Reviewerly/exHarmony huggingface dataset:
A Deep Learning Framework to Reconstruct Face under Mask

While deep learning-based image reconstruction methods have shown significant success in removing objects from pictures, they have yet to achieve acceptable results for attributing consistency to gender, ethnicity, expression, and other characteristics like the topological structure of the face. The purpose of this work is to extract the mask region from a masked image and rebuild the area that has been detected. This problem is complex because (i) it is difficult to determine the gender of an image hidden behind a mask, which causes the network to become confused and reconstruct the male face as a female or vice versa; (ii) we may receive images from multiple angles, making it extremely difficult to maintain the actual shape, topological structure of the face and a natural image; and (iii) there are problems with various mask forms because, in some cases, the area of the mask cannot be anticipated precisely; certain parts of the mask remain on the face after completion. To solve this complex task, we split the problem into three phases: landmark detection, object detection for the targeted mask area, and inpainting the addressed mask region. To begin, to solve the first problem, we have used gender classification, which detects the actual gender behind a mask, then we detect the landmark of the masked facial image. Second, we identified the non-face item, i.e., the mask, and used the Mask R-CNN network to create the binary mask of the observed mask area. Thirdly, we developed an inpainting network that uses anticipated landmarks to create realistic images. To segment the mask, this article uses a mask R-CNN and offers a binary segmentation map for identifying the mask area. Additionally, we generated the image utilizing landmarks as structural guidance through a GAN-based network. The studies presented in this paper use the FFHQ and CelebA datasets. This study outperformed all prior studies in terms of generating cutting-edge results for real-world pictures gathered from the web.
Graph-adaptive Rectified Linear Unit for Graph Neural Networks

Graph Neural Networks (GNNs) have achieved remarkable success by extending traditional convolution to learning on non-Euclidean data. The key to the GNNs is adopting the neural message-passing paradigm with two stages: aggregation and update. The current design of GNNs considers the topology information in the aggregation stage. However, in the updating stage, all nodes share the same updating function. The identical updating function treats each node embedding as i.i.d. random variables and thus ignores the implicit relationships between neighborhoods, which limits the capacity of the GNNs. The updating function is usually implemented with a linear transformation followed by a non-linear activation function. To make the updating function topology-aware, we inject the topological information into the non-linear activation function and propose Graph-adaptive Rectified Linear Unit (GReLU), which is a new parametric activation function incorporating the neighborhood information in a novel and efficient way. The parameters of GReLU are obtained from a hyperfunction based on both node features and the corresponding adjacent matrix. To reduce the risk of overfitting and the computational cost, we decompose the hyperfunction as two independent components for nodes and features respectively. We conduct comprehensive experiments to show that our plug-and-play GReLU method is efficient and effective given different GNN backbones and various downstream tasks.
Readiness of academic librarians towards the use of robotic technologies in Nigerian university libraries

Purpose The purpose of the article is to investigate readiness of academic librarians toward the use of robotic technologies in Nigerian university libraries. The article provides further insights on the following areas: •What are the levels of readiness of university libraries in Nigeria towards robotic technologies in terms of digital infrastructure, policy framework and human development for the adoption and use of robotics in the libraries? •What services are available in the university libraries that can be handled by robotic technologies? •Are academic librarians aware of the potential benefits of robotics in library operations? •What are the potential challenges that university libraries may face in the adoption and use of robotic technologies? Design/methodology/approach The study is a survey research anchored on positivism paradigm. The questionnaire was used as data collection instrument. Furthermore, 100 academic librarians were selected from the ten purposively selected ten universities. Snowballing sample technique was used to select ten academic librarians from each of the university libraries. Findings The findings of the study revealed that the readiness of university libraries in Nigeria towards the adoption and use of robotic technologies in Nigeria is nothing to write home about. It indicates that university libraries in the country are not ready to adopt the use of this technology despite the high benefits they have to offer in library operations. Research limitations/implications The limitations of the study lay on the fact that very few librarians from each of the selected university libraries were selected; and at the same time, it covers only ten university libraries in the country. Originality/value The study is very important to university library management on the necessary steps that need to be taken for the effective adoption of robotic technologies. This research can be a guide to them. In addition, the empirical analysis of the study contributes to social informatics development in Nigeria.
Predicting progression of Alzheimer’s disease using forward-to-backward bi-directional network with integrative imputation

