Reviewerly/exHarmony huggingface dataset:
A Novel Key Features Screening Method Based on Extreme Learning Machine for Alzheimer’s Disease Study

The Extreme Learning Machine (ELM) is a simple and efficient Single Hidden Layer Feedforward Neural Network(SLFN) algorithm. In recent years, it has been gradually used in the study of Alzheimer's disease (AD). When using ELM to diagnose AD based on high-dimensional features, there are often some features that have no positive impact on the diagnosis, while others have a significant impact on the diagnosis. In this paper, a novel Key Features Screening Method based on Extreme Learning Machine (KFS-ELM) is proposed. It can screen for key features that are relevant to the classification (diagnosis). It can also assign weights to key features based on their importance. We designed an experiment to screen for key features of AD. A total of 920 key functional connections screened from 4005 functional connections. Their weights were also obtained. The results of the experiment showed that: (1) Using all (4,005) features to diagnose AD, the accuracy is 95.33%. Using 920 key features to diagnose AD, the accuracy is 99.20%. The 3,085 (4,005 - 920) features that were screened out had a negative effect on the diagnosis of AD. This indicates the KFS-ELM is effective in screening key features. (2) The higher the weight of the key features and the smaller their number, the greater their impact on AD diagnosis. This indicates that the KFS-ELM is rational in assigning weights to the key features for their importance. Therefore, KFS-ELM can be used as a tool for studying features and also for improving classification accuracy.
Object Detection and Recognition using Amazon Rekognition with Boto3

In Image Processing, the problem of Object Recognition is regarded as difficult. It's an important aspect of PC vision because so many PC vision programmes rely on it for effectiveness. To address this issue, a number of identifying terminology and frameworks have been discussed for quite some time. The purpose of this paper is to give a briefoverview of a range of platforms in the field of computer vision and object recognition. For the most part this paper covers a relatively mature service from AWS(Amazon Web Services), Amazon Rekognition which is easy to integrate into other services like Lambda, providing huge volume of details in its analysis. The paper incorporates different viewpoints that have been utilized by various analysts for object spotting and recognition. Rekognition is able to recognise objects and scenarios within photographs in this article, returning a confidence measure ranging from 0 to 100% for each label returned. It accomplishes this by using AWS’ Python client Boto3 and the detect labels method. This works by supplying an image, a limit on the number of labels to return, and a confidence level for filtering out ambiguous results.
Design of an Intrinsically Motivating AR Experience for Environmental Awareness

Abstract Augmented Reality seems a promising tool to provide engaging and effective educational experiences, thanks to its potentiality in stimulating intrinsic motivation, that could influence the learning process and the attitude of the users towards behaviours. This paper presents the Resized Plastic Augmented Reality learning experience, designed on the basis of Dunleavy's framework to provide a systemic overview of the microplastics issue to allow users to understand its mechanisms, educate them about their role in the system and help them to connect this information to their everyday actions.
Automated Requirement Dependency Analysis for Complex Technical Systems

Abstract Requirements changes are a leading cause for project failures. Due to propagation effects, change management requires dependency analysis. Existing approaches have shortcomings regarding ability to process large requirement sets, availability of required data, differentiation of propagation behavior and consideration of higher order dependencies. This paper introduces a new method for advanced requirement dependency analysis based on machine learning. Evaluation proves applicability and high performance by means of a case example, 4 development projects and 3 workshops with industry experts.
Procedural rights as safeguard for human rights in platform regulation

Abstract In what way can coregulation and regulation (like that included in the NetzDG and envisaged by the Digital Services Act) ensure individual users' right to procedural fairness, through ensuring due process and transparency? In other words, how could users be treated fairly when it comes to removing their objectionable content: For example, by being appropriately informed, being able to raise complaints against removal of their content, and by being able to challenge the platform's decision? The current state of the play is to push the responsibility of deciding about questions related to freedom of expression on social media platform providers. In the triangle of freedom of expression social media platforms enjoy a disproportionate power to the detriment of the other two actors: authorities and individuals. This article takes note about how platforms act as proxies for state authority in content governance and examines how procedural fairness could ensure a safety zone to protect freedom of expression.
An Efficient CNN Accelerator for Low-Cost Edge Systems

