Reviewerly/exHarmony huggingface dataset:
Creative content digital literacy Community Information Group (KIM): Effects of information dissemination optimization training

Digital literacy is still needed in order to build a smart community in selecting and filing information that is useful for themselves. The Community Information Group (KIM) in Bandung City is one of the leaders and also the influencer in educating about digital literacy to the public, which is one of the priority program of the Bandung City Government. Various activities carried out by KIM are referred to as “ADINDA”, which stands for Information Access, Discussion, Implementation, Networking, Dissemination and Aspiration Advocacy. Optimizing the use of smartphones as a media for digital literacy education to the community in urban villages in Bandung City, can actually be done by the Community Information Group (KIM). However, most of the KIM administrators or members are not competent yet to create content creatively through smartphones to increase people's digital literacy. The Community Service Team provides training on Creative Content Digital Literacy through Smartphones for Optimizing Information Dissemination, so that the role and function of KIM members is more optimal through the emotional and psychological approach to the target audience of the Bandung City community who have unique characteristics. The materials provided in the training are: Attractive & Viral Content, Digital Literacy & Preventing. Hoaxes, Selection of Issues, Timing & Illustration, Making Listicles in Digital Literacy and Selection of Content Types via Smartphones: Blogs, Graphic Info, and Articles. The training provided has been successful in significantly increasing the competence and communication capacity of the KIM members of Bandung City, as evidenced by the pre-test and post-test with quantitative and qualitative methods.
On the Susceptibility of Texas Instruments SimpleLink Platform Microcontrollers to Non-invasive Physical Attacks

We investigate the susceptibility of the Texas Instruments SimpleLink platform microcontrollers to non-invasive physical attacks. We extracted the ROM bootloader of these microcontrollers and then analysed it using static analysis augmented with information obtained through emulation. We demonstrate a voltage fault injection attack targeting the ROM bootloader that allows to enable debug access on a previously locked microcontroller within seconds. Information provided by Texas Instruments reveals that one of our voltage fault injection attacks abuses functionality that is left over from the integrated circuit manufacturing process. The demonstrated physical attack allows an adversary to extract the firmware (i.e. intellectual property) and to bypass secure boot. Additionally, we mount side-channel attacks and differential fault analysis attacks on the hardware AES co-processor. To demonstrate the practical applicability of these attacks we extract the firmware from a Tesla Model 3 key fob. This paper describes a case study covering Texas Instruments SimpleLink microcontrollers. Similar attack techniques can be, and have been, applied to microcontrollers from other manufacturers. The goal of our work is to document our analysis methodology and to ensure that system designers are aware of these vulnerabilities. They will then be able to take these into account during the product design phase. All identified vulnerabilities were responsibly disclosed.
Scalable nonlinear optical computing with multimode fibers

An optical computing framework based on spatiotemporal nonlinear effects of multimode fibers is presented. Experimentally, a powerful computation engine can be realized using linear and nonlinear interactions of spatial fiber modes. With the present optical scheme, we demonstrated excellent performance on a variety of classification and regression tasks. Our studies showed that spatiotemporal fiber nonlinearities are as effective as digital neural network structures in challenging computational tasks. Featuring better energy efficiency and easy scalability, our method provides a new approach to optical computation.
Robust state estimation based on multi-kernel correntropy

In this paper, the problem of robust state estimation under multi-mode non-Gaussian noise environment is investigated. The standard Kalman filter is optimal under Gaussian noise assumption. However, when noise is generated by multiple sources and corrupted by outliers, it means that the noise distribution is multimodal and non-Gaussian, then the performance of the standard Kalman filter will be severely degraded. In this work, a multi-kernel correntropy based state filter is developed. Correntropy is a generalized similarity measure between two random variables, and is insensitive to outliers. Since the noise distribution is multimodal instead of unimodal, a new multi-kernel correntropy based optimization objective function is constructed. The proposed state filter mainly consists of two steps, prediction step and correction step, where priori estimate and posterior estimate are computed respectively. The capabilities of the proposed filter are demonstrated on a benchmark navigation problem.
Personalised Fashion Assistant

The research aims to build a personalised fashion assistant that provides users a personalised virtual try-on experience. The user inputs a simple text describing the outfit she wants to try along with her image and can end up trialing outfits of her choice virtually. This virtual option has been extended to earrings try on too which overpowers existing models with the use of a streamlined approach unlike the complicated ones to achieve the goal. The research stands out with two other specialised features which include “expert rating”, an intelligent rating approach showing how well the outfit suits the user and goes with the trend and “fit advisor”, a smart way to reduce the number of exchanges that happen often in this era of online shopping and is a better approach compared to the existing ones which recommend the right size that would fit the user. The personalised feel is not limited only to the people who can type, the description of the outfit can be provided with voice or simply by choosing the pictorial options shown, thus overcoming the language barrier problem. The scope of this research covers women's tops. This fashion assistant benefits the shoppers by providing a personalised virtual experience, hence saving time, effort and capital required for physical shopping.
Exploring the key roles for digital content management