If left untreated, Alzheimer's disease (AD) is a leading cause of slowly progressive dementia. Therefore, it is critical to detect AD to prevent its progression. In this study, we propose a bidirectional progressive recurrent network with imputation (BiPro) that uses longitudinal data, including patient demographics and biomarkers of magnetic resonance imaging (MRI), to forecast clinical diagnoses and phenotypic measurements at multiple timepoints. To compensate for missing observations in the longitudinal data, we use an imputation module to inspect both temporal and multivariate relations associated with the mean and forward relations inherent in the time series data. To encode the imputed information, we define a modification of the long short-term memory (LSTM) cell by using a progressive module to compute the progression score of each biomarker between the given timepoint and the baseline through a negative exponential function. These features are used for the prediction task. The proposed system is an end-to-end deep recurrent network that can accomplish multiple tasks at the same time, including (1) imputing missing values, (2) forecasting phenotypic measurements, and (3) predicting the clinical status of a patient based on longitudinal data. We experimented on 1,335 participants from The Alzheimer's Disease Prediction of Longitudinal Evolution (TADPOLE) challenge cohort. The proposed method achieved a mean area under the receiver-operating characteristic curve (mAUC) of 78% for predicting the clinical status of patients, a mean absolute error (MAE) of 3.5ml for forecasting MRI biomarkers, and an MAE of 6.9ml for missing value imputation. The results confirm that our proposed model outperforms prevalent approaches, and can be used to minimize the progression of Alzheimer's disease.
Chinese power dispatching text entity recognition based on a double-layer BiLSTM and multi-feature fusion

A large amount of unstructured data has been accumulated in the daily dispatching work of power systems as the form of text. In order to use these texts effectively, entities in the text need to be recognized, such as names of station and equipment. Because of the complex composition of the power dispatching text, this paper first summarizes the characteristics of the power dispatching text. A character-level entity recognition model based on multiple features is proposed, which is suitable for power text. Our model combines pretrained character embedding, left-neighbour entropy, and part-of-speech to represent the domain characteristics of power dispatching text. And we exploit the fusion method for multiple features inputting. The double-layer BiLSTM proposed in this paper is used to predict character sequence labels, and finally CRF is used to optimize the label predicted. This paper chooses a power outage maintenance application to recognize name entities. The results of experiments show that our model can increase the overall F1 value by 2.26% compared with the traditional models, and the recognition of lines and stations has increased by 3.88% and 3.99%. The recognition accuracy of each tag has been enhanced.
Quantum Verification and Estimation with Few Copies

As quantum technologies advance, the ability to generate increasingly large quantum states has experienced rapid development. In this context, the verification and estimation of large entangled systems represents one of the main challenges in the employment of such systems for reliable quantum information processing. Though the most complete technique is undoubtedly full tomography, the inherent exponential increase of experimental and post-processing resources with system size makes this approach infeasible even at moderate scales. For this reason, there is currently an urgent need to develop novel methods that surpass these limitations. This review article presents novel techniques focusing on a fixed number of resources (sampling complexity), and thus prove suitable for systems of arbitrary dimension. Specifically, a probabilistic framework requiring at best only a single copy for entanglement detection is reviewed, together with the concept of selective quantum state tomography, which enables the estimation of arbitrary elements of an unknown state with a number of copies that is low and independent of the system's size. These hyper-efficient techniques define a dimensional demarcation for partial tomography and open a path for novel applications.
Artificial Biphasic Synapses Based on Nonvolatile Phase‐Change Photonic Memory Cells

Nonvolatile photonic memory cells are basic building blocks for neuromorphic hardware enabling the realization of all‐optical synapses and artificial neurons. These devices commonly exploit chalcogenide phase‐change materials, which are evanescently coupled to photonic waveguides, and provide fast write/erase speeds and large storage capacity. Here, we report for the first time the programming of a nonvolatile photonic memory cell based on Ag 3 In 4 Sb 76 Te 17 (AIST) which is capable of mimicking biphasic synapses. We evaluate the underlying mechanism of biphasic behavior of AIST cells based on numerical simulations and correlate to experimental findings. Switching dynamics demonstrate enhanced performance with a post‐excitation dead time as short as 12.8 ns. Based on AIST double cells, we demonstrate reversible multilevel switching between 45 unique synaptic weights for long‐term depression (LTD) and long‐term potentiation (LTP). The observed biphasic programming and excellent switching performance render AIST‐based photonic memory cells promising for artificial neural networks and neuromorphic photonic computing hardware.
Critical Care Database Comprising Patients With Infection