Customized hardware based convolutional neural network ( CNN or ConvNet ) accelerators have attracted significant attention for applications in a low-cost, edge computing system. However, there is a lack of research that seeks to optimize at both the algorithm and hardware levels simultaneously in resource-constrained FPGA systems. In this paper, we first analyze ConvNet models to find one that is most suitable for a low-cost FPGA implementation. Based on the analysis, we select MobileNetV2 as the backbone of our research due to its hardware-friendly structure. We use a quantized implementation with 4-bit precision and optimize further with a smaller input resolution of 192 × 192 to obtain a 68.8% detection accuracy on ImageNet, which represents only a 3.2% accuracy loss compared to a floating-point model that uses the full input size. We then develop a hardware implementation that uses a low-cost FPGA. To accelerate the depth-wise separable ConvNet and utilize DRAM resources efficiently with parallel processing, we propose a novel scoreboard architecture to dynamically schedule DRAM data requests in order to maintain a high hardware utilization. The number of DSP blocks used is about six times smaller than in prior work. In addition, internal block RAM utilization is approximately nine times more efficient than in prior work. Our proposed design achieves 3.07 frames per second (FPS) on the low-cost and resource constrained FPGA system.
Different discrete-time noise-suppression Z-type models for online solving time-varying and time-invariant cube roots in real and complex domains: Application to fractals

Different from the conventional calculation method, the discrete-time noise-suppression zeroing neural dynamic (DTNSZND) model is developed and investigated for online solving the time-varying/time-invariant scalar cube root problem with different disturbances (bounded/unbounded disturbances) from a control perspective in this paper. The solving precision and robustness of different DTNSZND models are generated via various difference rules are discussed and analyzed, and numerical simulations verify that the convergence property of the developed models are closely related to the accuracy of difference rules. The zero-stable, consistent and convergent of different DTNSZND models are demonstrated via theoretical analyses. Furthermore, numerical simulation and comparative results prove that the convergence, robustness and superiority of the presented different DTNSZND models for online solving time-varying/time-invariant cube root problems with different interferences in real domain or complex domain. Finally, different fractals are generated by utilizing the developed DTNSZND model to solve the cube root problem with different noises in complex domain, which are quite different from Newton fractals produced by Newton–Raphson iteration. Therefore, the proposed different DTNSZND models can be considered as a novel iterative algorithm to generate new fractal.
Location-based mobile learning on relief mapping methods

Modern approaches in education extensively use recent technologies in the learning process: One of them is using mobile devices to make the learning more interesting to students and to allow them to learn outside the classroom. With a mobile learning application for a field exercise for cartography students, this paper builds on research in Location-Based Mobile Learning (LBML). The approach is based on (1) gamified learning in a real-world context and (2) applying the user experience theory to designing a concept of mobile learning. The outcome is the quiz-based mobile application mCartoLearn. The approach was tested with two classes of university students and the results are presented in this paper. This approach extends lecturing of cartography at universities from indoor traditional and e-learning to outdoor contextual mobile learning. The mobile application allows the learners to better connect with the real-world to understand the cartographic methods of representing relief on maps.
Video Content Classification Using Time-Sync Comments and Titles

The Time-Sync Comment (TSC) is a novel comment used by online video websites and has been gradually recognized by Internet users. Attracted by the active viewer-creator interaction, numerous content creators upload their videos to online TSC video websites. Facing the increasing number of videos, it is difficult to efficiently classify video in traditional manual review methods. Given the close comment-video correlation, achievements of natural language processing can be applied to the TSC video classification task, processing texts instead of images. In this paper, a new method of video classification based on TSCs and titles is proposed. It combines the BERT (Bidirectional Encoder Representation from Transformers) model with the machine learning classifier, and obtains classification results via analyzing TSCs and titles. In particular, this method can work with few TSCs and bypass the limit of the BERT for the input sequence length. The experimental results on the real-world dataset show that BERT-SVM can achieve a better performance than the baseline methods, and the maximum accuracy is up to 0.9396. This research can help online video websites to manage TSC videos more efficiently and intelligently, and provide novel ideas for researchers to study classical tasks.
Applying Deep Learning based Approach for Visual Saliency based Prediction

The human eye and its vision is one of the most significant aspects of one's ability to understand the nature. Acquisition of visual intelligence and predicting visible attention is present terms used to describe how fast the human eye focuses on salient aspects in a scene while analysing it. In recent years, neural networks used to predict visual acuity. A deep neural network architecture based on transfer learning approach is proposed in this paper for visual saliency prediction. Proposed model extracts visual information from raw photos using convolutional layers in order predicting apparent stiffness. Additionally, the proposed model uses a VGG-16 architecture with semantic separation, using a pixel based separation layer for predicting the label of each pixel category in the inserted image. The proposed method is used in most cases to demonstrate its effectiveness.
Extraction of Key Features and Enhanced Prediction Framework of Breast Cancer Occurrence

