Reviewerly/exHarmony huggingface dataset:
Complaint Management in Ethiopian Vocational and Technical Education Institutions: A Framework and Implementation of a Decision Support System

DecisionSupport Systems (DSSs) are interactive information systems that are based on computers; as such, they share many of the characteristics that make information systems successful. As the name suggests, an interactive system allows for two-way communication between the user and the software. There is a structure in place at many companies for dealing with customer concerns. This is an effort to improve the company's overall quality, customer happiness, and decision-making processes. Current processes for managing complaints are inadequate, and the organisation is unresponsive to them. Within the company, there is a lack of proper framework to deal with such situations. With the use of an Intelligent Decision Support System for Managing Complaints (IDSSMC), researchers were able to test how well Ethiopian Polytechnic TVET Colleges deal with complaints submitted through a web-based interface coupled to a mobile application with SMS notification. Users may sign up and file complaints using the system's website or mobile app. A descriptive developmental approach was used for this investigation. While the development approach was utilised to create the system, the descriptive approach was used for describing test findings. Users of the TVET system will be able to register, submit, and check the progress of their complaints through the system's website and mobile phone application. This makes it possible to gather complaints from a wide range of individuals at any time and from any location. In the conclusion, it is strongly suggested to implement a Model driven DSS approach that might offer additional intelligence to this company or another, with the expectation that this project could serve as a starting point.
Construction of Early Warning Model of Geological Disasters Based on Data Mining

In view of the problem that traditional geological disaster monitoring methods cannot adapt to the development of big data, an early warning model of geological disasters based on data mining was proposed. Firstly, the function of the early warning system was determined, and the system included login, data mining and cleaning, early warning and other functions. Secondly, the early warning evaluation system of geological disasters was constructed from the three aspects of susceptibility, hazard and risk, and then the risk was early warned and evaluated by analytic hierarchy process and fuzzy evaluation method. The experimental results showed that in the actual prediction, the early warning results obtained by the early warning model of geological disasters based on data mining are consistent with the actual situation. Moreover, compared with the historical data, the prediction accuracy of the medium above disaster data in the early warning model reaches 87.433%. Therefore, the early warning model of geological disasters based on data mining can accurately predict geological disasters.
Research on Time-Anchored Law Enforcement Video Correlation Technology

The correlation between law enforcement video and law enforcement task data is an essential step to achieving the goal of archival management of law enforcement video data. By matching law enforcement video with law enforcement task data and realizing the automatic correlation of the two data, law enforcement data’s management and use efficiency can be significantly improved. Although the GPS location-based automatic correlation method is applied in most cases, its performance in the task-intensive scene is unsatisfactory. Therefore, to achieve accurate correlation even in the case of intensive tasks, this paper proposes a novel solution, discusses the details of design ideas and core algorithms, and verifies the solution’s effectiveness through simulation experiments.
AgriBIoT: A Blockchain-Based IoT Architecture for Crop Insurance

The climate has always played a central role in the lives of farmers. This close link is reinforced by climate change. Faced with increasingly significant consequences and growing uncertainties, they must find strategies to anticipate and protect themselves. Parametric agricultural insurance, which has recently appeared on the market, can help them. No need to send an expert on-site to see the damage, it is based on a climatic index (temperature, rainfall, humidity rate, etc.). When a certain threshold is exceeded on the index scale, compensation is automatically triggered. Around the world, several innovative start-ups have recently invested in this niche, with the ambition of combining their know-how in insurance with technology to offer solutions in response to climate change. In this paper, we propose to mix the IoT technology with the Blockchain to enhance and simplify the insurance process...
A Roadmap to Blockchain Technology Adoption in Saudi Public Hospitals

Blockchain has great potential and promise in the healthcare sector, even though it has not yet seen widespread adoption. One of the main challenges with blockchain adoption is IT employees’ awareness and knowledge. IT knowledge reduces adoption uncertainty and a basic understanding of blockchain and its features is necessary. This study evaluated the knowledge of IT employees who work in Saudi public hospitals and suggested a roadmap to blockchain adoption in hospitals.
A Simple and Effective Method for Injecting Word-level Information into Character-aware Neural Language Models

In this study, we propose a simple and effective method to inject word-level information into character-aware neural language models. Unlike previous approaches, which typically inject word-level information as input to a long short-term memory (LSTM) network, we inject such information into the softmax function. The resultant model can be considered a combination of a character-aware language model and a simple word-level language model. Our injection method can be used in conjunction with previous methods. The results of experiments on 14 typologically diverse languages are provided to empirically show that our injection method performed better than previous methods that inject word-level information at the input, including a gating mechanism, averaging, and concatenation of word vectors. Our method can also be used together with previous injection methods. Finally, we provide a comprehensive comparison with previous injection methods and analyze the effectiveness of word-level information in character-aware language models and the properties of our injection method in detail.
Hardware-efficient, large-scale reconfigurable optical neural network (ONN) with backpropagation