With the development of technology, more and more intangible assets and intellectual property are produced and valued. The protection and management of these properties are also increasingly discussed. Early digital rights management focused on the design and discussion of a single role perspective. In this study, the three roles of the digital industry supply chain (platform provider, content producer, and consumer) are discussed together. Design and discussion of the life cycle of digital property and different pricing strategies for its competition model are presented. Finally, this research puts forward different opinions on and contributions to the implications of management and practice.
Design and Development of Framework for Big Data Based Smart Farming System

Improving the agricultural productivity is an imminent need to meet the food requirement of constantly growing population rate. It can be gracefully satisfied if the farming process is integrated through technologies such as big data and IoT. The integration of agricultural processes with modern technologies has emerged as the smart agriculture technology. This research work is focused on proving the suitability of the big data analytics for smart agricultural processes in terms of increasing production and quality of yields with less resources and overhead. This research paper expounds the extensive review carried out on the related works in smart agricultural farming, challenges in implementing the smart farming technologies at large scale, followed by the conceptual framework model for the effective implementation of big data together with IoT devices in smart farming.
Analysis Methods of the Design of Clothing Customization System in AR

Top-down from the super-set for design of a cheongsam customization system of this work, this paper analyses and connects its subsets of (a) the application advantages of augmented reality technology in the field of clothing in general, (b) the feasibility of introducing augmented reality into cheongsam customization, (c) enlightened by wisdom from cognitive task analysis, expert interviews, genetic engineering and other methods, (d) the process of cheongsam customization is analyzed in depth, and the results of the elements of cheongsam design and required knowledge elements, and finally, combined with (e) the development and application of Azure Kinect auxiliary equipment, Unity engine, and Kinect SDK. Forward looking, AR is destined to integrate technology into life, serving life, and changing life with high quality.
Using cosmic-ray neutron sensors with machine learning to improve predictions of evapotranspiration and carbon fluxes

&amp;lt;p&amp;gt;Estimates of evapotranspiration and carbon fluxes across the globe are increasingly important to understand the impacts of climate change on global eco-hydrology. Soil moisture has a significant influence in these processes, and if we want to accurately predict energy and carbon fluxes good quality soil moisture data is ultimately needed. Flux towers can measure key atmospheric fluxes and often include traditional point-scale soil moisture sensors within the sensor footprint. However, the spatial mismatch between the flux tower footprint and in-situ point scale soil moisture sensors, as well as a lack of standardisation of these sensors, could lead to biases in this data source.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;The Cosmic-Ray Neutron Sensor (CRNS) is a more recent technology that can provide field scale (~250m radius), root-zone depth soil moisture estimates at hourly intervals. This is possible through an indirect relationship between measured cosmic-ray neutrons scattered from the soil and the amount of hydrogen atoms contained in the soil. Recent efforts to harmonize around 200 publicly available CRNS sites across the globe, using a standardised and state-of-the-art methodology, now opens the possibility for global scale studies.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;We have identified co-located flux tower and CRNS sites across the globe, covering the USA, Europe, Australia, and Brazil. We use established machine learning methods to train predictive models of evapotranspiration and carbon fluxes. We employ the NGBoost algorithm using a decision tree base learner. A key aspect of the NGBoost algorithm is that it gives a probability distribution of the prediction rather than a single point estimate. This will enable us to better understand not only the predictive power but also the uncertainty associated with the model. Our hypothesis is that the information gained when using a spatially consistent, standardised soil moisture estimate will be non-uniform across the globe. For example, it may be of more importance in transition periods, such as during seasonal changes to average soil moisture, as well as sites located in transition zones from dry to wet. This research will provide us with a better understanding of the value of soil moisture in the prediction of atmospheric fluxes across the globe when spatiotemporal scales among different environmental variables are more comparable&amp;lt;/p&amp;gt;
Development of a Web-Based Research and Community Service Information System to Improve Higher Education Services