In this work, feature selection is implemented in combination with various classification models to predict the occurrence of breast cancer from morphological characteristics of the cell nuclei from Breast Cancer Wisconsin (Diagnostic). This proposed method enables recognizing the critical features by eliminating the redundant ones. By doing this, issues such as overfitting and reducing multi-collinearity leading are mitigated to confounding effects induced by having too many features-consequently, more certainty in estimating the parameters of the machine learning model. The Breast Cancer Wisconsin (Diagnostic) data contains 30 features from 10 traits of the cell nuclei, acquired from a digitized image utilizing a fine needle aspirate of breast tissue. Here, it is found that using the L1 regularization (L1) features selection method combined with the Support Vector Classifier (SVC) delivers the best prediction model in terms of both recall rate and accuracy compared to all our other proposed combinations of feature selection and classifier. The high overall accuracy of 97% has been achieved. Moreover, our proposed scheme has a 97% recall rate for the malignant class of cancer using only 12 key features out of 30 features. Thus, this technique could assist the pathologist in rapidly determining malignant cases as only 13 features need to be evaluated instead of 30 with a minimal false-negative rate.
Scalable Cryoelectronics for Superconducting Qubit Control and Readout

Quantum computing promises an exponentially higher computational power than classical computers; although all the building blocks have become available, certain constraints still prevent quantum advantage. The fundamental challenge in building a practical quantum computer is integrating thousands of highly coherent qubits with the control and readout electronics. The need for a high‐coherence qubit drives the effort for quantum error correction algorithms to create fault‐tolerant quantum systems. Error correction becomes tangible in a quantum processor only in large numbers of qubits. Thus, the other challenge is reducing the number of physical interconnects (coaxial lines) between the quantum–classical interface and bulky room‐temperature electronics. To interface thousands of qubits, interconnects can be reduced by bringing the control and readout electronics near the quantum processor. Cryogenic complementary metal–oxide–semiconductor (CMOS) technology has been an ideal candidate for this purpose. Integrated control and readout at cryogenic temperatures require low power dissipation circuit designs and techniques such as frequency‐division multiplexing (FDM) due to the finite cooling power of a dilution refrigerator. Herein, an overview of each building block in a superconducting quantum computer is provided, focusing on scalability. Furthermore, this article is concluded with an outlook discussing current challenges and future directions for the scalable superconducting control and readout.
CloudRCNN: A Framework Based on Deep Neural Networks for Semantic Segmentation of Satellite Cloud Images

Shadow cumulus clouds are widely distributed globally. They carry critical information to analyze environmental and climate changes. They can also shape the energy and water cycles of the global ecosystem at multiple scales by impacting solar radiation transfer and precipitation. Satellite images are an important source of cloud data. The accurate detection and segmentation of clouds is of great significance for climate and environmental monitoring. In this paper, we propose an improved MaskRCNN framework for the semantic segmentation of satellite images. We also explore two deep neural network architectures using auxiliary loss and feature fusion functions. We conduct comparative experiments on the dataset called “Understanding Clouds from Satellite Images”, sourced from the Kaggle competition. Compared to the baseline model, MaskRCNN, the mIoU of the CloudRCNN (auxiliary loss) model improves by 15.24%, and that of the CloudRCNN (feature fusion) model improves by 12.77%. More importantly, the two neural network architectures proposed in this paper can be widely applied to various semantic segmentation neural network models to improve the distinction between the foreground and the background.
An Intelligent Cloud Computing Data Processing System for College Innovation and Entrepreneurship Data Statistics

With the improvement of the enrollment rate in recent years, innovation and entrepreneurship in colleges and universities have become the focus of the country and people. However, the statistics of innovation and entrepreneurship in colleges and universities have become more and more complicated and difficult. The purpose of this paper is to study how to face the statistics of innovation and entrepreneurship in colleges and universities, then to study the intelligent cloud computing data processing system, and put forward the importance of innovation and entrepreneurship and cloud computing in colleges and universities. The experiment results show that it can be seen that the number of graduates has risen sharply from 2011 to 2020 and the percentage has risen from about 20% to about 90%, which has also led to a great increase in the difficulty of graduate entrepreneurial data management. The difficulty of graduate entrepreneurial data management has risen from around 18% at the beginning to around 87% in 2020. Therefore, it is necessary to study the data processing system to make data processing more efficient. The intelligent cloud computing data processing system significantly improves the quality and efficiency of entrepreneurial service management, and the data mining application in the system can also provide data support for the analysis and prediction of graduates’ entrepreneurial situations. The intelligent cloud computing data processing system significantly improves the quality and efficiency of entrepreneurial service management, and the data mining application in the system can also provide data support for the analysis and prediction of graduates’ entrepreneurial situations.
Privacy-Preserving Cloud Data Model based on Differential Approach