We developed and implemented a deep optical neural network (ONN) design capable of performing large-scale training and inference in situ. For each elementary building block in the ONN, we introduce trainable parameters in a programmable device, weight mixing with a diffuser, and nonlinear detection on the camera for activation and optical readout. With automated reconfigurable neural architecture search, we optimized the architecture of deep ONNs that can perform multiple tasks at high speed and at large scale. The task accuracies achieved by our experiments are close to state-of-the-art benchmarks with conventional multilayer neural networks.
Analysis of 3D nuclear spatial architecture for prostate cancer risk stratification (Conference Presentation)

Non-destructive 3D microscopy enables the accurate characterization of diagnostically and prognostically significant microstructures in clinical specimens with significantly increased volumetric coverage than traditional 2D histology. We are using open-top light-sheet microscopy to image prostate cancer biopsies and investigating the prognostic significance of 3D spatial features of nuclei within prostate cancer microstructures. Using a previously published 3D nuclear segmentation workflow, we identify a preliminary set of 3D graph-based nuclear features to quantify the 3D spatial arrangement of nuclei in prostate cancer biopsies. Using a machine classifier, we identify the features which prognosticate prostate cancer risk and demonstrate agreement with patient outcomes.
Image sensing with multilayer nonlinear optical neural networks

Linear optics has been long applied to image compression. However, it is widely known that nonlinear neural networks outperform linear models in terms of feature extraction and image compression. Here, we show a nonlinear multilayer optical neural network using a commercially available image intensifier as a scalable optical-to-optical nonlinear activation function. We experimentally demonstrated that nonlinear ONNs outperform linear optical linear encoders in a variety of non-trivial machine vision tasks at a high image compression ratio (up to 800:1). We have shown that nonlinear ONNs can directly process optical inputs from physical objects under natural illumination, which provides a new pathway towards high-volume, high-throughput, and low-latency machine vision processing.
Deep physical neural networks based on ultrafast nonlinear optics

Utilizing the input-output transformation of ultrafast nonlinear pulse propagation in quadratic media, we experimentally construct a multilayer physical neural network to perform both audio and handwritten image classification. We introduce a hybrid in-situ in-silico backpropagation algorithm, physics-aware training, that is resilient to the simulation-reality gap, to train physical neural networks. The methodology for constructing and training physical neural networks applies to generic complex physical systems. To demonstrate its generality, we also built and trained physical neural networks out of analog electronic circuits and multimode mechanical oscillators to perform image classification.
An Examination on Mapping of Potential Solar Energy in Areas of Camarines Sur Using Arc GIS

This study analysed sun-oriented power framework arranging and plan techniques in light of geographic data frameworks (GIS). Sunlight based energy is the most valuable sustainable asset for customary use. Sun oriented power plants are essential for ordinary use in homes and different structures. Using remote detecting and geographic data frameworks, potential sun-based power plant areas in Kolkata and the close by West Bengal district of India were tracked down in this review. The consequences of this study can be utilized by managers, metropolitan organizers, engineers, and different partners to execute and develop another sunlight-based power plant in the review region. Since GISs are reasonable for taking care of geospatial information connected with sun-based asset and site appropriateness conditions on different scales, their utilization in sun-oriented power framework arranging and configuration could be extended.
Credit card fraud detection using predictive features and machine learning algorithms

CCF can destabilise economies, reduce confidence between customers and banks, and severely affect other people and businesses. The primary objective of banks and businesses is to identify fraudulent transactions with a high level of accuracy and to also reduce false alerts and the costs of manual investigation activities. When identifying CCF in large datasets, feature selection is very important to improve accuracy performance and rapid detection of fraud. One of the most widely used methods of feature selection is the random forest classifier (RFC), which is well suited for large datasets. The RFC works well; it tends to identify more predictive features, which can significantly improve the classification performance for a CCF detection model. In this paper, we suggest a CCF detection method based on feature selection using random forest classifier and machine learning algorithms such as support vector machines (SVM), isolation forest (IF) to detect fraudulent transactions. The proposed model is applied to a large real-world dataset to study the accuracy of its fraud detection performance. A comparison is made between the proposed model and other machine learning methods.
Accelerates inference by separable max-pooling for object detection