Patients treated in the intensive care unit (ICU) are closely monitored and receive intensive treatment. Such aggressive monitoring and treatment will generate high-granularity data from both electronic healthcare records and nursing charts. These data not only provide infrastructure for daily clinical practice but also can help to inform clinical studies. It is technically challenging to integrate and cleanse medical data from a variety of sources. Although there are several open-access critical care databases from western countries, there is a lack of this kind of database for Chinese adult patients. We established a critical care database involving patients with infection. A large proportion of these patients have sepsis and/or septic shock. High-granularity data comprising laboratory findings, baseline characteristics, medications, international statistical classification of diseases (ICD) code, nursing charts, and follow-up results were integrated to generate a comprehensive database. The database can be utilized for a variety of clinical studies. The dataset is fully accessible at PhysioNet( https://physionet.org/content/icu-infection-zigong-fourth/1.0/ ).
Readiness and Perception of Pacific Students to Mobile Phones for Higher Education

The emergence and advancement of Information Communication Technologies have transformed facilitation and content delivery in higher education worldwide, the Pacific region being no exception. The extensive use of mobile phones in the Pacific, especially with the student-aged populace, is gradually creating a niche for mobile learning in the education landscape. However, there is a growing concern on the effectiveness of this innovative intervention keeping in mind the digital intelligence of Pacific students. This paper explores the readiness and perception of the Pacific students using mobile devices for learning. An online questionnaire was used to collect data for this exploratory research. While the results revealed the student’s willingness and positive perception to leverage on mobile phones for learning in higher education, their readiness for the mobile-driven education is rather dependent on various factors which are explored in detail. The paper concludes with recommendations for the higher education institutes and education ministries in the Pacific region.
Clinical Explainability Failure (CEF) &amp; Explainability Failure Ratio (EFR) – Changing the Way We Validate Classification Algorithms

Adoption of Artificial Intelligence (AI) algorithms into the clinical realm will depend on their inherent trustworthiness, which is built not only by robust validation studies but is also deeply linked to the explainability and interpretability of the algorithms. Most validation studies for medical imaging AI report the performance of algorithms on study-level labels and lay little emphasis on measuring the accuracy of explanations generated by these algorithms in the form of heat maps or bounding boxes, especially in true positive cases. We propose a new metric - Explainability Failure Ratio (EFR) - derived from Clinical Explainability Failure (CEF) to address this gap in AI evaluation. We define an Explainability Failure as a case where the classification generated by an AI algorithm matches with study-level ground truth but the explanation output generated by the algorithm is inadequate to explain the algorithm's output. We measured EFR for two algorithms that automatically detect consolidation on chest X-rays to determine the applicability of the metric and observed a lower EFR for the model that had lower sensitivity for identifying consolidation on chest X-rays, implying that the trustworthiness of a model should be determined not only by routine statistical metrics but also by novel 'clinically-oriented' models.
Internet-augmented language models through few-shot prompting for open-domain question answering

In this work, we aim to capitalize on the unique few-shot capabilities of large-scale language models (LSLMs) to overcome some of their challenges with respect to grounding to factual and up-to-date information. Motivated by semi-parametric language models (LMs), which ground their decisions in external retrieved evidence, we use few-shot prompting to learn to condition LMs on information returned from the web using Google Search, a broad and constantly updated knowledge source. Our approach does not involve fine-tuning or learning additional parameters, thus making it applicable to any LM, offering therefore a strong baseline. Indeed, we find that LMs conditioned on the web surpass performance of closed-book models of similar, or even larger, model sizes in open-domain question answering. Finally, we find that increasing the inference-time compute of models, achieved via using multiple retrieved evidences to generate multiple answers followed by a reranking stage that uses scores generated by the same LMs, leads to better performance and alleviates lower performance of smaller few-shot LMs. All in all, our findings suggest that it might be beneficial to slow down the race towards the biggest model and instead shift attention towards finding more effective ways to use models, including but not limited to, better prompting or increasing inference-time compute.
ARCANE: An Efficient Architecture for Exact Machine Unlearning