Currently, the Institute for Research and Community Service (LPPM), Universitas Negeri Medan (UNIMED), tried to continue developing a research and community service information system according to the needs and become the portfolio of each user. This study will create a new feature in the research and community service information system, namely the user portfolio intended for prospective researchers. The process of research and community service at Universitas Negeri Medan carries out in stages, although it implements conventionally. Weak control over the implementation of these activities causes research and community service archives poorly documented. It makes it difficult for stakeholders to find documents when they are needed. The current technological developments have indicated the utilization of online databases to support human activities. Based on this, the actuality of a web-based research and community service information system used to record the research and community service process is essential. This study uses the waterfall method to ensure that the final product meets expectations by completing the communication, planning, modeling, construction, and deployment stages.
Interpretable Optimization Training Strategy-Based DCNN and Its Application on CT Image Recognition

The theoretical basis of the discrete random sample batch classification is not clear and the sample division is not scientific during the process of Deep Convolutional Neural Network (DCNN) model training. Aiming at the problems above, starting from the DCNN detection recognition mechanism, the theory of random discrete samples is given and proved, and a scientific quantitative batch of sample input method is proposed. Combined with image preprocessing, based on the strategy of random dispersion of samples, and scientifically quantified sample input batches, the DCNN model is trained with limited label samples, and then the CT image recognition of pulmonary nodules is carried out. Experimental results based on the LIDC-ID-RI public dataset show that the sensitivity, specificity, and accuracy of the proposed method have reached 96.40%, 95.60%, and 96.00%, respectively. Compared with the multiscale convolutional neural network method and the multiscale multimode image fusion method, the recognition accuracy of the proposed method is improved by 1.6 and 3.49 percentage points, respectively.
Anatomy-guided deep learning for object localization in medical images

Image segmentation is the process of delineating the regions occupied by objects of interest in a given image. This operation is a fundamentally required first step in numerous applications of medical imagery. In the medical imaging field, this activity has a rich literature that spans over 45 years. In spite of numerous advances, including deep learning (DL) networks (DLNs) in recent years, the problem has defied a robust, fail-safe, and satisfactory solution, especially for objects that are manifest with low contrast, are spatially sparse, have variable shape among individuals, or are affected by imaging artifacts, pathology, or post-treatment change in the body. Although image processing techniques, notably DLNs, are uncanny in their ability to harness low-level intensity pattern information on objects, they fall short in the high-level task of identifying and localizing an entire object as a gestalt. This dilemma has been a fundamental unmet challenge in medical image segmentation. In this paper, we demonstrate that by synergistically marrying the unmatched strengths of high-level human knowledge (i.e., natural intelligence (NI)) with the capabilities of DL networks (i.e., artificial intelligence (AI)) in garnering intricate details, these challenges can be significantly overcome. Focusing on the object recognition task, we formulate an anatomy-guided DL object recognition approach named Automatic Anatomy Recognition-Deep Learning (AAR-DL) which combines an advanced anatomy-modeling strategy, model-based non-DL object recognition, and DL object detection networks to achieve expert human-like performance.
Edge Constraint and Location Mapping for Liver Tumor Segmentation from Nonenhanced Images

As there is no contrast enhancement, the liver tumor area in nonenhanced MRI exists with blurred edges and low contrast, which greatly affects the speed and accuracy of liver tumor diagnosis. As a result, precise segmentation of liver tumor from nonenhanced MRI has become an urgent and challenging task. In this paper, we propose an edge constraint and localization mapping segmentation model (ECLMS) to accurately segment liver tumor from nonenhanced MRI. It consists of two parts: localization network and dual-branch segmentation network. We build the localization network, which generates prior coarse masks to provide position mapping for the segmentation network. This part enhances the ability of the model to localize liver tumor in nonenhanced images. We design a dual-branch segmentation network, where the main decoding branch focuses on the feature representation in the core region of the tumor and the edge decoding branch concentrates on capturing the edge information of the tumor. To improve the ability of the model for capturing detailed features, sSE blocks and dense upward connections are introduced into it. We design the bottleneck multiscale module to construct multiscale feature representations using kernels of different sizes while integrating the location mapping of tumor. The ECLMS model is evaluated on a private nonenhanced MRI dataset that comprises 215 different subjects. The model achieves the best Dice coefficient, precision, and accuracy of 90.23%, 92.25%, and 92.39%, correspondingly. The effectiveness of our model is demonstrated by experiment results, and our model reaches superior results in the segmentation task of nonenhanced liver tumor compared to existing segmentation methods.
A Cross-Entropy Based Feature Selection Method for Binary Valued Data Classification

