The experimental results were compared with the sliding window approach, indicating that the proposed method outperformed the sliding window approach in terms of overall accuracy, which exceeded 5%, as well as model robustness. The results also demonstrated efficient physical activity segmentation using the proposed method, resulting in high classification performance for all activities considered.
Sentiment Analysis for Arabic Social Media News Polarity

In recent years, the use of social media has rapidly increased and developed significant influence on its users. In the study of the behavior, reactions, approval, and interactions of social media users, detecting the polarity (positive, negative, neutral) of news posts is of considerable importance. This proposed research aims to collect data from Arabic social media pages, with the posts comprising the main unit in the dataset, and to build a <i>corpus</i> of manually-processed data for training and testing. Applying Natural Language Processing to the data is crucial for the computer to understand and easily manipulate the data. Therefore, Stop-Word removal, Stemming, and Normalization are applied. Several classifiers, such as Support Vector Machine, Naïve Bayes, K-Nearest Neighbor, Random Frost, and Decision Tree are used to train the dataset, and their accuracy is determined by data testing. These two steps are carried out using the open-source WEKA tool. As a result, each post is categorized into three different classes: positive, negative, and neutral. This research concludes that among the classifiers, SVM reaches the highest level of accuracy with a percentage of 83% for the F1-measure.
Disruption prediction and model analysis using LightGBM on J-TEXT and HL-2A

Abstract Using machine learning (ML) techniques to develop disruption predictors is an effective way to avoid or mitigate the disruption in a large-scale tokamak. The recent ML-based disruption predictors have made great progress regarding accuracy, but most of them have not achieved acceptable cross-machine performance. Before we develop a cross-machine predictor, it is very important to investigate the method of developing a cross-tokamak ML-based disruption prediction model. To ascertain the elements which impact the model’s performance and achieve a deep understanding of the predictor, multiple models are trained using data from two different tokamaks, J-TEXT and HL-2A, based on an implementation of the gradient-boosted decision trees algorithm called LightGBM, which can provide detailed information about the model and input features. The predictor models are not only built and tested for performance, but also analyzed from a feature importance perspective as well as for model performance variation. The relative feature importance ranking of two tokamaks is caused by differences in disruption types between different tokamaks. The result of two models with seven inputs showed that common diagnostics is very important in building a cross-machine predictor. This provided a strategy for selecting diagnostics and shots data for developing cross-machine predictors.
Learning to Select Context in a Hierarchical and Global Perspective for Open-Domain Dialogue Generation

Open-domain multi-turn conversations mainly have three features, which are hierarchical semantic structure, redundant information, and long-term dependency. Grounded on these, selecting relevant context becomes a challenge step for multiturn dialogue generation. However, existing methods cannot differentiate both useful words and utterances in long distances from a response. Besides, previous work just performs context selection based on a state in the decoder, which lacks a global guidance and could lead some focuses on irrelevant or unnecessary information. In this paper, we propose a novel model with hierarchical self-attention mechanism and distant supervision to not only detect relevant words and utterances in short and long distances, but also discern related information globally when decoding. Experimental results on two public datasets of both automatic and human evaluations show that our model significantly outperforms other baselines in terms of fluency, coherence, and informativeness.
MIMONet: Structured-light 3D shape reconstruction by a multi-input multi-output network

Reconstructing 3D geometric representation of objects with deep learning frameworks has recently gained a great deal of interest in numerous fields. The existing deep-learning-based 3D shape reconstruction techniques generally use a single red–green–blue (RGB) image, and the depth reconstruction accuracy is often highly limited due to a variety of reasons. We present a 3D shape reconstruction technique with an accuracy enhancement strategy by integrating the structured-light scheme with deep convolutional neural networks (CNNs). The key idea is to transform multiple (typically two) grayscale images consisting of fringe and/or speckle patterns into a 3D depth map using an end-to-end artificial neural network. Distinct from the existing autoencoder-based networks, the proposed technique reconstructs the 3D shape of target using a refinement approach that fuses multiple feature maps to obtain multiple outputs with an accuracy-enhanced final output. A few experiments have been conducted to verify the robustness and capabilities of the proposed technique. The findings suggest that the proposed network approach can be a promising 3D reconstruction technique for future academic research and industrial applications.
The Integration of Blockchain Technology and Smart Grid: Framework and Application

Based on the diffusion of blockchain technology in the smart grid, this paper studies the framework and application of the blockchain technology in the smart grid, so as to combine the blockchain with the smart grid and establish a sustainable supply chain. However, the establishment of a sustainable supply chain is based on a layered theoretical framework. Not only should the framework take into account needless attributes and the relationship among various criteria and aspects but the application should also involve a balance of multiple stakeholders. For the above reasons, this paper uses a combination of Fuzzy-DEMATEL and ISM. The results show that (1) the hierarchical path of sustainable supply chain management of the smart grid under the blockchain starts from the social level, pays attention to system construction, grasps the technical standards, and defines the development goals of the power grid. (2) The development of green energy has become a new market growth point. (3) The control of the operation level becomes the focus of the smart grid. (4) The optimization and development of the economic structure are restricted by social factors. By integrating and optimizing the blockchain and supply chain, this paper puts forward a theoretical framework, establishes a sustainable GIP application system with multistakeholder participation at the supply chain level, and indicates the significance of the blockchain in the smart grid.
Self-Supervised Feature Enhancement Networks for Small Object Detection in Noisy Images

Recent CNN-based approaches have shown impressive improvements in object detection, but detecting small objects in images is still a challenging task. Small object detection becomes more difficult if the image contains a lot of noise, which is frequent in real environments. The main reason is that the ratio of visual signal to noise on small objects is very low, making it difficult to extract rich features for detection. To address this issue, we propose a feature enhancement network (FEN) that is trained in a self-supervised manner. Specifically, FEN takes features from input images whose values randomly were erased, then predicts the erased values by aggregating neighboring values. This scheme enables FEN to improve features using surrounding values, which have great effects on enriching features from small-object regions during the test phase. To verify the robustness of our method against small object detection from noisy images, we adopt vehicle detection in aerial images as the main target task. The proposed method consistently outperformed the baseline methods in our experiments. We further present a variety of empirical studies, quantitatively and qualitatively, for in-depth analysis.
Virtual Reality Public Speaking Training: Experimental Evaluation of Direct Feedback Technology Acceptance

Virtual Reality (VR) offers significant potential for public speaking training. Virtual Reality Speech Training (VR-ST) helps trainees develop presentation skills and practice their application in the real world. Additionally, participants with public speaking anxiety can improve their presentation skills in a safe virtual environment without fear of judgment. Another benefit is direct feedback based on gamification principles, which provides users with information about their performance during training and allows for the adjustment of behavior in real-time. However, it is not yet clear if direct feedback based on visualization through icons is accepted by participants, such that it may support learning transfer in VR training applications. As a result, we set out to investigate how direct feedback in a VR -ST affects the participants' technology acceptance based on the Technology Acceptance Model (TAM). We conducted a between-subjects experimental study in order to compare a VR-ST with direct feedback (n = 100) with a simulation-based VR-ST (n = 100). The resulting MANOVAs demonstrated a preference for the direct feedback version for all TAM determinations, showing that direct feedback offers benefits to trainees by improving technology acceptance, independent of location and without supervision by trainers. Further results show that VR-ST is generally more accepted by participants without public speaking anxiety. Our findings indicate that developers of VR public speaking applications should focus on the inclusion of meaningful direct feedback and consider individual differences between users in order to optimally implement training measures.