With the variety of cloud services, the cloud service provider delivers the machine learning service, which is used in many applications, including risk assessment, product recommen-dation, and image recognition. The cloud service provider initiates a protocol for the classification service to enable the data owners to request an evaluation of their data. The owners may not entirely rely on the cloud environment as the third parties manage it. However, protecting data privacy while sharing it is a significant challenge. A novel privacy-preserving model is proposed, which is based on differential privacy and machine learning approaches. The proposed model allows the various data owners for storage, sharing, and utilization in the cloud environment. The experiments are conducted on Blood transfusion service center, Phoneme, and Wilt datasets to lay down the proposed model's efficiency in accuracy, precision, recall, and Fl-score terms. The results exhibit that the proposed model specifies high accuracy, precision, recall, and Fl-score up to 97.72%, 98.04%, 97.72%, and 98.80%, respectively.
Detection and Summarization of Honest Reviews Using Text Mining

When it comes to purchasing and making business decisions, online reviews have become incredibly valuable. The reviewer can boost brand loyalty while also assisting other customers in understanding their product experience. As internet reviews are becoming more prominent, fraudulent reviews which refer to reviews submitted by authors who are rewarded for creating fake evaluations to influence readers&#x0027; perceptions, are becoming more common. This research paper aims to create a product review summarizer that generates a summary for amazon product reviews based on non-fake reviews. In the current work, we have compared supervised machine learning algorithms with SVD dimensionality reduction and a text mining approach for the summarization. The labeled amazon review dataset was used for building the model. This paper gives a novel idea of giving text summary of amazon reviews after filtering out the fake ones.
A Natural Language Processing Tool Offering Data Extraction for COVID-19 Related Information (DECOVRI)

A new natural language processing (NLP) application for COVID-19 related information extraction from clinical text notes is being developed as part of our pandemic response efforts. This NLP application called DECOVRI (Data Extraction for COVID-19 Related Information) will be released as a free and open source tool to convert unstructured notes into structured data within an OMOP CDM-based ecosystem. The DECOVRI prototype is being continuously improved and will be released early (beta) and in a full version.
Offline dominance and zeugmatic similarity normings of variably ambiguous words assessed against a neural language model (BERT)

For any research program examining how ambiguous words are processed in broader linguistic contexts, a first step is to establish factors relating to the frequency balance or dominance of those words' multiple meanings, as well as the similarity of those meanings to one other. Homonyms-words with divergent meanings-are one ambiguous word type commonly utilized in psycholinguistic research. In contrast, although polysemes-words with multiple related senses-are far more common in English, they have been less frequently used as tools for understanding one-to-many word-to-meaning mappings. The current paper details two norming studies of a relatively large number of ambiguous English words. In the first, offline dominance norming is detailed for 547 homonyms and polysemes via a free association task suitable for words across the ambiguity continuum, with a goal of identifying words with more equibiased meanings. The second norming assesses offline meaning similarity for a partial subset of 318 ambiguous words (including homonyms, unambiguous words, and polysemes divided into regular and irregular types) using a novel, continuous rating method reliant on the linguistic phenomenon of zeugma. In addition, we conduct computational analyses on the human similarity norming data using the BERT pretrained neural language model (Devlin et al., 2018, BERT: Pre-training of deep bidirectional transformers for language understanding. ArXiv Preprint. arXiv:1810.04805) to evaluate factors that may explain variance beyond that accounted for by dictionary-criteria ambiguity categories. Finally, we make available the summarized item dominance values and similarity ratings in resultant appendices (see supplementary material), as well as individual item and participant norming data, which can be accessed online ( https://osf.io/g7fmv/ ).
Towards Comprehensive European Agricultural Data Governance: Moving Beyond the “Data Ownership” Debate

Abstract Farming has become more productive and less costly thanks to the effective usage of data-driven “digital agriculture” services. However, this new form of “smart farming” has also brought about data-related concerns connected with the ambiguity regarding who owns the data, who has what rights over it, and ultimately whether there is a need for regulation. The idea of providing a “data ownership” right to farmers is predominantly defended by the sectoral literature and has even been adopted already through the voluntary agricultural data codes of conduct developed by stakeholders both in Europe and the US. This paper approaches this debate from the perspective of the sectoral market failures including data lock-in, data fragmentation, data access problems, and a lack of trust on the part of farmers. It demonstrates that the way in which ownership is framed is not adequate to address these problems and has the potential to exacerbate them. An alternative legal design should be flexible enough to remove the reasons for these problems. Creating rules/rights might not be enough to remove all the issues though. A holistic approach including a legal design and infrastructural opportunities assumes great importance in this regard on the eve of the Common European Agricultural Data Space and possible sectoral regulatory intervention following the horizontal framework provided by the recent Data Act.