Feature selection is a process of finding a meaningful subset of attributes from a given set of measurements for a purpose of revealing a coherent relation or causality in an event to facilitate an effective pattern classification. It can be treated as a pre-step before constructing the machine learning models in big data analytics for improving the accuracy of a prediction result. By selecting the most significant features, it will reduce the time of training and the complexity of the model, avoid data overfitting, and help user to better understand the source data and the modeling results. Though features are commonly dealt with in continuous values, many features appear to be binary valued, i.e., either 1 or 0, in many real-world machine learning applications. Inspired by existing feature selection methods, a new framework called FMC_SELECTOR was presented in this paper which addresses specifically the selection of significant features of binary values from highly imbalanced large datasets. The FMC_SELECTOR combines the fisher linear discriminant analysis with a cross-entropy concept to create an integrated mapping function to evaluate each individual features from a given dataset. A new formula called Mapping Based Cross-Entropy Evaluation (MCE) was derived. A Positive Case Prediction Score (PPS) is explored to verify the significance of the features selected in a classification process. The performance of FMC_SELECTOR is compared with two popular feature selection methods – the Univariate Importance (UI) and Recursive Feature Elimination (RFM), and shows a better performance on the datasets tested.
Investigation of Mechanical Engineering Academicians’ Use of Distance Education Technologies

The aim of this study is to determine the use of distance education technologies, conditions of use and how often they use various computer applications, and also to investigate the use of these applications by academicians who teach in the Department of Mechanical Engineering. In the research carried out with the scanning model, there were 370 volunteers from various universities in Russia, consisting of academicians who teach in the field of mechanical engineering. The research was carried out in the spring term of 2020–2021; before the research, a 6-week online training was given to mechanical engineer academicians. In the study, the ‘distance education technologies’ measurement tool developed by the researchers and compiled by experts in the field was used. The measurement tool was delivered to the academicians via the online method and collected. The analysis of the data was carried out by using the SPSS programme, frequency analysis, t-test and ANOVA test, and the results were added to the research with tables. Accord-ing to the results obtained from the research, although the distance education technologies of the academicians who teach in the field of mechanical engineering are satisfactory, the rate of academicians never using new technologies in the teaching process is quite low and the rate of using them very often is quite high.
A generative model for fBm with deep ReLU neural networks

We provide a large probability bound on the uniform approximation of fractional Brownian motion with Hurst parameter H, by a deep-feedforward ReLU neural network fed with a N-dimensional Gaussian vector, with bounds on the network design (number of hidden layers and total number of neurons). Essentially, up to log terms, achieving an uniform error of O(N−H) is possible with log⁡(N) hidden layers and O(Nlog⁡N) parameters. Our analysis relies, in the standard Brownian motion case (H=1/2), on the Levy construction and in the general fractional Brownian motion case (H≠1/2), on the Lemarié-Meyer wavelet representation. This work gives theoretical support on new generative models based on neural networks for simulating continuous-time processes.
Domain-Level Pairwise Semantic Interaction for Aspect-Based Sentiment Classification

Aspect-based sentiment classification (ABSC) is a very challenging subtask of sentiment analysis (SA) and suffers badly from the class-imbalance. Existing methods only process sentences independently, without considering the domain-level relationship between sentences, and fail to provide effective solutions to the problem of class-imbalance. From an intuitive point of view, sentences in the same domain often have high-level semantic connections. The interaction of their high-level semantic features can force the model to produce better semantic representations, and find the similarities and nuances between sentences better. Driven by this idea, we propose a plug-and-play Pairwise Semantic Interaction (PSI) module, which takes pairwise sentences as input, and obtains interactive information by learning the semantic vectors of the two sentences. Subsequently, different gates are generated to effectively highlight the key semantic features of each sentence. Finally, the adversarial interaction between the vectors is used to make the semantic representation of two sentences more distinguishable. Experimental results on four ABSC datasets show that, in most cases, PSI is superior to many competitive state-of-the-art baselines and can significantly alleviate the problem of class-imbalance.
Experimental quantum end-to-end learning on a superconducting processor

Abstract Machine learning can be enhanced by a quantum computer via its inherent quantum parallelism. In the pursuit of quantum advantages for machine learning with noisy intermediate-scale quantum devices, it was proposed that the learning model can be designed in an end-to-end fashion, i.e., the quantum ansatz is parameterized by directly manipulable control pulses without circuit design and compilation. Such gate-free models are hardware friendly and can fully exploit limited quantum resources. Here, we report the experimental realization of quantum end-to-end machine learning on a superconducting processor. The trained model can achieve 98% recognition accuracy for two handwritten digits (via two qubits) and 89% for four digits (via three qubits) in the MNIST (Mixed National Institute of Standards and Technology) database. The experimental results exhibit the great potential of quantum end-to-end learning for resolving complex real-world tasks when more qubits are available.