The object detector can achieve better performance, but it also requires high computational cost. In this work, we present an accelerated inference in the structure spatial pyramid pooling (SPP), called ACSPP, based on an optimised architecture that uses combinational separable max-pooling to replace the standard max-pooling operator, and apply it to the YOLOX model. For the MS COCO Val dataset, the average inference time the proposed ACSPP is 10.87 ms, while the average inference time of the YOLOX model is 12.46 ms when a single NVIDIA 1660ti GPU is used. For the Pascal VOC2007 val dataset, the average inference time of the proposed ACSPP is 33.35 ms, while the average inference time of the YOLOX model is 37.30 ms when a single NVIDIA 1050ti GPU is used.
Machine Translation of Selected Ghazals of Hafiz from Persian into English

Neural Machine Translation (NMT) is an end-to-end learning approach for automated translation with the potential to overcome many weaknesses in conventional phrase-based translation systems. However, there are still grey areas that need attention in the field of machine translation, mainly in the context of literary translation and poetic language. This research aims to identify the shortcomings of the NMT in addressing ambiguity and the semantic complexities of poetic expressions that are important in translating literature in a cross-cultural and multilingual context. Furthermore, while human translators struggle to retain naturalness and accuracy in translation, this research aims to identify to what extent machine translation could convey the message in translating literary-based texts from Persian into English. The corpus of this study consists of Forty ghazals from the Divan of Hafiz, which is translated by a computer-assisted translation software, memoQ and then compared to the authentic translation of Hafiz by Henry Wilberforce Clarke. This comparative descriptive study portrayed the challenges and issues related to Hafizâ€™s poems, which demonstrate that the literary touch is lost in MT products.
Unsupervised Cross-Domain White Blood Cells Classification Using DANN

The classification of white blood cells (WBCs) from microscopic blood image provides invaluable information for diagnosis of various diseases. Deep Convolutional Neural Networks are often used to classify WBCs automatically and have obtained certain achievements. However, when the training (source) dataset and test (target) dataset fall from different data distributions (i.e. domain shift), deep convolution neural networks adapt poorly. To solve the problem, we proposed a DANN-based method aiming to help our classifier learn domain-invariant information by using adversarial training. Two datasets were tested and our method achieved 97.1% accuracy, 97.2% recall, 97.2% precision and 97.4%f1-score, respectively. Domain adaptation verification shows that the proposed method has higher performance than other adaptive methods, and has broad application prospects in WBC classification.
630/28. EVALUACIÓN INICIAL DE UN PROGRAMA DE CONSULTA ELECTRÓNICA EN CARDIOLOGÍA PARA DERIVACIÓN DESDE ATENCIÓN PRIMARIA

The palynological assemblages of the Dalichai Formation were studied to reconstruct the paleoenvironmental evolution of the Middle Jurassic interval in the eastern part of the Alborz Mountains (N Iran). Dinoflagellate cyst biozones, planktonic foraminifera, and ammonites suggest a Bajocian-Callovian age for the Dalichai Formation. The palynodebris, mostly of terrestrial origin, are dominated by black opaque phytoclasts. Dinocysts, spores, pollen grains, and amorphous organic matters (AOM) are minor components in most samples. Terrestrial palynoflora are mainly represented by coastal gymnosperms (Araucariaceae, Cheirolepidiaceae), lowland simple monosulcate pollen producers (Cycadales/Bennettitales), ferns, upland seed ferns and conifers (Corystospermales and other bisaccate pollen producers). These assemblages indicate a warm climate with seasonally-dry conditions. However, an increase in Cheirolepidiaceae (Classopollis) in Bathonian assemblages reflects higher temperature during this interval. The dominance of the land-derived phytoclasts and presence of brackish water Botryococcus colonies with cells arranged irregularly suggest a proximal setting with high terrestrial and freshwater input during the Bajocian. The palynofacies analysis suggests a sea-level rise during the Bathonian, reflected by higher abundance and diversity of dinocysts as well as the presence of degraded small phytoclasts. This transgression phase is related to the increased subsidence of the Alborz region due to ongoing Neotethys subduction and creation of a large extensional back arc basin in the Eurasian margin. The syn-rift deposits are characterized by ammonitico rosso facies as well as hemipelagic carbonates containing planktonic foraminifera and pelagic bivalves.
Feasibility of Multiple Advanced Machine Learning Techniques for Synthetic Finite-fault Earthquake Ground Motion Data