Recently users’ right-to-be-forgotten is stipulated by many laws and regulations. However, only removing the data from the dataset is not enough, as machine learning models would memorize the training data once the data is involved in model training, increasing the risk of exposing users’ privacy. To solve this problem, currently, the straightforward method, naive retraining, is to discard these data and retrain the model from scratch, which is reliable but brings much computational and time overhead. In this paper, we propose an exact unlearning architecture called ARCANE. Based on ensemble learning, we transform the naive retraining into multiple one-class classiﬁcation tasks to reduce retraining cost while ensuring model performance, especially in the case of a large number of unlearning requests not considered by previous works. Then we further introduce data preprocessing methods to reduce the retraining overhead and speed up the unlearning, which includes representative data selection for redundancy removal, training state saving to reuse previous calculation results, and sorting to cope with unlearning requests of different distributions. We extensively evaluate ARCANE on three typical datasets with three common model architectures. Experiment results show the effectiveness and superiority of ARCANE over both the naive retraining and the state-of-the-art method in terms of model performance and unlearning speed.
Blockchain-Enabled Supply-Chain in Crop Production Framework

Abstract The purpose of this paper is to propose an approach to blockchain-enabled supply-chain model for a smart crop production framework. The defined tasks are: (1) analysis of blockchain ecosystem as a network of stakeholders and as an infrastructure of technical and logical elements; (2) definition of a supply-chain model; (3) design of blockchain reference infrastructure; (4) description of blockchain information channels with smart contracts basic functionalities. The results presented include: а supply-chain model facilitating seeds certification process, monitoring and supervision of the grain process, provenance and as optional interactions with regulatory bodies, logistics and financial services; the three level blockchain reference infrastructure and a blockchain-enabled supply-chain supporting five information channels with nine participants and smart contracts. An account management user application tool, the general descriptions of smart contract basic functionalities and a selected parts of one smart contract code are provided as examples.
Visualization of Customized Convolutional Neural Network for Natural Language Recognition

For analytical approach-based word recognition techniques, the task of segmenting the word into individual characters is a big challenge, specifically for cursive handwriting. For this, a holistic approach can be a better option, wherein the entire word is passed to an appropriate recognizer. Gurumukhi script is a complex script for which a holistic approach can be proposed for offline handwritten word recognition. In this paper, the authors propose a Convolutional Neural Network-based architecture for recognition of the Gurumukhi month names. The architecture is designed with five convolutional layers and three pooling layers. The authors also prepared a dataset of 24,000 images, each with a size of 50 × 50. The dataset was collected from 500 distinct writers of different age groups and professions. The proposed method achieved training and validation accuracies of about 97.03% and 99.50%, respectively for the proposed dataset.
Deep learning based classification of time series of Chen and Rössler chaotic systems over their graphic images

In this study, the graphic images of time series of different chaotic systems are classified with deep learning methods for the first time in the literature. For the classification, a datasetcontains images of time series of Chen and Rossler chaotic systems for different parameter values, initial conditions, step size and time length are generated. Then, high accuracy classifications are performed with transfer learning methods. The used transfer learning methods are SqueezeNet, VGG-19, AlexNet, ResNet50, ResNet-101, DenseNet-201, ShuffleNet, and GoogLeNet. According to the problem, classifications accuracy is varying between 89% and 99.7% in this study. Thus, this study shows that identifying a chaotic system from its graphic image of time series is possible.
Solar collector tilt angle optimization for solar power plant setup-able sites at Western Himalaya and correlation formulation

Abstract Tilt angle optimization of the solar collector is essential to achieve maximum power output. In this study, the performance analysis of monthly and yearly optimum tilt angles has been carried out for solar power plant setup-able sites in the Western Himalayan region of India. A mathematic model has been used for optimum tilt angle assessment. Annual average performance enhancement for monthly optimum tilt angles is 10–11%, 5–7% and 4–6% from horizontal, tilted at the latitude and tilted at an optimum tilt angle, respectively. Validation of the results has been carried out by mounting a polycrystalline PV panel at one of the suggested plant setup-able sites (ϕ30° 51′ 1.656′′, L 77° 3′ 41.508′′). The percentage variations found in experimental results are 8.85, 9.13 and 14.09 from horizontal, tilted at the latitude and tilted at yearly optimum tilt angle PV panel, respectively. To generalize the obtained result, correlations in terms of latitude and declination angle have also been formulated for yearly and monthly optimum tilt angles, respectively. The preciseness of the developed correlations has been validated by statistical tools. The results from this study have also been compared with the results of some previous studies, and good agreement has been obtained.