&amp;lt;p&amp;gt;The overwhelming success of data-driven models to solve complex predictive real-world problems has made them an effective alternative to the simulation-driven models. In addition to their computational cost, simulation-driven approaches need to be calibrated by actual data that links both to the physical theory and thereby improves both our knowledge and existing models. Similarly, data-driven methods allow deepening the knowledge by analyzing existing data. Therefore, to develop improved predictive models, one needs to pursue a balance between both data-driven and simulation-driven approaches, keeping data as a common pivot. This can be done by using Machine Learning (ML) which is a powerful tool to extract knowledge directly from the data and provide complementary information to the previously developed physics-based models. The main advantage of ML methods is their ability to process massive and complex structure data sets that are difficult to be processed by traditional data-processing methods. Therein lies also the main disadvantage of ML methods i.e., they need massive amounts of data that often are not available. In this study however we take advantage of a new physics-based model of the earthquake fault system of the Southwest Iceland transform zone and generate synthetic, but physically realistic, finite-fault earthquake catalogues. For each earthquake in the catalogues we simulate seismicground motion parameters at a large hypothetical station network&amp;amp;#160; thus generating a massive parametric dataset of synthetic seismic data from earthquakes of magnitude 5 to 7. We will apply multiple types of new generation machine learning techniques such as deep neural network (DNN), deep Bayesian neural networks (DBNN) and deep Gaussian processes (DGP) to investigate the ability and efficiency of the methods in capturing the characteristics of the synthetic dataset in terms of key parameters e.g., ground motion amplitudes, ground motion attenuation versus source-to-site distance and site effects and independent parameters such as earthquake magnitude, fault extend and depth, etc. The ML methods will be trained using a procedure known as greedy layer-wise pretraining where each layer is initialized via the unsupervised pretraining, and the output of the previous layer can be used as the input for the next one. A typical advantage of these pre-trained networks compared to the other deep learning models is that the weights initialization renders the optimization process more effective, providing faster convergence by initializing the network parameters near a convergence region. This can help to avoid underfitting/overfitting problems when the training samples are highly correlated. The results will provide a new insight into the efficiency and usefulness of ML methods on synthetic seismic datasets with implications for their use on actual and more sparser datasets.&amp;lt;/p&amp;gt;
Machine Learning based Estimator for ground Shaking maps

&amp;lt;p&amp;gt;Large earthquakes are among the most destructive natural phenomena. After a large-magnitude event occurs, a crucial task for hazard assessment is to rapidly and accurately estimate the ground shaking intensities in the affected region. To satisfy real-time constraints, ground shaking is traditionally evaluated with empirical relations called Ground Motion Prediction Equations (GMPE) which can be combined with local amplification factors and early data recordings, when available. Given their nature, GMPEs can be inaccurate to model rarely observed earthquakes, such as large earthquakes. Furthermore, even for very populated databases, GMPEs are characterized by large variances, as earthquakes of similar magnitude and location may have very different outcomes related to complex fault phenomena and wave physics.&amp;amp;#160;&amp;lt;/p&amp;gt; &amp;lt;p&amp;gt;The ML Estimator for Ground Shaking maps (MLESmap) workflow is proposed as a novel procedure that exploits the predictive power of ML algorithms to estimate ground acceleration values a few seconds after a large earthquake occurs. The inferred model can produce peak (spectral) ground motion maps for quasi-real-time applications. Due to its fast assessment, it can further be used to explore uncertainties quickly and reliably. MLESmap is based upon large databases of physics-based seismic scenarios to feed the algorithms.&amp;lt;/p&amp;gt; &amp;lt;p&amp;gt;Our approach (i.e. simulate, train, deploy) can help produce the next generation of ground shake maps, capturing physical information from wave propagation (directivity, topography, site effects) at the velocity of simple empirical GMPE. In this work, we will present the MLESmap workflow, its precision, and a use case.&amp;lt;/p&amp;gt;
Policy Gradient Methods

This extraordinary three-volume work, written in an engaging and rigorous style by a world authority in the field, provides an accessible, comprehensive introduction to the full spectrum of mathematical and statistical techniques underpinning contemporary methods in data-driven learning and inference. This second volume, Inference, builds on the foundational topics established in volume I to introduce students to techniques for inferring unknown variables and quantities, including Bayesian inference, Monte Carlo Markov Chain methods, maximum-likelihood estimation, hidden Markov models, Bayesian networks, and reinforcement learning. A consistent structure and pedagogy is employed throughout this volume to reinforce student understanding, with over 350 end-of-chapter problems (including solutions for instructors), 180 solved examples, almost 200 figures, datasets and downloadable Matlab code. Supported by sister volumes Foundations and Learning, and unique in its scale and depth, this textbook sequence is ideal for early-career researchers and graduate students across many courses in signal processing, machine learning, statistical analysis, data science and inference.